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FleetView's default when no agent name is typed. (Tools: *)","- claude-code-guide: Use this agent when the user asks questions (\"Can Claude...\", \"Does Claude...\", \"How do I...\") about: (1) Claude Code (the CLI tool) - features, hooks, slash commands, MCP servers, settings, IDE integrations, keyboard shortcuts; (2) Claude Agent SDK - building custom agents; (3) Claude API (formerly Anthropic API) - API usage, tool use, Anthropic SDK usage. **IMPORTANT:** Before spawning a new agent, check if there is already a running or recently completed claude-code-guide agent that you can continue via SendMessage. (Tools: Bash, Read, WebFetch, WebSearch)","- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)","- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)","- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)","- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)"],"removedTypes":[],"isInitial":true,"showConcurrencyNote":true},"type":"attachment","uuid":"f4ed9cbf-8b8e-41e9-a47c-9168e99e842d","timestamp":"2026-07-07T13:16:27.947Z","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/claude-projects/08-07","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"f4ed9cbf-8b8e-41e9-a47c-9168e99e842d","isSidechain":false,"attachment":{"type":"skill_listing","content":"- git-sync: Commit + Push aller persistent konfigurierten Projekt-Repos\n- deep-research: Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report. - When the user wants a deep, multi-source, fact-checked research report on any topic. BEFORE invoking, check if the question is specific enough to research directly — if underspecified (e.g., \"what car to buy\" without budget/use-case/region), ask 2-3 clarifying questions to narrow scope. Then pass the refined question as args, weaving the answers in.\n- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- artifact-design: Design guidance and fundamentals for Artifacts.\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- verify: Verify that a code change actually does what it's supposed to by exercising it end-to-end and observing behavior — drive the affected flow, not just tests or typecheck. Run before committing nontrivial changes; bootstraps this repo's project verify skill if none exists yet. Don't invoke it on a diff that only touches tests, docs, or other code with no runtime surface to drive (a change to product source always has one) — there's nothing to observe.\n- code-review: Review the current diff for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings; ultra: deep multi-agent review in the cloud). Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- run: Launch and drive this project's app to see a change working. 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Der Frage-Assistent in der Tauri-App ist davon getrennt — er macht rohe API-Calls an Sonnet 5 aus dem Rust-Kern. Skills greifen dort nicht.\n\nWas du meinst, geht trotzdem — über den **System-Prompt** des API-Calls. Das ist genau der Hebel:\n\n**Jargon-Verständnis:** In den System-Prompt gehört ohnehin das Schema (Tabellen, Spalten). Dazu ein Glossar: was „Modul\", „Bereich\", „Position\", Fahrzeugtyp etc. in euren Daten bedeuten und auf welche Spalten/Werte sie abbilden. Damit trifft das Text→SQL den richtigen Join/Filter statt zu raten. Das ist der wirksamste Teil.\n\n**Verbotene Fragen:** Zwei Ebenen, und die Unterscheidung ist wichtig:\n- Im System-Prompt formulierte Verbote sind *weich* — das Modell hält sich meistens daran, aber nicht garantiert. Für eine Demo reicht das.\n- *Hart* durchsetzbar ist nur an der SQL-Schicht: read-only Verbindung, Whitelist erlaubter Tabellen/Spalten, generiertes SQL vor Ausführung validieren. Wenn „darf nicht\" wirklich „kann nicht\" heißen soll, gehört es dorthin, nicht in den Prompt.\n\nKein Unsinn also — nur das Etikett „Skills\" passt nicht; die Spezialisierung läuft über System-Prompt plus 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{"parentUuid":"8be0c9d1-1a07-4b26-b5a3-eeba2c82043d","isSidechain":false,"promptId":"10b24b4b-0c5e-4983-8b29-2c147ad75b7d","type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_012FuPd6L7cnJWsVmD2hYzRx","content":"Launching skill: artifact-design"}]},"uuid":"01d68dac-b111-43cd-be49-3afdcdaae0b0","timestamp":"2026-07-07T13:18:52.464Z","toolUseResult":{"success":true,"commandName":"artifact-design"},"sourceToolAssistantUUID":"8be0c9d1-1a07-4b26-b5a3-eeba2c82043d","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/claude-projects/08-07","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"01d68dac-b111-43cd-be49-3afdcdaae0b0","isSidechain":false,"promptId":"10b24b4b-0c5e-4983-8b29-2c147ad75b7d","type":"user","message":{"role":"user","content":[{"type":"text","text":"Approach this as the design lead at a small studio known for their versatility, giving every client a visual identity pitched at the treatment the task actually calls for. Make deliberate choices about palette, typography, and layout that are specific to this subject, and avoid templated designs.\n\n## Read the request first\n\nCalibrate treatment, not whether to design. A doc deserves the same craft as a landing page — what changes is the treatment that craft is delivered in.\n\nMany requests call for a more utilitarian treatment: a plan, a memo, a demo. Make it polished: include real typographic hierarchy, considered spacing, and a proper palette, but avoid over-designing. Most pages do not need a flashy, gigantic hero. Keep flourishes tasteful and limited.\n\nSome requests call for an editorial treatment: a landing page, a game, an app or tool they'll keep or share.\n\nWhen unsure: a well-composed page is never the wrong answer; an over-designed visual identity sometimes is.\n\nFundamentals below apply to everything. The editorial process after that runs only when the read above says so.\n\n## Fundamentals for every artifact\n\n**Honor what's already there** Look for an existing design system first — CLAUDE.md, a tokens or theme file, existing component styles. When one exists, apply it; everything below fills gaps and never overrides. Precedence is always: the user's own words, then the project's existing system, then your choices.\n\n**Ground it in the subject.** If the subject isn't already clear, pin it: one concrete subject, its audience, and the page's single job. The subject's own world — its materials, instruments, vernacular — is where distinctive choices come from. Build with real content throughout, never lorem.\n\n**Pair typefaces** Typography carries the page even when the page isn't about typography. The Artifact CSP blocks font CDNs, so don't link a webfont URL and risk a silent fallback. Instead inline the face as a @font-face data URI. Keep running text near 65 characters wide; set a type scale and stay on it; give headings `text-wrap: balance`, body text room to breathe, and uppercase labels a touch of letter-spacing.\n\n**Choose neutrals, don't default to them.** A pure mid-grey reads as unconsidered; a grey with a slight hue bias toward the page's accent reads as chosen. Pure white and near-black are fine grounds when they suit the subject — the point is that the neutral was picked, not inherited.\n\n**Design both themes.** The page renders in the viewer's theme: `prefers-color-scheme` carries the OS preference, and the viewer's toggle stamps `data-theme=\"dark\"` / `data-theme=\"light\"` on the root element, which must override the media query in both directions. The robust pattern is token-level: define the palette as custom properties on `:root`, redefine only the tokens under `@media (prefers-color-scheme: dark)` — style components through the tokens, never directly inside the media query — then redefine them again under `:root[data-theme=\"dark\"]` and `:root[data-theme=\"light\"]`. Give the second theme the same care as the first — don't naively invert; keep contrast legible and the accent working on both grounds. A design that deliberately commits to one visual world (a neon arcade screen, a letterpress invitation) may stay single-theme — make it a choice, not an omission.\n\n**Let layout do the spacing.** Lay out sibling groups with flex or grid and `gap`, not per-element margins that silently collapse or double. Wide content — tables, code, diagrams — gets `overflow-x: auto` on its own container so the page body never scrolls sideways. Reach for `font-variant-numeric: tabular-nums` wherever digits line up in columns.\n\n**Avoid AI-generated design** AI-generated design currently clusters around a few looks: warm cream (#F4F1EA) with a serif display and terracotta accent; near-black with a lone acid-green or vermilion pop; broadsheet hairline rules with dense columns; a purple-to-blue gradient hero on white; Inter or Space Grotesk as the \"safe\" face; emoji as section markers; everything centered; `rounded-lg` everywhere; accent bar/rail on rounded cards. Where the user pins down a visual direction, follow it exactly — their words always win, including when they ask for one of these looks. Where nothing is specified, don't spend that freedom on one of these defaults.\n\n**Build cleanly** Be cognizant of overlapping elements, cascade collisions, silent font fallbacks; visual bugs hide in the gap between source and output. Close every non-void element, double-quote attributes, give keyboard focus a visible state, respect `prefers-reduced-motion`. For generative or decorative graphics, reach for Canvas or WebGL rather than hand-authoring long SVG path data.\n\n**CSS rules** When writing the CSS, watch your selector specificities. It is easy to generate classes that cancel each other out — a type-based selector like `.section` fighting an element-based one like `.cta` over padding and margins between sections. Structure the cascade so it doesn't silently undo your spacing.\n\n**Writing the copy** Words are design material, not decoration. Write from the user's side of the screen — name things by what people recognize, not how the system is built (a person manages *notifications*, not *webhook config*). Active voice; a control says exactly what happens (\"Publish\", then a toast that says \"Published\"). Errors explain what went wrong and how to fix it — no apologies, no vagueness. Specific beats clever.\n\n**Structure is information** Structural devices, numbering, eyebrows, dividers, labels, should encode something true about the content, not decorate it. Many generic designs use numbered markers (01 / 02 / 03), but that's only appropriate if the content actually is a sequence - like a real process or a typed timeline where order carries information the reader needs. Question if choices like numbered markers actually make sense before incorporating them.\n\n**When it's a UI, not a document** A dashboard or tool is scanned and operated, not read top-to-bottom, so the craft shifts from typography to information design. Surface the summary before the detail; encode state in form as well as number — a pill, a chip, a severity stripe — so what needs attention reads at a glance. Semantic color (good / warning / critical) is separate from the accent hue and doesn't count as your accent. Give sparklines and charts the same care as type: an area fill, a faint grid, an emphasized endpoint. What's interactive should look interactive.\n\n\n\n## Process\n\nBefore writing code, sketch a short design plan — a compact token system with color, type, and layout:\n- **Color**: describe the palette as 4–6 named hex values.\n- **Type**: typefaces for 2+ roles — a characterful display face used with restraint, a complementary body face, and a utility face for captions or data if needed.\n- **Layout**: a layout concept in one or two sentences.\n\nThen build, following the plan and deriving every color and type decision from it.\n\n## When the request is editorial\n\nThe stance shifts: the client has already rejected proposals that felt templated, and is paying for a distinctive point of view. Make opinionated calls, and take one real aesthetic risk where it serves the work.\n\nReview the design plan against the subject before building: if any part of it reads like the generic default you would produce for any similar page, revise that part, and note what you changed and why. Only after you've confirmed the plan's uniqueness do you write the code, following the revised plan exactly.\n\n**Principles** \n\n- The hero is a thesis: open with the most characteristic thing in the subject's world — headline, image, live demo, interactive moment. \n- Typography carries the personality of the page. Pair the display and body faces deliberately, not the same families you would reach for on any other project, and set a clear type scale with intentional weights, widths, and spacing. Make the type treatment itself a memorable part of the design, not a neutral delivery vehicle for the content. \n- Leverage motion deliberately. Think about where and if animation can serve the subject: a page-load sequence, a scroll-triggered reveal, hover micro-interactions, ambient atmosphere. An orchestrated moment usually lands harder than scattered effects; choose what the direction calls for. However, sometimes less is more, and extra animation contributes to the feeling that the design is AI-generated. \n- Match complexity to the vision. Maximalist directions need elaborate execution; minimal directions need precision in spacing, type, and detail. Elegance is executing the chosen vision well.\n- Spend your boldness in one place; keep everything around it quiet. 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"}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4060,"cache_read_input_tokens":30341,"output_tokens":8543,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4060},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":8543,"cache_read_input_tokens":30341,"cache_creation_input_tokens":4060,"cache_creation":{"ephemeral_5m_input_tokens":4060,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcnmEhSDTvQ8CWdFLx7VA","attributionSkill":"artifact-design","type":"assistant","uuid":"8fb489db-a517-4f88-bf9c-9abb74e1487d","timestamp":"2026-07-07T13:19:46.692Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/claude-projects/08-07","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"8fb489db-a517-4f88-bf9c-9abb74e1487d","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_011CcnmEiwzhvPfJ9SGhGNZs","type":"message","role":"assistant","content":[{"type":"text","text":"Design-Plan: technisches Referenzdokument, utilitaristisch aber sauber. Würth-Rot als Akzent (im Thema verankert), warm-neutrale Grautöne mit leichtem Rotstich, Monospace für den Prompt-Text, System-Sans für Annotationen. Ein zentrierter Lesefluss, jeder Prompt-Abschnitt als beschrifteter Block mit „Warum\"-Notiz; Glossar und Guardrails farblich hervorgehoben, weil das deine zwei Punkte sind."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4060,"cache_read_input_tokens":30341,"output_tokens":8543,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4060},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":8543,"cache_read_input_tokens":30341,"cache_creation_input_tokens":4060,"cache_creation":{"ephemeral_5m_input_tokens":4060,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcnmEhSDTvQ8CWdFLx7VA","attributionSkill":"artifact-design","type":"assistant","uuid":"53ccf0ab-867b-445f-9398-ba61ceed7a08","timestamp":"2026-07-07T13:19:50.780Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/claude-projects/08-07","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"53ccf0ab-867b-445f-9398-ba61ceed7a08","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_011CcnmEiwzhvPfJ9SGhGNZs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RLBEvFPkZ2kjMvUTGUxLbC","name":"Write","input":{"file_path":"/private/tmp/claude-502/-Users-marcus-hinz-claude-projects-08-07/0891e8e1-97a0-46c9-8283-ebcf659af06b/scratchpad/system-prompt.html","content":"Frage-Assistent — Beispiel-System-Prompt\n\n\n
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Frage-Assistent · Text → SQL → Visualisierung

\n

Beispiel-System-Prompt für die Würth-Auftragsdatenbank

\n

Der komplette System-Prompt, der bei jedem API-Call an Sonnet 5 vorangestellt wird — in Abschnitte zerlegt, jeweils mit dem „Warum\". Zwei Blöcke sind hervorgehoben: das Glossar für den Jargon und die Guardrails für verbotene Fragen.

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\n SQLite · read-only\n Ausgabe: JSON\n Modell: claude-sonnet-5\n
\n
\n\n
\n
01

Rolle & Auftrag

\n

Legt die einzige Aufgabe fest und schließt Fließtext-Antworten aus. Das Modell soll genau eine Abfrage liefern, nicht diskutieren.

\n
Du bist der Datenassistent für die Würth-Auftragsdatenbank.\nDu übersetzt eine Nutzerfrage in genau eine SQLite-SELECT-Abfrage\nund schlägst eine passende Visualisierung vor.\nDu antwortest ausschließlich mit dem unten definierten JSON.
\n
\n\n
\n
02

Schema

\n

Ohne Schema rät das Modell Spaltennamen. Tabellen, Typen und Beziehungen werden vollständig genannt, damit die Joins stimmen.

\n
auftraege(auftrag_id PK, kunde_id, auftrag_nr, datum DATE, gesamt_betrag REAL)\npositionen(position_id PK, auftrag_id, modul_id, menge INT, einzelpreis REAL)\nmodule(modul_id PK, code, klartext, kategorie)\nkunden(kunde_id PK, name, branche, plz, ort, bundesland)\nauftrag_fzg(auftrag_id PK, fahrzeug_typ, aufbau)\n\n# Beziehungen\npositionen.auftrag_id  → auftraege.auftrag_id\npositionen.modul_id    → module.modul_id\nauftraege.kunde_id     → kunden.kunde_id\nauftrag_fzg.auftrag_id → auftraege.auftrag_id
\n
\n\n
\n
03

Glossar

dein Punkt: Jargon
\n

Bildet Nutzersprache auf Spalten und Werte ab. Hier steckt der größte Hebel: „Modul\" trifft klartext, nicht die interne code-Nummer; „Bereich\" meint die Kundenbranche.

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# Nutzersprache → Daten\n„Modul\", „Bauteil\", „Einrichtung\"   → module.klartext  (NICHT module.code)\n„Bereich\", „Sparte\", „Branche\"       → kunden.branche\n„Region\", „wo\"                       → kunden.bundesland, ersatzw. ort/plz\n„Fahrzeug\", „Fzg\", „Aufbau\"          → auftrag_fzg.fahrzeug_typ / .aufbau\n                                       (Werte meist Transporter/Van)\n„Umsatz\"/„Wert\" eines Auftrags       → auftraege.gesamt_betrag\n„Umsatz\"/„Wert\" einer Position       → positionen.menge * positionen.einzelpreis\n„Auftrag\"  = Bestellung als Ganzes\n„Position\" = eine Zeile darin\nZeitangaben                          → auftraege.datum
\n
\n\n
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04

SQL-Regeln

\n

Grenzt Dialekt und Form ein. Bei nicht beantwortbaren Fragen soll sql: null kommen, statt Spalten zu erfinden.

\n
- Genau eine Anweisung, ausschließlich SELECT.\n- SQLite-Dialekt (Datum: strftime). Keine Fremd-DB-Funktionen.\n- Immer LIMIT 1000, außer bei Aggregaten.\n- Nur die oben genannten Tabellen/Spalten. Keine erfinden.\n- Nicht beantwortbar mit dem Schema → \"sql\": null + kurzer Hinweis.\n- Gruppierungen sinnvoll sortieren (ORDER BY … DESC).
\n
\n\n
\n
05

Verbotene Fragen

dein Punkt: Guardrails
\n

Die weiche Ebene der Sperren. Das Modell hält sich meistens daran — hart durchgesetzt wird read-only und die Tabellen-Whitelist in der Anwendung, nicht hier.

\n
- Keine namentliche Einzelkunden-Auswertung mit Umsatz-/Kontaktbezug.\n  Ausnahme: Aggregat über ≥ 5 Kunden.\n- Keine Fragen außerhalb der Auftragsdaten\n  (Wetter, Allgemeinwissen, Recht, Preisverhandlung) → \"sql\": null.\n- Keine Spekulation/Prognose über die Daten hinaus.\n\n# Hinweis im Prompt selbst:\n# Diese Regeln sind weich. Harte Durchsetzung (read-only,\n# Tabellen-Whitelist, SQL-Validierung) liegt in der Anwendung.
\n
\n\n
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06

Ausgabe-Vertrag

\n

Erzwingt maschinenlesbares JSON. Der chart-Block steuert die Auto-Visualisierung „auf Knopfdruck\" — das Frontend rendert Balken, Linie, Kreis oder Tabelle direkt daraus.

\n
{\n  \"sql\":    \"…\" | null,\n  \"chart\":  { \"typ\": \"balken|linie|kreis|tabelle\", \"x\": \"spalte\", \"y\": \"spalte\" },\n  \"titel\":  \"kurzer Diagrammtitel\",\n  \"hinweis\": \"nur wenn sql null ist oder etwas eingeschränkt wurde\"\n}
\n
\n\n \n
\n"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":4060,"cache_read_input_tokens":30341,"output_tokens":8543,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":4060},"inference_geo":"not_available","iterations":[{"input_tokens":1,"output_tokens":8543,"cache_read_input_tokens":30341,"cache_creation_input_tokens":4060,"cache_creation":{"ephemeral_5m_input_tokens":4060,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcnmEhSDTvQ8CWdFLx7VA","attributionSkill":"artifact-design","type":"assistant","uuid":"2b677681-83fa-4feb-adf3-922dd21d853f","timestamp":"2026-07-07T13:20:46.191Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/claude-projects/08-07","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"2b677681-83fa-4feb-adf3-922dd21d853f","isSidechain":false,"promptId":"10b24b4b-0c5e-4983-8b29-2c147ad75b7d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RLBEvFPkZ2kjMvUTGUxLbC","type":"tool_result","content":"File created successfully at: /private/tmp/claude-502/-Users-marcus-hinz-claude-projects-08-07/0891e8e1-97a0-46c9-8283-ebcf659af06b/scratchpad/system-prompt.html (file state is current in your context — no need to Read it back)"}]},"uuid":"d860ab50-150b-4239-bdc2-d3a90e9c0f1e","timestamp":"2026-07-07T13:20:46.234Z","toolUseResult":{"type":"create","filePath":"/private/tmp/claude-502/-Users-marcus-hinz-claude-projects-08-07/0891e8e1-97a0-46c9-8283-ebcf659af06b/scratchpad/system-prompt.html","content":"Frage-Assistent — Beispiel-System-Prompt\n\n\n
\n
\n

Frage-Assistent · Text → SQL → Visualisierung

\n

Beispiel-System-Prompt für die Würth-Auftragsdatenbank

\n

Der komplette System-Prompt, der bei jedem API-Call an Sonnet 5 vorangestellt wird — in Abschnitte zerlegt, jeweils mit dem „Warum\". Zwei Blöcke sind hervorgehoben: das Glossar für den Jargon und die Guardrails für verbotene Fragen.

\n
\n SQLite · read-only\n Ausgabe: JSON\n Modell: claude-sonnet-5\n
\n
\n\n
\n
01

Rolle & Auftrag

\n

Legt die einzige Aufgabe fest und schließt Fließtext-Antworten aus. Das Modell soll genau eine Abfrage liefern, nicht diskutieren.

\n
Du bist der Datenassistent für die Würth-Auftragsdatenbank.\nDu übersetzt eine Nutzerfrage in genau eine SQLite-SELECT-Abfrage\nund schlägst eine passende Visualisierung vor.\nDu antwortest ausschließlich mit dem unten definierten JSON.
\n
\n\n
\n
02

Schema

\n

Ohne Schema rät das Modell Spaltennamen. Tabellen, Typen und Beziehungen werden vollständig genannt, damit die Joins stimmen.

\n
auftraege(auftrag_id PK, kunde_id, auftrag_nr, datum DATE, gesamt_betrag REAL)\npositionen(position_id PK, auftrag_id, modul_id, menge INT, einzelpreis REAL)\nmodule(modul_id PK, code, klartext, kategorie)\nkunden(kunde_id PK, name, branche, plz, ort, bundesland)\nauftrag_fzg(auftrag_id PK, fahrzeug_typ, aufbau)\n\n# Beziehungen\npositionen.auftrag_id  → auftraege.auftrag_id\npositionen.modul_id    → module.modul_id\nauftraege.kunde_id     → kunden.kunde_id\nauftrag_fzg.auftrag_id → auftraege.auftrag_id
\n
\n\n
\n
03

Glossar

dein Punkt: Jargon
\n

Bildet Nutzersprache auf Spalten und Werte ab. Hier steckt der größte Hebel: „Modul\" trifft klartext, nicht die interne code-Nummer; „Bereich\" meint die Kundenbranche.

\n
# Nutzersprache → Daten\n„Modul\", „Bauteil\", „Einrichtung\"   → module.klartext  (NICHT module.code)\n„Bereich\", „Sparte\", „Branche\"       → kunden.branche\n„Region\", „wo\"                       → kunden.bundesland, ersatzw. ort/plz\n„Fahrzeug\", „Fzg\", „Aufbau\"          → auftrag_fzg.fahrzeug_typ / .aufbau\n                                       (Werte meist Transporter/Van)\n„Umsatz\"/„Wert\" eines Auftrags       → auftraege.gesamt_betrag\n„Umsatz\"/„Wert\" einer Position       → positionen.menge * positionen.einzelpreis\n„Auftrag\"  = Bestellung als Ganzes\n„Position\" = eine Zeile darin\nZeitangaben                          → auftraege.datum
\n
\n\n
\n
04

SQL-Regeln

\n

Grenzt Dialekt und Form ein. Bei nicht beantwortbaren Fragen soll sql: null kommen, statt Spalten zu erfinden.

\n
- Genau eine Anweisung, ausschließlich SELECT.\n- SQLite-Dialekt (Datum: strftime). Keine Fremd-DB-Funktionen.\n- Immer LIMIT 1000, außer bei Aggregaten.\n- Nur die oben genannten Tabellen/Spalten. Keine erfinden.\n- Nicht beantwortbar mit dem Schema → \"sql\": null + kurzer Hinweis.\n- Gruppierungen sinnvoll sortieren (ORDER BY … DESC).
\n
\n\n
\n
05

Verbotene Fragen

dein Punkt: Guardrails
\n

Die weiche Ebene der Sperren. Das Modell hält sich meistens daran — hart durchgesetzt wird read-only und die Tabellen-Whitelist in der Anwendung, nicht hier.

\n
- Keine namentliche Einzelkunden-Auswertung mit Umsatz-/Kontaktbezug.\n  Ausnahme: Aggregat über ≥ 5 Kunden.\n- Keine Fragen außerhalb der Auftragsdaten\n  (Wetter, Allgemeinwissen, Recht, Preisverhandlung) → \"sql\": null.\n- Keine Spekulation/Prognose über die Daten hinaus.\n\n# Hinweis im Prompt selbst:\n# Diese Regeln sind weich. Harte Durchsetzung (read-only,\n# Tabellen-Whitelist, SQL-Validierung) liegt in der Anwendung.
\n
\n\n
\n
06

Ausgabe-Vertrag

\n

Erzwingt maschinenlesbares JSON. Der chart-Block steuert die Auto-Visualisierung „auf Knopfdruck\" — das Frontend rendert Balken, Linie, Kreis oder Tabelle direkt daraus.

\n
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\n
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{"parentUuid":"a9570edd-77fe-4046-be8c-5e234a54bfe6","isSidechain":false,"promptId":"10b24b4b-0c5e-4983-8b29-2c147ad75b7d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013vfMq4XRoxPD28vNHoqDU3","type":"tool_result","content":"Published /private/tmp/claude-502/-Users-marcus-hinz-claude-projects-08-07/0891e8e1-97a0-46c9-8283-ebcf659af06b/scratchpad/system-prompt.html at https://claude.ai/code/artifact/3945a9d2-52ec-4030-877a-a6788c0faa4b"}]},"uuid":"526a8113-c786-486d-8a31-4fd754afe737","timestamp":"2026-07-07T13:21:47.336Z","toolUseResult":{"url":"https://claude.ai/code/artifact/3945a9d2-52ec-4030-877a-a6788c0faa4b","path":"/private/tmp/claude-502/-Users-marcus-hinz-claude-projects-08-07/0891e8e1-97a0-46c9-8283-ebcf659af06b/scratchpad/system-prompt.html","title":"Frage-Assistent — 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Das war mein Fehler.\n\nDas `Artifact`-Tool veröffentlicht die Seite auf claude.ai (extern gehostet, wenn auch privat) — das ist die einzige Funktion des Tools. Du hast weder eine Ablage dort noch überhaupt ein Hochladen angefordert; „auf Knopfdruck visualisieren\" habe ich eigenmächtig als Artifact interpretiert. 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\n

Vorschlagsmodell · Schichten

\n

Wie ein Konfigurationsvorschlag entsteht

\n

Fünf Schichten. Das LLM sitzt nur an den Rändern — es deutet die Eingabe und formuliert das Ergebnis. Was vorgeschlagen wird, entscheidet der deterministische Kern aus SQL-Korrelation und harten Regeln. So kann der Vorschlag keine unmögliche oder unvollständige Konfiguration erfinden.

\n\n
\n

\n Mindestannahme (harte Regel)\n Jede Fahrzeugbestückung braucht z. B. einen Unterboden — fehlt er, wird er erzwungen.\n

\n

\n Ausschluss (harte Regel)\n Nicht jeder Boden passt in jedes Fahrzeug — inkompatible Kombinationen fallen raus.\n

\n
\n\n
\n LLM — deutet / formuliert\n SQL — rechnet\n Harte Regel — deterministisch\n
\n
\n\n
\n\n
\n
\n 01\n

Datenanalyse

\n LLM\n
\n

Deutet das Konfigurationsprofil und die bisherige Bestückung, leitet die Analysefrage ab: welches Segment, welche offenen Slots. Trifft keine Auswahl — bereitet nur vor.

\n
\n Profil: Hersteller/Typ/Dach/Land\n \n strukturierte Analyseanfrage\n
\n
# aus: Renault · Trafic · Hochdach · DE\nSegment  = Transporter, Hochdach\noffen    = Boden, Verkleidung, Regal\n
\n
\n\n
\n\n
\n
\n 02\n

Aggregation & Korrelationsanalyse

\n SQL\n
\n

Zählt über die realen Aufträge, was im Segment zusammen gewählt wird — Korrelation, nicht Filter-und-Kopie. Merkmale ohne Einfluss auf die Wahl bleiben außen vor.

\n
\n auftraege ⋈ positionen ⋈ module\n \n Kandidaten + Häufigkeit/Konfidenz\n
\n
# Kandidaten im Segment, nach Konfidenz\nBodenplatte Sperrholz   0.88\nSeitenverkleidung       0.81\nRegalsystem R2          0.63\nTrennwand               0.41\n
\n
\n\n
\n\n
\n
\n 03\n

Ausschlussfilter

\n Harte Regel\n
\n

Streicht inkompatible Kombinationen aus den Kandidaten. Kompatibilität nach Fahrzeugtyp/Dach — deterministisch, keine Statistik.

\n
\n Kandidaten\n \n kompatible Kandidaten\n
\n
# Nicht jeder Boden passt in jedes Fahrzeug\nBodenplatte Sperrholz   ✓ passt (Trafic)\nBoden XL (L3)           ✗ raus — nur L3, hier L2\n
\n
\n\n
\n\n
\n
\n 04\n

Mindestannahmen

\n Harte Regel\n
\n

Erzwingt Pflichtbestandteile, die jede Bestückung braucht — auch wenn die Korrelation sie nicht oben hat. Ergänzt Fehlendes, statt es dem Zufall zu überlassen.

\n
\n kompatible Kandidaten\n \n vollständige Konfiguration\n
\n
# Jede Bestückung braucht einen Unterboden\nUnterboden im Vorschlag?  nein\n→ Bodenplatte Sperrholz erzwungen\n
\n
\n\n
\n\n
\n
\n 05\n

Natürlichsprachliche Formulierung

\n LLM\n
\n

Formuliert die fertige Konfiguration und die Begründung in lesbarem Deutsch. Ändert die Auswahl nicht mehr — nur Text.

\n
\n Konfiguration + Kennzahlen\n \n Vorschlagstext\n
\n
# Ausgabe an die Maske\n„Für den Trafic Hochdach: Bodenplatte Sperrholz\n (Pflicht), Seitenverkleidung und Regal R2 —\n in 81 % vergleichbarer Aufträge so gewählt.\"\n
\n
\n\n
\n\n
\n Warum die Reihenfolge: Das LLM rahmt nur ein. Zahlen kommen aus SQL, Zulässigkeit und Vollständigkeit aus den harten Regeln. Der letzte LLM-Schritt formuliert, ohne die Auswahl noch zu verändern — deshalb kann kein unmöglicher oder lückenhafter Vorschlag herauskommen.\n
\n
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\n
\n

Vorschlagsmodell · Schichten

\n

Wie ein Konfigurationsvorschlag entsteht

\n

Fünf Schichten. Das LLM sitzt nur an den Rändern — es deutet die Eingabe und formuliert das Ergebnis. Was vorgeschlagen wird, entscheidet der deterministische Kern aus SQL-Korrelation und harten Regeln. So kann der Vorschlag keine unmögliche oder unvollständige Konfiguration erfinden.

\n\n
\n

\n Mindestannahme (harte Regel)\n Jede Fahrzeugbestückung braucht z. B. einen Unterboden — fehlt er, wird er erzwungen.\n

\n

\n Ausschluss (harte Regel)\n Nicht jeder Boden passt in jedes Fahrzeug — inkompatible Kombinationen fallen raus.\n

\n
\n\n
\n LLM — deutet / formuliert\n SQL — rechnet\n Harte Regel — deterministisch\n
\n
\n\n
\n\n
\n
\n 01\n

Datenanalyse

\n LLM\n
\n

Deutet das Konfigurationsprofil und die bisherige Bestückung, leitet die Analysefrage ab: welches Segment, welche offenen Slots. Trifft keine Auswahl — bereitet nur vor.

\n
\n Profil: Hersteller/Typ/Dach/Land\n \n strukturierte Analyseanfrage\n
\n
# aus: Renault · Trafic · Hochdach · DE\nSegment  = Transporter, Hochdach\noffen    = Boden, Verkleidung, Regal\n
\n
\n\n
\n\n
\n
\n 02\n

Aggregation & Korrelationsanalyse

\n SQL\n
\n

Zählt über die realen Aufträge, was im Segment zusammen gewählt wird — Korrelation, nicht Filter-und-Kopie. Merkmale ohne Einfluss auf die Wahl bleiben außen vor.

\n
\n auftraege ⋈ positionen ⋈ module\n \n Kandidaten + Häufigkeit/Konfidenz\n
\n
# Kandidaten im Segment, nach Konfidenz\nBodenplatte Sperrholz   0.88\nSeitenverkleidung       0.81\nRegalsystem R2          0.63\nTrennwand               0.41\n
\n
\n\n
\n\n
\n
\n 03\n

Ausschlussfilter

\n Harte Regel\n
\n

Streicht inkompatible Kombinationen aus den Kandidaten. Kompatibilität nach Fahrzeugtyp/Dach — deterministisch, keine Statistik.

\n
\n Kandidaten\n \n kompatible Kandidaten\n
\n
# Nicht jeder Boden passt in jedes Fahrzeug\nBodenplatte Sperrholz   ✓ passt (Trafic)\nBoden XL (L3)           ✗ raus — nur L3, hier L2\n
\n
\n\n
\n\n
\n
\n 04\n

Mindestannahmen

\n Harte Regel\n
\n

Erzwingt Pflichtbestandteile, die jede Bestückung braucht — auch wenn die Korrelation sie nicht oben hat. Ergänzt Fehlendes, statt es dem Zufall zu überlassen.

\n
\n kompatible Kandidaten\n \n vollständige Konfiguration\n
\n
# Jede Bestückung braucht einen Unterboden\nUnterboden im Vorschlag?  nein\n→ Bodenplatte Sperrholz erzwungen\n
\n
\n\n
\n\n
\n
\n 05\n

Natürlichsprachliche Formulierung

\n LLM\n
\n

Formuliert die fertige Konfiguration und die Begründung in lesbarem Deutsch. Ändert die Auswahl nicht mehr — nur Text.

\n
\n Konfiguration + Kennzahlen\n \n Vorschlagstext\n
\n
# Ausgabe an die Maske\n„Für den Trafic Hochdach: Bodenplatte Sperrholz\n (Pflicht), Seitenverkleidung und Regal R2 —\n in 81 % vergleichbarer Aufträge so gewählt.\"\n
\n
\n\n
\n\n
\n Warum die Reihenfolge: Das LLM rahmt nur ein. Zahlen kommen aus SQL, Zulässigkeit und Vollständigkeit aus den harten Regeln. Der letzte LLM-Schritt formuliert, ohne die Auswahl noch zu verändern — deshalb kann kein unmöglicher oder lückenhafter Vorschlag herauskommen.\n
\n
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files"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":60144,"cache_read_input_tokens":18461,"output_tokens":1418,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":60144},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1418,"cache_read_input_tokens":18461,"cache_creation_input_tokens":60144,"cache_creation":{"ephemeral_5m_input_tokens":60144,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":{"cache_miss_reason":{"type":"previous_message_not_found"}}},"requestId":"req_011CcnuugzSb9EpLQmZDHBFU","type":"assistant","uuid":"d0599598-55ca-481b-beb4-3c6c7a92dde3","timestamp":"2026-07-07T15:12:57.867Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"d0599598-55ca-481b-beb4-3c6c7a92dde3","isSidechain":false,"promptId":"ddb25f5d-1c5f-4bb0-834c-8d4902352404","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FY45M4e7MbBfAfXE5tDgVf","type":"tool_result","content":"total 464\ndrwxr-xr-x 17 marcus.hinz staff 544 Jul 7 13:07 .\ndrwxr-xr-x 22 marcus.hinz staff 704 Jul 6 09:15 ..\n-rw-r--r-- 1 marcus.hinz staff 3877 Jun 30 16:18 collect_fzg_text.py\n-rw-r--r-- 1 marcus.hinz staff 7210 Jul 7 13:07 enrich_branche.py\n-rw-r--r-- 1 marcus.hinz staff 3931 Jul 1 11:04 enrich_currency.py\n-rw-r--r-- 1 marcus.hinz staff 3696 Jul 7 12:49 enrich_fzg_from_json.py\n-rw-r--r-- 1 marcus.hinz staff 1745 Jun 30 16:30 enrich_fzg_load.py\n-rw-r--r-- 1 marcus.hinz staff 2802 Jun 30 14:49 enrich_load.py\ndrwxr-xr-x 12 marcus.hinz staff 384 Jun 30 14:48 enrichment\ndrwxr-xr-x 3 marcus.hinz staff 96 Jul 7 12:56 enrichment_neu\n-rw-r--r-- 1 marcus.hinz staff 6049 Jul 1 10:26 extract_stueckliste.py\n-rw-r--r-- 1 marcus.hinz staff 992 Jul 1 12:08 fx_cache.json\ndrwxr-xr-x 10 marcus.hinz staff 320 Jun 30 16:30 fzg_enrichment\n-rw-r--r-- 1 marcus.hinz staff 98805 Jun 30 16:18 fzg_texts.json\n-rw-r--r-- 1 marcus.hinz staff 6801 Jul 7 12:01 import.py\n-rw-r--r-- 1 marcus.hinz staff 8236 Jul 6 11:19 schema.sql\n-rw-r--r-- 1 marcus.hinz staff 73349 Jul 1 12:08 stueckliste.json\n=== grep für Anreicherung/Branche/Modell ===\n(eval):1: no matches found: --include=*.py","is_error":false}]},"uuid":"cf9cc15d-e331-4ab8-919d-3c08b615c234","timestamp":"2026-07-07T15:13:10.481Z","toolUseResult":{"stdout":"total 464\ndrwxr-xr-x 17 marcus.hinz staff 544 Jul 7 13:07 .\ndrwxr-xr-x 22 marcus.hinz staff 704 Jul 6 09:15 ..\n-rw-r--r-- 1 marcus.hinz staff 3877 Jun 30 16:18 collect_fzg_text.py\n-rw-r--r-- 1 marcus.hinz staff 7210 Jul 7 13:07 enrich_branche.py\n-rw-r--r-- 1 marcus.hinz staff 3931 Jul 1 11:04 enrich_currency.py\n-rw-r--r-- 1 marcus.hinz staff 3696 Jul 7 12:49 enrich_fzg_from_json.py\n-rw-r--r-- 1 marcus.hinz staff 1745 Jun 30 16:30 enrich_fzg_load.py\n-rw-r--r-- 1 marcus.hinz staff 2802 Jun 30 14:49 enrich_load.py\ndrwxr-xr-x 12 marcus.hinz staff 384 Jun 30 14:48 enrichment\ndrwxr-xr-x 3 marcus.hinz staff 96 Jul 7 12:56 enrichment_neu\n-rw-r--r-- 1 marcus.hinz staff 6049 Jul 1 10:26 extract_stueckliste.py\n-rw-r--r-- 1 marcus.hinz staff 992 Jul 1 12:08 fx_cache.json\ndrwxr-xr-x 10 marcus.hinz staff 320 Jun 30 16:30 fzg_enrichment\n-rw-r--r-- 1 marcus.hinz staff 98805 Jun 30 16:18 fzg_texts.json\n-rw-r--r-- 1 marcus.hinz staff 6801 Jul 7 12:01 import.py\n-rw-r--r-- 1 marcus.hinz staff 8236 Jul 6 11:19 schema.sql\n-rw-r--r-- 1 marcus.hinz staff 73349 Jul 1 12:08 stueckliste.json\n=== grep für Anreicherung/Branche/Modell ===\n(eval):1: no matches found: --include=*.py","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sourceToolAssistantUUID":"d0599598-55ca-481b-beb4-3c6c7a92dde3","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"cf9cc15d-e331-4ab8-919d-3c08b615c234","isSidechain":false,"attachment":{"type":"task_reminder","content":[],"itemCount":0},"type":"attachment","uuid":"1b590b46-24f6-4fac-afd0-9639969d85c5","timestamp":"2026-07-07T15:13:10.484Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"1b590b46-24f6-4fac-afd0-9639969d85c5","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_011CcnuxiMPBmCGt5Dq2gB4T","type":"message","role":"assistant","content":[{"type":"text","text":"There's already `enrich_branche.py`. Let me read it and the model it uses."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":2196,"cache_read_input_tokens":78605,"output_tokens":106,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2196},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":106,"cache_read_input_tokens":78605,"cache_creation_input_tokens":2196,"cache_creation":{"ephemeral_5m_input_tokens":2196,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcnuxEruGcjdHHWTx3g81","type":"assistant","uuid":"a8ed939d-0468-4d10-a2b6-9432620bebea","timestamp":"2026-07-07T15:13:19.593Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"a8ed939d-0468-4d10-a2b6-9432620bebea","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_011CcnuxiMPBmCGt5Dq2gB4T","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01L6RrFEJ9bvs3HS1rsBStdH","name":"Read","input":{"file_path":"/Users/marcus.hinz/projects/08-07/pipeline/enrich_branche.py"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":131,"cache_creation_input_tokens":2196,"cache_read_input_tokens":78605,"output_tokens":106,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2196},"inference_geo":"not_available","iterations":[{"input_tokens":131,"output_tokens":106,"cache_read_input_tokens":78605,"cache_creation_input_tokens":2196,"cache_creation":{"ephemeral_5m_input_tokens":2196,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcnuxEruGcjdHHWTx3g81","type":"assistant","uuid":"5d40d4ef-e220-4bc7-9cdd-d4266d2d41e4","timestamp":"2026-07-07T15:13:20.161Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"5d40d4ef-e220-4bc7-9cdd-d4266d2d41e4","isSidechain":false,"promptId":"ddb25f5d-1c5f-4bb0-834c-8d4902352404","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L6RrFEJ9bvs3HS1rsBStdH","type":"tool_result","content":"1\t#!/usr/bin/env python3\n2\t\"\"\"Firmendaten je Kunde per Web-Suche + LLM -> enrichment_neu/branche.jsonl.\n3\t\n4\tPro Kunde ein Sonnet-Aufruf mit server-seitigem web_search-Tool: sucht die\n5\tFirma (Name + Ort/PLZ/Land), liest die Treffer und gibt die Felder aus\n6\tkunde_firmendaten strukturiert zurück. Resumierbar: bereits erledigte\n7\tkunde_ids (aus der JSONL) werden übersprungen. Danach --pack -> result_000.json\n8\tfür enrich_load.py.\n9\t\n10\tKey: ~/.ssh/anthropic_api_key. Modell: claude-sonnet-5.\n11\t\"\"\"\n12\timport argparse\n13\timport json\n14\timport os\n15\timport re\n16\timport sys\n17\timport urllib.request\n18\tfrom pathlib import Path\n19\t\n20\tMODELL = \"claude-sonnet-5\"\n21\tLAND_NAMES = {\n22\t \"DE\": \"Deutschland\", \"AT\": \"Österreich\", \"CH\": \"Schweiz\", \"NO\": \"Norwegen\",\n23\t \"FR\": \"Frankreich\", \"ES\": \"Spanien\", \"IT\": \"Italien\", \"PL\": \"Polen\",\n24\t}\n25\t\n26\tSYSTEM = \"\"\"Du recherchierst zu einer Firma die Stammdaten und ordnest sie in ein festes Vokabular ein.\n27\tNutze das web_search-Tool (Name + Ort/PLZ/Land), lies die Treffer und gib danach AUSSCHLIESSLICH\n28\tein JSON-Objekt zurück — keine Erklärung davor/danach, keine Markdown-Zäune.\n29\t\n30\tgewerk (genau einer): Kfz | SHK/HLK | Bau-Dach | Elektro | Holz-Zimmerei | Fördertechnik | Industrie | Handel | öffentlich | Sonstige\n31\tsegment (genau einer): Handwerk | Industrie | Flotte | öffentlich\n32\t\n33\tFelder:\n34\t{\"branche_text\": \"\", \"branche_nace\": \"\",\n35\t \"gewerk\": \"\", \"segment\": \"\", \"mitarbeiter_kl\": \"\",\n36\t \"umsatz_kl\": \"\", \"gruendungsjahr\": , \"rechtsform\": \"\",\n37\t \"website\": \"\", \"ort\": \"\", \"bundesland\": \"\", \"hr_nummer\": \"\",\n38\t \"konfidenz\": <0..1>, \"bestaetigt\": <0 oder 1, 1 nur bei eindeutigem Registertreffer zur Adresse>,\n39\t \"quelle_primaer\": \"\", \"belege\": \"<1-3 Sätze Begründung inkl. Quell-URLs>\"}\n40\t\n41\tBei unsicherer Zuordnung niedrige konfidenz und bestaetigt=0. Rate nicht ins Blaue — lieber null.\"\"\"\n42\t\n43\tFELDER = [\"branche_text\", \"branche_nace\", \"gewerk\", \"segment\", \"mitarbeiter_kl\", \"umsatz_kl\",\n44\t \"gruendungsjahr\", \"rechtsform\", \"website\", \"ort\", \"bundesland\", \"hr_nummer\",\n45\t \"konfidenz\", \"bestaetigt\", \"quelle_primaer\", \"belege\"]\n46\t\n47\t\n48\tdef api_key():\n49\t path = Path.home() / \".ssh\" / \"anthropic_api_key\"\n50\t for line in path.read_text().splitlines():\n51\t line = line.strip()\n52\t if line.startswith(\"sk-ant-\"):\n53\t return line\n54\t sys.exit(f\"kein sk-ant-Key in {path}\")\n55\t\n56\t\n57\tdef json_objekt(text):\n58\t text = text.strip().removeprefix(\"```json\").removeprefix(\"```\").removesuffix(\"```\").strip()\n59\t start = text.find(\"{\")\n60\t end = text.rfind(\"}\")\n61\t return json.loads(text[start:end + 1])\n62\t\n63\t\n64\tdef frage(key, kunde):\n65\t teile = [kunde[\"name_roh\"]]\n66\t if kunde.get(\"name_1\"):\n67\t teile.append(kunde[\"name_1\"])\n68\t adr = \", \".join(x for x in [kunde.get(\"strasse\"), kunde.get(\"plz\"),\n69\t LAND_NAMES.get(kunde.get(\"land\"), kunde.get(\"land\"))] if x)\n70\t user = f\"Firma: {' / '.join(teile)}\\nAdresse: {adr or 'unbekannt'}\"\n71\t\n72\t body = json.dumps({\n73\t \"model\": MODELL,\n74\t \"max_tokens\": 2500,\n75\t \"system\": SYSTEM,\n76\t \"tools\": [{\"type\": \"web_search_20250305\", \"name\": \"web_search\", \"max_uses\": 4}],\n77\t \"messages\": [{\"role\": \"user\", \"content\": user}],\n78\t }).encode()\n79\t\n80\t req = urllib.request.Request(\n81\t \"https://api.anthropic.com/v1/messages\", data=body,\n82\t headers={\"x-api-key\": key, \"anthropic-version\": \"2023-06-01\",\n83\t \"content-type\": \"application/json\"})\n84\t with urllib.request.urlopen(req, timeout=200) as r:\n85\t resp = json.load(r)\n86\t # web_search erzeugt mehrere Text-Blöcke; das JSON steht in irgendeinem davon\n87\t # (finale Antwort, evtl. mit nachgestelltem Kommentar) -> rückwärts suchen.\n88\t texte = [b[\"text\"] for b in resp[\"content\"] if b[\"type\"] == \"text\" and b[\"text\"].strip()]\n89\t for t in reversed(texte):\n90\t try:\n91\t obj = json_objekt(t)\n92\t except (ValueError, json.JSONDecodeError):\n93\t continue\n94\t if isinstance(obj, dict) and (\"gewerk\" in obj or \"branche_text\" in obj):\n95\t return obj\n96\t raise ValueError(\"keine JSON-Antwort in den Text-Blöcken\")\n97\t\n98\t\n99\tdef main():\n100\t ap = argparse.ArgumentParser()\n101\t here = Path(__file__).resolve().parent\n102\t ap.add_argument(\"--db\", default=here.parent / \"data\" / \"wuerth.db\")\n103\t ap.add_argument(\"--out\", default=here / \"enrichment_neu\")\n104\t ap.add_argument(\"--limit\", type=int, default=0, help=\"max Kunden dieser Lauf (0 = alle)\")\n105\t ap.add_argument(\"--workers\", type=int, default=6, help=\"parallele Aufrufe\")\n106\t ap.add_argument(\"--pack\", action=\"store_true\", help=\"JSONL -> result_000.json\")\n107\t args = ap.parse_args()\n108\t\n109\t out = Path(args.out)\n110\t out.mkdir(exist_ok=True)\n111\t jsonl = out / \"branche.jsonl\"\n112\t\n113\t if args.pack:\n114\t # bei Retries kann eine kunde_id mehrfach in der JSONL stehen -> letzte gewinnt\n115\t by_id = {}\n116\t for l in jsonl.read_text().splitlines():\n117\t if l.strip():\n118\t r = json.loads(l)\n119\t by_id[r[\"kunde_id\"]] = r\n120\t recs = list(by_id.values())\n121\t (out / \"result_000.json\").write_text(json.dumps(recs, ensure_ascii=False, indent=1))\n122\t print(f\"gepackt: {len(recs)} Records -> {out/'result_000.json'}\")\n123\t return\n124\t\n125\t import sqlite3\n126\t con = sqlite3.connect(args.db)\n127\t con.row_factory = sqlite3.Row\n128\t kunden = con.execute(\n129\t \"SELECT kunde_id, name_roh, name_1, strasse, plz, land FROM kunden ORDER BY kunde_id\"\n130\t ).fetchall()\n131\t\n132\t # Als erledigt gilt nur, wer ein belastbares Ergebnis hat (konfidenz gesetzt);\n133\t # null-Ergebnisse (Parse-/Netzfehler) werden erneut gezogen.\n134\t erledigt = set()\n135\t if jsonl.exists():\n136\t for l in jsonl.read_text().splitlines():\n137\t if l.strip():\n138\t r = json.loads(l)\n139\t if r.get(\"konfidenz\") is not None:\n140\t erledigt.add(r[\"kunde_id\"])\n141\t\n142\t global key\n143\t key = api_key()\n144\t offen = [dict(k) for k in kunden if k[\"kunde_id\"] not in erledigt]\n145\t if args.limit:\n146\t offen = offen[:args.limit]\n147\t\n148\t import threading\n149\t from concurrent.futures import ThreadPoolExecutor\n150\t lock = threading.Lock()\n151\t zaehler = {\"n\": 0}\n152\t fh = jsonl.open(\"a\")\n153\t\n154\t def bearbeite(k):\n155\t try:\n156\t roh = frage(key, k)\n157\t except Exception as e: # Einzelfehler nicht den ganzen Lauf abbrechen lassen\n158\t roh = {\"gewerk\": None, \"segment\": None, \"konfidenz\": None, \"belege\": f\"FEHLER: {e}\"}\n159\t rec = {\"kunde_id\": k[\"kunde_id\"], **{f: roh.get(f) for f in FELDER}}\n160\t with lock:\n161\t fh.write(json.dumps(rec, ensure_ascii=False) + \"\\n\")\n162\t fh.flush()\n163\t zaehler[\"n\"] += 1\n164\t print(f\"[{zaehler['n']}/{len(offen)}] kunde {k['kunde_id']:>4} \"\n165\t f\"{(k['name_roh'] or '')[:38]:<38} -> {rec['gewerk']} / {rec['segment']} ({rec['konfidenz']})\",\n166\t flush=True)\n167\t\n168\t with ThreadPoolExecutor(max_workers=args.workers) as ex:\n169\t list(ex.map(bearbeite, offen))\n170\t fh.close()\n171\t print(f\"fertig: {len(offen)} Kunden bearbeitet, {len(erledigt)+len(offen)}/{len(kunden)} gesamt\")\n172\t\n173\t\n174\tif __name__ == \"__main__\":\n175\t main()\n176\t"}]},"uuid":"5d672e73-f846-4c39-ba7a-d836960539ee","timestamp":"2026-07-07T15:13:20.207Z","toolUseResult":{"type":"text","file":{"filePath":"/Users/marcus.hinz/projects/08-07/pipeline/enrich_branche.py","content":"#!/usr/bin/env python3\n\"\"\"Firmendaten je Kunde per Web-Suche + LLM -> enrichment_neu/branche.jsonl.\n\nPro Kunde ein Sonnet-Aufruf mit server-seitigem web_search-Tool: sucht die\nFirma (Name + Ort/PLZ/Land), liest die Treffer und gibt die Felder aus\nkunde_firmendaten strukturiert zurück. Resumierbar: bereits erledigte\nkunde_ids (aus der JSONL) werden übersprungen. Danach --pack -> result_000.json\nfür enrich_load.py.\n\nKey: ~/.ssh/anthropic_api_key. Modell: claude-sonnet-5.\n\"\"\"\nimport argparse\nimport json\nimport os\nimport re\nimport sys\nimport urllib.request\nfrom pathlib import Path\n\nMODELL = \"claude-sonnet-5\"\nLAND_NAMES = {\n \"DE\": \"Deutschland\", \"AT\": \"Österreich\", \"CH\": \"Schweiz\", \"NO\": \"Norwegen\",\n \"FR\": \"Frankreich\", \"ES\": \"Spanien\", \"IT\": \"Italien\", \"PL\": \"Polen\",\n}\n\nSYSTEM = \"\"\"Du recherchierst zu einer Firma die Stammdaten und ordnest sie in ein festes Vokabular ein.\nNutze das web_search-Tool (Name + Ort/PLZ/Land), lies die Treffer und gib danach AUSSCHLIESSLICH\nein JSON-Objekt zurück — keine Erklärung davor/danach, keine Markdown-Zäune.\n\ngewerk (genau einer): Kfz | SHK/HLK | Bau-Dach | Elektro | Holz-Zimmerei | Fördertechnik | Industrie | Handel | öffentlich | Sonstige\nsegment (genau einer): Handwerk | Industrie | Flotte | öffentlich\n\nFelder:\n{\"branche_text\": \"\", \"branche_nace\": \"\",\n \"gewerk\": \"\", \"segment\": \"\", \"mitarbeiter_kl\": \"\",\n \"umsatz_kl\": \"\", \"gruendungsjahr\": , \"rechtsform\": \"\",\n \"website\": \"\", \"ort\": \"\", \"bundesland\": \"\", \"hr_nummer\": \"\",\n \"konfidenz\": <0..1>, \"bestaetigt\": <0 oder 1, 1 nur bei eindeutigem Registertreffer zur Adresse>,\n \"quelle_primaer\": \"\", \"belege\": \"<1-3 Sätze Begründung inkl. Quell-URLs>\"}\n\nBei unsicherer Zuordnung niedrige konfidenz und bestaetigt=0. Rate nicht ins Blaue — lieber null.\"\"\"\n\nFELDER = [\"branche_text\", \"branche_nace\", \"gewerk\", \"segment\", \"mitarbeiter_kl\", \"umsatz_kl\",\n \"gruendungsjahr\", \"rechtsform\", \"website\", \"ort\", \"bundesland\", \"hr_nummer\",\n \"konfidenz\", \"bestaetigt\", \"quelle_primaer\", \"belege\"]\n\n\ndef api_key():\n path = Path.home() / \".ssh\" / \"anthropic_api_key\"\n for line in path.read_text().splitlines():\n line = line.strip()\n if line.startswith(\"sk-ant-\"):\n return line\n sys.exit(f\"kein sk-ant-Key in {path}\")\n\n\ndef json_objekt(text):\n text = text.strip().removeprefix(\"```json\").removeprefix(\"```\").removesuffix(\"```\").strip()\n start = text.find(\"{\")\n end = text.rfind(\"}\")\n return json.loads(text[start:end + 1])\n\n\ndef frage(key, kunde):\n teile = [kunde[\"name_roh\"]]\n if kunde.get(\"name_1\"):\n teile.append(kunde[\"name_1\"])\n adr = \", \".join(x for x in [kunde.get(\"strasse\"), kunde.get(\"plz\"),\n LAND_NAMES.get(kunde.get(\"land\"), kunde.get(\"land\"))] if x)\n user = f\"Firma: {' / '.join(teile)}\\nAdresse: {adr or 'unbekannt'}\"\n\n body = json.dumps({\n \"model\": MODELL,\n \"max_tokens\": 2500,\n \"system\": SYSTEM,\n \"tools\": [{\"type\": \"web_search_20250305\", \"name\": \"web_search\", \"max_uses\": 4}],\n \"messages\": [{\"role\": \"user\", \"content\": user}],\n }).encode()\n\n req = urllib.request.Request(\n \"https://api.anthropic.com/v1/messages\", data=body,\n headers={\"x-api-key\": key, \"anthropic-version\": \"2023-06-01\",\n \"content-type\": \"application/json\"})\n with urllib.request.urlopen(req, timeout=200) as r:\n resp = json.load(r)\n # web_search erzeugt mehrere Text-Blöcke; das JSON steht in irgendeinem davon\n # (finale Antwort, evtl. mit nachgestelltem Kommentar) -> rückwärts suchen.\n texte = [b[\"text\"] for b in resp[\"content\"] if b[\"type\"] == \"text\" and b[\"text\"].strip()]\n for t in reversed(texte):\n try:\n obj = json_objekt(t)\n except (ValueError, json.JSONDecodeError):\n continue\n if isinstance(obj, dict) and (\"gewerk\" in obj or \"branche_text\" in obj):\n return obj\n raise ValueError(\"keine JSON-Antwort in den Text-Blöcken\")\n\n\ndef main():\n ap = argparse.ArgumentParser()\n here = Path(__file__).resolve().parent\n ap.add_argument(\"--db\", default=here.parent / \"data\" / \"wuerth.db\")\n ap.add_argument(\"--out\", default=here / \"enrichment_neu\")\n ap.add_argument(\"--limit\", type=int, default=0, help=\"max Kunden dieser Lauf (0 = alle)\")\n ap.add_argument(\"--workers\", type=int, default=6, help=\"parallele Aufrufe\")\n ap.add_argument(\"--pack\", action=\"store_true\", help=\"JSONL -> result_000.json\")\n args = ap.parse_args()\n\n out = Path(args.out)\n out.mkdir(exist_ok=True)\n jsonl = out / \"branche.jsonl\"\n\n if args.pack:\n # bei Retries kann eine kunde_id mehrfach in der JSONL stehen -> letzte gewinnt\n by_id = {}\n for l in jsonl.read_text().splitlines():\n if l.strip():\n r = json.loads(l)\n by_id[r[\"kunde_id\"]] = r\n recs = list(by_id.values())\n (out / \"result_000.json\").write_text(json.dumps(recs, ensure_ascii=False, indent=1))\n print(f\"gepackt: {len(recs)} Records -> {out/'result_000.json'}\")\n return\n\n import sqlite3\n con = sqlite3.connect(args.db)\n con.row_factory = sqlite3.Row\n kunden = con.execute(\n \"SELECT kunde_id, name_roh, name_1, strasse, plz, land FROM kunden ORDER BY kunde_id\"\n ).fetchall()\n\n # Als erledigt gilt nur, wer ein belastbares Ergebnis hat (konfidenz gesetzt);\n # null-Ergebnisse (Parse-/Netzfehler) werden erneut gezogen.\n erledigt = set()\n if jsonl.exists():\n for l in jsonl.read_text().splitlines():\n if l.strip():\n r = json.loads(l)\n if r.get(\"konfidenz\") is not None:\n erledigt.add(r[\"kunde_id\"])\n\n global key\n key = api_key()\n offen = [dict(k) for k in kunden if k[\"kunde_id\"] not in erledigt]\n if args.limit:\n offen = offen[:args.limit]\n\n import threading\n from concurrent.futures import ThreadPoolExecutor\n lock = threading.Lock()\n zaehler = {\"n\": 0}\n fh = jsonl.open(\"a\")\n\n def bearbeite(k):\n try:\n roh = frage(key, k)\n except Exception as e: # Einzelfehler nicht den ganzen Lauf abbrechen lassen\n roh = {\"gewerk\": None, \"segment\": None, \"konfidenz\": None, \"belege\": f\"FEHLER: {e}\"}\n rec = {\"kunde_id\": k[\"kunde_id\"], **{f: roh.get(f) for f in FELDER}}\n with lock:\n fh.write(json.dumps(rec, ensure_ascii=False) + \"\\n\")\n fh.flush()\n zaehler[\"n\"] += 1\n print(f\"[{zaehler['n']}/{len(offen)}] kunde {k['kunde_id']:>4} \"\n f\"{(k['name_roh'] or '')[:38]:<38} -> {rec['gewerk']} / {rec['segment']} ({rec['konfidenz']})\",\n flush=True)\n\n with ThreadPoolExecutor(max_workers=args.workers) as ex:\n list(ex.map(bearbeite, offen))\n fh.close()\n print(f\"fertig: {len(offen)} Kunden bearbeitet, {len(erledigt)+len(offen)}/{len(kunden)} gesamt\")\n\n\nif __name__ == \"__main__\":\n main()\n","numLines":176,"startLine":1,"totalLines":176}},"sourceToolAssistantUUID":"5d40d4ef-e220-4bc7-9cdd-d4266d2d41e4","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"type":"last-prompt","lastPrompt":"kanst du die kundenreicherung vorbereiten, so dass die calls von Haiku gemacht werden können, oder hilft das nicht?","leafUuid":"5d672e73-f846-4c39-ba7a-d836960539ee","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b"} {"type":"ai-title","aiTitle":"Clarify question assistant customization via skills","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b"} {"type":"mode","mode":"normal","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b"} {"type":"permission-mode","permissionMode":"default","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b"} 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antworten."}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3971,"cache_read_input_tokens":80801,"output_tokens":1634,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3971},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1634,"cache_read_input_tokens":80801,"cache_creation_input_tokens":3971,"cache_creation":{"ephemeral_5m_input_tokens":3971,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcnuxxNz7RHgJ5sNya52B","type":"assistant","uuid":"170c5485-2ed8-4938-b6da-6fa3f8c4dba5","timestamp":"2026-07-07T15:13:47.916Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"170c5485-2ed8-4938-b6da-6fa3f8c4dba5","isSidechain":false,"message":{"model":"claude-opus-4-8","id":"msg_011CcnuxzLp651LvKYaoNAvi","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ko1goG3RD8dRHndwkax8Qc","name":"Skill","input":{"skill":"claude-api"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3971,"cache_read_input_tokens":80801,"output_tokens":1634,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3971},"inference_geo":"not_available","iterations":[{"input_tokens":2,"output_tokens":1634,"cache_read_input_tokens":80801,"cache_creation_input_tokens":3971,"cache_creation":{"ephemeral_5m_input_tokens":3971,"ephemeral_1h_input_tokens":0},"type":"message"}],"speed":"standard"},"diagnostics":null},"requestId":"req_011CcnuxxNz7RHgJ5sNya52B","type":"assistant","uuid":"d200c628-a08f-47f0-9e50-31e85027df8c","timestamp":"2026-07-07T15:13:47.918Z","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"d200c628-a08f-47f0-9e50-31e85027df8c","isSidechain":false,"promptId":"ddb25f5d-1c5f-4bb0-834c-8d4902352404","type":"user","message":{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01Ko1goG3RD8dRHndwkax8Qc","content":"Launching skill: claude-api"}]},"uuid":"01913fff-d124-4441-861f-f741759c7311","timestamp":"2026-07-07T15:14:00.930Z","toolUseResult":{"success":true,"commandName":"claude-api","allowedTools":["Read","Grep","Glob","WebFetch"]},"sourceToolAssistantUUID":"d200c628-a08f-47f0-9e50-31e85027df8c","session_id":"0891e8e1-97a0-46c9-8283-ebcf659af06b","userType":"external","entrypoint":"cli","cwd":"/Users/marcus.hinz/projects/08-07/pipeline","sessionId":"0891e8e1-97a0-46c9-8283-ebcf659af06b","version":"2.1.202","gitBranch":"main"} {"parentUuid":"01913fff-d124-4441-861f-f741759c7311","isSidechain":false,"promptId":"ddb25f5d-1c5f-4bb0-834c-8d4902352404","type":"user","message":{"role":"user","content":[{"type":"text","text":"Base directory for this skill: /private/tmp/claude-502/bundled-skills/2.1.202/385dab59f40c50e75ef10d66c74ec730/claude-api\n\n# Building LLM-Powered Applications with Claude\n\nThis skill helps you build LLM-powered applications with Claude. Choose the right surface based on your needs, detect the project language, then read the relevant language-specific documentation.\n\n## Before You Start\n\nScan the target file (or, if no target file, the prompt and project) for non-Anthropic provider markers — `import openai`, `from openai`, `langchain_openai`, `OpenAI(`, `gpt-4`, `gpt-5`, file names like `agent-openai.py` or `*-generic.py`, or any explicit instruction to keep the code provider-neutral. If you find any, stop and tell the user that this skill produces Claude/Anthropic SDK code; ask whether they want to switch the file to Claude or want a non-Claude implementation. Do not edit a non-Anthropic file with Anthropic SDK calls.\n\n## Output Requirement\n\nWhen the user asks you to add, modify, or implement a Claude feature, your code must call Claude through one of:\n\n1. **The official Anthropic SDK** for the project's language (`anthropic`, `@anthropic-ai/sdk`, `com.anthropic.*`, etc.). This is the default whenever a supported SDK exists for the project.\n2. **Raw HTTP** (`curl`, `requests`, `fetch`, `httpx`, etc.) — only when the user explicitly asks for cURL/REST/raw HTTP, the project is a shell/cURL project, or the language has no official SDK.\n\nNever mix the two — don't reach for `requests`/`fetch` in a Python or TypeScript project just because it feels lighter. Never fall back to OpenAI-compatible shims.\n\n**Never guess SDK usage.** Function names, class names, namespaces, method signatures, and import paths must come from explicit documentation — either the `{lang}/` files in this skill or the official SDK repositories or documentation links listed in `shared/live-sources.md`. If the binding you need is not explicitly documented in the skill files, WebFetch the relevant SDK repo from `shared/live-sources.md` before writing code. Do not infer Ruby/Java/Go/PHP/C# APIs from cURL shapes or from another language's SDK.\n\n**If WebFetch or repository access fails** (network restricted, timeouts, clone blocked): do not keep retrying — write code from the patterns and namespace/package tables in the `{lang}/` file, run the compiler or interpreter on it, and iterate on the error output. For statically-typed SDKs (C#, Java, Go) a compile-fix loop against local errors reaches working code faster than blocked network research.\n\n## Defaults\n\nUnless the user requests otherwise:\n\nFor the Claude model version, please use Claude Opus 4.8, which you can access via the exact model string `claude-opus-4-8`. Please default to using adaptive thinking (`thinking: {type: \"adaptive\"}`) for anything remotely complicated. And finally, please default to streaming for any request that may involve long input, long output, or high `max_tokens` — it prevents hitting request timeouts. Use the SDK's `.get_final_message()` / `.finalMessage()` helper to get the complete response if you don't need to handle individual stream events\n\n## ⚠️ API Drift — Your Training Prior May Be Stale\n\nSeveral common Claude API shapes changed in 2025–2026. If you recall a pattern from training, verify it against the `{lang}/` files in this skill before writing — the rows below are the most frequent drift points:\n\n| Area | Stale prior | Current API |\n|---|---|---|\n| Extended thinking | `thinking: {type: \"enabled\", budget_tokens: N}` | On Claude 4.6+ models: `thinking: {type: \"adaptive\"}`. `budget_tokens` is deprecated on Opus 4.6 / Sonnet 4.6 and **rejected with a 400** on Fable 5 / Sonnet 5 / Opus 4.8 / 4.7. Pre-4.6 models still use `budget_tokens`. |\n| Web search / web fetch tool type | `web_search_20250305`, `web_fetch_20250910` | `web_search_20260209`, `web_fetch_20260209` (dynamic filtering) on Opus 4.8/4.7/4.6, Sonnet 5, and Sonnet 4.6. Older models keep the basic variants; on Vertex AI only basic `web_search_20250305` is available (web fetch is not on Vertex) — see the Server Tools QR below. |\n| PHP parameter names | snake_case wire names as named args (`max_tokens`) | Top-level named args are camelCase (`maxTokens`). Nested array keys vary by feature (e.g. `'taskBudget'`, `'skillID'`, `'mcp_server_name'`) — copy the exact key from the documented example; do not bulk-convert. |\n\nThe `{lang}/` files in this skill are authoritative over recalled patterns.\n\n---\n\n## Subcommands\n\nIf the User Request at the bottom of this prompt is a bare subcommand string (no prose), search every **Subcommands** table in this document — including any in sections appended below — and follow the matching Action column directly. This lets users invoke specific flows via `/claude-api `. If no table in the document matches, treat the request as normal prose.\n\n| Subcommand | Action |\n|---|---|\n| `migrate` | Migrate existing Claude API code to a newer model. **Read `shared/model-migration.md` immediately** and follow it in order: Step 0 (confirm scope — ask which files/directories before any edit), Step 1 (classify each file), then the per-target breaking-changes section. Do not summarize the guide — execute it. If the user did not name a target model, ask which model to migrate to in the same turn as the scope question. |\n\n---\n\n## Language Detection\n\nBefore reading code examples, determine which language the user is working in:\n\n1. **Look at project files** to infer the language:\n\n - `*.py`, `requirements.txt`, `pyproject.toml`, `setup.py`, `Pipfile` → **Python** — read from `python/`\n - `*.ts`, `*.tsx`, `package.json`, `tsconfig.json` → **TypeScript** — read from `typescript/`\n - `*.js`, `*.jsx` (no `.ts` files present) → **TypeScript** — JS uses the same SDK, read from `typescript/`\n - `*.java`, `pom.xml`, `build.gradle` → **Java** — read from `java/`\n - `*.kt`, `*.kts`, `build.gradle.kts` → **Java** — Kotlin uses the Java SDK, read from `java/`\n - `*.scala`, `build.sbt` → **Java** — Scala uses the Java SDK, read from `java/`\n - `*.go`, `go.mod` → **Go** — read from `go/`\n - `*.rb`, `Gemfile` → **Ruby** — read from `ruby/`\n - `*.cs`, `*.csproj` → **C#** — read from `csharp/`\n - `*.php`, `composer.json` → **PHP** — read from `php/`\n\n2. **If multiple languages detected** (e.g., both Python and TypeScript files):\n\n - Check which language the user's current file or question relates to\n - If still ambiguous, ask: \"I detected both Python and TypeScript files. Which language are you using for the Claude API integration?\"\n\n3. **If language can't be inferred** (empty project, no source files, or unsupported language):\n\n - Use AskUserQuestion with options: Python, TypeScript, Java, Go, Ruby, cURL/raw HTTP, C#, PHP\n - If AskUserQuestion is unavailable, default to Python examples and note: \"Showing Python examples. Let me know if you need a different language.\"\n\n4. **If unsupported language detected** (Rust, Swift, C++, Elixir, etc.):\n\n - Suggest cURL/raw HTTP examples from `curl/` and note that community SDKs may exist\n - Offer to show Python or TypeScript examples as reference implementations\n\n5. **If user needs cURL/raw HTTP examples**, read from `curl/`.\n\n### Language-Specific Feature Support\n\n| Language | Tool Runner | Managed Agents | Notes |\n| ---------- | ----------- | -------------- | ------------------------------------- |\n| Python | Yes (beta) | Yes (beta) | Full support — `@beta_tool` decorator |\n| TypeScript | Yes (beta) | Yes (beta) | Full support — `betaZodTool` + Zod |\n| Java | Yes (beta) | Yes (beta) | Beta tool use with annotated classes |\n| Go | Yes (beta) | Yes (beta) | `BetaToolRunner` in `toolrunner` pkg |\n| Ruby | Yes (beta) | Yes (beta) | `BaseTool` + `tool_runner` in beta |\n| C# | Yes (beta) | Yes (beta) | `BetaToolRunner` + raw JSON schema |\n| PHP | Yes (beta) | Yes (beta) | `BetaRunnableTool` + `toolRunner()` |\n| cURL | N/A | Yes (beta) | Raw HTTP, no SDK features |\n\n> **Managed Agents code examples**: dedicated language-specific READMEs are provided for Python, TypeScript, Go, Ruby, PHP, Java, and cURL (`{lang}/managed-agents/README.md`, `curl/managed-agents.md`). Read your language's README plus the language-agnostic `shared/managed-agents-*.md` concept files. **Agents are persistent — create once, reference by ID.** Store the agent ID returned by `agents.create` and pass it to every subsequent `sessions.create`; do not call `agents.create` in the request path. The Anthropic CLI (`ant`) is one convenient way to create agents and environments from version-controlled YAML — see `shared/anthropic-cli.md`. If a binding you need isn't shown in the README, WebFetch the relevant entry from `shared/live-sources.md` rather than guess. C# has beta Managed Agents support via `client.Beta.Agents` and related namespaces.\n\n---\n\n## Which Surface Should I Use?\n\n> **Start simple.** Default to the simplest tier that meets your needs. Single API calls and workflows handle most use cases — only reach for agents when the task genuinely requires open-ended, model-driven exploration.\n\n| Use Case | Tier | Recommended Surface | Why |\n| ----------------------------------------------- | --------------- | ------------------------- | ------------------------------------------------------------ |\n| Classification, summarization, extraction, Q&A | Single LLM call | **Claude API** | One request, one response |\n| Batch processing or embeddings | Single LLM call | **Claude API** | Specialized endpoints |\n| Multi-step pipelines with code-controlled logic | Workflow | **Claude API + tool use** | You orchestrate the loop |\n| Custom agent with your own tools | Agent | **Claude API + tool use** | Maximum flexibility |\n| Server-managed stateful agent with workspace | Agent | **Managed Agents** | Anthropic runs the loop and hosts the tool-execution sandbox |\n| Persisted, versioned agent configs | Agent | **Managed Agents** | Agents are stored objects; sessions pin to a version |\n| Long-running multi-turn agent with file mounts | Agent | **Managed Agents** | Per-session containers, SSE event stream, Skills + MCP |\n\n> **Note:** Managed Agents is the right choice when you want Anthropic to run the agent loop *and* host the container where tools execute — file ops, bash, code execution all run in the per-session workspace. If you want to host the compute yourself or run your own custom tool runtime, Claude API + tool use is the right choice — use the tool runner for automatic loop handling, or the manual loop for fine-grained control (approval gates, custom logging, conditional execution).\n\n> **Cloud-provider access.** **Claude Platform on AWS** is Anthropic-operated with same-day API parity — see `shared/claude-platform-on-aws.md` for client setup. For per-feature availability on **Claude Platform on AWS**, **Amazon Bedrock**, **Google Vertex AI**, and **Microsoft Foundry**, see `shared/platform-availability.md` — that table is the single source of truth in this skill; do not infer availability from anywhere else.\n\n### Decision Tree\n\n```\nWhat does your application need?\n\n0. Which provider?\n ├── First-party API or Claude Platform on AWS → continue (full surface available; per-feature exceptions in shared/platform-availability.md).\n └── Amazon Bedrock, Google Vertex AI, or Microsoft Foundry → Claude API (+ tool use for agents); see shared/platform-availability.md for per-feature support.\n\n1. Single LLM call (classification, summarization, extraction, Q&A)\n └── Claude API — one request, one response\n\n2. Do you want Anthropic to run the agent loop and host a per-session\n container where Claude executes tools (bash, file ops, code)?\n └── Yes → Managed Agents — server-managed sessions, persisted agent configs,\n SSE event stream, Skills + MCP, file mounts.\n Examples: \"stateful coding agent with a workspace per task\",\n \"long-running research agent that streams events to a UI\",\n \"agent with persisted, versioned config used across many sessions\"\n\n3. Workflow (multi-step, code-orchestrated, with your own tools)\n └── Claude API with tool use — you control the loop\n\n4. Open-ended agent (model decides its own trajectory, your own tools, you host the compute)\n └── Claude API agentic loop (maximum flexibility)\n```\n\n### Should I Build an Agent?\n\nBefore choosing the agent tier, check all four criteria:\n\n- **Complexity** — Is the task multi-step and hard to fully specify in advance? (e.g., \"turn this design doc into a PR\" vs. \"extract the title from this PDF\")\n- **Value** — Does the outcome justify higher cost and latency?\n- **Viability** — Is Claude capable at this task type?\n- **Cost of error** — Can errors be caught and recovered from? (tests, review, rollback)\n\nIf the answer is \"no\" to any of these, stay at a simpler tier (single call or workflow).\n\n---\n\n## Architecture\n\nEverything goes through `POST /v1/messages`. Tools and output constraints are features of this single endpoint — not separate APIs.\n\n**User-defined tools** — You define tools (via decorators, Zod schemas, or raw JSON), and the SDK's tool runner handles calling the API, executing your functions, and looping until Claude is done. For full control, you can write the loop manually.\n\n**Server-side tools** — Anthropic-hosted tools that run on Anthropic's infrastructure. Code execution is fully server-side (declare it in `tools`, Claude runs code automatically). Computer use can be server-hosted or self-hosted.\n\n**Structured outputs** — Constrains the Messages API response format (`output_config.format`) and/or tool parameter validation (`strict: true`). The recommended approach is `client.messages.parse()` which validates responses against your schema automatically. Note: the old `output_format` parameter is deprecated; use `output_config: {format: {...}}` on `messages.create()`.\n\n**Supporting endpoints** — Batches (`POST /v1/messages/batches`), Files (`POST /v1/files`), Token Counting (`POST /v1/messages/count_tokens` — see `shared/token-counting.md`), and Models (`GET /v1/models`, `GET /v1/models/{id}` — live capability/context-window discovery) feed into or support Messages API requests.\n\n---\n\n## Current Models (cached: 2026-06-24)\n\n| Model | Model ID | Context | Input $/1M | Output $/1M |\n| ----------------- | ------------------- | -------------- | ---------- | ----------- |\n| Claude Fable 5 | `claude-fable-5` | 1M | $10.00 | $50.00 |\n| Claude Mythos 5 (Project Glasswing only) | `claude-mythos-5` | 1M | $10.00 | $50.00 |\n| Claude Opus 4.8 | `claude-opus-4-8` | 1M | $5.00 | $25.00 |\n| Claude Opus 4.7 | `claude-opus-4-7` | 1M | $5.00 | $25.00 |\n| Claude Opus 4.6 | `claude-opus-4-6` | 1M | $5.00 | $25.00 |\n| Claude Sonnet 5 | `claude-sonnet-5` | 1M | $3.00 ($2.00 intro through 2026-08-31) | $15.00 ($10.00 intro) |\n| Claude Sonnet 4.6 | `claude-sonnet-4-6` | 1M | $3.00 | $15.00 |\n| Claude Haiku 4.5 | `claude-haiku-4-5` | 200K | $1.00 | $5.00 |\n\n**ALWAYS use `claude-opus-4-8` unless the user explicitly names a different model.** This is non-negotiable. Do not use `claude-sonnet-5`, `claude-sonnet-4-6`, or any other model unless the user literally says \"use sonnet\" or \"use haiku\". Never downgrade for cost — that's the user's decision, not yours. Use `claude-fable-5` only when the user explicitly asks for Claude Fable 5, \"fable\", or Anthropic's most capable model — it has different API behavior than the Opus family (see below) and pricing that exceeds Opus-tier.\n\n### Claude Fable 5 (`claude-fable-5`) — most capable widely released model\n\nClaude Fable 5 is Anthropic's most capable widely released model, for the most demanding reasoning and long-horizon agentic work. **Claude Mythos 5** (`claude-mythos-5`) offers the same capabilities, pricing, and API surface through Project Glasswing (participation is the only way to access it), succeeding the invitation-only Claude Mythos Preview (`claude-mythos-preview`) — everything below applies to both models. 1M context window (the maximum is also the default), 128K max output. Key API differences from Opus-tier — see `shared/model-migration.md` → Migrating to Claude Fable 5 for details:\n\n- **Thinking is always on** — omit the `thinking` parameter entirely (or send `{type: \"adaptive\"}`). Any other explicit configuration is rejected: `{type: \"disabled\"}` and `{type: \"enabled\", budget_tokens: N}` both return a 400. Control depth with `output_config.effort` (supports `low` through `xhigh` and `max`).\n- **The raw chain of thought is never returned** — responses carry regular `thinking` blocks (not `redacted_thinking`): `display: \"summarized\"` returns a readable summary, `\"omitted\"` (the default) leaves the `thinking` field as an empty string. Replay rules: pass thinking blocks back exactly as received on the same model (including empty-text blocks — the API rejects *modified* blocks, not read ones); a **different** model **drops** them from the prompt (typically silently — not an error; the drop happens before pricing, so dropped blocks aren't billed and there's nothing to strip). Regular thinking blocks from other models replay across models freely.\n- **Tokenizer** — same tokenizer as Opus 4.8 (introduced with Opus 4.7). Token counts are roughly unchanged when migrating from Opus 4.7/4.8; per-token pricing differs. Coming from Opus 4.6, Sonnet, Haiku, or older, re-baseline with `count_tokens`.\n- **`refusal` stop reason — handle it, and opt into fallbacks by default** — safety classifiers may decline a request (HTTP 200, `stop_reason: \"refusal\"`, with a `stop_details` category). A pre-output refusal has an empty `content` array and is not billed at all; a mid-stream refusal bills the already-streamed output — discard the partial output. Always check `stop_reason` before reading `content`. Recovery is **opt-in on the API**: most Claude consumer surfaces ship with built-in Claude Opus 4.8 fallbacks, but an API request that doesn't opt in simply stops on a refusal — and false positives on benign adjacent work (security tooling, life-sciences tasks) do happen. **When you write `claude-fable-5` code, include the server-side `fallbacks` parameter by default** (`betas: [\"server-side-fallback-2026-06-01\"]` + `fallbacks: [{\"model\": \"claude-opus-4-8\"}]`; Claude API and Claude Platform on AWS): a declined request is transparently re-served by the fallback model inside the same call, with credit-style repricing applied automatically (a decline before any output isn't billed; the rescue bills at the fallback model's own rates). Tell the user you've enabled it; drop it only if they decline. The GA SDKs' client-side `BetaRefusalFallbackMiddleware` + `BetaFallbackState` handle retry everywhere server-side fallbacks aren't supported (incl. Amazon Bedrock, Vertex AI, Microsoft Foundry); fallback credit refunds the cache-switch cost of client-side retries. Code examples: the Refusal Fallbacks section of your language's claude-api doc; full semantics in the migration guide's refusal section.\n- **No assistant prefill** — same as the rest of the 4.6+ family.\n- **30-day data retention required** — Claude Fable 5 is not available under zero data retention; requests from an org whose retention configuration doesn't meet the requirement return `400 invalid_request_error`.\n- **Longer turns, different prompting** — single requests on hard tasks can run many minutes (plan timeouts/streaming/progress UX); effort sweeps should include low/medium for routine work; prompts written for prior models are often too prescriptive and reduce output quality. See `shared/model-migration.md` → Migrating to Claude Fable 5 → Behavioral shifts (prompt-tunable) for the recommended prompt snippets (anti-overplanning, no-tidying, grounded progress claims, boundaries, async sub-agents, memory, `send_to_user`).\n\n**CRITICAL: Use only the exact model ID strings from the table above — they are complete as-is. Do not append date suffixes.** For example, use `claude-sonnet-4-6`, never `claude-sonnet-4-6-20251114` or any other date-suffixed variant you might recall from training data. If the user requests an older model not in the table (e.g., \"opus 4.5\", \"sonnet 3.7\"), read `shared/models.md` for the exact ID — do not construct one yourself.\n\nA note: if any of the model strings above look unfamiliar to you, that's to be expected — that just means they were released after your training data cutoff. Rest assured they are real models; we wouldn't mess with you like that.\n\n**Live capability lookup:** The table above is cached. When the user asks \"what's the context window for X\", \"does X support vision/thinking/effort\", or \"which models support Y\", query the Models API (`client.models.retrieve(id)` / `client.models.list()`) — see `shared/models.md` for the field reference and capability-filter examples.\n\n---\n\n## Authentication (Quick Reference)\n\n**An unset `ANTHROPIC_API_KEY` does NOT mean there are no credentials.** The SDKs and the `ant` CLI resolve credentials in this order (first match wins): `ANTHROPIC_API_KEY` → `ANTHROPIC_AUTH_TOKEN` → the `ANTHROPIC_PROFILE`-selected or active OAuth profile from `ant auth login` → Workload Identity Federation env vars → the default profile on disk. A bare `Anthropic()` / `new Anthropic()` / `anthropic.NewClient()` works after `ant auth login` with no env var set.\n\n**When you need to call the API and `ANTHROPIC_API_KEY` is unset, don't ask the user for a key.** First run `ant auth status` — it shows which credential source and profile is active. If it reports an active profile:\n\n- **SDK code or `ant` CLI:** just run it. The zero-arg client constructor and every `ant …` subcommand pick up the profile automatically — no env var needed.\n- **Raw `curl` / HTTP:** get a short-lived token with `ant auth print-credentials --access-token` and send it as `Authorization: Bearer ` **plus** the header `anthropic-beta: oauth-2025-04-20` (OAuth tokens go on `Authorization: Bearer`, not `x-api-key:` — converting a curl from an API key is a header change, not a key swap). Always pass `--access-token`; the no-flag form prints JSON, not a bare token.\n\nOnly ask the user for a key if `ant auth status` reports no active credential source (or `ant` itself isn't installed). Suggest `ant auth login` as the first option — it stores a profile under `~/.config/anthropic/` that the SDKs read automatically — and an exported `ANTHROPIC_API_KEY` as the alternative.\n\nFull auth details (named profiles, scopes, the API-key-shadows-profile trap, refresh-token expiry): `shared/anthropic-cli.md`.\n\n---\n\n## Thinking & Effort (Quick Reference)\n\n**Fable 5 / Opus 4.8 / 4.7 / Sonnet 5 — Adaptive thinking only:** Use `thinking: {type: \"adaptive\"}`. `thinking: {type: \"enabled\", budget_tokens: N}` returns a 400 — adaptive is the only on-mode. On Opus 4.8, Opus 4.7, and Sonnet 5, `{type: \"disabled\"}` and omitting `thinking` both work (on Sonnet 5, omitting runs adaptive; on Opus 4.7/4.8, omitting runs without thinking — set `{type: \"adaptive\"}` explicitly); on Fable 5, an explicit `{type: \"disabled\"}` returns a 400 — omit the `thinking` param entirely instead. Sampling parameters (`temperature`, `top_p`, `top_k`) are also removed and will 400. Opus 4.8 keeps the same request surface as 4.7 (no new breaking changes) — see `shared/model-migration.md` → Migrating to Opus 4.8 for the behavioral re-tuning, and → Migrating to Opus 4.7 for the full breaking-change list when coming from 4.6 or earlier. Note: with `thinking` disabled, Opus 4.8 may write longer reasoning into the visible response — leave adaptive thinking on, or add a final-answer-only instruction (see the migration guide).\n**Opus 4.6 — Adaptive thinking (recommended):** Use `thinking: {type: \"adaptive\"}`. Claude dynamically decides when and how much to think. No `budget_tokens` needed — `budget_tokens` is deprecated on Opus 4.6 and Sonnet 4.6 and should not be used for new code. Adaptive thinking also automatically enables interleaved thinking (no beta header needed). **When the user asks for \"extended thinking\", a \"thinking budget\", or `budget_tokens`: always use Fable 5, Opus 4.8, 4.7, or 4.6 with `thinking: {type: \"adaptive\"}`. The concept of a fixed token budget for thinking is deprecated — adaptive thinking replaces it. Do NOT use `budget_tokens` for new 4.6/4.7/4.8 code and do NOT switch to an older model.** *Gradual-migration carve-out:* `budget_tokens` is still functional on Opus 4.6 and Sonnet 4.6 as a transitional escape hatch — if you're migrating existing code and need a hard token ceiling before you've tuned `effort`, see `shared/model-migration.md` → Transitional escape hatch. Note: this carve-out does **not** apply to Fable 5, Opus 4.7 or 4.8 — `budget_tokens` is fully removed there.\n**Effort parameter (GA, no beta header):** Controls thinking depth and overall token spend via `output_config: {effort: \"low\"|\"medium\"|\"high\"|\"max\"}` (inside `output_config`, not top-level). Default is `high` (equivalent to omitting it). `max` is supported on Fable 5, Opus 4.6 and later, Sonnet 5, and Sonnet 4.6 (not Haiku or earlier Sonnets). Opus 4.7 added `\"xhigh\"` (between `high` and `max`) — the best setting for most coding and agentic use cases on Fable 5 / Opus 4.7/4.8 / Sonnet 5, and the default in Claude Code; use a minimum of `high` for most intelligence-sensitive work. Works on Fable 5, Opus 4.5, Opus 4.6, Opus 4.7, Opus 4.8, Sonnet 5, and Sonnet 4.6. Will error on Sonnet 4.5 / Haiku 4.5. On Fable 5, Opus 4.7/4.8, and Sonnet 5, effort matters more than on any prior model in their tier — re-tune it when migrating, and run long-horizon/agentic tasks at `high`/`xhigh` with the full task spec given up front. Combine with adaptive thinking for the best cost-quality tradeoffs. Lower effort means fewer and more-consolidated tool calls, less preamble, and terser confirmations — `high` is often the sweet spot balancing quality and token efficiency; use `max` when correctness matters more than cost; use `low` for subagents or simple tasks.\n\n**Thinking display — `\"omitted\"` by default on Fable 5 / Mythos 5 / Opus 4.8 / 4.7 / Sonnet 5:** `display: \"summarized\"` returns a readable summary of the reasoning; `\"omitted\"` (the default on all five — a silent change from Opus 4.6 and Sonnet 4.6, where it was `\"summarized\"`) streams `thinking` blocks with empty text. `display` controls visibility only — thinking happens and is billed the same under every setting; the raw chain of thought is never exposed on any model. If you stream reasoning to users, the default looks like a long pause before output — set `thinking: {type: \"adaptive\", display: \"summarized\"}` explicitly. (Independent of display, echo thinking blocks back unchanged when continuing on the same model; other models silently ignore them — see the migration guide.)\n\n**Task Budgets (beta, Fable 5 / Opus 4.7 / 4.8 / Sonnet 5):** `output_config: {task_budget: {type: \"tokens\", total: N}}` tells the model how many tokens it has for a full agentic loop — it sees a running countdown and self-moderates (minimum 20,000; beta header `task-budgets-2026-03-13`). Distinct from `max_tokens`, which is an enforced per-response ceiling the model is not aware of. See `shared/model-migration.md` → Task Budgets.\n\n**Sonnet 4.6:** Supports adaptive thinking (`thinking: {type: \"adaptive\"}`). `budget_tokens` is deprecated on Sonnet 4.6 — use adaptive thinking instead.\n\n**Older models (only if explicitly requested):** If the user specifically asks for Sonnet 4.5 or another older model, use `thinking: {type: \"enabled\", budget_tokens: N}`. `budget_tokens` must be less than `max_tokens` (minimum 1024). Never choose an older model just because the user mentions `budget_tokens` — use Opus 4.8 with adaptive thinking instead.\n\n---\n\n## Compaction (Quick Reference)\n\n**Beta, Fable 5, Opus 4.8, Opus 4.7, Opus 4.6, Sonnet 5, and Sonnet 4.6.** For long-running conversations that may exceed the 1M context window, enable server-side compaction. The API automatically summarizes earlier context when it approaches the trigger threshold (default: 150K tokens). Requires beta header `compact-2026-01-12`.\n\n**Critical:** Append `response.content` (not just the text) back to your messages on every turn. Compaction blocks in the response must be preserved — the API uses them to replace the compacted history on the next request. Extracting only the text string and appending that will silently lose the compaction state.\n\nSee `{lang}/claude-api/README.md` (Compaction section) for code examples. Full docs via WebFetch in `shared/live-sources.md`.\n\n---\n\n## Prompt Caching (Quick Reference)\n\n**Prefix match.** Any byte change anywhere in the prefix invalidates everything after it. Render order is `tools` → `system` → `messages`. Keep stable content first (frozen system prompt, deterministic tool list), put volatile content (timestamps, per-request IDs, varying questions) after the last `cache_control` breakpoint.\n\n**Mid-conversation operator instructions** (Claude Opus 4.8 only; no beta header): append `{\"role\": \"system\", ...}` to `messages[]` instead of editing top-level `system`. Preserves the cached history prefix and is the prompt-injection-safe operator channel. See `shared/prompt-caching.md` § Mid-conversation system messages.\n\n**Top-level auto-caching** (`cache_control: {type: \"ephemeral\"}` on `messages.create()`) is the simplest option when you don't need fine-grained placement. Max 4 breakpoints per request. Minimum cacheable prefix is ~1024 tokens — shorter prefixes silently won't cache.\n\n**Verify with `usage.cache_read_input_tokens`** — if it's zero across repeated requests, a silent invalidator is at work (`datetime.now()` in system prompt, unsorted JSON, varying tool set).\n\nFor placement patterns, architectural guidance, and the silent-invalidator audit checklist: read `shared/prompt-caching.md`. Language-specific syntax: `{lang}/claude-api/README.md` (Prompt Caching section).\n\n---\n\n## Fast Mode (Quick Reference)\n\n**Research preview, Opus 4.8 / 4.7 only.** Opus 4.7 fast mode is deprecated — after removal, `speed: \"fast\"` on 4.7 returns an error. Opus 4.8 is the durable fast-capable tier. Fast mode runs the same model at up to 2.5x higher output tokens per second, at premium pricing. Three things are required on every request: use the **beta** messages endpoint (`client.beta.messages.…`), pass the beta flag `fast-mode-2026-02-01`, and set `speed: \"fast\"` as a top-level request parameter (not a header, not in `extra_body`).\n\n```python\nclient.beta.messages.create(\n model=\"claude-opus-4-8\", max_tokens=4096,\n speed=\"fast\", betas=[\"fast-mode-2026-02-01\"],\n messages=[...],\n)\n```\n\n| Language | Beta flag | Speed parameter |\n|---|---|---|\n| Python | `betas=[\"fast-mode-2026-02-01\"]` | `speed=\"fast\"` |\n| TypeScript / Ruby | `betas: [\"fast-mode-2026-02-01\"]` | `speed: \"fast\"` |\n| Go | `[]anthropic.AnthropicBeta{anthropic.AnthropicBetaFastMode2026_02_01}` | `Speed: anthropic.BetaMessageNewParamsSpeedFast` |\n| Java | `.addBeta(AnthropicBeta.FAST_MODE_2026_02_01)` | `.speed(MessageCreateParams.Speed.FAST)` |\n| C# | `Betas = [\"fast-mode-2026-02-01\"]` | `Speed = Speed.Fast` (`Anthropic.Models.Beta.Messages`) |\n| PHP | `betas: ['fast-mode-2026-02-01']` | `speed: 'fast'` |\n| cURL | `anthropic-beta: fast-mode-2026-02-01` header | `\"speed\": \"fast\"` in body |\n\n`response.usage.speed` reports which speed was used. Fast mode has its own rate limit separate from standard Opus; on 429, either retry after the `retry-after` delay or drop `speed` and fall back to standard (note: switching speed invalidates prompt cache). Not available with Batch API, Priority Tier, Claude Platform on AWS, or third-party platforms.\n\n---\n\n## Task Budgets (Quick Reference)\n\n**Beta, Fable 5 / Sonnet 5 / Opus 4.8 / 4.7.** A task budget gives Claude a token ceiling for an agentic loop so it paces itself and finishes gracefully instead of being cut off. Set `task_budget` inside `output_config` on `client.beta.messages.stream(...)` with beta flag `task-budgets-2026-03-13` — use streaming so the large `max_tokens` doesn't hit HTTP timeouts:\n\n```python\nwith client.beta.messages.stream(\n model=\"claude-opus-4-8\", max_tokens=128000,\n output_config={\"effort\": \"high\", \"task_budget\": {\"type\": \"tokens\", \"total\": 64000}},\n betas=[\"task-budgets-2026-03-13\"],\n messages=[...], tools=[...],\n) as stream:\n response = stream.get_final_message()\n```\n\n`task_budget` fields: `type` (always `\"tokens\"`), `total`, and optional `remaining` (defaults to `total`). The server injects a countdown marker Claude sees during generation; the budget counts what Claude generates and the tool results it reads this turn — **not** the full history you resend each request.\n\n**Observing spend:** accumulate `response.usage.output_tokens` (plus the token count of the tool-result blocks you append) across loop iterations if you want to display progress. Leave `remaining` unset in the normal loop — the server tracks the countdown itself, and passing a client-computed `remaining` while also resending full history under-reports the budget. **Only pass `remaining`** when you compact or rewrite history between requests and the server can no longer derive prior spend.\n\n---\n\n## Provider Clients (Quick Reference)\n\nWhen targeting Claude on a third-party platform, use that platform's dedicated client class — not the first-party `Anthropic()` client with a `base_url` override. After construction the client exposes the same `messages.create` / `.stream` surface as the first-party SDK.\n\n### Amazon Bedrock\n\nUse the **Mantle** client (Messages-API Bedrock endpoint). Bedrock model IDs take an `anthropic.` prefix (e.g. `\"anthropic.claude-opus-4-8\"`). Region is required.\n\n| Language | Client |\n|---|---|\n| Python | `from anthropic import AnthropicBedrockMantle` → `AnthropicBedrockMantle(aws_region=\"…\")` |\n| TypeScript | `import { AnthropicBedrockMantle } from \"@anthropic-ai/bedrock-sdk\"` → `new AnthropicBedrockMantle({ awsRegion: \"…\" })` |\n| Go | `bedrock.NewMantleClient(ctx, bedrock.MantleClientConfig{ AWSRegion: \"…\" })` |\n| Java | `AnthropicOkHttpClient.builder().backend(BedrockMantleBackend.fromEnv()).build()` (from `com.anthropic.bedrock.backends`) |\n| C# | `new AnthropicBedrockMantleClient(new() { AwsRegion = \"…\" })` (package `Anthropic.Bedrock`) |\n| PHP | `use Anthropic\\Bedrock\\MantleClient;` → `new MantleClient(awsRegion: '…')` |\n| Ruby | `Anthropic::BedrockMantleClient.new(aws_region: \"…\")` |\n\n`AnthropicBedrock` / `BedrockClient` / `BedrockBackend` (without `Mantle`) are the legacy `bedrock-runtime` InvokeModel path — prefer the Mantle client for new code.\n\n### Microsoft Foundry\n\n| Language | Client |\n|---|---|\n| Python | `from anthropic import AnthropicFoundry` → `AnthropicFoundry(api_key=…, resource=\"…\")` |\n| TypeScript | `import AnthropicFoundry from \"@anthropic-ai/foundry-sdk\"` → `new AnthropicFoundry({ … })` |\n| Java | `AnthropicOkHttpClient.builder().backend(FoundryBackend.fromEnv()).build()` (from `com.anthropic.foundry.backends`) |\n| C# | `new AnthropicFoundryClient(new AnthropicFoundryApiKeyCredentials(…))` (package `Anthropic.Foundry`) |\n| PHP | `Foundry\\Client::withCredentials(…)` |\n\nThe Go and Ruby SDKs do not currently support Foundry. For Ruby, use the standard `Anthropic::Client.new(base_url: \"\")` as a fallback (Entra ID auth is not built in). For Claude Platform on AWS, see `shared/claude-platform-on-aws.md`.\n\n### Google Cloud Vertex AI\n\nTwo required constructor args: GCP `project_id` and `region`. Vertex model IDs take **no prefix** — current-generation models (Opus 4.8/4.7/4.6, Sonnet 5, Sonnet 4.6) use the bare first-party ID (e.g. `\"claude-opus-4-8\"`); dated-snapshot models use an `@` version separator (e.g. `claude-opus-4-5@20251101`, **not** `claude-opus-4-5-20251101`). Auth is GCP ADC (`gcloud auth application-default login`); no Anthropic API key. `region` can be `\"global\"` (recommended), a multi-region (`\"us\"`/`\"eu\"`), or a specific region. After construction, use the same `messages.create` / `.stream` surface.\n\n| Language | Client |\n|---|---|\n| Python | `from anthropic import AnthropicVertex` → `AnthropicVertex(project_id=\"…\", region=\"…\")` (install `\"anthropic[vertex]\"`) |\n| TypeScript | `import { AnthropicVertex } from \"@anthropic-ai/vertex-sdk\"` → `new AnthropicVertex({ projectId, region })` |\n| Go | `import \"github.com/anthropics/anthropic-sdk-go/vertex\"` → `anthropic.NewClient(vertex.WithGoogleAuth(ctx, region, projectID))` |\n| Java | `AnthropicOkHttpClient.builder().backend(VertexBackend.builder().region(\"…\").project(\"…\").build()).build()` (from `com.anthropic.vertex.backends`) |\n| C# | `new AnthropicClient { Backend = new VertexBackend(projectId, region) }` (package `Anthropic.Vertex`) |\n| PHP | `use Anthropic\\Vertex;` → `Vertex\\Client::fromEnvironment(location: '…', projectId: '…')` — note `location`, not `region` |\n| Ruby | `Anthropic::VertexClient.new(region: \"…\", project_id: \"…\")` |\n\n---\n\n## Context Editing (Quick Reference)\n\n**Beta.** Context editing **clears** old tool results or thinking blocks from the conversation before the model sees it; it is **not compaction** (which summarizes). On `client.beta.messages.*` with beta `context-management-2025-06-27`, pass `context_management.edits` with a strategy type:\n\n```python\nclient.beta.messages.create(\n model=\"claude-opus-4-8\", max_tokens=4096,\n betas=[\"context-management-2025-06-27\"],\n context_management={\"edits\": [{\"type\": \"clear_tool_uses_20250919\"}]},\n tools=[...], messages=[...],\n)\n```\n\nStrategy types: `clear_tool_uses_20250919` (clears old tool results; optional `clear_tool_inputs: true` also clears the tool_use params) and `clear_thinking_20251015` (clears thinking blocks). Do **not** use `compact_20260112` or beta `compact-2026-01-12` — those are the separate compaction feature.\n\n---\n\n## Mid-Conversation System Messages (Quick Reference)\n\n**Claude Opus 4.8 only; no beta header.** Append `{\"role\": \"system\", \"content\": \"…\"}` to the `messages` array (not the top-level `system` field) to add an operator instruction mid-conversation without invalidating the cached prefix. Use the regular `client.messages.create` — there is no beta. A mid-conversation system message must follow a `user` message (or an `assistant` message ending in server-tool use), and must be either the last entry in `messages` or be followed by an `assistant` turn — it cannot be `messages[0]`. Availability: `shared/platform-availability.md`. See `shared/prompt-caching.md` § Mid-conversation system messages.\n\n---\n\n## Managed Agents (Beta)\n\n**Managed Agents** is a third surface: server-managed stateful agents with Anthropic-hosted tool execution. You create a persisted, versioned Agent config (`POST /v1/agents`), then start Sessions that reference it. Each session provisions a container as the agent's workspace — bash, file ops, and code execution run there; the agent loop itself runs on Anthropic's orchestration layer and acts on the container via tools. The session streams events; you send messages and tool results back.\n\nAvailability: `shared/platform-availability.md`. For agents on Bedrock / Vertex / Foundry (where Managed Agents is unsupported), use Claude API + tool use.\n\n**Mandatory flow:** Agent (once) → Session (every run). `model`/`system`/`tools` live on the agent, never the session. See `shared/managed-agents-overview.md` for the full reading guide, beta headers, and pitfalls.\n\n**Beta headers:** `managed-agents-2026-04-01` — the SDK sets this automatically for all `client.beta.{agents,environments,sessions,vaults,memory_stores,deployments,deployment_runs}.*` calls. Skills API uses `skills-2025-10-02` and Files API uses `files-api-2025-04-14`, but you don't need to explicitly pass those in for endpoints other than `/v1/skills` and `/v1/files`.\n\n**Subcommands** — invoke directly with `/claude-api `:\n\n| Subcommand | Action |\n|---|---|\n| `managed-agents-onboard` | Walk the user through setting up a Managed Agent from scratch. **Read `shared/managed-agents-onboarding.md` immediately** and follow its interview script: **describe → configure the agent (propose, don't interrogate) → environment → session** (same arc as the Console quickstart, auth deferred to the session step) — defaults and inline suggestions do the work, with a silent viability gate (job vs tools/credentials/data) before any code is emitted. Do not summarize — run the interview. |\n\n**Reading guide:** Start with `shared/managed-agents-overview.md`, then the topical `shared/managed-agents-*.md` files (core, environments, tools, events, outcomes, multiagent, webhooks, memory, scheduled-deployments, client-patterns, onboarding, api-reference). For Python, TypeScript, Go, Ruby, PHP, and Java, read `{lang}/managed-agents/README.md` for code examples. For cURL, read `curl/managed-agents.md`. **Agents are persistent — create once, reference by ID.** Store the agent ID returned by `agents.create` and pass it to every subsequent `sessions.create`; do not call `agents.create` in the request path. The Anthropic CLI (`ant`) is one convenient way to create agents and environments from version-controlled YAML — see `shared/anthropic-cli.md`. If a binding you need isn't shown in the language README, WebFetch the relevant entry from `shared/live-sources.md` rather than guess. C# has beta Managed Agents support via `client.Beta.Agents` and related namespaces.\n\n**When the user wants to set up a Managed Agent from scratch** (e.g. \"how do I get started\", \"walk me through creating one\", \"set up a new agent\"): read `shared/managed-agents-onboarding.md` and run its interview — same flow as the `managed-agents-onboard` subcommand.\n\n**When the user asks \"how do I write the client code for X\":** reach for `shared/managed-agents-client-patterns.md` — covers lossless stream reconnect, `processed_at` queued/processed gate, interrupt, `tool_confirmation` round-trip, the correct idle/terminated break gate, post-idle status race, stream-first ordering, file-mount gotchas, keeping credentials host-side via custom tools, etc.\n\n**When the user wants the agent to run on a schedule** (cron, \"every night\", \"weekly report\"): read `shared/managed-agents-scheduled-deployments.md` — deployments fire sessions autonomously on a cron cadence, with per-firing run records and lifecycle controls (pause/unpause/archive).\n\n---\n\n## Server Tools (Quick Reference)\n\nServer-side tools run on Anthropic's infrastructure — no client-side execution loop. Declare in `tools`; results arrive as content blocks in the same response. **No beta header** unless noted. **Prefer the latest type variant your model supports.** The `_20260209` web search / web fetch variants below (dynamic filtering) require Opus 4.8/4.7/4.6, Sonnet 5, or Sonnet 4.6; the basic variants for older models are listed after the table.\n\n| Tool | `type` | `name` | Key optional params | Result block type |\n|---|---|---|---|---|\n| Web search | `web_search_20260209` | `web_search` | `max_uses`, `allowed_domains`/`blocked_domains`, `user_location` | `web_search_tool_result` → `.content` is a list of `web_search_result` |\n| Web fetch | `web_fetch_20260209` | `web_fetch` | `max_uses`, `allowed_domains`/`blocked_domains`, `citations`, `max_content_tokens` | `web_fetch_tool_result` → `.content` is a `web_fetch_result` with a `document` block |\n| Code execution | `code_execution_20260521` | `code_execution` | none | `bash_code_execution_tool_result` → `.content.stdout` / `.stderr` / `.return_code` |\n| Tool search (regex) | `tool_search_tool_regex_20251119` | `tool_search_tool_regex` | mark other tools `defer_loading: true` | `tool_search_tool_result` |\n| Tool search (BM25) | `tool_search_tool_bm25_20251119` | `tool_search_tool_bm25` | mark other tools `defer_loading: true` | `tool_search_tool_result` |\n\n`web_search_20260209` / `web_fetch_20260209` have built-in dynamic filtering — code execution runs under the hood, so do **not** separately declare `code_execution` in `tools` (a second execution environment confuses the model). For models older than Opus 4.6 / Sonnet 4.6, use the basic variants `web_search_20250305` / `web_fetch_20250910` instead; on Vertex AI only basic `web_search_20250305` is available. `code_execution_20260120` (REPL persistence + programmatic tool calling) runs on Opus 4.5+ / Sonnet 4.5+. **Go SDK only**: `code_execution_20260521` lives under `client.Beta.Messages.New` with `Betas: []anthropic.AnthropicBeta{\"code-execution-2025-08-25\"}` (other languages use plain `client.messages.create`); `code_execution_20260120` uses the non-beta `client.Messages.New` in Go like everywhere else. Web fetch only fetches URLs already present in the conversation. Provider availability varies by tool — see `shared/platform-availability.md`. See `shared/tool-use-concepts.md` for `pause_turn` handling.\n\n## Document & File Input (Quick Reference)\n\n**PDF (base64, no beta):** `{\"type\": \"document\", \"source\": {\"type\": \"base64\", \"media_type\": \"application/pdf\", \"data\": }}` in user content, placed before the text block. Base64 string must have no newlines. Limits: 32 MB request, 600 pages (100 for 200k-context models). Java: `ContentBlockParam.ofDocument(DocumentBlockParam... Base64PdfSource.builder().data(...))`.\n\n**Files API (beta `files-api-2025-04-14`):** upload via `client.beta.files.upload(...)` → response `id` is the `file_id`. Reference it as `{\"type\": \"document\", \"source\": {\"type\": \"file\", \"file_id\": \"...\"}}` for PDF/text, or `{\"type\": \"image\", ...}` for images — the content-block type must match the file's MIME type. The beta header is required on **both** the upload and the `messages.create` that references the file. Availability: `shared/platform-availability.md`.\n\n**Citations (no beta):** set `citations: {enabled: true}` on each `document` content block (all or none). Response splits into multiple `text` blocks; cited blocks carry a `citations` array. Each citation has `cited_text`, `document_index`, `document_title`, and a location by `type`: `char_location` (`start_char_index`/`end_char_index`) for plain text, `page_location` (`start_page_number`/`end_page_number`, 1-indexed) for PDF, `content_block_location` for custom content. Incompatible with `output_config.format`.\n\n## Tool Use Patterns (Quick Reference)\n\n**Strict tool use (no beta):** set `strict: true` as a top-level field on the tool definition (alongside `name`/`description`/`input_schema`), **not** on `tool_choice`. Schema must have `additionalProperties: false` + `required`. Guarantees `tool_use.input` validates exactly. Go: `Strict: anthropic.Bool(true)` + `additionalProperties` via `InputSchema.ExtraFields`; Java: `.strict(true)` + `.putAdditionalProperty(\"additionalProperties\", JsonValue.from(false))`.\n\n**Parallel tool use (default on):** one assistant message may contain multiple `tool_use` blocks. Execute them concurrently, then return **all** `tool_result` blocks in a **single** user message (don't split across multiple messages). For a failed tool, return `tool_result` with `is_error: true` — don't drop it.\n\n**Tool Runner (SDK beta helper):** drives the tool-call loop for you via `client.beta.messages.*`. Python: `@beta_tool` decorator + `client.beta.messages.tool_runner(...)` → `runner.until_done()`. TypeScript: `betaZodTool({...})` from `@anthropic-ai/sdk/helpers/beta/zod` + `client.beta.messages.toolRunner(...)` → `await runner`. Go: `toolrunner.NewBetaToolFromJSONSchema(...)` + `client.Beta.Messages.NewToolRunner(...)` → `.RunToCompletion(ctx)`. Java requires `.addBeta(\"structured-outputs-2025-11-13\")`. Ruby: `Anthropic::BaseTool` subclass + `client.beta.messages.tool_runner(...)`. PHP: `BetaRunnableTool` + `->toolRunner(...)`. C#: raw JSON-schema tools + `BetaToolRunner` via `client.Beta.Messages.ToolRunner(...)`.\n\n**Programmatic tool calling (no beta header):** Claude calls your custom tool from inside code execution. Add `{\"type\": \"code_execution_20260120\", \"name\": \"code_execution\"}` **and** set `\"allowed_callers\": [\"code_execution_20260120\"]` on your custom tool. Opus 4.5+ / Sonnet 4.5+ (availability: `shared/platform-availability.md`). When responding to a pending programmatic call, the user message must contain **only** `tool_result` blocks (no text). Not compatible with `strict: true`, `disable_parallel_tool_use`, forced `tool_choice`, or MCP tools.\n\n## Other API Surfaces (Quick Reference)\n\n**Message Batches (no beta; availability: `shared/platform-availability.md`):** `client.messages.batches.create(requests=[{custom_id, params}, ...])` → poll `client.messages.batches.retrieve(id).processing_status` until `\"ended\"` → stream `client.messages.batches.results(id)`. Each result has `.custom_id` + `.result.type` (`succeeded`/`errored`/`canceled`/`expired`); on success read `.result.message.content`. Python wraps requests as `Request(custom_id=..., params=MessageCreateParamsNonStreaming(...))`. Results arrive in **any order** — key by `custom_id`, never by position.\n\n**Models API (no beta; availability: `shared/platform-availability.md`):** `client.models.list()` (auto-paginates) and `client.models.retrieve(\"claude-opus-4-8\")`. Each model object has `id`, `display_name`, `created_at`, and — since Mar 2026 — `max_input_tokens` (the context window), `max_tokens` (the output cap), and `capabilities`. There is no `context_window` field.\n\n**Stop details (GA, Opus 4.7+):** `response.stop_details` is populated **only when `stop_reason == \"refusal\"`** (fields: `type: \"refusal\"`, `category: \"cyber\"|\"bio\"|null`, `explanation`). It is `null` for every other `stop_reason` (`end_turn`, `max_tokens`, `tool_use`, `pause_turn`, …) — always guard before reading.\n\n**Client config (no beta):** `timeout` default 10 min; **units differ by SDK** — Python/Ruby: seconds; TypeScript: **milliseconds**; Go `option.WithRequestTimeout(time.Duration)`; Java `Duration`; C# `TimeSpan`. TS scales the default up to 60 min for large `max_tokens` on non-streaming requests; Java does so for streaming requests (Java non-streaming scales 30s–10 min). `max_retries`/`maxRetries` default 2 (retries 408/409/429/5xx + connection errors). `base_url` (or `ANTHROPIC_BASE_URL` env). Per-request override: Python `client.with_options(timeout=5.0).messages.create(...)`; TS `client.messages.create({...}, {timeout: 5_000})`; Ruby `request_options: {timeout: 5}`. Timeouts are retried — wall-clock can reach `timeout × (max_retries+1)`.\n\n## Workload Identity Federation (Quick Reference)\n\n**GA, no beta header.** Construct the normal zero-arg client (`Anthropic()` / `new Anthropic()` / `anthropic.NewClient()` / `AnthropicOkHttpClient.fromEnv()`); the SDK auto-detects WIF when **all** of `ANTHROPIC_FEDERATION_RULE_ID`, `ANTHROPIC_ORGANIZATION_ID`, `ANTHROPIC_SERVICE_ACCOUNT_ID`, and `ANTHROPIC_IDENTITY_TOKEN_FILE` (or `ANTHROPIC_IDENTITY_TOKEN`) are set, exchanges the JWT at `/v1/oauth/token`, and auto-refreshes. `ANTHROPIC_WORKSPACE_ID` does not gate activation — required only when the federation rule spans multiple workspaces (else 400 `workspace_id_required`), optional for single-workspace rules. `ANTHROPIC_API_KEY` or `ANTHROPIC_AUTH_TOKEN` (even empty) outrank WIF, and a set `ANTHROPIC_PROFILE` also wins over the federation env vars (a missing named profile is an error, not a fall-through) — unset all three.\n\n---\n\n## Reference Documentation\n\nThe relevant documentation for your detected language is included below in `` tags. Each tag has a `path` attribute showing its original file path. Use this to find the right section:\n\n### Quick Task Reference\n\n> All SDK languages use the same per-language `claude-api/` directory layout (cURL: `curl/examples.md`). Not every language has every file — if a file is absent, that feature's example is not yet documented for that language; fall back to the cURL shape or WebFetch the SDK repo.\n\n**Single text classification/summarization/extraction/Q&A:**\n→ Refer to `python/claude-api/README.md`\n\n**Chat UI or real-time response display:**\n→ Refer to `python/claude-api/README.md` + `python/claude-api/streaming.md`\n\n**Long-running conversations (may exceed context window):**\n→ Refer to `python/claude-api/README.md` — see Compaction section\n\n**Migrating to a newer model or replacing a retired model:**\n→ Refer to `shared/model-migration.md`\n\n**Prompt caching / optimize caching / \"why is my cache hit rate low\":**\n→ Refer to `shared/prompt-caching.md` + `python/claude-api/README.md` (Prompt Caching section)\n\n**Count tokens in a file / prompt / diff (\"how many tokens is X\"):**\n→ Refer to `shared/token-counting.md` — use `messages.count_tokens`, never `tiktoken`\n\n**Function calling / tool use / agents:**\n→ Refer to `python/claude-api/README.md` + `shared/tool-use-concepts.md` + `python/claude-api/tool-use.md`\n\n**Batch processing (non-latency-sensitive):**\n→ Refer to `python/claude-api/README.md` + `python/claude-api/batches.md`\n\n**File uploads across multiple requests:**\n→ Refer to `python/claude-api/README.md` + `python/claude-api/files-api.md`\n\n**Agent design (tool surface, context management, caching strategy):**\n→ Refer to `shared/agent-design.md`\n\n**Anthropic CLI (`ant`) — terminal access, version-controlled agent/environment YAML, scripting:**\n→ Refer to `shared/anthropic-cli.md`\n\n**Managed Agents (server-managed stateful agents):**\n→ Refer to `shared/managed-agents-overview.md` and the rest of the `shared/managed-agents-*.md` files. For Python, TypeScript, Go, Ruby, PHP, and Java, read the `managed-agents/README.md` in the language folder for code examples. For cURL, read `curl/managed-agents.md`. C# has beta Managed Agents support — use `curl/managed-agents.md` as the wire-level reference (the C# SDK mirrors it via `client.Beta.Agents`; see `csharp/claude-api/README.md`).\n\n**Error handling:**\n→ Refer to `shared/error-codes.md`\n\n**Latest docs via WebFetch:**\n→ Refer to `shared/live-sources.md` for URLs\n\n---\n\n## Included Documentation\n\n\n# Claude API — Python\n\n## Installation\n\n```bash\npip install anthropic\n```\n\n## Client Initialization\n\n```python\nimport anthropic\n\n# Default — resolves credentials from the environment:\n# ANTHROPIC_API_KEY, or ANTHROPIC_AUTH_TOKEN, or an `ant auth login` profile.\n# Prefer this for local dev; don't hardcode a key.\nclient = anthropic.Anthropic()\n\n# Explicit API key (only when you must inject a specific key)\nclient = anthropic.Anthropic(api_key=\"your-api-key\")\n\n# Async client\nasync_client = anthropic.AsyncAnthropic()\n```\n\n---\n\n## Client Configuration\n\n### Per-request overrides\n\nUse `with_options()` to override client settings for a single call without mutating the client:\n\n```python\nclient.with_options(timeout=5.0, max_retries=5).messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n)\n```\n\n### Timeouts\n\nDefault request timeout is 10 minutes. Pass a float (seconds) or an `httpx.Timeout` for granular control. On timeout the SDK raises `anthropic.APITimeoutError` (and retries per `max_retries`).\n\n```python\nimport httpx\n\nclient = anthropic.Anthropic(timeout=20.0)\nclient = anthropic.Anthropic(\n timeout=httpx.Timeout(60.0, read=5.0, write=10.0, connect=2.0),\n)\n```\n\n### Retries\n\nThe SDK auto-retries connection errors, 408, 409, 429, and ≥500 with exponential backoff (default 2 retries). Set `max_retries` on the client or via `with_options()`; `max_retries=0` disables.\n\n### Async performance (aiohttp backend)\n\nFor high-concurrency async workloads, install `anthropic[aiohttp]` and pass `DefaultAioHttpClient` instead of the default httpx backend:\n\n```python\nfrom anthropic import AsyncAnthropic, DefaultAioHttpClient\n\nasync with AsyncAnthropic(http_client=DefaultAioHttpClient()) as client:\n ...\n```\n\n### Custom HTTP client (proxy, base URL)\n\nUse `DefaultHttpxClient` / `DefaultAsyncHttpxClient` — not raw `httpx.Client` — so the SDK's default timeouts and connection limits are preserved:\n\n```python\nfrom anthropic import Anthropic, DefaultHttpxClient\n\nclient = Anthropic(\n base_url=\"http://my.test.server.example.com:8083\", # or ANTHROPIC_BASE_URL env var\n http_client=DefaultHttpxClient(proxy=\"http://my.test.proxy.example.com\"),\n)\n```\n\n### Logging\n\nSet `ANTHROPIC_LOG=debug` (or `info`) to enable SDK logging via the standard `logging` module.\n\n---\n\n## Basic Message Request\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[\n {\"role\": \"user\", \"content\": \"What is the capital of France?\"}\n ]\n)\n# response.content is a list of content block objects (TextBlock, ThinkingBlock,\n# ToolUseBlock, ...). Check .type before accessing .text.\nfor block in response.content:\n if block.type == \"text\":\n print(block.text)\n```\n\n---\n\n## System Prompts\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n system=\"You are a helpful coding assistant. Always provide examples in Python.\",\n messages=[{\"role\": \"user\", \"content\": \"How do I read a JSON file?\"}]\n)\n```\n\n### Mid-conversation system messages (model-gated)\n\nFor operator instructions that arrive mid-conversation (mode switches, injected state), append `{\"role\": \"system\", ...}` to `messages` instead of editing top-level `system` — this preserves the cached prefix and carries operator authority. Must follow a user message (or an `assistant` message ending in server-tool use), and must be either the last entry in `messages` or be followed by an `assistant` turn; cannot be `messages[0]`. Unsupported models return a 400 (`role 'system' is not supported on this model`). See `shared/prompt-caching.md` for when to use this vs. top-level `system`.\n\n```python\nresponse = client.messages.create(\n model=MODEL_ID, # must support mid-conversation system messages\n max_tokens=16000,\n system=[{\"type\": \"text\", \"text\": STABLE_SYSTEM, \"cache_control\": {\"type\": \"ephemeral\"}}],\n messages=history + [\n {\"role\": \"user\", \"content\": user_message},\n {\"role\": \"system\", \"content\": \"Terse mode enabled — keep responses under 40 words.\"},\n ],\n) # No beta header needed — use regular client.messages.create\n```\n\n---\n\n## Vision (Images)\n\n### Base64\n\n```python\nimport base64\n\nwith open(\"image.png\", \"rb\") as f:\n image_data = base64.standard_b64encode(f.read()).decode(\"utf-8\")\n\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"image\",\n \"source\": {\n \"type\": \"base64\",\n \"media_type\": \"image/png\",\n \"data\": image_data\n }\n },\n {\"type\": \"text\", \"text\": \"What's in this image?\"}\n ]\n }]\n)\n```\n\n### URL\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"image\",\n \"source\": {\n \"type\": \"url\",\n \"url\": \"https://example.com/image.png\"\n }\n },\n {\"type\": \"text\", \"text\": \"Describe this image\"}\n ]\n }]\n)\n```\n\n---\n\n## Prompt Caching\n\nCache large context to reduce costs (up to 90% savings). **Caching is a prefix match** — any byte change anywhere in the prefix invalidates everything after it. For placement patterns, architectural guidance (frozen system prompt, deterministic tool order, where to put volatile content), and the silent-invalidator audit checklist, read `shared/prompt-caching.md`.\n\n### Automatic Caching (Recommended)\n\nUse top-level `cache_control` to automatically cache the last cacheable block in the request — no need to annotate individual content blocks:\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n cache_control={\"type\": \"ephemeral\"}, # auto-caches the last cacheable block\n system=\"You are an expert on this large document...\",\n messages=[{\"role\": \"user\", \"content\": \"Summarize the key points\"}]\n)\n```\n\n### Manual Cache Control\n\nFor fine-grained control, add `cache_control` to specific content blocks:\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n system=[{\n \"type\": \"text\",\n \"text\": \"You are an expert on this large document...\",\n \"cache_control\": {\"type\": \"ephemeral\"} # default TTL is 5 minutes\n }],\n messages=[{\"role\": \"user\", \"content\": \"Summarize the key points\"}]\n)\n\n# With explicit TTL (time-to-live)\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n system=[{\n \"type\": \"text\",\n \"text\": \"You are an expert on this large document...\",\n \"cache_control\": {\"type\": \"ephemeral\", \"ttl\": \"1h\"} # 1 hour TTL\n }],\n messages=[{\"role\": \"user\", \"content\": \"Summarize the key points\"}]\n)\n```\n\n### Verifying Cache Hits\n\n```python\nprint(response.usage.cache_creation_input_tokens) # tokens written to cache (~1.25x cost)\nprint(response.usage.cache_read_input_tokens) # tokens served from cache (~0.1x cost)\nprint(response.usage.input_tokens) # uncached tokens (full cost)\n```\n\nIf `cache_read_input_tokens` is zero across repeated identical-prefix requests, a silent invalidator is at work — `datetime.now()` or a UUID in the system prompt, unsorted `json.dumps()`, or a varying tool set. See `shared/prompt-caching.md` for the full audit table.\n\n---\n\n## Extended Thinking\n\n> **Fable 5, Opus 4.8, Opus 4.7, Opus 4.6, and Sonnet 4.6:** Use adaptive thinking. `budget_tokens` is removed on Fable 5, Opus 4.8, and 4.7 (400 if sent); deprecated on Opus 4.6 and Sonnet 4.6.\n> **Older models:** Use `thinking: {type: \"enabled\", budget_tokens: N}` (must be < `max_tokens`, min 1024).\n\n```python\n# Fable 5 / Opus 4.8 / 4.7 / 4.6: adaptive thinking (recommended)\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n thinking={\"type\": \"adaptive\", \"display\": \"summarized\"}, # display opt-in: default is omitted (empty thinking text) on Fable 5 / Mythos 5 / Opus 4.8 / 4.7\n output_config={\"effort\": \"high\"}, # low | medium | high | max\n messages=[{\"role\": \"user\", \"content\": \"Solve this step by step...\"}]\n)\n\n# Access thinking and response\nfor block in response.content:\n if block.type == \"thinking\":\n print(f\"Thinking: {block.thinking}\")\n elif block.type == \"text\":\n print(f\"Response: {block.text}\")\n```\n\n---\n\n## Error Handling\n\n```python\nimport anthropic\n\ntry:\n response = client.messages.create(...)\nexcept anthropic.BadRequestError as e:\n print(f\"Bad request: {e.message}\")\nexcept anthropic.AuthenticationError:\n print(\"Invalid API key\")\nexcept anthropic.PermissionDeniedError:\n print(\"API key lacks required permissions\")\nexcept anthropic.NotFoundError:\n print(\"Invalid model or endpoint\")\nexcept anthropic.RateLimitError as e:\n retry_after = int(e.response.headers.get(\"retry-after\", \"60\"))\n print(f\"Rate limited. Retry after {retry_after}s.\")\nexcept anthropic.APIStatusError as e:\n if e.status_code >= 500:\n print(f\"Server error ({e.status_code}). Retry later.\")\n else:\n print(f\"API error: {e.message}\")\nexcept anthropic.APIConnectionError:\n print(\"Network error. Check internet connection.\")\n```\n\n---\n\n## Response Helpers\n\nEvery response object exposes `_request_id` (populated from the `request-id` header) — log it when reporting failures to Anthropic. Despite the underscore prefix, this property is public.\n\n```python\nmessage = client.messages.create(...)\nprint(message._request_id) # req_018EeWyXxfu5pfWkrYcMdjWG\nprint(message.to_json()) # serialize the Pydantic model\nprint(message.to_dict()) # plain dict\n```\n\nTo access raw headers or other response metadata, use `.with_raw_response`:\n\n```python\nraw = client.messages.with_raw_response.create(\n model=\"claude-opus-4-8\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n)\nprint(raw.headers.get(\"request-id\"))\nmessage = raw.parse() # the Message object messages.create() would have returned\n```\n\n---\n\n## Multi-Turn Conversations\n\nThe API is stateless — send the full conversation history each time.\n\n```python\nclass ConversationManager:\n \"\"\"Manage multi-turn conversations with the Claude API.\"\"\"\n\n def __init__(self, client: anthropic.Anthropic, model: str, system: str = None):\n self.client = client\n self.model = model\n self.system = system\n self.messages = []\n\n def send(self, user_message: str, **kwargs) -> str:\n \"\"\"Send a message and get a response.\"\"\"\n self.messages.append({\"role\": \"user\", \"content\": user_message})\n\n response = self.client.messages.create(\n model=self.model,\n max_tokens=kwargs.get(\"max_tokens\", 16000),\n system=self.system,\n messages=self.messages,\n **kwargs\n )\n\n assistant_message = next(\n (b.text for b in response.content if b.type == \"text\"), \"\"\n )\n self.messages.append({\"role\": \"assistant\", \"content\": assistant_message})\n\n return assistant_message\n\n# Usage\nconversation = ConversationManager(\n client=anthropic.Anthropic(),\n model=\"claude-opus-4-8\",\n system=\"You are a helpful assistant.\"\n)\n\nresponse1 = conversation.send(\"My name is Alice.\")\nresponse2 = conversation.send(\"What's my name?\") # Claude remembers \"Alice\"\n```\n\n**Rules:**\n\n- Consecutive same-role messages are allowed — the API combines them into a single turn\n- First message must be `user`\n- `role: \"system\"` messages are allowed mid-conversation on supporting models (no beta header needed) — see § Mid-conversation system messages above\n\n---\n\n### Compaction (long conversations)\n\n> **Beta, Fable 5, Opus 4.8, Opus 4.7, Opus 4.6, and Sonnet 4.6.** When conversations approach the 200K context window, compaction automatically summarizes earlier context server-side. The API returns a `compaction` block; you must pass it back on subsequent requests — append `response.content`, not just the text.\n\n```python\nimport anthropic\n\nclient = anthropic.Anthropic()\nmessages = []\n\ndef chat(user_message: str) -> str:\n messages.append({\"role\": \"user\", \"content\": user_message})\n\n response = client.beta.messages.create(\n betas=[\"compact-2026-01-12\"],\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=messages,\n context_management={\n \"edits\": [{\"type\": \"compact_20260112\"}]\n }\n )\n\n # Append full content — compaction blocks must be preserved\n messages.append({\"role\": \"assistant\", \"content\": response.content})\n\n return next(block.text for block in response.content if block.type == \"text\")\n\n# Compaction triggers automatically when context grows large\nprint(chat(\"Help me build a Python web scraper\"))\nprint(chat(\"Add support for JavaScript-rendered pages\"))\nprint(chat(\"Now add rate limiting and error handling\"))\n```\n\n---\n\n## Stop Reasons\n\nThe `stop_reason` field in the response indicates why the model stopped generating:\n\n| Value | Meaning |\n|-------|---------|\n| `end_turn` | Claude finished its response naturally |\n| `max_tokens` | Hit the `max_tokens` limit — increase it or use streaming |\n| `stop_sequence` | Hit a custom stop sequence |\n| `tool_use` | Claude wants to call a tool — execute it and continue |\n| `pause_turn` | Model paused and can be resumed (agentic flows) |\n| `refusal` | Claude refused for safety reasons — check `stop_details` |\n\n### Structured Stop Details\n\nWhen `stop_reason` is `\"refusal\"`, the response includes a `stop_details` object with structured information about the refusal:\n\n```python\nif response.stop_reason == \"refusal\" and response.stop_details:\n print(f\"Category: {response.stop_details.category}\") # e.g. \"cyber\", \"bio\", \"reasoning_extraction\", \"frontier_llm\", or None — see docs for the full set\n print(f\"Explanation: {response.stop_details.explanation}\")\n```\n\n### Refusal Fallbacks (Claude Fable 5) — opt in by default\n\nFallbacks are **opt-in**: without them a refused request simply stops. Include the server-side `fallbacks` parameter in `claude-fable-5` code by default — on a policy decline the API re-runs the same request on the fallback model inside the same call. A decline before any output isn't billed (a mid-stream decline bills the streamed partial); the rescue bills at the fallback model's own rates, with cache repricing applied automatically.\n\n```python\nresponse = client.beta.messages.create(\n model=\"claude-fable-5\",\n max_tokens=16000,\n betas=[\"server-side-fallback-2026-06-01\"],\n fallbacks=[{\"model\": \"claude-opus-4-8\"}],\n messages=[{\"role\": \"user\", \"content\": \"...\"}],\n)\n\n# Switch points: one fallback block per model that ran and declined this turn\nfor block in response.content:\n if block.type == \"fallback\":\n print(f\"{block.from_.model} declined; {block.to.model} continued\")\n\n# Served-by signal — covers sticky turns, which carry no fallback block.\n# Pair with stop_reason: the fallback model can itself refuse.\nfallback_ran = any(\n entry.type == \"fallback_message\" for entry in response.usage.iterations or []\n)\nif fallback_ran and response.stop_reason != \"refusal\":\n print(f\"Served by {response.model}\")\n```\n\nA `stop_reason: \"refusal\"` on the final response means the whole chain refused. The header must be exactly `server-side-fallback-2026-06-01`; the parameter is rejected on the Batches API and unavailable on Amazon Bedrock, Vertex AI, and Microsoft Foundry — register the client-side `BetaRefusalFallbackMiddleware` on the client there instead. Full semantics (sticky routing, billing, streaming, echoing fallback turns back): `shared/model-migration.md` → Migrating to Claude Fable 5 → `refusal` stop reason.\n\n---\n\n## Cost Optimization Strategies\n\n### 1. Use Prompt Caching for Repeated Context\n\n```python\n# Automatic caching (simplest — caches the last cacheable block)\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n cache_control={\"type\": \"ephemeral\"},\n system=large_document_text, # e.g., 50KB of context\n messages=[{\"role\": \"user\", \"content\": \"Summarize the key points\"}]\n)\n\n# First request: full cost\n# Subsequent requests: ~90% cheaper for cached portion\n```\n\n### 2. Choose the Right Model\n\n```python\n# Default to Opus for most tasks\nresponse = client.messages.create(\n model=\"claude-opus-4-8\", # $5.00/$25.00 per 1M tokens\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Explain quantum computing\"}]\n)\n\n# Use Sonnet for high-volume production workloads\nstandard_response = client.messages.create(\n model=\"claude-sonnet-5\", # $3.00/$15.00 per 1M tokens\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Summarize this document\"}]\n)\n\n# Use Haiku only for simple, speed-critical tasks\nsimple_response = client.messages.create(\n model=\"claude-haiku-4-5\", # $1.00/$5.00 per 1M tokens\n max_tokens=256,\n messages=[{\"role\": \"user\", \"content\": \"Classify this as positive or negative\"}]\n)\n```\n\n### 3. Use Token Counting Before Requests\n\n```python\ncount_response = client.messages.count_tokens(\n model=\"claude-opus-4-8\",\n messages=messages,\n system=system\n)\n\nestimated_input_cost = count_response.input_tokens * 0.000005 # $5/1M tokens\nprint(f\"Estimated input cost: ${estimated_input_cost:.4f}\")\n```\n\n---\n\n## Retry with Exponential Backoff\n\n> **Note:** The Anthropic SDK automatically retries rate limit (429) and server errors (5xx) with exponential backoff. You can configure this with `max_retries` (default: 2). Only implement custom retry logic if you need behavior beyond what the SDK provides.\n\n```python\nimport time\nimport random\nimport anthropic\n\ndef call_with_retry(\n client: anthropic.Anthropic,\n max_retries: int = 5,\n base_delay: float = 1.0,\n max_delay: float = 60.0,\n **kwargs\n):\n \"\"\"Call the API with exponential backoff retry.\"\"\"\n last_exception = None\n\n for attempt in range(max_retries):\n try:\n return client.messages.create(**kwargs)\n except anthropic.RateLimitError as e:\n last_exception = e\n except anthropic.APIStatusError as e:\n if e.status_code >= 500:\n last_exception = e\n else:\n raise # Client errors (4xx except 429) should not be retried\n\n delay = min(base_delay * (2 ** attempt) + random.uniform(0, 1), max_delay)\n print(f\"Retry {attempt + 1}/{max_retries} after {delay:.1f}s\")\n time.sleep(delay)\n\n raise last_exception\n```\n\n\n\n# Message Batches API — Python\n\nThe Batches API (`POST /v1/messages/batches`) processes Messages API requests asynchronously at 50% of standard prices.\n\n## Key Facts\n\n- Up to 100,000 requests or 256 MB per batch\n- Most batches complete within 1 hour; maximum 24 hours\n- Results available for 29 days after creation\n- 50% cost reduction on all token usage\n- All Messages API features supported (vision, tools, caching, etc.)\n\n---\n\n## Create a Batch\n\n```python\nimport anthropic\nfrom anthropic.types.message_create_params import MessageCreateParamsNonStreaming\nfrom anthropic.types.messages.batch_create_params import Request\n\nclient = anthropic.Anthropic()\n\nmessage_batch = client.messages.batches.create(\n requests=[\n Request(\n custom_id=\"request-1\",\n params=MessageCreateParamsNonStreaming(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Summarize climate change impacts\"}]\n )\n ),\n Request(\n custom_id=\"request-2\",\n params=MessageCreateParamsNonStreaming(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Explain quantum computing basics\"}]\n )\n ),\n ]\n)\n\nprint(f\"Batch ID: {message_batch.id}\")\nprint(f\"Status: {message_batch.processing_status}\")\n```\n\n---\n\n## Poll for Completion\n\n```python\nimport time\n\nwhile True:\n batch = client.messages.batches.retrieve(message_batch.id)\n if batch.processing_status == \"ended\":\n break\n print(f\"Status: {batch.processing_status}, processing: {batch.request_counts.processing}\")\n time.sleep(60)\n\nprint(\"Batch complete!\")\nprint(f\"Succeeded: {batch.request_counts.succeeded}\")\nprint(f\"Errored: {batch.request_counts.errored}\")\n```\n\n---\n\n## Retrieve Results\n\n> **Note:** Examples below use `match/case` syntax, requiring Python 3.10+. For earlier versions, use `if/elif` chains instead.\n\n```python\nfor result in client.messages.batches.results(message_batch.id):\n match result.result.type:\n case \"succeeded\":\n msg = result.result.message\n text = next((b.text for b in msg.content if b.type == \"text\"), \"\")\n print(f\"[{result.custom_id}] {text[:100]}\")\n case \"errored\":\n if result.result.error.type == \"invalid_request\":\n print(f\"[{result.custom_id}] Validation error - fix request and retry\")\n else:\n print(f\"[{result.custom_id}] Server error - safe to retry\")\n case \"canceled\":\n print(f\"[{result.custom_id}] Canceled\")\n case \"expired\":\n print(f\"[{result.custom_id}] Expired - resubmit\")\n```\n\n---\n\n## Cancel a Batch\n\n```python\ncancelled = client.messages.batches.cancel(message_batch.id)\nprint(f\"Status: {cancelled.processing_status}\") # \"canceling\"\n```\n\n---\n\n## List Batches (auto-pagination)\n\nIterating the return value of any `list()` call auto-paginates across all pages — do not index into `.data` if you want the full set:\n\n```python\nfor batch in client.messages.batches.list(limit=20):\n print(batch.id, batch.processing_status)\n```\n\nFor manual control, use `first_page.has_next_page()` / `first_page.get_next_page()` / `first_page.next_page_info()`; `first_page.data` holds the current page's items and `first_page.last_id` is the cursor.\n\n---\n\n## Batch with Prompt Caching\n\n```python\nshared_system = [\n {\"type\": \"text\", \"text\": \"You are a literary analyst.\"},\n {\n \"type\": \"text\",\n \"text\": large_document_text, # Shared across all requests\n \"cache_control\": {\"type\": \"ephemeral\"}\n }\n]\n\nmessage_batch = client.messages.batches.create(\n requests=[\n Request(\n custom_id=f\"analysis-{i}\",\n params=MessageCreateParamsNonStreaming(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n system=shared_system,\n messages=[{\"role\": \"user\", \"content\": question}]\n )\n )\n for i, question in enumerate(questions)\n ]\n)\n```\n\n---\n\n## Full End-to-End Example\n\n```python\nimport anthropic\nimport time\nfrom anthropic.types.message_create_params import MessageCreateParamsNonStreaming\nfrom anthropic.types.messages.batch_create_params import Request\n\nclient = anthropic.Anthropic()\n\n# 1. Prepare requests\nitems_to_classify = [\n \"The product quality is excellent!\",\n \"Terrible customer service, never again.\",\n \"It's okay, nothing special.\",\n]\n\nrequests = [\n Request(\n custom_id=f\"classify-{i}\",\n params=MessageCreateParamsNonStreaming(\n model=\"claude-haiku-4-5\",\n max_tokens=50,\n messages=[{\n \"role\": \"user\",\n \"content\": f\"Classify as positive/negative/neutral (one word): {text}\"\n }]\n )\n )\n for i, text in enumerate(items_to_classify)\n]\n\n# 2. Create batch\nbatch = client.messages.batches.create(requests=requests)\nprint(f\"Created batch: {batch.id}\")\n\n# 3. Wait for completion\nwhile True:\n batch = client.messages.batches.retrieve(batch.id)\n if batch.processing_status == \"ended\":\n break\n time.sleep(10)\n\n# 4. Collect results\nresults = {}\nfor result in client.messages.batches.results(batch.id):\n if result.result.type == \"succeeded\":\n msg = result.result.message\n results[result.custom_id] = next((b.text for b in msg.content if b.type == \"text\"), \"\")\n\nfor custom_id, classification in sorted(results.items()):\n print(f\"{custom_id}: {classification}\")\n```\n\n\n\n# Files API — Python\n\nThe Files API uploads files for use in Messages API requests. Reference files via `file_id` in content blocks, avoiding re-uploads across multiple API calls.\n\n**Beta:** Pass `betas=[\"files-api-2025-04-14\"]` in your API calls (the SDK sets the required header automatically).\n\n## Key Facts\n\n- Maximum file size: 500 MB\n- Total storage: 100 GB per organization\n- Files persist until deleted\n- File operations (upload, list, delete) are free; content used in messages is billed as input tokens\n- Not available on Amazon Bedrock or Google Vertex AI\n\n---\n\n## Upload a File\n\nThe `file` argument accepts a `(filename, content, content_type)` tuple, a `pathlib.Path` (or any `PathLike` — read for you, async-safe with `AsyncAnthropic`), or an open binary file object.\n\n```python\nimport anthropic\nfrom pathlib import Path\n\nclient = anthropic.Anthropic()\n\nuploaded = client.beta.files.upload(\n file=(\"report.pdf\", open(\"report.pdf\", \"rb\"), \"application/pdf\"),\n)\n# or: client.beta.files.upload(file=Path(\"report.pdf\"))\nprint(f\"File ID: {uploaded.id}\")\nprint(f\"Size: {uploaded.size_bytes} bytes\")\n```\n\n---\n\n## Use a File in Messages\n\n### PDF / Text Document\n\n```python\nresponse = client.beta.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"Summarize the key findings in this report.\"},\n {\n \"type\": \"document\",\n \"source\": {\"type\": \"file\", \"file_id\": uploaded.id},\n \"title\": \"Q4 Report\", # optional\n \"citations\": {\"enabled\": True} # optional, enables citations\n }\n ]\n }],\n betas=[\"files-api-2025-04-14\"],\n)\nfor block in response.content:\n if block.type == \"text\":\n print(block.text)\n```\n\n### Image\n\n```python\nimage_file = client.beta.files.upload(\n file=(\"photo.png\", open(\"photo.png\", \"rb\"), \"image/png\"),\n)\n\nresponse = client.beta.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"What's in this image?\"},\n {\n \"type\": \"image\",\n \"source\": {\"type\": \"file\", \"file_id\": image_file.id}\n }\n ]\n }],\n betas=[\"files-api-2025-04-14\"],\n)\n```\n\n---\n\n## Manage Files\n\n### List Files\n\nIterate the list result directly — the SDK auto-paginates across all pages. Only use `.data` if you want the first page only.\n\n```python\nfor f in client.beta.files.list():\n print(f\"{f.id}: {f.filename} ({f.size_bytes} bytes)\")\n```\n\n### Get File Metadata\n\n```python\nfile_info = client.beta.files.retrieve_metadata(\"file_011CNha8iCJcU1wXNR6q4V8w\")\nprint(f\"Filename: {file_info.filename}\")\nprint(f\"MIME type: {file_info.mime_type}\")\n```\n\n### Delete a File\n\n```python\nclient.beta.files.delete(\"file_011CNha8iCJcU1wXNR6q4V8w\")\n```\n\n### Download a File\n\nOnly files created by the code execution tool or skills can be downloaded (not user-uploaded files).\n\n```python\nfile_content = client.beta.files.download(\"file_011CNha8iCJcU1wXNR6q4V8w\")\nfile_content.write_to_file(\"output.txt\")\n```\n\n---\n\n## Full End-to-End Example\n\nUpload a document once, ask multiple questions about it:\n\n```python\nimport anthropic\n\nclient = anthropic.Anthropic()\n\n# 1. Upload once\nuploaded = client.beta.files.upload(\n file=(\"contract.pdf\", open(\"contract.pdf\", \"rb\"), \"application/pdf\"),\n)\nprint(f\"Uploaded: {uploaded.id}\")\n\n# 2. Ask multiple questions using the same file_id\nquestions = [\n \"What are the key terms and conditions?\",\n \"What is the termination clause?\",\n \"Summarize the payment schedule.\",\n]\n\nfor question in questions:\n response = client.beta.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": question},\n {\n \"type\": \"document\",\n \"source\": {\"type\": \"file\", \"file_id\": uploaded.id}\n }\n ]\n }],\n betas=[\"files-api-2025-04-14\"],\n )\n print(f\"\\nQ: {question}\")\n text = next((b.text for b in response.content if b.type == \"text\"), \"\")\n print(f\"A: {text[:200]}\")\n\n# 3. Clean up when done\nclient.beta.files.delete(uploaded.id)\n```\n\n\n\n# Streaming — Python\n\n## Quick Start\n\n```python\nwith client.messages.stream(\n model=\"claude-opus-4-8\",\n max_tokens=64000,\n messages=[{\"role\": \"user\", \"content\": \"Write a story\"}]\n) as stream:\n for text in stream.text_stream:\n print(text, end=\"\", flush=True)\n```\n\n### Async\n\n```python\nasync with async_client.messages.stream(\n model=\"claude-opus-4-8\",\n max_tokens=64000,\n messages=[{\"role\": \"user\", \"content\": \"Write a story\"}]\n) as stream:\n async for text in stream.text_stream:\n print(text, end=\"\", flush=True)\n```\n\n### Low-level: `stream=True`\n\n`messages.stream()` (above) is the recommended helper — it accumulates state and exposes `text_stream` / `get_final_message()`. If you only need the raw event iterator and want lower memory use, pass `stream=True` to `messages.create()` instead:\n\n```python\nfor event in client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=64000,\n messages=[{\"role\": \"user\", \"content\": \"Write a story\"}],\n stream=True,\n):\n print(event.type)\n```\n\nNo final-message accumulation is done for you in this form.\n\n---\n\n## Handling Different Content Types\n\nClaude may return text, thinking blocks, or tool use. Handle each appropriately:\n\n> **Fable 5 / Opus 4.8 / Opus 4.7 / Opus 4.6:** Use `thinking: {type: \"adaptive\"}`. On older models, use `thinking: {type: \"enabled\", budget_tokens: N}` instead.\n\n```python\nwith client.messages.stream(\n model=\"claude-opus-4-8\",\n max_tokens=64000,\n thinking={\"type\": \"adaptive\", \"display\": \"summarized\"}, # display opt-in: default is omitted (empty thinking text) on Fable 5 / Mythos 5 / Opus 4.8 / 4.7\n messages=[{\"role\": \"user\", \"content\": \"Analyze this problem\"}]\n) as stream:\n for event in stream:\n if event.type == \"content_block_start\":\n if event.content_block.type == \"thinking\":\n print(\"\\n[Thinking...]\")\n elif event.content_block.type == \"text\":\n print(\"\\n[Response:]\")\n\n elif event.type == \"content_block_delta\":\n if event.delta.type == \"thinking_delta\":\n print(event.delta.thinking, end=\"\", flush=True)\n elif event.delta.type == \"text_delta\":\n print(event.delta.text, end=\"\", flush=True)\n```\n\n---\n\n## Streaming with Tool Use\n\nThe Python tool runner currently returns complete messages. Use streaming for individual API calls within a manual loop if you need per-token streaming with tools:\n\n```python\nwith client.messages.stream(\n model=\"claude-opus-4-8\",\n max_tokens=64000,\n tools=tools,\n messages=messages\n) as stream:\n for text in stream.text_stream:\n print(text, end=\"\", flush=True)\n\n response = stream.get_final_message()\n # Continue with tool execution if response.stop_reason == \"tool_use\"\n```\n\n---\n\n## Getting the Final Message\n\n```python\nwith client.messages.stream(\n model=\"claude-opus-4-8\",\n max_tokens=64000,\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}]\n) as stream:\n for text in stream.text_stream:\n print(text, end=\"\", flush=True)\n\n # Get full message after streaming\n final_message = stream.get_final_message()\n print(f\"\\n\\nTokens used: {final_message.usage.output_tokens}\")\n```\n\n---\n\n## Streaming with Progress Updates\n\n```python\ndef stream_with_progress(client, **kwargs):\n \"\"\"Stream a response with progress updates.\"\"\"\n total_tokens = 0\n content_parts = []\n\n with client.messages.stream(**kwargs) as stream:\n for event in stream:\n if event.type == \"content_block_delta\":\n if event.delta.type == \"text_delta\":\n text = event.delta.text\n content_parts.append(text)\n print(text, end=\"\", flush=True)\n\n elif event.type == \"message_delta\":\n if event.usage and event.usage.output_tokens is not None:\n total_tokens = event.usage.output_tokens\n\n final_message = stream.get_final_message()\n\n print(f\"\\n\\n[Tokens used: {total_tokens}]\")\n return \"\".join(content_parts)\n```\n\n---\n\n## Error Handling in Streams\n\n```python\ntry:\n with client.messages.stream(\n model=\"claude-opus-4-8\",\n max_tokens=64000,\n messages=[{\"role\": \"user\", \"content\": \"Write a story\"}]\n ) as stream:\n for text in stream.text_stream:\n print(text, end=\"\", flush=True)\nexcept anthropic.APIConnectionError:\n print(\"\\nConnection lost. Please retry.\")\nexcept anthropic.RateLimitError:\n print(\"\\nRate limited. Please wait and retry.\")\nexcept anthropic.APIStatusError as e:\n print(f\"\\nAPI error: {e.status_code}\")\n```\n\n---\n\n## Stream Event Types\n\n| Event Type | Description | When it fires |\n| --------------------- | --------------------------- | --------------------------------- |\n| `message_start` | Contains message metadata | Once at the beginning |\n| `content_block_start` | New content block beginning | When a text/tool_use block starts |\n| `content_block_delta` | Incremental content update | For each token/chunk |\n| `content_block_stop` | Content block complete | When a block finishes |\n| `message_delta` | Message-level updates | Contains `stop_reason`, usage |\n| `message_stop` | Message complete | Once at the end |\n\n## Best Practices\n\n1. **Always flush output** — Use `flush=True` to show tokens immediately\n2. **Handle partial responses** — If the stream is interrupted, you may have incomplete content\n3. **Track token usage** — The `message_delta` event contains usage information\n4. **Use timeouts** — Set appropriate timeouts for your application\n5. **Default to streaming** — Use `.get_final_message()` to get the complete response even when streaming, giving you timeout protection without needing to handle individual events\n6. **Large `max_tokens` without streaming raises `ValueError`** — The SDK refuses non-streaming requests it estimates will exceed ~10 minutes (idle connections drop). Pass `stream=True` / use `messages.stream()`, or explicitly override `timeout`, to suppress the guard.\n\n\n\n# Tool Use — Python\n\nFor conceptual overview (tool definitions, tool choice, tips), see [shared/tool-use-concepts.md](../../shared/tool-use-concepts.md).\n\n## Tool Runner (Recommended)\n\n**Beta:** The tool runner is in beta in the Python SDK.\n\nUse the `@beta_tool` decorator to define tools as typed functions, then pass them to `client.beta.messages.tool_runner()`:\n\n```python\nimport anthropic\nfrom anthropic import beta_tool\n\nclient = anthropic.Anthropic()\n\n@beta_tool\ndef get_weather(location: str, unit: str = \"celsius\") -> str:\n \"\"\"Get current weather for a location.\n\n Args:\n location: City and state, e.g., San Francisco, CA.\n unit: Temperature unit, either \"celsius\" or \"fahrenheit\".\n \"\"\"\n # Your implementation here\n return f\"72°F and sunny in {location}\"\n\n# The tool runner handles the agentic loop automatically\nrunner = client.beta.messages.tool_runner(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n tools=[get_weather],\n messages=[{\"role\": \"user\", \"content\": \"What's the weather in Paris?\"}],\n)\n\n# Each iteration yields a BetaMessage; iteration stops when Claude is done\nfor message in runner:\n print(message)\n```\n\nFor async usage, use `@beta_async_tool` with `async def` functions.\n\n**Key benefits of the tool runner:**\n\n- No manual loop — the SDK handles calling tools and feeding results back\n- Type-safe tool inputs via decorators\n- Tool schemas are generated automatically from function signatures\n- Iteration stops automatically when Claude has no more tool calls\n\n---\n\n## MCP Tool Conversion Helpers\n\n**Beta.** Convert [MCP (Model Context Protocol)](https://modelcontextprotocol.io/) tools, prompts, and resources to Anthropic API types for use with the tool runner. Requires `pip install anthropic[mcp]` (Python 3.10+).\n\n> **Note:** The Claude API also supports an `mcp_servers` parameter that lets Claude connect directly to remote MCP servers. Use these helpers instead when you need local MCP servers, prompts, resources, or more control over the MCP connection.\n\n### MCP Tools with Tool Runner\n\n```python\nfrom anthropic import AsyncAnthropic\nfrom anthropic.lib.tools.mcp import async_mcp_tool\nfrom mcp import ClientSession\nfrom mcp.client.stdio import stdio_client, StdioServerParameters\n\nclient = AsyncAnthropic()\n\nasync with stdio_client(StdioServerParameters(command=\"mcp-server\")) as (read, write):\n async with ClientSession(read, write) as mcp_client:\n await mcp_client.initialize()\n\n tools_result = await mcp_client.list_tools()\n # tool_runner is sync — returns the runner, not a coroutine\n runner = client.beta.messages.tool_runner(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Use the available tools\"}],\n tools=[async_mcp_tool(t, mcp_client) for t in tools_result.tools],\n )\n async for message in runner:\n print(message)\n```\n\nFor sync usage, use `mcp_tool` instead of `async_mcp_tool`.\n\n### MCP Prompts\n\n```python\nfrom anthropic.lib.tools.mcp import mcp_message\n\nprompt = await mcp_client.get_prompt(name=\"my-prompt\")\nresponse = await client.beta.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[mcp_message(m) for m in prompt.messages],\n)\n```\n\n### MCP Resources as Content\n\n```python\nfrom anthropic.lib.tools.mcp import mcp_resource_to_content\n\nresource = await mcp_client.read_resource(uri=\"file:///path/to/doc.txt\")\nresponse = await client.beta.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": [\n mcp_resource_to_content(resource),\n {\"type\": \"text\", \"text\": \"Summarize this document\"},\n ],\n }],\n)\n```\n\n### Upload MCP Resources as Files\n\n```python\nfrom anthropic.lib.tools.mcp import mcp_resource_to_file\n\nresource = await mcp_client.read_resource(uri=\"file:///path/to/data.json\")\nuploaded = await client.beta.files.upload(file=mcp_resource_to_file(resource))\n```\n\nConversion functions raise `UnsupportedMCPValueError` if an MCP value cannot be converted (e.g., unsupported content types like audio, unsupported MIME types).\n\n---\n\n## Manual Agentic Loop\n\nUse this when you need fine-grained control over the loop (e.g., custom logging, conditional tool execution, human-in-the-loop approval):\n\n```python\nimport anthropic\n\nclient = anthropic.Anthropic()\ntools = [...] # Your tool definitions\nmessages = [{\"role\": \"user\", \"content\": user_input}]\n\n# Agentic loop: keep going until Claude stops calling tools\nwhile True:\n response = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n tools=tools,\n messages=messages\n )\n\n # If Claude is done (no more tool calls), break\n if response.stop_reason == \"end_turn\":\n break\n\n # Server-side tool hit iteration limit; re-send to continue\n if response.stop_reason == \"pause_turn\":\n messages = [\n {\"role\": \"user\", \"content\": user_input},\n {\"role\": \"assistant\", \"content\": response.content},\n ]\n continue\n\n # Extract tool use blocks from the response\n tool_use_blocks = [b for b in response.content if b.type == \"tool_use\"]\n\n # Append assistant's response (including tool_use blocks)\n messages.append({\"role\": \"assistant\", \"content\": response.content})\n\n # Execute each tool and collect results\n tool_results = []\n for tool in tool_use_blocks:\n result = execute_tool(tool.name, tool.input) # Your implementation\n tool_results.append({\n \"type\": \"tool_result\",\n \"tool_use_id\": tool.id, # Must match the tool_use block's id\n \"content\": result\n })\n\n # Append tool results as a user message\n messages.append({\"role\": \"user\", \"content\": tool_results})\n\n# Final response text\nfinal_text = next(b.text for b in response.content if b.type == \"text\")\n```\n\n---\n\n## Handling Tool Results\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n tools=tools,\n messages=[{\"role\": \"user\", \"content\": \"What's the weather in Paris?\"}]\n)\n\nfor block in response.content:\n if block.type == \"tool_use\":\n tool_name = block.name\n tool_input = block.input\n tool_use_id = block.id\n\n result = execute_tool(tool_name, tool_input)\n\n followup = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n tools=tools,\n messages=[\n {\"role\": \"user\", \"content\": \"What's the weather in Paris?\"},\n {\"role\": \"assistant\", \"content\": response.content},\n {\n \"role\": \"user\",\n \"content\": [{\n \"type\": \"tool_result\",\n \"tool_use_id\": tool_use_id,\n \"content\": result\n }]\n }\n ]\n )\n```\n\n---\n\n## Multiple Tool Calls\n\n```python\ntool_results = []\n\nfor block in response.content:\n if block.type == \"tool_use\":\n result = execute_tool(block.name, block.input)\n tool_results.append({\n \"type\": \"tool_result\",\n \"tool_use_id\": block.id,\n \"content\": result\n })\n\n# Send all results back at once\nif tool_results:\n followup = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n tools=tools,\n messages=[\n *previous_messages,\n {\"role\": \"assistant\", \"content\": response.content},\n {\"role\": \"user\", \"content\": tool_results}\n ]\n )\n```\n\n---\n\n## Error Handling in Tool Results\n\n```python\ntool_result = {\n \"type\": \"tool_result\",\n \"tool_use_id\": tool_use_id,\n \"content\": \"Error: Location 'xyz' not found. Please provide a valid city name.\",\n \"is_error\": True\n}\n```\n\n---\n\n## Tool Choice\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n tools=tools,\n tool_choice={\"type\": \"tool\", \"name\": \"get_weather\"}, # Force specific tool\n messages=[{\"role\": \"user\", \"content\": \"What's the weather in Paris?\"}]\n)\n```\n\n---\n\n## Code Execution\n\n### Basic Usage\n\n```python\nimport anthropic\n\nclient = anthropic.Anthropic()\n\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": \"Calculate the mean and standard deviation of [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\"\n }],\n tools=[{\n \"type\": \"code_execution_20260120\",\n \"name\": \"code_execution\"\n }]\n)\n\nfor block in response.content:\n if block.type == \"text\":\n print(block.text)\n elif block.type == \"bash_code_execution_tool_result\":\n print(f\"stdout: {block.content.stdout}\")\n```\n\n### Upload Files for Analysis\n\n```python\n# 1. Upload a file\nuploaded = client.beta.files.upload(file=open(\"sales_data.csv\", \"rb\"))\n\n# 2. Pass to code execution via container_upload block\n# Code execution is GA; Files API is still beta (pass via extra_headers)\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n extra_headers={\"anthropic-beta\": \"files-api-2025-04-14\"},\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"Analyze this sales data. Show trends and create a visualization.\"},\n {\"type\": \"container_upload\", \"file_id\": uploaded.id}\n ]\n }],\n tools=[{\"type\": \"code_execution_20260120\", \"name\": \"code_execution\"}]\n)\n```\n\n### Retrieve Generated Files\n\n```python\nimport os\n\nOUTPUT_DIR = \"./claude_outputs\"\nos.makedirs(OUTPUT_DIR, exist_ok=True)\n\nfor block in response.content:\n if block.type == \"bash_code_execution_tool_result\":\n result = block.content\n if result.type == \"bash_code_execution_result\" and result.content:\n for file_ref in result.content:\n if file_ref.type == \"bash_code_execution_output\":\n metadata = client.beta.files.retrieve_metadata(file_ref.file_id)\n file_content = client.beta.files.download(file_ref.file_id)\n # Use basename to prevent path traversal; validate result\n safe_name = os.path.basename(metadata.filename)\n if not safe_name or safe_name in (\".\", \"..\"):\n print(f\"Skipping invalid filename: {metadata.filename}\")\n continue\n output_path = os.path.join(OUTPUT_DIR, safe_name)\n file_content.write_to_file(output_path)\n print(f\"Saved: {output_path}\")\n```\n\n### Container Reuse\n\n```python\n# First request: set up environment\nresponse1 = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Install tabulate and create data.json with sample data\"}],\n tools=[{\"type\": \"code_execution_20260120\", \"name\": \"code_execution\"}]\n)\n\n# Get container ID from response\ncontainer_id = response1.container.id\n\n# Second request: reuse the same container\nresponse2 = client.messages.create(\n container=container_id,\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Read data.json and display as a formatted table\"}],\n tools=[{\"type\": \"code_execution_20260120\", \"name\": \"code_execution\"}]\n)\n```\n\n### Response Structure\n\n```python\nfor block in response.content:\n if block.type == \"text\":\n print(block.text) # Claude's explanation\n elif block.type == \"server_tool_use\":\n print(f\"Running: {block.name} - {block.input}\") # What Claude is doing\n elif block.type == \"bash_code_execution_tool_result\":\n result = block.content\n if result.type == \"bash_code_execution_result\":\n if result.return_code == 0:\n print(f\"Output: {result.stdout}\")\n else:\n print(f\"Error: {result.stderr}\")\n else:\n print(f\"Tool error: {result.error_code}\")\n elif block.type == \"text_editor_code_execution_tool_result\":\n print(f\"File operation: {block.content}\")\n```\n\n---\n\n## Memory Tool\n\n### Basic Usage\n\n```python\nimport anthropic\n\nclient = anthropic.Anthropic()\n\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Remember that my preferred language is Python.\"}],\n tools=[{\"type\": \"memory_20250818\", \"name\": \"memory\"}],\n)\n```\n\n### SDK Memory Helper\n\nSubclass `BetaAbstractMemoryTool`:\n\n```python\nfrom anthropic.lib.tools import BetaAbstractMemoryTool\n\nclass MyMemoryTool(BetaAbstractMemoryTool):\n def view(self, command): ...\n def create(self, command): ...\n def str_replace(self, command): ...\n def insert(self, command): ...\n def delete(self, command): ...\n def rename(self, command): ...\n\nmemory = MyMemoryTool()\n\n# Use with tool runner\nrunner = client.beta.messages.tool_runner(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n tools=[memory],\n messages=[{\"role\": \"user\", \"content\": \"Remember my preferences\"}],\n)\n\nfor message in runner:\n print(message)\n```\n\nFor full implementation examples, use WebFetch:\n\n- `https://github.com/anthropics/anthropic-sdk-python/blob/main/examples/memory/basic.py`\n\n---\n\n## Structured Outputs\n\n### JSON Outputs (Pydantic — Recommended)\n\n```python\nfrom pydantic import BaseModel\nfrom typing import List\nimport anthropic\n\nclass ContactInfo(BaseModel):\n name: str\n email: str\n plan: str\n interests: List[str]\n demo_requested: bool\n\nclient = anthropic.Anthropic()\n\nresponse = client.messages.parse(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": \"Extract: Jane Doe (jane@co.com) wants Enterprise, interested in API and SDKs, wants a demo.\"\n }],\n output_format=ContactInfo,\n)\n\n# response.parsed_output is a validated ContactInfo instance\ncontact = response.parsed_output\nprint(contact.name) # \"Jane Doe\"\nprint(contact.interests) # [\"API\", \"SDKs\"]\n```\n\n### Raw Schema\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\n \"role\": \"user\",\n \"content\": \"Extract info: John Smith (john@example.com) wants the Enterprise plan.\"\n }],\n output_config={\n \"format\": {\n \"type\": \"json_schema\",\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\"type\": \"string\"},\n \"email\": {\"type\": \"string\"},\n \"plan\": {\"type\": \"string\"},\n \"demo_requested\": {\"type\": \"boolean\"}\n },\n \"required\": [\"name\", \"email\", \"plan\", \"demo_requested\"],\n \"additionalProperties\": False\n }\n }\n }\n)\n\nimport json\n# output_config.format guarantees the first block is text with valid JSON\ntext = next(b.text for b in response.content if b.type == \"text\")\ndata = json.loads(text)\n```\n\n### Strict Tool Use\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Book a flight to Tokyo for 2 passengers on March 15\"}],\n tools=[{\n \"name\": \"book_flight\",\n \"description\": \"Book a flight to a destination\",\n \"strict\": True,\n \"input_schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"destination\": {\"type\": \"string\"},\n \"date\": {\"type\": \"string\", \"format\": \"date\"},\n \"passengers\": {\"type\": \"integer\", \"enum\": [1, 2, 3, 4, 5, 6, 7, 8]}\n },\n \"required\": [\"destination\", \"date\", \"passengers\"],\n \"additionalProperties\": False\n }\n }]\n)\n```\n\n### Using Both Together\n\n```python\nresponse = client.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=16000,\n messages=[{\"role\": \"user\", \"content\": \"Plan a trip to Paris next month\"}],\n output_config={\n \"format\": {\n \"type\": \"json_schema\",\n \"schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"summary\": {\"type\": \"string\"},\n \"next_steps\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}\n },\n \"required\": [\"summary\", \"next_steps\"],\n \"additionalProperties\": False\n }\n }\n },\n tools=[{\n \"name\": \"search_flights\",\n \"description\": \"Search for available flights\",\n \"strict\": True,\n \"input_schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"destination\": {\"type\": \"string\"},\n \"date\": {\"type\": \"string\", \"format\": \"date\"}\n },\n \"required\": [\"destination\", \"date\"],\n \"additionalProperties\": False\n }\n }]\n)\n```\n\n\n\n# Managed Agents — Python\n\n> **Bindings not shown here:** This README covers the most common managed-agents flows for Python. If you need a class, method, namespace, field, or behavior that isn't shown, WebFetch the Python SDK repo **or the relevant docs page** from `shared/live-sources.md` rather than guess. Do not extrapolate from cURL shapes or another language's SDK.\n\n> **Agents are persistent — create once, reference by ID.** Store the agent ID returned by `agents.create` and pass it to every subsequent `sessions.create`; do not call `agents.create` in the request path. The Anthropic CLI is one convenient way to create agents and environments from version-controlled YAML — its URL is in `shared/live-sources.md`. The examples below show in-code creation for completeness; in production the create call belongs in setup, not in the request path.\n\n## Installation\n\n```bash\npip install anthropic\n```\n\n## Client Initialization\n\n```python\nimport anthropic\n\n# Default — resolves credentials from the environment:\n# ANTHROPIC_API_KEY, or ANTHROPIC_AUTH_TOKEN, or an `ant auth login` profile.\n# Prefer this for local dev; don't hardcode a key.\nclient = anthropic.Anthropic()\n\n# Explicit API key (only when you must inject a specific key)\nclient = anthropic.Anthropic(api_key=\"your-api-key\")\n```\n\n---\n\n## Create an Environment\n\n```python\nenvironment = client.beta.environments.create(\n name=\"my-dev-env\",\n config={\n \"type\": \"cloud\",\n \"networking\": {\"type\": \"unrestricted\"},\n },\n)\nprint(environment.id) # env_...\n```\n\n---\n\n## Create an Agent (required first step)\n\n> ⚠️ **There is no inline agent config.** `model`/`system`/`tools` live on the agent object, not the session. Always start with `agents.create()` — the session only takes `agent={\"type\": \"agent\", \"id\": agent.id}`.\n\n### Minimal\n\n```python\n# 1. Create the agent (reusable, versioned)\nagent = client.beta.agents.create(\n name=\"Coding Assistant\",\n model=\"claude-opus-4-8\",\n tools=[{\"type\": \"agent_toolset_20260401\", \"default_config\": {\"enabled\": True}}],\n)\n\n# 2. Start a session\nsession = client.beta.sessions.create(\n agent={\"type\": \"agent\", \"id\": agent.id, \"version\": agent.version},\n environment_id=environment.id,\n)\nprint(session.id, session.status)\nprint(f\"Trace: https://platform.claude.com/workspaces/default/sessions/{session.id}\")\n```\n\n### With system prompt and custom tools\n\n```python\nimport os\n\nagent = client.beta.agents.create(\n name=\"Code Reviewer\",\n model=\"claude-opus-4-8\",\n system=\"You are a senior code reviewer.\",\n tools=[\n {\"type\": \"agent_toolset_20260401\"},\n {\n \"type\": \"custom\",\n \"name\": \"run_tests\",\n \"description\": \"Run the test suite\",\n \"input_schema\": {\n \"type\": \"object\",\n \"properties\": {\n \"test_path\": {\"type\": \"string\", \"description\": \"Path to test file\"}\n },\n \"required\": [\"test_path\"],\n },\n },\n ],\n)\n\nsession = client.beta.sessions.create(\n agent={\"type\": \"agent\", \"id\": agent.id, \"version\": agent.version},\n environment_id=environment.id,\n title=\"Code review session\",\n resources=[\n {\n \"type\": \"github_repository\",\n \"url\": \"https://github.com/owner/repo\",\n \"mount_path\": \"/workspace/repo\",\n \"authorization_token\": os.environ[\"GITHUB_TOKEN\"],\n \"branch\": \"main\",\n }\n ],\n)\n```\n\n---\n\n## Send a User Message\n\n```python\nclient.beta.sessions.events.send(\n session_id=session.id,\n events=[\n {\n \"type\": \"user.message\",\n \"content\": [{\"type\": \"text\", \"text\": \"Review the auth module\"}],\n }\n ],\n)\n```\n\n> 💡 **Stream-first:** Open the stream *before* (or concurrently with) sending the message. The stream only delivers events that occur after it opens — stream-after-send means early events arrive buffered in one batch. See [Steering Patterns](../../shared/managed-agents-events.md#steering-patterns).\n\n---\n\n## Stream Events (SSE)\n\n```python\nimport json\n\n# Stream-first: open stream, then send while stream is live\nwith client.beta.sessions.events.stream(\n session_id=session.id,\n) as stream:\n client.beta.sessions.events.send(\n session_id=session.id,\n events=[{\"type\": \"user.message\", \"content\": [{\"type\": \"text\", \"text\": \"...\"}]}],\n )\n for event in stream:\n ... # process events\n\n# Standalone stream iteration:\nwith client.beta.sessions.events.stream(\n session_id=session.id,\n) as stream:\n for event in stream:\n if event.type == \"agent.message\":\n for block in event.content:\n if block.type == \"text\":\n print(block.text, end=\"\", flush=True)\n elif event.type == \"agent.custom_tool_use\":\n # Custom tool invocation — session is now idle\n print(f\"\\nCustom tool call: {event.name}\")\n print(f\"Input: {json.dumps(event.input)}\")\n # Send result back (see below)\n elif event.type == \"session.status_idle\":\n print(\"\\n--- Agent idle ---\")\n elif event.type == \"session.status_terminated\":\n print(\"\\n--- Session terminated ---\")\n break\n```\n\n---\n\n## Provide Custom Tool Result\n\n```python\nclient.beta.sessions.events.send(\n session_id=session.id,\n events=[\n {\n \"type\": \"user.custom_tool_result\",\n \"custom_tool_use_id\": \"sevt_abc123\",\n \"content\": [{\"type\": \"text\", \"text\": \"All 42 tests passed.\"}],\n }\n ],\n)\n```\n\n---\n\n## Poll Events\n\n```python\nevents = client.beta.sessions.events.list(\n session_id=session.id,\n)\nfor event in events.data:\n print(f\"{event.type}: {event.id}\")\n```\n\n> ⚠️ **Prefer the SDK over raw `requests`/`httpx`.** If you hand-roll a poll loop, don't assume `timeout=(5, 60)` or `httpx.Timeout(120)` caps total call duration — both are **per-chunk** read timeouts (reset on every byte), so a trickling response can block forever. For a hard wall-clock deadline, track `time.monotonic()` at the loop level and bail explicitly, or wrap with `asyncio.wait_for()`. See [Receiving Events](../../shared/managed-agents-events.md#receiving-events).\n\n---\n\n## Full Streaming Loop with Custom Tools\n\n```python\nimport json\n\n\ndef run_custom_tool(tool_name: str, tool_input: dict) -> str:\n \"\"\"Execute a custom tool and return the result.\"\"\"\n if tool_name == \"run_tests\":\n # Your tool implementation here\n return \"All tests passed.\"\n return f\"Unknown tool: {tool_name}\"\n\n\ndef run_session(client, session_id: str):\n \"\"\"Stream events and handle custom tool calls.\"\"\"\n while True:\n with client.beta.sessions.events.stream(\n session_id=session_id,\n ) as stream:\n tool_calls = []\n for event in stream:\n if event.type == \"agent.message\":\n for block in event.content:\n if block.type == \"text\":\n print(block.text, end=\"\", flush=True)\n elif event.type == \"agent.custom_tool_use\":\n tool_calls.append(event)\n elif event.type == \"session.status_idle\":\n break\n elif event.type == \"session.status_terminated\":\n return\n\n if not tool_calls:\n break\n\n # Process custom tool calls\n results = []\n for call in tool_calls:\n result = run_custom_tool(call.name, call.input)\n results.append({\n \"type\": \"user.custom_tool_result\",\n \"custom_tool_use_id\": call.id,\n \"content\": [{\"type\": \"text\", \"text\": result}],\n })\n\n client.beta.sessions.events.send(\n session_id=session_id,\n events=results,\n )\n```\n\n---\n\n## Upload a File\n\n```python\nwith open(\"data.csv\", \"rb\") as f:\n file = client.beta.files.upload(\n file=f,\n )\n\n# Use in a session\nsession = client.beta.sessions.create(\n agent={\"type\": \"agent\", \"id\": agent.id, \"version\": agent.version},\n environment_id=environment.id,\n resources=[{\"type\": \"file\", \"file_id\": file.id, \"mount_path\": \"/workspace/data.csv\"}],\n)\n```\n\n---\n\n## List and Download Session Files\n\nList files the agent wrote to `/mnt/session/outputs/` during a session, then download them.\n\n```python\n# List files associated with a session\nfiles = client.beta.files.list(\n scope_id=session.id,\n betas=[\"managed-agents-2026-04-01\"],\n)\nfor f in files.data:\n print(f.filename, f.size_bytes)\n # Download each file and save to disk\n file_content = client.beta.files.download(f.id)\n file_content.write_to_file(f.filename)\n```\n\n> 💡 There's a brief indexing lag (~1–3s) between `session.status_idle` and output files appearing in `files.list`. Retry once or twice if the list is empty.\n\n---\n\n## Session Management\n\n```python\n# Get session details\nsession = client.beta.sessions.retrieve(session_id=\"sesn_011CZxAbc123Def456\")\nprint(session.status, session.usage)\n\n# List sessions\nsessions = client.beta.sessions.list()\n\n# Delete a session\nclient.beta.sessions.delete(session_id=\"sesn_011CZxAbc123Def456\")\n\n# Archive a session\nclient.beta.sessions.archive(session_id=\"sesn_011CZxAbc123Def456\")\n```\n\n---\n\n## MCP Server Integration\n\n```python\n# Agent declares MCP server (no auth here — auth goes in a vault)\nagent = client.beta.agents.create(\n name=\"MCP Agent\",\n model=\"claude-opus-4-8\",\n mcp_servers=[\n {\"type\": \"url\", \"name\": \"my-tools\", \"url\": \"https://my-mcp-server.example.com/sse\"},\n ],\n tools=[\n {\"type\": \"agent_toolset_20260401\", \"default_config\": {\"enabled\": True}},\n {\"type\": \"mcp_toolset\", \"mcp_server_name\": \"my-tools\"},\n ],\n)\n\n# Session attaches vault(s) containing credentials for those MCP server URLs\nsession = client.beta.sessions.create(\n agent=agent.id,\n environment_id=environment.id,\n vault_ids=[vault.id],\n)\n```\n\nSee `shared/managed-agents-tools.md` §Vaults for creating vaults and adding credentials.\n\n\n\n# Agent Design Patterns\n\nThis file covers decision heuristics for building agents on the Claude API: which primitives to reach for, how to design your tool surface, and how to manage context and cost over long runs. For per-tool mechanics and code examples, see `tool-use-concepts.md` and the language-specific folders.\n\n---\n\n## Model Parameters\n\n| Parameter | When to use it | What to expect |\n| --- | --- | --- |\n| **Adaptive thinking** (`thinking: {type: \"adaptive\"}`) | When you want Claude to control when and how much to think. | Claude determines thinking depth per request and automatically interleaves thinking between tool calls. No token budget to tune. |\n| **Effort** (`output_config: {effort: ...}`) | When adjusting the tradeoff between thoroughness and token efficiency. | Lower effort → fewer and more-consolidated tool calls, less preamble, terser confirmations. `medium` is often a favorable balance. Use `max` when correctness matters more than cost. |\n\nSee `SKILL.md` §Thinking & Effort for model support and parameter details.\n\n---\n\n## Designing Your Tool Surface\n\n### Bash vs. dedicated tools\n\nClaude doesn't know your application's security boundary, approval policy, or UX surface. Claude emits tool calls; your harness handles them. The shape of those tool calls determines what the harness can do.\n\nA **bash tool** gives Claude broad programmatic leverage — it can perform almost any action. But it gives the harness only an opaque command string, the same shape for every action. Promoting an action to a **dedicated tool** gives the harness an action-specific hook with typed arguments it can intercept, gate, render, or audit.\n\n**When to promote an action to a dedicated tool:**\n\n- **Security boundary.** Actions that require gating are natural candidates. Reversibility is a useful criterion: hard-to-reverse actions (external API calls, sending messages, deleting data) can be gated behind user confirmation. A `send_email` tool is easy to gate; `bash -c \"curl -X POST ...\"` is not.\n- **Staleness checks.** A dedicated `edit` tool can reject writes if the file changed since Claude last read it. Bash can't enforce that invariant.\n- **Rendering.** Some actions benefit from custom UI. Claude Code promotes question-asking to a tool so it can render as a modal, present options, and block the agent loop until answered.\n- **Scheduling.** Read-only tools like `glob` and `grep` can be marked parallel-safe. When the same actions run through bash, the harness can't tell a parallel-safe `grep` from a parallel-unsafe `git push`, so it must serialize.\n\n**Rule of thumb:** Start with bash for breadth. Promote to dedicated tools when you need to gate, render, audit, or parallelize the action.\n\n---\n\n## Anthropic-Provided Tools\n\n| Tool | Side | When to use it | What to expect |\n| --- | --- | --- | --- |\n| **Bash** | Client | Claude needs to execute shell commands. | Claude emits commands; your harness executes them. Reference implementation provided. |\n| **Text editor** | Client | Claude needs to read or edit files. | Claude views, creates, and edits files via your implementation. Reference implementation provided. |\n| **Computer use** | Client or Server | Claude needs to interact with GUIs, web apps, or visual interfaces. | Claude takes screenshots and issues mouse/keyboard commands. Can be self-hosted (you run the environment) or Anthropic-hosted. |\n| **Code execution** | Server | Claude needs to run code in a sandbox you don't want to manage. | Anthropic-hosted container with built-in file and bash sub-tools. No client-side execution. |\n| **Web search / fetch** | Server | Claude needs information past its training cutoff (news, current events, recent docs) or the content of a specific URL. | Claude issues a query or URL; Anthropic executes it and returns results with citations. |\n| **Memory** | Client | Claude needs to save context across sessions. | Claude reads/writes a `/memories` directory. You implement the storage backend. |\n\n**Client-side** tools are defined by Anthropic (name, schema, Claude's usage pattern) but executed by your harness. Anthropic provides reference implementations. **Server-side** tools run entirely on Anthropic infrastructure — declare them in `tools` and Claude handles the rest.\n\n---\n\n## Composing Tool Calls: Programmatic Tool Calling\n\nWith standard tool use, each tool call is a round trip: Claude calls the tool, the result lands in Claude's context, Claude reasons about it, then calls the next tool. Three sequential actions (read profile → look up orders → check inventory) means three round trips. Each adds latency and tokens, and most of the intermediate data is never needed again.\n\n**Programmatic tool calling (PTC)** lets Claude compose those calls into a script instead. The script runs in the code execution container. When the script calls a tool, the container pauses, the call is executed (client-side or server-side), and the result returns to the running code — not to Claude's context. The script processes it with normal control flow (loops, filters, branches). Only the script's final output returns to Claude.\n\n| When to use it | What to expect |\n| --- | --- |\n| Many sequential tool calls, or large intermediate results you want filtered before they hit the context window. | Claude writes code that invokes tools as functions. Runs in the code execution container. Token cost scales with final output, not intermediate results. |\n\n---\n\n## Scaling the Tool and Instruction Set\n\n| Feature | When to use it | What to expect |\n| --- | --- | --- |\n| **Tool search** | Many tools available, but only a few relevant per request. Don't want all schemas in context upfront. | Claude searches the tool set and loads only relevant schemas. Tool definitions are appended, not swapped — preserves cache (see Caching below). |\n| **Skills** | Task-specific instructions Claude should load only when relevant. | Each skill is a folder with a `SKILL.md`. The skill's description sits in context by default; Claude reads the full file when the task calls for it. |\n\nBoth patterns keep the fixed context small and load detail on demand.\n\n---\n\n## Long-Running Agents: Managing Context\n\n| Pattern | When to use it | What to expect |\n| --- | --- | --- |\n| **Context editing** | Context grows stale over many turns (old tool results, completed thinking). | Tool results and thinking blocks are cleared based on configurable thresholds. Keeps the transcript lean without summarizing. |\n| **Compaction** | Conversation likely to reach or exceed the context window limit. | Earlier context is summarized into a compaction block server-side. See `SKILL.md` §Compaction for the critical `response.content` handling. |\n| **Memory** | State must persist across sessions (not just within one conversation). | Claude reads/writes files in a memory directory. Survives process restarts. |\n\n**Choosing between them:** Context editing and compaction operate within a session — editing prunes stale turns, compaction summarizes when you're near the limit. Memory is for cross-session persistence. Many long-running agents use all three.\n\n---\n\n## Caching for Agents\n\n**Read `prompt-caching.md` first.** It covers the prefix-match invariant, breakpoint placement, the silent-invalidator audit, and why changing tools or models mid-session breaks the cache. This section covers only the agent-specific workarounds for those constraints.\n\n| Constraint (from `prompt-caching.md`) | Agent-specific workaround |\n| --- | --- |\n| Editing the system prompt mid-session invalidates the cache. | Append a `{\"role\": \"system\", ...}` message to `messages[]` instead (no beta header; on supporting models — see `prompt-caching.md` § Mid-conversation system messages). The cached prefix stays intact, and the model treats it as an operator-authority instruction rather than user text. On models that don't support it, fall back to a `` text block in the user turn. |\n| Switching models mid-session invalidates the cache. | Spawn a **subagent** with the cheaper model for the sub-task; keep the main loop on one model. |\n| Adding/removing tools mid-session invalidates the cache. | Use **tool search** for dynamic discovery — it appends tool schemas rather than swapping them, so the existing prefix is preserved. |\n\nFor multi-turn breakpoint placement, use top-level auto-caching — see `prompt-caching.md` §Placement patterns.\n\n---\n\nFor live documentation on any of these features, see `live-sources.md`.\n\n\n\n# Anthropic CLI (`ant`)\n\nThe `ant` CLI exposes every Claude API resource as a shell subcommand. Compared to `curl`: request bodies are built from typed flags or piped YAML instead of hand-written JSON, `@path` inlines file contents into any string field, `--transform` extracts fields with a GJSON path (no `jq`), list endpoints auto-paginate (cap total results with `--max-items N`; `--limit` only sets the server page size), and the `beta:` prefix auto-sets the right `anthropic-beta` header.\n\n## When to use the CLI vs the SDK\n\n**CLI for the control plane, SDK for the data plane.** Agents and environments are relatively static resources you define, configure, and debug with `ant` — check the YAML into your repo, apply from CI, inspect from a terminal. Sessions are dynamic and driven by your application through the SDK — create per task, stream events, react to tool calls, integrate into your product. Both hit the same API; the split is about where the call lives, not what's possible.\n\n| | Control plane → `ant` | Data plane → SDK |\n|---|---|---|\n| Resources | agents, environments, skills, vaults, files | sessions, events |\n| Cadence | Once per deploy / ad-hoc | Every task / every turn |\n| Lives in | `*.yaml` in your repo + CI + terminal | Application code |\n| Typical calls | `create < agent.yaml`, `update --version N`, `list`, `retrieve`, `archive`, `--debug` | `sessions.create()`, `events.stream()`, `events.send()` |\n\n## Install and auth\n\n```sh\n# macOS\nbrew install anthropics/tap/ant\nxattr -d com.apple.quarantine \"$(brew --prefix)/bin/ant\"\n\n# Linux / WSL — pick the release from github.com/anthropics/anthropic-cli/releases\ncurl -fsSL \"https://github.com/anthropics/anthropic-cli/releases/download/v${VERSION}/ant_${VERSION}_$(uname -s | tr A-Z a-z)_$(uname -m | sed -e s/x86_64/amd64/ -e s/aarch64/arm64/).tar.gz\" \\\n | sudo tar -xz -C /usr/local/bin ant\n\n# Or from source (Go 1.22+)\ngo install github.com/anthropics/anthropic-cli/cmd/ant@latest\n```\n\n**Auth** — the CLI resolves credentials the same way the SDKs do (first match wins): explicit flags, then `ANTHROPIC_API_KEY`, then `ANTHROPIC_AUTH_TOKEN`, then the `ANTHROPIC_PROFILE`-selected or active profile, then Workload Identity Federation env vars, then the default profile on disk. Override the host with `ANTHROPIC_BASE_URL` or `--base-url`.\n\n- **API key**: set `ANTHROPIC_API_KEY` in the environment.\n- **OAuth profile** (no static key to manage): `ant auth login` opens a browser, exchanges for a short-lived token, and stores a profile under `$ANTHROPIC_CONFIG_DIR` (default `~/.config/anthropic/` on Linux/macOS, `%APPDATA%\\Anthropic` on Windows — `configs/.json` for settings, `credentials/.json` for tokens). Subsequent `ant` (and SDK) calls pick it up automatically — a bare `Anthropic()` client works after login, but scripts that read `ANTHROPIC_API_KEY` directly do not. Claude Code and the Claude Agent SDK honor the same profile resolution. `ant auth status` shows which credential source and profile won (it reports status only — don't script against its exit code as a health check); `ant auth logout` clears the active profile (`--all` for every profile). On a remote host without a browser, `ant auth login --no-browser` prints the authorize URL and accepts the code back in the terminal.\n- **Non-interactive workloads** (CI, servers, containers): interactive login is for development on your own machine — use Workload Identity Federation instead (see the authentication docs via `shared/live-sources.md`).\n\n> **The #1 auth trap:** profiles are only consulted when no API key is set. A stale exported `ANTHROPIC_API_KEY` silently overrides every profile — requests hit whatever org/workspace that key is scoped to. `ant auth status` shows which source won; unset the key (or per-command: `env -u ANTHROPIC_API_KEY ant …`) before relying on a profile. Truly **unset** it — an empty `ANTHROPIC_API_KEY=\"\"` still wins its precedence slot and authenticates with an empty key. The same shadowing applies in reverse to Claude Code: after `ant auth login`, Claude Code may warn about an auth conflict between the profile and its own `/login` credential — keep one (use the profile and `/logout` in Claude Code, or `ant auth logout` to keep Claude Code's own login).\n\n**Named profiles** — an interactive-login token is bound to a single org+workspace, and the API only shows resources belonging to that workspace. If an agent, session, or file you created \"disappears\", the usual cause is a token scoped to a different workspace than the one that created it (`ant auth status` shows the active workspace). Multi-workspace work means one profile per workspace:\n\n```sh\nant auth login --profile # creates the profile if it doesn't exist; org/workspace picker in browser\nant auth login --profile --workspace-id wrkspc_01... # bind directly, skip the picker\nant profile activate # switch the default profile\nant --profile models list # one-off; equivalent: ANTHROPIC_PROFILE= ant models list\nant profile list # inspect\nant profile set workspace_id wrkspc_01... --profile # edit config keys (workspace_id, base_url, organization_id, …)\n```\n\n`ant profile set` edits an existing profile's config — it never creates one, and it does **not** rebind already-issued credentials; run `ant auth login` again under that profile to mint a token for the new target. Pointing `ANTHROPIC_PROFILE` at a profile that doesn't exist is an error, not a fall-through. Refresh tokens eventually hard-expire (they don't slide with use) — when a previously working profile starts failing auth, re-run `ant auth login` before debugging anything else.\n\n**Scopes** — a profile's OAuth scope set is requested at login (`--scope`) and persists on the profile (`scope` is also a `profile set` config key; like other config edits, changing it requires a fresh `ant auth login` to take effect). Privileged scopes — e.g. `org:admin` for organization-administration endpoints — are **not** in the default scope set: pass the full set you want explicitly (`ant auth login --profile admin --scope \"... org:admin\"`), and the server grants a privileged scope only if your role actually has it. Because the scope set rides on every token the profile mints, keep privileged work on a dedicated profile (`admin` vs `default`) and do day-to-day inference on the unprivileged one, switching with `--profile`/`ANTHROPIC_PROFILE`. Check `ant auth login --help` for the current scope list, and `ant auth status` to see what the active token carries.\n\nTo hand the active credential to a subprocess or raw-HTTP script:\n\n```sh\n# Bare access token — for curl's Authorization header\ncurl https://api.anthropic.com/v1/messages \\\n -H \"Authorization: Bearer $(ant auth print-credentials --access-token)\" \\\n -H \"anthropic-version: 2023-06-01\" \\\n -H \"anthropic-beta: oauth-2025-04-20\" \\\n -H \"content-type: application/json\" \\\n -d '{\"model\": \"claude-opus-4-8\", \"max_tokens\": 1024, \"messages\": [{\"role\": \"user\", \"content\": \"Hello\"}]}'\n\n# .env format — sets ANTHROPIC_AUTH_TOKEN (and ANTHROPIC_BASE_URL if the profile has one).\n# Output is bare KEY=value (no `export`), so use `set -a` to auto-export for child processes:\nset -a; eval \"$(ant auth print-credentials --env)\"; set +a\npython my_script.py # SDK picks up ANTHROPIC_AUTH_TOKEN\n```\n\nOAuth tokens go on `Authorization: Bearer` (not `x-api-key:`) **plus the `anthropic-beta: oauth-2025-04-20` header** — converting a raw curl/httpx script from an API key is a header change, not a key swap. The beta header requirement is endpoint-dependent (some endpoints happen to work without it; `/v1/messages` does not) — always send it so requests don't break when you switch endpoints. The token is short-lived and not auto-refreshed when passed via env var, so re-run `print-credentials` before it expires for long-running scripts (`print-credentials` itself refreshes the token if needed). If both `ANTHROPIC_API_KEY` and `ANTHROPIC_AUTH_TOKEN` are set, the SDKs send both and the API rejects the request — unset `ANTHROPIC_API_KEY` before `eval`ing the `--env` output.\n\n**Foot-gun:** `ant auth print-credentials` with **no flags** prints the entire credentials JSON, not the bare token — putting that in an `Authorization` header yields an empty response or HTTP/2 protocol error. Always use `--access-token` for headers (it always reads the named/active profile; a set `ANTHROPIC_API_KEY` doesn't override credential printing).\n\n## Command structure\n\n```\nant [:] [flags]\n```\n\nBeta resources (agents, sessions, environments, deployments, skills, vaults, memory stores) live under `beta:` — the CLI auto-sends the right `anthropic-beta` header, so don't pass it yourself unless overriding with `--beta
`. For self-hosted environments, `ant beta:worker poll/run` and `ant beta:environments:work stats/stop` drive and monitor the work queue — see `shared/managed-agents-self-hosted-sandboxes.md`.\n\n```sh\nant models list\nant messages create --model claude-opus-4-8 --max-tokens 1024 --message '{role: user, content: \"Hello\"}'\nant beta:agents retrieve --agent-id agent_01...\nant beta:sessions:events list --session-id session_01...\n```\n\n`ant --help` lists resources; append `--help` to any subcommand for its flags.\n\n## Global flags\n\n| Flag | Purpose |\n| --- | --- |\n| `--format` | `auto` (default: pretty if TTY, compact if piped), `json`, `jsonl`, `yaml`, `pretty`, `raw`, `explore` (interactive TUI) |\n| `--transform` | GJSON path applied to the response (per-item on list endpoints). Not applied when `--format raw`. |\n| `-r`, `--raw-output` | If the transformed result is a string, print it without quotes (jq semantics). Pair with `--transform` for scalar capture. |\n| `--max-items` | Cap total results returned from auto-paginating list endpoints (distinct from `--limit`, which is the server page size). |\n| `--format-error` / `--transform-error` | Same as `--format`/`--transform`, applied to error responses. `-r` does not apply to the error path — use `--format-error yaml` for unquoted error scalars. |\n| `--base-url` | Override API host |\n| `--debug` | Print full HTTP request + response to stderr (API key redacted) |\n\n## Output — `--transform` + `--format`\n\n`--transform` takes a [GJSON path](https://github.com/tidwall/gjson/blob/master/SYNTAX.md). On list endpoints it runs **per item**, not on the envelope.\n\n```sh\nant beta:agents list --transform '{id,name,model}' --format jsonl\n```\n\n**Extract a scalar for shell use:** pair `--transform` with `-r` (`--raw-output` — prints strings unquoted, jq-style):\n\n```sh\nAGENT_ID=$(ant beta:agents create --name \"My Agent\" --model '{id: claude-sonnet-5}' \\\n --transform id -r)\n```\n\n## Input — flags, stdin, `@file`\n\n**Flags** — scalar fields map directly. Structured fields accept relaxed-YAML syntax (unquoted keys) or strict JSON. Repeatable flags build arrays (each `--tool`, `--event`, `--message` appends one element):\n\n```sh\nant beta:agents create \\\n --name \"Research Agent\" \\\n --model '{id: claude-opus-4-8}' \\\n --tool '{type: agent_toolset_20260401}' \\\n --tool '{type: custom, name: search_docs, input_schema: {type: object, properties: {query: {type: string}}}}'\n```\n\n**Stdin** — pipe a full JSON or YAML body. Merged with flags; flags win on conflict (for array fields, any flag **replaces** the stdin array entirely — it does not append). Quote the heredoc delimiter (`<<'YAML'`) to disable shell expansion inside the body:\n\n```sh\nant beta:agents create <<'YAML'\nname: Research Agent\nmodel: claude-opus-4-8\nsystem: |\n You are a research assistant. Cite sources for every claim.\ntools:\n - type: agent_toolset_20260401\nYAML\n```\n\n**`@file` references** — inline a file's contents into any string-valued field. Inside structured flag values, quote the path. Binary files are auto-base64'd; force with `@file://` (text) or `@data://` (base64). Escape a literal leading `@` as `\\@`.\n\n```sh\nant beta:agents create --name \"Researcher\" --model '{id: claude-sonnet-5}' --system @./prompts/researcher.txt\n\nant messages create --model claude-opus-4-8 --max-tokens 1024 \\\n --message '{role: user, content: [\n {type: document, source: {type: base64, media_type: application/pdf, data: \"@./scan.pdf\"}},\n {type: text, text: \"Extract the text from this scanned document.\"}\n ]}' \\\n --transform 'content.0.text' -r\n```\n\nFlags that natively take a file path (e.g. `--file` on `beta:files upload`) accept a bare path without `@`.\n\n## Version-controlled Managed Agents resources\n\nThis is the recommended flow for defining agents and environments — check the YAML into your repo and sync via `create` (first time) / `update` (thereafter). See `shared/managed-agents-core.md` for the field reference.\n\n```yaml\n# summarizer.agent.yaml\nname: Summarizer\nmodel: claude-sonnet-5\nsystem: |\n You are a helpful assistant that writes concise summaries.\ntools:\n - type: agent_toolset_20260401\n```\n\n```sh\n# Create (once) — capture the ID\nAGENT_ID=$(ant beta:agents create < summarizer.agent.yaml --transform id -r)\n\n# Update (CI) — needs ID + current version (optimistic lock)\nant beta:agents update --agent-id \"$AGENT_ID\" --version 1 < summarizer.agent.yaml\n```\n\nSame pattern for environments (`ant beta:environments create|update < env.yaml`), then start a session with both IDs:\n\n```sh\nant beta:sessions create --agent \"$AGENT_ID\" --environment-id \"$ENV_ID\" --title \"Task\"\nant beta:sessions:events send --session-id \"$SID\" \\\n --event '{type: user.message, content: [{type: text, text: \"Summarize X\"}]}'\nant beta:sessions:events list --session-id \"$SID\" --transform 'content.0.text' -r\nant beta:sessions:events stream --session-id \"$SID\" # live event stream\n```\n\n### Interactive session loop (stream-before-send)\n\n`ant beta:sessions:events stream` only delivers events emitted *after* the stream opens — so open it **before** sending the kickoff to avoid missing early events. Use process substitution to hold the stream on a file descriptor, send, then read:\n\n```sh\nexec {stream}< <(ant beta:sessions:events stream --session-id \"$SID\" \\\n --transform '{type,text:content.#(type==\"text\").text,err:error.message}' --format yaml)\n\nant beta:sessions:events send --session-id \"$SID\" > /dev/null <<'YAML'\nevents:\n - type: user.message\n content:\n - type: text\n text: Summarize the repo README\nYAML\n\ntype=\nwhile IFS= read -r -u \"$stream\" line; do\n case \"$line\" in\n type:\\ session.status_idle) break ;;\n type:\\ session.error)\n IFS= read -r -u \"$stream\" next || next=\n case \"$next\" in err:\\ *) msg=${next#err: } ;; *) msg=unknown ;; esac\n printf '\\n[Error: %s]\\n' \"$msg\"; break ;;\n type:\\ *) type=${line#type: } ;;\n text:*)\n [[ $type == agent.message ]] || continue\n val=${line#text: }\n case \"$val\" in '|-'|'|') ;; *) printf '%s' \"$val\" ;; esac ;;\n \\ \\ *)\n if [[ $type == agent.message ]]; then printf '%s\\n' \"${line# }\"; fi ;;\n esac\ndone\nexec {stream}<&-\n```\n\nThis works for interactive exploration and demos. For application code that needs to react to `agent.tool_use` / `agent.custom_tool_use` events, reconnect after drops, or dedup against `events.list`, use the SDK — see `shared/managed-agents-client-patterns.md`.\n\n## Scripting patterns\n\n`--transform id -r` on a list endpoint emits one bare ID per line — compose with `xargs`, or use `--max-items N` to bound the result set without piping through `head`:\n\n```sh\nFIRST=$(ant beta:agents list --transform id -r --max-items 1)\nant beta:agents:versions list --agent-id \"$FIRST\" --transform '{version,created_at}' --format jsonl\n```\n\nError shaping mirrors the success path (note: `-r` does not apply to error output — use `--format-error yaml` for an unquoted scalar here):\n\n```sh\nant beta:agents retrieve --agent-id bogus --transform-error error.message --format-error yaml 2>&1\n```\n\nShell completion: `ant @completion {zsh|bash|fish|powershell}`.\n\nFor the full, always-current reference (including per-endpoint flags), WebFetch the **Anthropic CLI** URL in `shared/live-sources.md`.\n\n\n\n# Claude Platform on AWS\n\n**Anthropic-operated** access to the Claude Developer Platform through AWS infrastructure — SigV4 authentication, AWS IAM access control, and AWS Marketplace billing. Because Anthropic operates it, **the API surface matches first-party with same-day parity** — for per-feature exceptions, see `shared/platform-availability.md` (the single source of truth; do not rely on an inline exception list here). Model IDs are the bare first-party strings (`claude-opus-4-8`, `claude-sonnet-5`) — **no provider prefix**.\n\n> **Not the same as Amazon Bedrock.** Bedrock is partner-operated (AWS runs the service; release schedules vary, feature subset, `anthropic.`-prefixed model IDs). Claude Platform on AWS and Bedrock coexist; pick by whether you need AWS-native IAM/billing with full Anthropic API parity (this page) vs. Bedrock's own ecosystem.\n\n---\n\n## Client & install\n\n| Language | Install | Client |\n|---|---|---|\n| Python | `pip install -U \"anthropic[aws]\"` | `from anthropic import AnthropicAWS` → `AnthropicAWS()` |\n| TypeScript | `npm install @anthropic-ai/aws-sdk` | `import AnthropicAws from \"@anthropic-ai/aws-sdk\"` → `new AnthropicAws()` |\n| Go | `go get github.com/anthropics/anthropic-sdk-go` | `import anthropicaws \"github.com/anthropics/anthropic-sdk-go/aws\"` → `anthropicaws.NewClient(ctx, anthropicaws.ClientConfig{})` |\n| C# | `dotnet add package Anthropic.Aws` | `new AnthropicAwsClient()` |\n| Java | See SDK repo in `shared/live-sources.md` | See SDK repo in `shared/live-sources.md` |\n| Ruby | `gem install anthropic aws-sdk-core` | See SDK repo in `shared/live-sources.md` |\n| PHP | `composer require anthropic-ai/sdk aws/aws-sdk-php` | See SDK repo in `shared/live-sources.md` |\n\nAfter construction, **use the client exactly as you would `Anthropic()`** — `client.messages.create(...)`, `client.beta.sessions.*`, etc., with bare model IDs.\n\n```python\nfrom anthropic import AnthropicAWS\n\nclient = AnthropicAWS() # region + workspace_id from env; see below\nclient.messages.create(\n model=\"claude-opus-4-8\",\n max_tokens=1024,\n messages=[{\"role\": \"user\", \"content\": \"Hello\"}],\n)\n```\n\n---\n\n## Required configuration\n\nTwo values must be available (constructor args or environment) — **there is no default fallback** for either:\n\n| Value | Env var | Notes |\n|---|---|---|\n| AWS region | `AWS_REGION` | Required. Unlike `AnthropicBedrock`, there is no `us-east-1` fallback. |\n| Workspace ID | `ANTHROPIC_AWS_WORKSPACE_ID` | Required. Routes requests to your Claude workspace. |\n\nEndpoint pattern: `https://aws-external-anthropic.{region}.api.aws/v1/...`. Requests are SigV4-signed with service name `aws-external-anthropic`.\n\n## Authentication\n\nThe client resolves AWS credentials via the standard precedence chain: explicit constructor args → environment (`AWS_ACCESS_KEY_ID`/`AWS_SECRET_ACCESS_KEY`/`AWS_SESSION_TOKEN`) → shared profile → assumed role / instance metadata.\n\n**Short-term API keys** are also supported for cases where SigV4 isn't practical (e.g., browser, simple scripts). Mint one with the per-language token-generator package; pass it as `api_key` on the client. Lifetime is the **lesser of** the requested duration, the underlying credential's expiry, and **12 hours**. For package names and IAM details, WebFetch the Claude Platform on AWS page in `shared/live-sources.md`.\n\n---\n\n## What to tell users\n\n- Treat it as first-party: every section of this skill applies unchanged. Do **not** apply Bedrock's feature-availability mask.\n- Model IDs are bare (`claude-opus-4-8`). Do **not** add an `anthropic.` prefix.\n- A missing region or `workspace_id` throws at client-construction time (no request is sent). A **403** means the request reached the server — check for a **wrong** `workspace_id` or a missing IAM action on the principal. See the IAM actions reference in `shared/live-sources.md`.\n\n\n\n# HTTP Error Codes Reference\n\nThis file documents HTTP error codes returned by the Claude API, their common causes, and how to handle them. For language-specific error handling examples, see the `python/` or `typescript/` folders.\n\n## Error Code Summary\n\n| Code | Error Type | Retryable | Common Cause |\n| ---- | ----------------------- | --------- | ------------------------------------ |\n| 400 | `invalid_request_error` | No | Invalid request format or parameters |\n| 401 | `authentication_error` | No | Invalid or missing API key |\n| 403 | `permission_error` | No | API key lacks permission |\n| 404 | `not_found_error` | No | Invalid endpoint or model ID |\n| 413 | `request_too_large` | No | Request exceeds size limits |\n| 429 | `rate_limit_error` | Yes | Too many requests |\n| 500 | `api_error` | Yes | Anthropic service issue |\n| 529 | `overloaded_error` | Yes | API is temporarily overloaded |\n\n## Detailed Error Information\n\n### 400 Bad Request\n\n**Causes:**\n\n- Malformed JSON in request body\n- Missing required parameters (`model`, `max_tokens`, `messages`)\n- Invalid parameter types (e.g., string where integer expected)\n- Empty messages array\n- Messages not alternating user/assistant\n\n**Example error:**\n\n```json\n{\n \"type\": \"error\",\n \"error\": {\n \"type\": \"invalid_request_error\",\n \"message\": \"messages: roles must alternate between \\\"user\\\" and \\\"assistant\\\"\"\n },\n \"request_id\": \"req_011CSHoEeqs5C35K2UUqR7Fy\"\n}\n```\n\n**Fix:** Validate request structure before sending. Check that:\n\n- `model` is a valid model ID\n- `max_tokens` is a positive integer\n- `messages` array is non-empty and alternates correctly\n\n---\n\n### 401 Unauthorized\n\n**Causes:**\n\n- Missing `x-api-key` header or `Authorization` header\n- Invalid API key format\n- Revoked or deleted API key\n- OAuth bearer token sent via `x-api-key` instead of `Authorization: Bearer`\n- Both `ANTHROPIC_API_KEY` and `ANTHROPIC_AUTH_TOKEN` set — the SDK sends both headers and the API rejects the request\n\n**Fix:** Set `ANTHROPIC_API_KEY`, or run `ant auth login` and leave the client constructor empty. For raw HTTP with an OAuth token, use `Authorization: Bearer ` (not `x-api-key:`).\n\n---\n\n### 403 Forbidden\n\n**Causes:**\n\n- API key doesn't have access to the requested model\n- Organization-level restrictions\n- Attempting to access beta features without beta access\n\n**Fix:** Check your API key permissions in the Console. You may need a different API key or to request access to specific features.\n\n---\n\n### 404 Not Found\n\n**Causes:**\n\n- Typo in model ID (e.g., `claude-sonnet-4.6` instead of `claude-sonnet-4-6`)\n- Using deprecated model ID\n- Invalid API endpoint\n\n**Fix:** Use exact model IDs from the models documentation. You can use aliases (e.g., `claude-opus-4-8`).\n\n---\n\n### 413 Request Too Large\n\n**Causes:**\n\n- Request body exceeds maximum size\n- Too many tokens in input\n- Image data too large\n\n**Fix:** Reduce input size — truncate conversation history, compress/resize images, or split large documents into chunks.\n\n---\n\n### 400 Validation Errors\n\nSome 400 errors are specifically related to parameter validation:\n\n- `max_tokens` exceeds model's limit\n- Invalid `temperature` value (must be 0.0-1.0)\n- `budget_tokens` >= `max_tokens` in extended thinking\n- Invalid tool definition schema\n\n**Model-specific 400s on Fable 5 / Opus 4.8 / 4.7:**\n\n- `temperature`, `top_p`, `top_k` are removed — sending any of them returns 400. Delete the parameter; see `shared/model-migration.md` → Per-SDK Syntax Reference.\n- `thinking: {type: \"enabled\", budget_tokens: N}` is removed — sending it returns 400. Use `thinking: {type: \"adaptive\"}` instead.\n- **Fable 5 only:** an explicit `thinking: {type: \"disabled\"}` returns 400 (it is accepted on Opus 4.8/4.7). Omit the `thinking` param entirely instead.\n- **Fable 5 only:** if the organization is set to zero data retention (ZDR) — or any retention below the required 30 days — then **all** Fable 5 requests return `400 invalid_request_error`, even with a perfectly valid payload. Check the org's retention configuration before debugging the request body.\n\n**Common mistake with extended thinking on older models (Opus 4.6 and earlier):**\n\n```\n# Wrong: budget_tokens must be < max_tokens\nthinking: budget_tokens=10000, max_tokens=1000 → Error!\n\n# Correct\nthinking: budget_tokens=10000, max_tokens=16000\n```\n\n---\n\n### 429 Rate Limited\n\n**Causes:**\n\n- Exceeded requests per minute (RPM)\n- Exceeded tokens per minute (TPM)\n- Exceeded tokens per day (TPD)\n\n**Headers to check:**\n\n- `retry-after`: Seconds to wait before retrying\n- `x-ratelimit-limit-*`: Your limits\n- `x-ratelimit-remaining-*`: Remaining quota\n\n**Fix:** The Anthropic SDKs automatically retry 429 and 5xx errors with exponential backoff (default: `max_retries=2`). For custom retry behavior, see the language-specific error handling examples.\n\n---\n\n### 500 Internal Server Error\n\n**Causes:**\n\n- Temporary Anthropic service issue\n- Bug in API processing\n\n**Fix:** Retry with exponential backoff. If persistent, check [status.anthropic.com](https://status.anthropic.com).\n\n---\n\n### 529 Overloaded\n\n**Causes:**\n\n- High API demand\n- Service capacity reached\n\n**Fix:** Retry with exponential backoff. Consider using a different model (Haiku is often less loaded), spreading requests over time, or implementing request queuing.\n\n---\n\n## Common Mistakes and Fixes\n\n| Mistake | Error | Fix |\n| ------------------------------- | ---------------- | ------------------------------------------------------- |\n| `temperature`/`top_p`/`top_k` on Fable 5 / Opus 4.8 / 4.7 | 400 | Remove the parameter (see `shared/model-migration.md`) |\n| `budget_tokens` on Fable 5 / Opus 4.8 / 4.7 | 400 | Use `thinking: {type: \"adaptive\"}` |\n| `thinking: {type: \"disabled\"}` on Fable 5 | 400 | Omit the `thinking` param entirely (accepted on Opus 4.8/4.7) |\n| Org set to ZDR / retention below 30 days (Fable 5) | 400 on every request | Fix the org's data-retention configuration — the payload isn't the problem |\n| `budget_tokens` >= `max_tokens` (older models) | 400 | Ensure `budget_tokens` < `max_tokens` |\n| Typo in model ID | 404 | Use valid model ID like `claude-opus-4-8` |\n| First message is `assistant` | 400 | First message must be `user` |\n| Consecutive same-role messages | 400 | Alternate `user` and `assistant` |\n| API key in code | 401 (leaked key) | Use environment variable |\n| Custom retry needs | 429/5xx | SDK retries automatically; customize with `max_retries` |\n\n## Typed Exceptions in SDKs\n\n**Always use the SDK's typed exception classes** instead of checking error messages with string matching. Each HTTP status code maps to a specific exception class per SDK.\n\n### Exception class names by language\n\n| HTTP | Python (`anthropic.*`) / TypeScript (`Anthropic.*`) | Ruby (`Anthropic::Errors::*`) | Java (`com.anthropic.errors.*`) | C# | PHP (`Anthropic\\Core\\Exceptions\\*`) |\n|---|---|---|---|---|---|\n| 400 | `BadRequestError` | `BadRequestError` | `BadRequestException` | `AnthropicBadRequestException` | `BadRequestException` |\n| 401 | `AuthenticationError` | `AuthenticationError` | `UnauthorizedException` | `AnthropicUnauthorizedException` | `AuthenticationException` |\n| 403 | `PermissionDeniedError` | `PermissionDeniedError` | `PermissionDeniedException` | `AnthropicForbiddenException` | `PermissionDeniedException` |\n| 404 | `NotFoundError` | `NotFoundError` | `NotFoundException` | `AnthropicNotFoundException` | `NotFoundException` |\n| 422 | `UnprocessableEntityError` | `UnprocessableEntityError` | `UnprocessableEntityException` | `AnthropicUnprocessableEntityException` | `UnprocessableEntityException` |\n| 429 | `RateLimitError` | `RateLimitError` | `RateLimitException` | `AnthropicRateLimitException` | `RateLimitException` |\n| ≥500 | `InternalServerError` | `InternalServerError` | `InternalServerException` | `Anthropic5xxException` | `InternalServerException` |\n| net | `APIConnectionError` | `APIConnectionError` | `AnthropicIoException` | `AnthropicIOException` | `APIConnectionException` |\n| base | `APIError` (both); `APIStatusError` (Python only) | `APIStatusError` / `APIError` | `AnthropicServiceException` | `AnthropicApiException` | `APIStatusException` / `APIException` |\n\nThe Ruby and PHP classes live in a dedicated errors namespace — write `Anthropic::Errors::RateLimitError` and `Anthropic\\Core\\Exceptions\\RateLimitException` (not bare `Anthropic::RateLimitError`). All 4xx C# exceptions also inherit from `Anthropic4xxException`.\n\n### Catch most-specific first, in a chain\n\nOrder `catch`/`except`/`rescue` clauses from the most specific subclass to the base class, with a separate clause for each category you handle differently — retryable (429, ≥500, network) vs. non-retryable (4xx). The SDK defines a distinct class per status for exactly this reason; a single broad catch-all discards that information.\n\n```python\ntry:\n msg = client.messages.create(...)\nexcept anthropic.NotFoundError as e: # 404 — e.g. bad model ID\n ...\nexcept anthropic.RateLimitError as e: # 429 — back off and retry\n ...\nexcept anthropic.APIStatusError as e: # any other non-2xx HTTP response\n print(e.status_code, e.message)\nexcept anthropic.APIConnectionError as e: # network failure before a response\n ...\n```\n\nThe same chain shape applies in every SDK: TypeScript `instanceof Anthropic.NotFoundError` → `RateLimitError` → `APIConnectionError` → `APIError` (check `APIConnectionError` before `APIError` — in the TypeScript SDK it's a subclass of `APIError`, unlike Python where it's a sibling); Ruby `rescue Anthropic::Errors::NotFoundError` → `…::RateLimitError` → `…::APIStatusError`; Java `catch (NotFoundException) … catch (RateLimitException) … catch (AnthropicServiceException)`; C# `catch (AnthropicNotFoundException) … catch (AnthropicRateLimitException) … catch (AnthropicApiException)`; PHP `catch (NotFoundException) … catch (RateLimitException) … catch (APIStatusException)`.\n\n### Go — `errors.As` then branch on status\n\nThe Go SDK returns a single `*anthropic.Error` for all non-2xx responses. Unwrap it with `errors.As`, then branch on `StatusCode`:\n\n```go\n_, err := client.Messages.New(ctx, params)\nif err != nil {\n var apierr *anthropic.Error\n if errors.As(err, &apierr) {\n switch apierr.StatusCode {\n case 404:\n // bad model ID / resource\n case 429:\n // back off and retry\n default:\n // other API error — apierr.StatusCode, apierr.RequestID\n }\n } else {\n // transport-level error (*url.Error wrapping *net.OpError, etc.)\n }\n}\n```\n\n### Error `.type` Field\n\nAll `APIStatusError` subclasses now expose a `.type` property (Python: `.type`, TypeScript: `.type`, Java: `.errorType()`, Go: `.Type()`, Ruby: `.type`, PHP: `.type`) that returns the API error type string (e.g., `\"invalid_request_error\"`, `\"authentication_error\"`, `\"rate_limit_error\"`, `\"overloaded_error\"`). Use this for programmatic error classification when you need finer granularity than the HTTP status code — for example, distinguishing `\"billing_error\"` from `\"permission_error\"` (both map to 403).\n\n```python\nexcept anthropic.APIStatusError as e:\n if e.type == \"rate_limit_error\":\n # handle rate limiting\n elif e.type == \"overloaded_error\":\n # handle overload\n```\n\n\n\n# Live Documentation Sources\n\nThis file contains WebFetch URLs for fetching current information from platform.claude.com and Agent SDK repositories. Use these when users need the latest data that may have changed since the cached content was last updated.\n\n## When to Use WebFetch\n\n- User explicitly asks for \"latest\" or \"current\" information\n- Cached data seems incorrect\n- User asks about features not covered in cached content\n- User needs specific API details or examples\n\n## Claude API Documentation URLs\n\n### Models & Pricing\n\n| Topic | URL | Extraction Prompt |\n| --------------- | ---------------------------------------------------------------------------- | ------------------------------------------------------------------------------- |\n| Models Overview | `https://platform.claude.com/docs/en/about-claude/models/overview.md` | \"Extract current model IDs, context windows, and pricing for all Claude models\" |\n| Migration Guide | `https://platform.claude.com/docs/en/about-claude/models/migration-guide.md` | \"Extract breaking changes, deprecated parameters, and per-model migration steps when moving to a newer Claude model\" |\n| Introducing Claude Fable 5 | `https://platform.claude.com/docs/en/about-claude/models/introducing-claude-fable-5.md` | \"Extract capabilities, API changes, and availability stages for Claude Fable 5 and Claude Mythos 5\" |\n| Pricing | `https://platform.claude.com/docs/en/pricing.md` | \"Extract current pricing per million tokens for input and output\" |\n\n### Core Features\n\n| Topic | URL | Extraction Prompt |\n| ----------------- | ---------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- |\n| Extended Thinking | `https://platform.claude.com/docs/en/build-with-claude/extended-thinking.md` | \"Extract extended thinking parameters, budget_tokens requirements, and usage examples\" |\n| Adaptive Thinking | `https://platform.claude.com/docs/en/build-with-claude/adaptive-thinking.md` | \"Extract adaptive thinking setup, effort levels, and Claude Opus 4.8 usage examples\" |\n| Effort Parameter | `https://platform.claude.com/docs/en/build-with-claude/effort.md` | \"Extract effort levels, cost-quality tradeoffs, and interaction with thinking\" |\n| Tool Use | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview.md` | \"Extract tool definition schema, tool_choice options, and handling tool results\" |\n| Streaming | `https://platform.claude.com/docs/en/build-with-claude/streaming.md` | \"Extract streaming event types, SDK examples, and best practices\" |\n| Prompt Caching | `https://platform.claude.com/docs/en/build-with-claude/prompt-caching.md` | \"Extract cache_control usage, pricing benefits, and implementation examples\" |\n\n### Media & Files\n\n| Topic | URL | Extraction Prompt |\n| ----------- | ---------------------------------------------------------------------- | ----------------------------------------------------------------- |\n| Vision | `https://platform.claude.com/docs/en/build-with-claude/vision.md` | \"Extract supported image formats, size limits, and code examples\" |\n| PDF Support | `https://platform.claude.com/docs/en/build-with-claude/pdf-support.md` | \"Extract PDF handling capabilities, limits, and examples\" |\n\n### API Operations\n\n| Topic | URL | Extraction Prompt |\n| ---------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- |\n| Batch Processing | `https://platform.claude.com/docs/en/build-with-claude/batch-processing.md` | \"Extract batch API endpoints, request format, and polling for results\" |\n| Files API | `https://platform.claude.com/docs/en/build-with-claude/files.md` | \"Extract file upload, download, and referencing in messages, including supported types and beta header\" |\n| Token Counting | `https://platform.claude.com/docs/en/build-with-claude/token-counting.md` | \"Extract token counting API usage and examples\" |\n| Rate Limits | `https://platform.claude.com/docs/en/api/rate-limits.md` | \"Extract current rate limits by tier and model\" |\n| Errors | `https://platform.claude.com/docs/en/api/errors.md` | \"Extract HTTP error codes, meanings, and retry guidance\" |\n| Amazon Bedrock | `https://platform.claude.com/docs/en/build-with-claude/claude-on-amazon-bedrock.md` | \"Extract the AnthropicBedrockMantle client per language, `anthropic.`-prefixed model IDs, auth paths, feature availability, and regions\" |\n| Claude Platform on AWS | `https://platform.claude.com/docs/en/build-with-claude/claude-platform-on-aws.md` | \"Extract the AnthropicAWS client per language, SigV4 auth, credential precedence, short-term API keys, workspace_id, and region requirements\" |\n| Claude Platform on AWS — IAM actions | `https://platform.claude.com/docs/en/api/claude-platform-on-aws-iam-actions.md` | \"Extract the IAM action names, resource ARNs, and policy examples required for each API capability\" |\n\n### Tools\n\n| Topic | URL | Extraction Prompt |\n| -------------- | -------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- |\n| Code Execution | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/code-execution-tool.md` | \"Extract code execution tool setup, file upload, container reuse, and response handling\" |\n| Computer Use | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/computer-use.md` | \"Extract computer use tool setup, capabilities, and implementation examples\" |\n| Bash Tool | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/bash-tool.md` | \"Extract bash tool schema, reference implementation, and security considerations\" |\n| Text Editor | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/text-editor-tool.md` | \"Extract text editor tool commands, schema, and reference implementation\" |\n| Memory Tool | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/memory-tool.md` | \"Extract memory tool commands, directory structure, and implementation patterns\" |\n| Tool Search | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-search-tool.md` | \"Extract tool search setup, when to use, and cache interaction\" |\n| Programmatic Tool Calling | `https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling.md` | \"Extract PTC setup, script execution model, and tool invocation from code\" |\n| Skills | `https://platform.claude.com/docs/en/agents-and-tools/skills.md` | \"Extract skill folder structure, SKILL.md format, and loading behavior\" |\n\n### Advanced Features\n\n| Topic | URL | Extraction Prompt |\n| ------------------ | ----------------------------------------------------------------------------- | --------------------------------------------------- |\n| Structured Outputs | `https://platform.claude.com/docs/en/build-with-claude/structured-outputs.md` | \"Extract output_config.format usage and schema enforcement\" |\n| Compaction | `https://platform.claude.com/docs/en/build-with-claude/compaction.md` | \"Extract compaction setup, trigger config, and streaming with compaction\" |\n| Context Editing | `https://platform.claude.com/docs/en/build-with-claude/context-editing.md` | \"Extract context editing thresholds, what gets cleared, and configuration\" |\n| Citations | `https://platform.claude.com/docs/en/build-with-claude/citations.md` | \"Extract citation format and implementation\" |\n| Context Windows | `https://platform.claude.com/docs/en/build-with-claude/context-windows.md` | \"Extract context window sizes and token management\" |\n\n### Managed Agents\n\nUse these when a managed-agents binding, behavior, or wire-level detail isn't covered in the cached `shared/managed-agents-*.md` concept files or in `{lang}/managed-agents/README.md`.\n\n| Topic | URL | Extraction Prompt |\n| --------------------- | -------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |\n| Overview | `https://platform.claude.com/docs/en/managed-agents/overview.md` | \"Extract the high-level architecture and how agents/sessions/environments/vaults fit together\" |\n| Quickstart | `https://platform.claude.com/docs/en/managed-agents/quickstart.md` | \"Extract the minimal end-to-end agent → environment → session → stream code path\" |\n| Agent Setup | `https://platform.claude.com/docs/en/managed-agents/agent-setup.md` | \"Extract agent create/update/list-versions/archive lifecycle and parameters\" |\n| Define Outcomes | `https://platform.claude.com/docs/en/managed-agents/define-outcomes.md` | \"Extract outcome definitions, evaluation hooks, and success criteria configuration\" |\n| Sessions | `https://platform.claude.com/docs/en/managed-agents/sessions.md` | \"Extract session lifecycle, status transitions, idle/terminated semantics, and resume rules\" |\n| Environments | `https://platform.claude.com/docs/en/managed-agents/environments.md` | \"Extract environment config (cloud/networking), management endpoints, and reuse model\" |\n| Self-Hosted Sandboxes | `https://platform.claude.com/docs/en/managed-agents/self-hosted-sandboxes.md` | \"Extract config:{type:self_hosted}, ANTHROPIC_ENVIRONMENT_KEY, EnvironmentWorker.run/run_one, beta_agent_toolset, ant beta:worker poll/run, webhook-driven wake\" |\n| Self-Hosted Sandboxes — Security | `https://platform.claude.com/docs/en/managed-agents/self-hosted-sandboxes-security.md` | \"Extract what the customer owns (hardening, egress, key custody, trust boundaries) vs what Anthropic cannot do\" |\n| Events and Streaming | `https://platform.claude.com/docs/en/managed-agents/events-and-streaming.md` | \"Extract event stream types, stream-first ordering, reconnect/dedupe, and steering patterns\" |\n| Tools | `https://platform.claude.com/docs/en/managed-agents/tools.md` | \"Extract built-in toolset, custom tool definitions, and tool result wire format\" |\n| Files | `https://platform.claude.com/docs/en/managed-agents/files.md` | \"Extract file upload, mount paths, session resources, and listing/downloading session outputs\" |\n| Permission Policies | `https://platform.claude.com/docs/en/managed-agents/permission-policies.md` | \"Extract permission policy types (allow/deny/confirm) and per-tool config\" |\n| Multi-Agent | `https://platform.claude.com/docs/en/managed-agents/multi-agent.md` | \"Extract multi-agent composition patterns, sub-agent invocation, and result handoff\" |\n| Observability | `https://platform.claude.com/docs/en/managed-agents/observability.md` | \"Extract logging, tracing, and usage telemetry exposed by managed agents\" |\n| Webhooks | `https://platform.claude.com/docs/en/managed-agents/webhooks.md` | \"Extract webhook endpoint registration, HMAC signature verification, supported event types, and delivery semantics\" |\n| GitHub | `https://platform.claude.com/docs/en/managed-agents/github.md` | \"Extract github_repository resource shape, multi-repo mounting, and token rotation\" |\n| MCP Connector | `https://platform.claude.com/docs/en/managed-agents/mcp-connector.md` | \"Extract MCP server declaration on agents and vault-based credential injection at session\" |\n| Vaults | `https://platform.claude.com/docs/en/managed-agents/vaults.md` | \"Extract vault create, credential add/rotate, OAuth refresh shape, and archive\" |\n| Skills | `https://platform.claude.com/docs/en/managed-agents/skills.md` | \"Extract skill packaging and loading model for managed agents\" |\n| Memory | `https://platform.claude.com/docs/en/managed-agents/memory.md` | \"Extract memory resource shape, scoping, and lifecycle\" |\n| Onboarding | `https://platform.claude.com/docs/en/managed-agents/onboarding.md` | \"Extract first-run setup, prerequisites, and account/region requirements\" |\n| Cloud Containers | `https://platform.claude.com/docs/en/managed-agents/cloud-containers.md` | \"Extract cloud container runtime, image config, and network/storage knobs\" |\n| Migration | `https://platform.claude.com/docs/en/managed-agents/migration.md` | \"Extract migration paths from earlier APIs/preview shapes to GA managed agents\" |\n\n### Anthropic CLI\n\nThe `ant` CLI provides terminal access to the Claude API. Every API resource is exposed as a subcommand. It is one convenient way to create agents, environments, sessions, and other resources from version-controlled YAML, and to inspect responses interactively.\n\n| Topic | URL | Extraction Prompt |\n| ------------- | ------------------------------------------------------- | -------------------------------------------------------------------------------------------------- |\n| Anthropic CLI | `https://platform.claude.com/docs/en/api/sdks/cli.md` | \"Extract CLI install, authentication, command structure, and the beta:agents/environments/sessions commands\" |\n| Authentication overview | `https://platform.claude.com/docs/en/manage-claude/authentication.md` | \"Extract the credential options (API keys, interactive OAuth login, Workload Identity Federation) and when to use each\" |\n| WIF reference | `https://platform.claude.com/docs/en/manage-claude/wif-reference.md` | \"Extract credential precedence order, the profile configuration file schema, and the configuration directory layout\" |\n\n---\n\n## Claude API SDK Repositories\n\nWebFetch these when a binding (class, method, namespace, field) isn't covered in the cached `{lang}/` skill files or in the managed-agents docs above. The SDKs include beta managed-agents support for `/v1/agents`, `/v1/sessions`, `/v1/environments`, and related resources — search the repo for `BetaManagedAgents`, `beta.agents`, `beta.sessions`, or the equivalent namespace for that language.\n\n| SDK | URL | Extraction Prompt |\n| ---------- | -------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |\n| Python | `https://github.com/anthropics/anthropic-sdk-python` | \"Extract beta managed-agents namespaces, classes, and method signatures (`client.beta.agents`, `client.beta.sessions`)\" |\n| TypeScript | `https://github.com/anthropics/anthropic-sdk-typescript` | \"Extract beta managed-agents namespaces, classes, and method signatures (`client.beta.agents`, `client.beta.sessions`)\" |\n| Java | `https://github.com/anthropics/anthropic-sdk-java` | \"Extract beta managed-agents classes, builders, and method signatures (`client.beta().agents()`, `BetaManagedAgents*`)\" |\n| Go | `https://github.com/anthropics/anthropic-sdk-go` | \"Extract beta managed-agents types and method signatures (`client.Beta.Agents`, `BetaManagedAgents*` event types)\" |\n| Ruby | `https://github.com/anthropics/anthropic-sdk-ruby` | \"Extract beta managed-agents methods and parameter shapes (`client.beta.agents`, `client.beta.sessions`)\" |\n| C# | `https://github.com/anthropics/anthropic-sdk-csharp` | \"Extract beta managed-agents classes and method signatures (NuGet package, `BetaManagedAgents*` types)\" |\n| PHP | `https://github.com/anthropics/anthropic-sdk-php` | \"Extract beta managed-agents classes and method signatures (`$client->beta->agents`, `BetaManagedAgents*` params)\" |\n\nEach SDK repo also ships runnable programs under `examples/` — including the refusal-fallback / `fallbacks` examples (client-side middleware registration, fallback state, server-side `fallbacks` param). Fetch those for exact per-language syntax instead of translating another language's example.\n\n---\n\n## Fallback Strategy\n\nIf WebFetch fails (network issues, URL changed):\n\n1. Use cached content from the language-specific files (note the cache date)\n2. Inform user the data may be outdated\n3. Suggest they check platform.claude.com or the GitHub repos directly\n\n\n\n# Managed Agents — Endpoint Reference\n\nAll endpoints require `x-api-key` and `anthropic-version: 2023-06-01` headers. Managed Agents endpoints additionally require the `anthropic-beta` header.\n\n## Beta Headers\n\n```\nanthropic-beta: managed-agents-2026-04-01\n```\n\nThe SDK adds this header automatically for all `client.beta.{agents,environments,sessions,vaults,memory_stores,deployments,deployment_runs}.*` calls. Skills endpoints use `skills-2025-10-02`; Files endpoints use `files-api-2025-04-14`.\n\n---\n\n## SDK Method Reference\n\nAll resources are under the `beta` namespace. Python and TypeScript share identical method names.\n\n| Resource | Python / TypeScript (`client.beta.*`) | Go (`client.Beta.*`) |\n| --- | --- | --- |\n| Agents | `agents.create` / `retrieve` / `update` / `list` / `archive` | `Agents.New` / `Get` / `Update` / `List` / `Archive` |\n| Agent Versions | `agents.versions.list` | `Agents.Versions.List` |\n| Environments | `environments.create` / `retrieve` / `update` / `list` / `delete` / `archive` | `Environments.New` / `Get` / `Update` / `List` / `Delete` / `Archive` |\n| Environment Work (self-hosted) | `environments.work.poller` / `stats` / `stop` | See `shared/managed-agents-self-hosted-sandboxes.md` |\n| Sessions | `sessions.create` / `retrieve` / `update` / `list` / `delete` / `archive` | `Sessions.New` / `Get` / `Update` / `List` / `Delete` / `Archive` |\n| Session Events | `sessions.events.list` / `send` / `stream` | `Sessions.Events.List` / `Send` / `StreamEvents` |\n| Session Threads | `sessions.threads.list` / `retrieve` / `archive`; `sessions.threads.events.list` / `stream` | `Sessions.Threads.List` / `Get` / `Archive`; `Sessions.Threads.Events.List` / `StreamEvents` |\n| Session Resources | `sessions.resources.add` / `retrieve` / `update` / `list` / `delete` | `Sessions.Resources.Add` / `Get` / `Update` / `List` / `Delete` |\n| Deployments | `deployments.create` / `pause` / `unpause` / `archive` / `run` | Not yet documented — WebFetch the SDK repo (`shared/live-sources.md`) |\n| Deployment Runs | `deployment_runs.list` / `retrieve` (TS: `deploymentRuns.*`) | Not yet documented — WebFetch the SDK repo (`shared/live-sources.md`) |\n| Vaults | `vaults.create` / `retrieve` / `update` / `list` / `delete` / `archive` | `Vaults.New` / `Get` / `Update` / `List` / `Delete` / `Archive` |\n| Credentials | `vaults.credentials.create` / `retrieve` / `update` / `list` / `delete` / `archive` / `mcp_oauth_validate` | `Vaults.Credentials.New` / `Get` / `Update` / `List` / `Delete` / `Archive` / `McpOauthValidate` |\n| Memory Stores | `memory_stores.create` / `retrieve` / `update` / `list` / `delete` / `archive` | `MemoryStores.New` / `Get` / `Update` / `List` / `Delete` / `Archive` |\n| Memories | `memory_stores.memories.create` / `retrieve` / `update` / `list` / `delete` | `MemoryStores.Memories.New` / `Get` / `Update` / `List` / `Delete` |\n| Memory Versions | `memory_stores.memory_versions.list` / `retrieve` / `redact` | `MemoryStores.MemoryVersions.List` / `Get` / `Redact` |\n\n**Naming quirks to watch for:**\n- Agents and Session Threads have **no delete** — only `archive`. Archive is **permanent**: the agent becomes read-only, new sessions cannot reference it, and there is no unarchive. Confirm with the user before archiving a production agent. Environments, Sessions, Vaults, Credentials, and Memory Stores have both `delete` and `archive`; Session Resources, Files, Skills, and Memories are `delete`-only; Memory Versions have neither — only `redact`.\n- Session resources use `add` (not `create`).\n- Go's event stream is `StreamEvents` (not `Stream`).\n- The self-hosted worker is **not** under `client.beta.*` — it's `EnvironmentWorker` from `anthropic.lib.environments` / `@anthropic-ai/sdk/helpers/beta/environments`; only `environments.work.poller/stats/stop` are client methods.\n\n**Agent shorthand:** `agent` on session create accepts three forms — a bare string (`agent=\"agent_abc123\"`, latest version), a pinned reference `{type: \"agent\", id, version}`, or `{type: \"agent_with_overrides\", id, version?, model?, system?, tools?, mcp_servers?, skills?}` to override those fields for this session only (see `shared/managed-agents-core.md` → Override agent configuration for a session).\n\n**Model shorthand:** `model` on agent create accepts either a bare string (`model=\"claude-opus-4-8\"` — uses `standard` speed) or the full config object (`{id: \"claude-opus-4-8\", speed: \"fast\"}`). Note: `speed: \"fast\"` is supported only on Opus 4.8 and Opus 4.7. Opus 4.7 fast mode is deprecated; after removal, `speed: \"fast\"` on Opus 4.7 returns an error. Opus 4.8 is the durable fast-capable tier.\n\n---\n\n## Agents\n\n**Step one of every flow.** Sessions require a pre-created agent — there is no inline agent config under `managed-agents-2026-04-01`.\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `GET` | `/v1/agents` | ListAgents | List agents |\n| `POST` | `/v1/agents` | CreateAgent | Create a saved agent configuration |\n| `GET` | `/v1/agents/{agent_id}` | GetAgent | Get agent details |\n| `POST` | `/v1/agents/{agent_id}` | UpdateAgent | Update agent configuration |\n| `POST` | `/v1/agents/{agent_id}/archive` | ArchiveAgent | Archive an agent. Makes it **read-only**; existing sessions continue, new sessions cannot reference it. No unarchive — this is the terminal state. |\n| `GET` | `/v1/agents/{agent_id}/versions` | ListAgentVersions | List agent versions |\n\n## Sessions\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `GET` | `/v1/sessions` | ListSessions | List sessions (paginated) |\n| `POST` | `/v1/sessions` | CreateSession | Create a new session |\n| `GET` | `/v1/sessions/{session_id}` | GetSession | Get session details |\n| `POST` | `/v1/sessions/{session_id}` | UpdateSession | Update session `metadata`/`title`, or `agent.tools`/`agent.mcp_servers`/`vault_ids` (session-local override; session must be `idle`). See `shared/managed-agents-core.md` → Updating the agent configuration mid-session. |\n| `DELETE` | `/v1/sessions/{session_id}` | DeleteSession | Delete a session |\n| `POST` | `/v1/sessions/{session_id}/archive` | ArchiveSession | Archive a session |\n\n## Events\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `GET` | `/v1/sessions/{session_id}/events` | ListEvents | List events (polling, paginated) |\n| `POST` | `/v1/sessions/{session_id}/events` | SendEvents | Send events (user message, tool result) |\n| `GET` | `/v1/sessions/{session_id}/events/stream` | StreamEvents | Stream events via SSE. Optional `event_deltas[]=agent.message` / `agent.thinking` opts in to live-preview `event_start`/`event_delta` events — see `shared/managed-agents-events.md` § Live previews. |\n\n## Session Threads\n\nPer-subagent event streams in multiagent sessions. See `shared/managed-agents-multiagent.md`.\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `GET` | `/v1/sessions/{session_id}/threads` | ListThreads | List threads (paginated) |\n| `GET` | `/v1/sessions/{session_id}/threads/{thread_id}` | GetThread | Retrieve one thread (carries `agent` snapshot, `status`, `parent_thread_id`, `stats`, `usage`) |\n| `POST` | `/v1/sessions/{session_id}/threads/{thread_id}/archive` | ArchiveThread | Archive a thread |\n| `GET` | `/v1/sessions/{session_id}/threads/{thread_id}/events` | ListThreadEvents | List past events for one thread (paginated) |\n| `GET` | `/v1/sessions/{session_id}/threads/{thread_id}/stream` | StreamThreadEvents | Stream one thread via SSE (SDK: `threads.events.stream`) |\n\n## Session Resources\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------------- | ---------------- | ---------------------------------------- |\n| `GET` | `/v1/sessions/{session_id}/resources` | ListResources | List resources attached to session |\n| `POST` | `/v1/sessions/{session_id}/resources` | AddResource | Attach `file` or `github_repository` resource (SDK method: `add`, not `create`). `memory_store` resources attach at session-create time only. |\n| `GET` | `/v1/sessions/{session_id}/resources/{resource_id}` | GetResource | Get a single resource |\n| `POST` | `/v1/sessions/{session_id}/resources/{resource_id}` | UpdateResource | Update resource |\n| `DELETE` | `/v1/sessions/{session_id}/resources/{resource_id}` | DeleteResource | Remove resource from session |\n\n## Environments\n\n| Method | Path | Operation | Description |\n| -------- | ---------------------------------------------------------------- | -------------------- | ----------------------------------- |\n| `POST` | `/v1/environments` | CreateEnvironment | Create environment |\n| `GET` | `/v1/environments` | ListEnvironments | List environments |\n| `GET` | `/v1/environments/{environment_id}` | GetEnvironment | Get environment details |\n| `POST` | `/v1/environments/{environment_id}` | UpdateEnvironment | Update environment |\n| `DELETE` | `/v1/environments/{environment_id}` | DeleteEnvironment | Delete environment. Returns 204. |\n| `POST` | `/v1/environments/{environment_id}/archive` | ArchiveEnvironment | Archive environment. Makes it **read-only**; existing sessions continue, new sessions cannot reference it. No unarchive — this is the terminal state. |\n| `GET` | `/v1/environments/{environment_id}/work/stats` | WorkQueueStats | Self-hosted work-queue depth/pending/workers. `x-api-key` auth. See `shared/managed-agents-self-hosted-sandboxes.md`. |\n| `POST` | `/v1/environments/{environment_id}/work/{work_id}/stop` | StopWork | Self-hosted: stop a claimed work item. `x-api-key` auth. |\n\nFor `type: \"self_hosted\"`, `config` is the bare `{\"type\": \"self_hosted\"}` — `networking` and `packages` do not apply.\n\n## Deployments\n\nScheduled deployments (`depl_` IDs) run an agent on a recurring cron schedule — each firing creates a session. See `shared/managed-agents-scheduled-deployments.md` for the conceptual guide (cron/DST semantics, failure behavior, lifecycle).\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `POST` | `/v1/deployments` | CreateDeployment | Create a scheduled deployment |\n| `POST` | `/v1/deployments/{deployment_id}/pause` | PauseDeployment | Suppress scheduled triggers (reversible; manual runs still allowed) |\n| `POST` | `/v1/deployments/{deployment_id}/unpause` | UnpauseDeployment | Resume from the next occurrence (no backfill) |\n| `POST` | `/v1/deployments/{deployment_id}/archive` | ArchiveDeployment | **Terminal** — schedule stops, deployment becomes immutable |\n| `POST` | `/v1/deployments/{deployment_id}/run` | RunDeployment | Trigger a manual run immediately (`trigger_context.type: \"manual\"`); works while paused |\n\n## Deployment Runs\n\nEach trigger attempt (scheduled or manual) writes a `deployment_run` record (`drun_` IDs) carrying either the created `session_id` or an `error.type` (`environment_archived`, `agent_archived`, `vault_not_found`, `session_rate_limited`, `service_unavailable`).\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `GET` | `/v1/deployment_runs?deployment_id=...` | ListDeploymentRuns | List runs for a deployment (paginated; filter failures with `has_error=true`) |\n| `GET` | `/v1/deployment_runs/{deployment_run_id}` | GetDeploymentRun | Retrieve a single run by ID (a `deployment_run.*` webhook event carries this as `data.id`) |\n\n## Vaults\n\nVaults store credentials that Anthropic manages on your behalf — MCP credentials (OAuth with auto-refresh, or static bearer tokens) and `environment_variable` credentials substituted into outbound requests at egress. Attach to sessions via `vault_ids`. See `managed-agents-tools.md` §Vaults for the conceptual guide and credential shapes.\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `POST` | `/v1/vaults` | CreateVault | Create a vault |\n| `GET` | `/v1/vaults` | ListVaults | List vaults |\n| `GET` | `/v1/vaults/{vault_id}` | GetVault | Get vault details |\n| `POST` | `/v1/vaults/{vault_id}` | UpdateVault | Update vault |\n| `DELETE` | `/v1/vaults/{vault_id}` | DeleteVault | Delete vault |\n| `POST` | `/v1/vaults/{vault_id}/archive` | ArchiveVault | Archive vault |\n\n## Credentials\n\nCredentials are individual secrets stored inside a vault.\n\n| Method | Path | Operation | Description |\n| -------- | ----------------------------------------------------------------- | ------------------ | ---------------------------- |\n| `POST` | `/v1/vaults/{vault_id}/credentials` | CreateCredential | Create a credential |\n| `GET` | `/v1/vaults/{vault_id}/credentials` | ListCredentials | List credentials in vault |\n| `GET` | `/v1/vaults/{vault_id}/credentials/{credential_id}` | GetCredential | Get credential metadata |\n| `POST` | `/v1/vaults/{vault_id}/credentials/{credential_id}` | UpdateCredential | Update credential |\n| `DELETE` | `/v1/vaults/{vault_id}/credentials/{credential_id}` | DeleteCredential | Delete credential |\n| `POST` | `/v1/vaults/{vault_id}/credentials/{credential_id}/archive` | ArchiveCredential | Archive credential |\n| `POST` | `/v1/vaults/{vault_id}/credentials/{credential_id}/mcp_oauth_validate` | McpOauthValidate | Validate an MCP OAuth credential |\n\n## Memory Stores\n\nWorkspace-scoped persistent memory that survives across sessions. Attach to a session via a `{\"type\": \"memory_store\", \"memory_store_id\": ...}` entry in `resources[]` (session-create time only). See `shared/managed-agents-memory.md` for the conceptual guide, the FUSE-mount agent interface, preconditions, and versioning.\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ------------------ | ---------------------------------------- |\n| `POST` | `/v1/memory_stores` | CreateMemoryStore | Create a store (`name`, `description`, `metadata`) |\n| `GET` | `/v1/memory_stores` | ListMemoryStores | List stores (`include_archived`, `created_at_{gte,lte}`) |\n| `GET` | `/v1/memory_stores/{memory_store_id}` | GetMemoryStore | Get store details |\n| `POST` | `/v1/memory_stores/{memory_store_id}` | UpdateMemoryStore | Update store |\n| `DELETE` | `/v1/memory_stores/{memory_store_id}` | DeleteMemoryStore | Delete store |\n| `POST` | `/v1/memory_stores/{memory_store_id}/archive` | ArchiveMemoryStore | Archive store. Makes it **read-only**; existing sessions continue, new sessions cannot reference it. No unarchive. |\n\n## Memories\n\nIndividual text documents inside a store (≤ 100KB each). `create` creates at a `path` and returns `409` (`memory_path_conflict_error`, with `conflicting_memory_id`) if the path is occupied; `update` mutates by `mem_...` ID (rename and/or content). Only `update` accepts a `precondition` (`{\"type\": \"content_sha256\", \"content_sha256\": ...}`) — on mismatch returns `409` (`memory_precondition_failed_error`). List endpoints accept `view: \"basic\"|\"full\"` (controls whether `content` is populated; `retrieve` defaults to `full`).\n\n| Method | Path | Operation | Description |\n| -------- | ----------------------------------------------------------------- | -------------- | ---------------------------------------- |\n| `GET` | `/v1/memory_stores/{memory_store_id}/memories` | ListMemories | Returns `Memory \\| MemoryPrefix`; filter by `path_prefix`, `depth`, `order_by`/`order` |\n| `POST` | `/v1/memory_stores/{memory_store_id}/memories` | CreateMemory | Create at `path` (SDK: `memories.create`); `409 memory_path_conflict_error` if occupied |\n| `GET` | `/v1/memory_stores/{memory_store_id}/memories/{memory_id}` | GetMemory | Read one memory (defaults to `view=\"full\"`) |\n| `PATCH` | `/v1/memory_stores/{memory_store_id}/memories/{memory_id}` | UpdateMemory | Change `content`, `path`, or both by ID; optional `precondition` |\n| `DELETE` | `/v1/memory_stores/{memory_store_id}/memories/{memory_id}` | DeleteMemory | Delete (optional `expected_content_sha256`) |\n\n## Memory Versions\n\nImmutable per-mutation snapshots (`memver_...`) — the audit and rollback surface. `operation` ∈ `created` / `modified` / `deleted`.\n\n| Method | Path | Operation | Description |\n| -------- | ----------------------------------------------------------------------------- | --------------------- | ---------------------------------------- |\n| `GET` | `/v1/memory_stores/{memory_store_id}/memory_versions` | ListMemoryVersions | Newest-first; filter by `memory_id`, `operation`, `session_id`, `api_key_id`, `created_at_{gte,lte}` |\n| `GET` | `/v1/memory_stores/{memory_store_id}/memory_versions/{version_id}` | GetMemoryVersion | List fields + full `content` |\n| `POST` | `/v1/memory_stores/{memory_store_id}/memory_versions/{version_id}/redact` | RedactMemoryVersion | Clear `content`/`content_sha256`/`content_size_bytes`/`path`; preserve actor + timestamps |\n\n## Files\n\n| Method | Path | Operation | Description |\n| -------- | ------------------------------------------------ | ---------------- | ---------------------------------------- |\n| `POST` | `/v1/files` | UploadFile | Upload a file |\n| `GET` | `/v1/files` | ListFiles | List files |\n| `GET` | `/v1/files/{file_id}` | GetFile | Get file metadata (SDK method: `retrieve_metadata`) |\n| `GET` | `/v1/files/{file_id}/content` | DownloadFile | Download file content |\n| `DELETE` | `/v1/files/{file_id}` | DeleteFile | Delete a file |\n\n## Skills\n\n| Method | Path | Operation | Description |\n| -------- | --------------------------------------------------------------- | ------------------ | ---------------------------- |\n| `POST` | `/v1/skills` | CreateSkill | Create a skill |\n| `GET` | `/v1/skills` | ListSkills | List skills |\n| `GET` | `/v1/skills/{skill_id}` | GetSkill | Get skill details |\n| `DELETE` | `/v1/skills/{skill_id}` | DeleteSkill | Delete a skill |\n| `POST` | `/v1/skills/{skill_id}/versions` | CreateVersion | Create skill version |\n| `GET` | `/v1/skills/{skill_id}/versions` | ListVersions | List skill versions |\n| `GET` | `/v1/skills/{skill_id}/versions/{version}` | GetVersion | Get skill version |\n| `DELETE` | `/v1/skills/{skill_id}/versions/{version}` | DeleteVersion | Delete skill version |\n\n---\n\n## Request/Response Schema Quick Reference\n\n### CreateAgent Request Body\n\n**Always start here.** `model`, `system`, `tools`, `mcp_servers`, `skills` are top-level fields on this object — they do NOT go on the session.\n\n```json\n{\n \"name\": \"string (required, 1-256 chars)\",\n \"model\": \"claude-opus-4-8 (required — bare string, or {id, speed} object)\",\n \"description\": \"string (optional, up to 2048 chars)\",\n \"system\": \"string (optional, up to 100,000 chars)\",\n \"tools\": [\n { \"type\": \"agent_toolset_20260401\" }\n ],\n \"skills\": [\n { \"type\": \"anthropic\", \"skill_id\": \"xlsx\" },\n { \"type\": \"custom\", \"skill_id\": \"skill_abc123\", \"version\": \"1\" }\n ],\n \"mcp_servers\": [\n {\n \"type\": \"url\",\n \"name\": \"github\",\n \"url\": \"https://api.githubcopilot.com/mcp/\"\n }\n ],\n \"multiagent\": {\n \"type\": \"coordinator\",\n \"agents\": [\n \"agent_abc123\",\n { \"type\": \"agent\", \"id\": \"agent_def456\", \"version\": 4 },\n { \"type\": \"self\" }\n ]\n },\n \"metadata\": {\n \"key\": \"value (max 16 pairs, keys ≤64 chars, values ≤512 chars)\"\n }\n}\n```\n\n> Limits: `tools` max 128, `skills` max 20, `mcp_servers` max 20 (unique names). `multiagent.agents` 1–20 entries (string ID | `{type:\"agent\",id,version?}` | `{type:\"self\"}`) — see `shared/managed-agents-multiagent.md`.\n\n### CreateSession Request Body\n\n```json\n{\n \"agent\": \"agent_abc123 (required — string shorthand for latest version, or {type: \\\"agent\\\", id, version} object)\",\n \"environment_id\": \"env_abc123 (required)\",\n \"title\": \"string (optional)\",\n \"resources\": [\n {\n \"type\": \"github_repository\",\n \"url\": \"https://github.com/owner/repo (required)\",\n \"authorization_token\": \"ghp_... (required)\",\n \"mount_path\": \"/workspace/repo (optional — defaults to /workspace/)\",\n \"checkout\": { \"type\": \"branch\", \"name\": \"main\" }\n }\n ],\n \"vault_ids\": [\"vlt_abc123 (optional — vault credentials: MCP auth + environment variables)\"],\n \"metadata\": {\n \"key\": \"value\"\n }\n}\n```\n\n> The `agent` field accepts a string ID, `{type: \"agent\", id, version}`, or `{type: \"agent_with_overrides\", id, version?, ...}` for session-local overrides of `model`/`system`/`tools`/`mcp_servers`/`skills`. Outside the overrides form, those fields live on the agent, not here.\n>\n> **`checkout`** accepts `{type: \"branch\", name: \"...\"}` or `{type: \"commit\", sha: \"...\"}`. Omit for the repo's default branch.\n\n### CreateEnvironment Request Body\n\n```json\n{\n \"name\": \"string (required)\",\n \"description\": \"string (optional)\",\n \"config\": {\n \"type\": \"cloud | self_hosted\",\n \"networking\": {\n \"type\": \"unrestricted | limited (union — see SDK types)\"\n },\n \"packages\": { }\n },\n \"metadata\": { \"key\": \"value\" }\n}\n```\n\n### CreateDeployment Request Body\n\n```json\n{\n \"name\": \"Weekly compliance scan\",\n \"agent\": \"agent_abc123 (required — same shapes as CreateSession)\",\n \"environment_id\": \"env_abc123 (required)\",\n \"initial_events\": [\n { \"type\": \"user.message\", \"content\": [{ \"type\": \"text\", \"text\": \"Run the weekly compliance scan.\" }] }\n ],\n \"schedule\": {\n \"type\": \"cron\",\n \"expression\": \"0 20 * * 5\",\n \"timezone\": \"America/New_York\"\n }\n}\n```\n\n> Optional session config (`resources`, `vault_ids`, etc.) is supported the same way as on CreateSession. Response includes `status`, `paused_reason`, and `schedule.upcoming_runs_at` (next fire times). See `shared/managed-agents-scheduled-deployments.md`.\n\n### SendEvents Request Body\n\n```json\n{\n \"events\": [\n {\n \"type\": \"user.message\",\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"Hello\"\n }\n ]\n }\n ]\n}\n```\n\n> `system.message` events (update the system prompt between turns) use the same envelope with `type: \"system.message\"` — Claude Opus 4.8 only; see `shared/managed-agents-events.md` § Updating the system prompt mid-session.\n\n### Define Outcome Event\n\n```json\n{\n \"type\": \"user.define_outcome\",\n \"description\": \"Build a DCF model for Costco in .xlsx\",\n \"rubric\": { \"type\": \"file\", \"file_id\": \"file_01...\" },\n \"max_iterations\": 5\n}\n```\n\n> `rubric` is required: `{type: \"text\", content}` or `{type: \"file\", file_id}`. `max_iterations` default 3, max 20. Echoed back with `outcome_id` + `processed_at`. See `shared/managed-agents-outcomes.md`.\n\n### Tool Result Event\n\n```json\n{\n \"type\": \"user.custom_tool_result\",\n \"custom_tool_use_id\": \"sevt_abc123\",\n \"content\": [{ \"type\": \"text\", \"text\": \"Result data\" }],\n \"is_error\": false\n}\n```\n\n---\n\n## Error Handling\n\nManaged Agents endpoints use the standard Anthropic API error format. Errors are returned with an HTTP status code and a JSON body containing `type`, `error`, and `request_id`:\n\n```json\n{\n \"type\": \"error\",\n \"error\": {\n \"type\": \"invalid_request_error\",\n \"message\": \"Description of what went wrong\"\n },\n \"request_id\": \"req_011CRv1W3XQ8XpFikNYG7RnE\"\n}\n```\n\nInclude the `request_id` when reporting issues to Anthropic — it lets us trace the request end-to-end. The inner `error.type` is one of the following:\n\n| Status | Error type | Description |\n|---|---|---|\n| 400 | `invalid_request_error` | The request was malformed or missing required parameters |\n| 401 | `authentication_error` | Invalid or missing API key |\n| 403 | `permission_error` | The API key doesn't have permission for this operation |\n| 404 | `not_found_error` | The requested resource doesn't exist |\n| 409 | `invalid_request_error` | The request conflicts with the resource's current state (e.g., sending to an archived session) |\n| 413 | `request_too_large` | The request body exceeds the maximum allowed size |\n| 429 | `rate_limit_error` | Too many requests — check rate limit headers for retry timing |\n| 500 | `api_error` | An internal server error occurred |\n| 529 | `overloaded_error` | The service is temporarily overloaded — retry with backoff |\n\nNote that `409 Conflict` carries `error.type: \"invalid_request_error\"` (there is no separate `conflict_error` type); inspect both the HTTP status and the `message` to distinguish conflicts from other invalid requests.\n\n---\n\n## Pagination\n\nMost Managed Agents list endpoints use the `page` / `next_page` cursor scheme:\n\n| Field | Where | Notes |\n|---|---|---|\n| `limit` | query | Max items per page |\n| `page` | query | Opaque cursor from a previous response — pass a `next_page` or `prev_page` value here |\n| `order` | query | `asc` / `desc` on endpoints that support sorting. A cursor encodes the `order` of the request that produced it — reusing it with a different `order` returns 400. Other params (filters, `limit`) can change between paginated requests. |\n| `next_page` | response | Cursor for the next page; `null` when there are no more results |\n| `prev_page` | response | Cursor for the previous page on endpoints that support backward pagination — currently **only `GET /v1/sessions`**. `null` on the first page. On endpoints that don't support it, the field is **absent** (not `null`). |\n\nEvery SDK exposes an auto-paginating iterator that follows `next_page`. In Python and TypeScript, iterate the list result directly; the other SDKs expose the iterator via a separate method (iterating the plain list result returns one page). SDK auto-pagination is **forward-only** — to go back a page, read `prev_page` from the response and pass it back as the `page` parameter yourself.\n\n> ⚠️ Some endpoints use a **different** cursor scheme: Message Batches, Files, Models, and several Admin API endpoints take `after_id`/`before_id` and return `has_more`/`first_id`/`last_id` instead of `page`/`next_page`. Some `page`-scheme endpoints (e.g. `GET /v1/skills`) also return a `has_more` boolean alongside `next_page`. Check the endpoint's reference page for its exact pagination fields.\n\n---\n\n## Rate Limits\n\nManaged Agents endpoints have per-organization request-per-minute (RPM) limits, separate from your [Messages API token limits](https://platform.claude.com/docs/en/api/rate-limits). Model inference inside a session still draws from your organization's standard ITPM/OTPM limits.\n\n| Endpoint group | Scope | RPM | Max concurrent |\n|---|---|---|---|\n| Create operations (Agents, Sessions, Vaults) | organization | 300 | — |\n| All other operations (Agents, Sessions, Vaults) | organization | 600 | — |\n| All operations (Environments) | organization | 60 | 5 |\n\nFiles and Skills endpoints use the standard tier-based [rate limits](https://platform.claude.com/docs/en/api/rate-limits).\n\nWhen a limit is exceeded the API returns `429` with a `rate_limit_error` (see [Error Handling](#error-handling) for the response envelope) and a `retry-after` header indicating how many seconds to wait before retrying. The Anthropic SDK reads this header and retries automatically.\n\n\n\n# Managed Agents — Common Client Patterns\n\nPatterns you'll write on the client side when driving a Managed Agent session, grounded in working SDK examples.\n\nCode samples are TypeScript — other languages follow the same shape; see `{lang}/managed-agents/README.md` (cURL and C#: `curl/managed-agents.md`) for equivalents.\n\n---\n\n## 1. Lossless stream reconnect\n\n**Problem:** SSE has no replay. If the connection drops mid-session, a naive reconnect re-opens the stream from \"now\" and you silently miss every event emitted in between.\n\n**Solution:** on reconnect, fetch the full event history via `events.list()` *before* consuming the live stream, and dedupe on event ID as the live stream catches up.\n\n```ts\nconst seenEventIds = new Set()\nconst stream = await client.beta.sessions.events.stream(session.id)\n\n// Stream is now open and buffering server-side. Read history first.\nfor await (const event of client.beta.sessions.events.list(session.id)) {\n seenEventIds.add(event.id)\n handle(event)\n}\n\n// Tail the live stream. Dedupe only gates handle() — terminal checks must run\n// even for already-seen events, or a terminal event that was in the history\n// response gets skipped by `continue` and the loop never exits.\nfor await (const event of stream) {\n if (!seenEventIds.has(event.id)) {\n seenEventIds.add(event.id)\n handle(event)\n }\n if (event.type === 'session.status_terminated') break\n if (event.type === 'session.status_idle' && event.stop_reason.type !== 'requires_action') break\n}\n```\n\n---\n\n## 2. `processed_at` — queued vs processed\n\nEvery event on the stream carries `processed_at` (ISO 8601). For client-sent events (`user.message`, `user.interrupt`, `user.tool_confirmation`, `user.custom_tool_result`) it's `null` when the event has been queued but not yet picked up by the agent, and populated once the agent processes it. The same event appears on the stream twice — once with `processed_at: null`, once with a timestamp.\n\n```ts\nfor await (const event of stream) {\n if (event.type === 'user.message') {\n if (event.processed_at == null) onQueued(event.id)\n else onProcessed(event.id, event.processed_at)\n }\n}\n```\n\nUse this to drive pending → acknowledged UI state for anything you send. How you map a locally-rendered optimistic message to the server-assigned `event.id` is application-specific (typically via the return value of `events.send()` or FIFO ordering).\n\n---\n\n## 3. Interrupt a running session\n\nSend `user.interrupt` as a normal event. The session keeps running until it reaches a safe boundary, then goes idle.\n\n```ts\nawait client.beta.sessions.events.send(session.id, {\n events: [{ type: 'user.interrupt' }],\n})\n\n// Drain until the session is truly done — see Pattern 5 for the full gate.\nfor await (const event of stream) {\n if (event.type === 'session.status_terminated') break\n if (\n event.type === 'session.status_idle' &&\n event.stop_reason.type !== 'requires_action'\n ) break\n}\n```\n\nReference: `interrupt.ts` — sends the interrupt the moment it sees `span.model_request_start`, drains to idle, then verifies via `sessions.retrieve()`.\n\n---\n\n## 4. `tool_confirmation` round-trip\n\nWhen the agent has `permission_policy: { type: 'always_ask' }`, any call to that tool fires an `agent.tool_use` event with `evaluated_permission === 'ask'` and the session goes idle waiting for a decision. Respond with `user.tool_confirmation`.\n\n```ts\nfor await (const event of stream) {\n if (event.type === 'agent.tool_use' && event.evaluated_permission === 'ask') {\n await client.beta.sessions.events.send(session.id, {\n events: [{\n type: 'user.tool_confirmation',\n tool_use_id: event.id, // not a toolu_ id — use event.id\n result: 'allow', // or 'deny'\n // deny_message: '...', // optional, only with result: 'deny'\n }],\n })\n }\n}\n```\n\nKey points:\n- `tool_use_id` is `event.id` (typically `sevt_...`), **not** a `toolu_...` ID.\n- `result` is `'allow' | 'deny'`. Use `deny_message` to tell the model *why* you denied — it gets surfaced back to the agent.\n- Multiple pending tools: respond once per `agent.tool_use` event with `evaluated_permission === 'ask'`.\n\nReference: `tool-permissions.ts`.\n\n---\n\n## 5. Correct idle-break gate\n\nDo not break on `session.status_idle` alone. The session goes idle transiently — e.g. between parallel tool executions, while waiting for a `user.tool_confirmation`, or while awaiting a `user.custom_tool_result`. Break when idle with a terminal `stop_reason`, or on `session.status_terminated`.\n\n```ts\nfor await (const event of stream) {\n handle(event)\n if (event.type === 'session.status_terminated') break\n if (event.type === 'session.status_idle') {\n if (event.stop_reason.type === 'requires_action') continue // waiting on you — handle it\n break // end_turn or retries_exhausted — both terminal\n }\n}\n```\n\n`stop_reason.type` values on `session.status_idle`:\n- `requires_action` — agent is waiting on a client-side event (tool confirmation, custom tool result). Handle it, don't break.\n- `retries_exhausted` — terminal failure. Break, then check `sessions.retrieve()` for the error state.\n- `end_turn` — normal completion.\n\n---\n\n## 6. Post-idle status-write race\n\nThe SSE stream emits `session.status_idle` slightly before the session's queryable status reflects it. Clients that break on idle and immediately call `sessions.delete()` or `sessions.archive()` will intermittently 400 with \"cannot delete/archive while running.\"\n\nPoll before cleanup:\n\n```ts\nlet s\nfor (let i = 0; i < 10; i++) {\n s = await client.beta.sessions.retrieve(session.id)\n if (s.status !== 'running') break\n await new Promise(r => setTimeout(r, 200))\n}\nif (s?.status !== 'running') {\n await client.beta.sessions.archive(session.id)\n} // else: still running after 2s — don't archive, let it settle or escalate\n```\n\n---\n\n## 7. Stream-first, then send\n\nAlways open the stream **before** sending the kickoff event. Otherwise the agent may process the event and emit the first events before your consumer is attached, and you'll miss them.\n\n```ts\nconst stream = await client.beta.sessions.events.stream(session.id)\nawait client.beta.sessions.events.send(session.id, {\n events: [{ type: 'user.message', content: [{ type: 'text', text: 'Hello' }] }],\n})\nfor await (const event of stream) { /* ... */ }\n```\n\nThe `Promise.all([stream, send])` shape works too, but stream-first is simpler and has the same effect — the stream starts buffering the moment it's opened.\n\n---\n\n## 8. File-mount gotchas\n\n**The mounted resource has a different `file_id` than the file you uploaded.** Session creation makes a session-scoped copy.\n\n```ts\nconst uploaded = await client.beta.files.upload({ file, purpose: 'agent_resource' })\n// uploaded.id → the original file\nconst session = await client.beta.sessions.create({\n /* ... */\n resources: [{ type: 'file', file_id: uploaded.id, mount_path: '/workspace/data.csv' }],\n})\n// session.resources[0].file_id !== uploaded.id ← different IDs\n```\n\nDelete the original via `files.delete(uploaded.id)`; the session-scoped copy is garbage-collected with the session. `mount_path` must be absolute — see `shared/managed-agents-environments.md`.\n\n---\n\n## 9. Secrets for non-MCP APIs and CLIs — keep them host-side via custom tools\n\n**Problem:** you want the agent to call a third-party API or run a CLI that needs a secret (API key, token, service-account credential), but you can't or don't want to hand the secret to a vault.\n\n**First check:** for cloud environments, the first-class answer is now a vault `environment_variable` credential — the agent's shell sees an opaque placeholder and the real secret is substituted at egress. See `shared/managed-agents-tools.md` → Vaults. Use this pattern instead when that doesn't fit: **self-hosted sandboxes** (env-var credentials not yet supported there), clients that reject the placeholder via local format validation, secrets that must never leave your infrastructure, or calls that need host-side binaries.\n\n**Solution:** move the authenticated call to your side. Declare a custom tool on the agent; when the agent emits `agent.custom_tool_use`, your orchestrator (the process reading the SSE stream) executes the call with its own credentials and responds with `user.custom_tool_result`. The container never sees the key.\n\n```ts\n// Agent template: declare the tool, no credentials\ntools: [{ type: 'custom', name: 'linear_graphql', input_schema: { /* query, vars */ } }]\n\n// Orchestrator: handle the call with host-side creds\nfor await (const event of stream) {\n if (event.type === 'agent.custom_tool_use' && event.name === 'linear_graphql') {\n const result = await linear.request(event.input.query, event.input.vars) // host's key\n await client.beta.sessions.events.send(session.id, {\n events: [{ type: 'user.custom_tool_result', tool_use_id: event.id, result }],\n })\n }\n}\n```\n\nSame shape works for `gh` CLI, local eval scripts, or anything else that needs host-side auth or binaries.\n\n**Security note:** this does not expose a public endpoint. `agent.custom_tool_use` arrives on the SSE stream your orchestrator already holds open with your Anthropic API key, and `user.custom_tool_result` goes back via `events.send()` under the same key. Your orchestrator is a client, not a server — nothing unauthenticated is listening.\n\n**Do not embed API keys in the system prompt or user messages as a workaround.** Prompts and messages are stored in the session's event history, returned by `events.list()`, and included in compaction summaries — a secret placed there is durably persisted and readable via the API for the life of the session.\n\n\n\n# Managed Agents — Core Concepts\n\n## Architecture\n\nManaged Agents is built around four core concepts:\n\n| Concept | Endpoint | What it is |\n|---|---|---|\n| **Agent** | `/v1/agents` | A persisted, versioned object defining the agent's capabilities and persona: model, system prompt, tools, MCP servers, skills. **Must be created before starting a session.** See the Agents section below. |\n| **Session** | `/v1/sessions` | A stateful interaction with an agent. References a pre-created agent by ID + an environment + initial instructions. Produces an event stream. |\n| **Environment** | `/v1/environments` | A template defining the configuration for container provisioning. |\n| **Container** | N/A | An isolated compute instance where the agent's **tools** execute (bash, file ops, code). The agent loop does not run here — it runs on Anthropic's orchestration layer and acts on the container via tool calls. |\n\n```\n ┌─────────────────────────────────────┐\n │ Anthropic orchestration layer │\nAgent (config) ───────▶│ (agent loop: Claude + tool calls) │\n └──────────────┬──────────────────────┘\n │ tool calls\n ▼\nEnvironment (template) ──▶ Container (tool execution workspace)\n │\n Session ─┤\n ├── Resources (files, repos, memory stores — attached at startup)\n ├── Vault IDs (MCP credential references)\n └── Conversation (event stream in/out)\n```\n\n> **Agent creation is a prerequisite.** Sessions reference a pre-created agent by ID — `model`/`system`/`tools` live on the agent object, never on the session. Every flow starts with `POST /v1/agents`.\n\n---\n\n## Session Lifecycle\n\n```\nrescheduling → running ↔ idle → terminated\n```\n\n| Status | Description |\n| -------------- | ------------------------------------------------------------------ |\n| `idle` | Agent has finished the current task, and is awaiting input. It's either waiting for input to continue working via a `user.message` or blocked awaiting a `user.custom_tool_result` or `user.tool_confirmation`. The `stop_reason` attached contains more information about why the Agent has stopped working. |\n| `running` | Session has starting running, and the Agent is actively doing work. |\n| `rescheduling` | Session is (re)scheduling after a retryable error has occurred, ready to be picked up by the orchestration system. |\n| `terminated` | Session has terminated, entering an irreversible and unusable state. |\n\n- Events can be sent when the session is `running` or `idle`. Messages are queued and processed in order.\n- The agent transitions `idle → running` when it receives a new event, then back to `idle` when done.\n- Errors surface as `session.error` events in the stream, not as a status value.\n\nEvery session has a live trace view in the Anthropic Console at `https://platform.claude.com/workspaces/default/sessions/{session_id}`. Print this URL immediately after creating a session so the user can watch tool calls and messages stream in real time. The `default` workspace segment auto-resolves to the session's actual workspace on load, so you don't need the workspace id.\n\n### Built-in session features\n\n- **Context compaction** — if you approach max context, the API automatically condenses session history to keep the interaction going\n- **Prompt caching** — historical repeated tokens are cached, reducing processing time and cost\n- **Extended thinking** — on by default, returned as `agent.thinking` events\n\n### Session operations\n\n| Operation | Notes |\n|---|---|\n| List / fetch | Paginated list or single resource by ID |\n| Update | Only `title` is updatable |\n| Archive | Session becomes **read-only**. Not reversible. |\n| Delete | Permanently deletes session, event history, container, and checkpoints. |\n\nThese are ops/inspection calls — typically made from a terminal, not application code. From the shell (see `shared/anthropic-cli.md`):\n\n```sh\nant beta:sessions list --transform '{id,title,status,created_at}' --format jsonl\nant beta:sessions retrieve --session-id \"$SID\"\nant beta:sessions:events stream --session-id \"$SID\" # watch events live\nant beta:sessions archive --session-id \"$SID\"\nant beta:sessions delete --session-id \"$SID\"\n```\n\n---\n\n## Sessions\n\nA session is a running agent instance inside an environment.\n\n### Session Object\n\nKey fields returned by the API:\n\n| Field | Type | Description |\n| --------------- | -------- | --------------------------------------------------- |\n| `type` | string | Always `\"session\"` |\n| `id` | string | Unique session ID |\n| `title` | string | Human-readable title |\n| `status` | string | `idle`, `running`, `rescheduling`, `terminated` |\n| `created_at` | string | ISO 8601 timestamp |\n| `updated_at` | string | ISO 8601 timestamp |\n| `archived_at` | string | ISO 8601 timestamp (nullable) |\n| `environment_id` | string | Environment ID |\n| `agent` | object | Agent configuration |\n| `resources` | array | Attached files, repos, and memory stores |\n| `metadata` | object | User-provided key-value pairs (max 8 keys) |\n| `usage` | object | Token usage statistics |\n\n### Creating a session\n\n**A session is meaningless without an agent.** Sessions reference a pre-created agent by ID. Create the agent first via `agents.create()`, then reference it:\n\n```ts\n// 1. Create the agent (reusable, versioned)\nconst agent = await client.beta.agents.create(\n {\n name: \"Coding Assistant\",\n model: \"claude-opus-4-8\",\n system: \"You are a helpful coding agent.\",\n tools: [{ type: \"agent_toolset_20260401\"}],\n },\n);\n\n// 2. Start a session that references it\nconst session = await client.beta.sessions.create(\n {\n agent: agent.id, // string shorthand → latest version. Or: { type: \"agent\", id: agent.id, version: agent.version }\n environment_id: environmentId,\n title: \"Hello World Session\",\n },\n);\n```\n\n> 💡 **Watch it live in Console.** While developing, print a link so you can click through to the session UI instead of parsing the event stream: `https://platform.claude.com/workspaces/default/sessions/{session.id}`. The `default` workspace segment auto-resolves to the session's actual workspace on load, so you don't need to know the workspace id. Include this `print`/`console.log` in code you generate for users who are iterating locally.\n\n**Session creation parameters:**\n\n| Field | Type | Required | Description |\n| --------------- | -------- | -------- | ---------------------------------------------- |\n| `agent` | string or object | **Yes** | Three forms: string shorthand `\"agent_abc123\"` (latest version); pinned `{type: \"agent\", id, version}`; or `{type: \"agent_with_overrides\", id, version?, ...}` to override `model`/`system`/`tools`/`mcp_servers`/`skills` for this session only — see § Override agent configuration for a session |\n| `environment_id`| string | **Yes** | Environment ID |\n| `title` | string | No | Human-readable name (appears in logs/dashboards) |\n| `resources` | array | No | Files, GitHub repos, or memory stores, attached to the container at startup. Memory stores are session-create-only (not addable via `resources.add()`). |\n| `vault_ids` | array | No | Vault IDs (`vlt_*`) — MCP credentials with auto-refresh + `environment_variable` secrets substituted at egress. See `shared/managed-agents-tools.md` → Vaults. |\n| `metadata` | object | No | User-provided key-value pairs |\n\n**Agent configuration fields** (passed to `agents.create()`, not `sessions.create()`):\n\n| Field | Type | Required | Description |\n| ------------- | -------- | -------- | ---------------------------------------------- |\n| `name` | string | **Yes** | Human-readable name (1-256 chars) |\n| `model` | string or object | **Yes** | Claude model ID (bare string, or `{id, speed}` object). All Claude 4.5+ models supported. |\n| `system` | string | No | System prompt — defines the agent's behavior (up to 100K chars) |\n| `tools` | array | No | Encompasses three kinds: (1) pre-built Claude Agent tools (`agent_toolset_20260401`), (2) MCP tools (`mcp_toolset`), and (3) custom client-side tools. Max 128. |\n| `mcp_servers` | array | No | MCP server connections — standardized third-party capabilities (e.g. GitHub, Asana). Max 20, unique names. See `shared/managed-agents-tools.md` → MCP Servers. |\n| `skills` | array | No | Customized \"best-practices\" context with progressive disclosure. Max 20. See `shared/managed-agents-tools.md` → Skills. |\n| `description` | string | No | Description of the agent (up to 2048 chars) |\n| `multiagent` | object | No | `{type: \"coordinator\", agents: [...]}` — roster this agent may delegate to. See `shared/managed-agents-multiagent.md`. |\n| `metadata` | object | No | Arbitrary key-value pairs (max 16, keys ≤64 chars, values ≤512 chars) |\n\n---\n\n## Agents\n\n**This is where every Managed Agents flow begins.** The agent object is a persisted, versioned configuration — you create it once, then reference it by ID every time you start a session. No agent → no session.\n\n### Agent Object\n\nThe API is **flat** — `model`, `system`, `tools` etc. are top-level fields, not wrapped in an `agent:{}` sub-object.\n\n| Field | Type | Required | Description |\n| ------------------ | -------- | -------- | -------------------------------------------------- |\n| `name` | string | Yes | Human-readable name |\n| `model` | string | Yes | Claude model ID |\n| `system` | string | No | System prompt |\n| `tools` | array | No | Agent toolset / MCP toolset / custom tools |\n| `mcp_servers` | array | No | MCP server connections |\n| `skills` | array | No | Skill references (max 20) |\n| `description` | string | No | Description of the agent |\n| `multiagent` | object | No | Coordinator roster — see `shared/managed-agents-multiagent.md` |\n| `metadata` | object | No | Arbitrary key-value pairs |\n\n### Lifecycle: create once, run many, update in place\n\nThe agent is a **persistent resource**, not a per-run parameter. The intended pattern:\n\n```\n┌─ setup (once) ─────────┐ ┌─ runtime (every invocation) ─┐\n│ agents.create() │ │ sessions.create( │\n│ → store agent_id │ ──→ │ agent={type:..., id: ID} │\n│ in config/env/db │ │ ) │\n└────────────────────────┘ └──────────────────────────────┘\n```\n\n**Anti-pattern:** calling `agents.create()` at the top of every script run. This accumulates orphaned agent objects, pays create latency on every invocation, and defeats the versioning model. If you see `agents.create()` in a function that's called per-request or per-cron-tick, that's wrong — hoist it to one-time setup and persist the ID.\n\n> **Recommended — define agents and environments as YAML + apply via the `ant` CLI.** The split is **CLI for the control plane, SDK for the data plane**: agents and environments are relatively static resources you manage with `ant` (version-controlled YAML, applied from CI); sessions are dynamic and driven by your application through the SDK. See `shared/anthropic-cli.md` → *Version-controlled Managed Agents resources* for the `ant beta:agents create < agent.yaml` / `update --version N` flow. The SDK `agents.create()` call shown elsewhere in this doc is the in-code equivalent — use it when you need to provision programmatically, but prefer the YAML flow for anything a human maintains.\n\n### Versioning\n\nEach `POST /v1/agents/{id}` (update) creates a new immutable version (numeric timestamp, e.g. `1772585501101368014`). The agent's history is append-only — you can't edit a past version.\n\n**Why version:**\n- **Reproducibility** — pin a session to a known-good config: `{type: \"agent\", id, version: 3}`\n- **Safe iteration** — update the agent without breaking sessions already running on the old version\n- **Rollback** — if a new system prompt regresses, pin new sessions back to the prior version while you debug\n\n**`version` is optional.** Omit it (or use the string shorthand `agent=\"agent_abc123\"`) to get the latest version at session-creation time. Pass it explicitly (`{type: \"agent\", id, version: N}`) to pin for reproducibility.\n\n**Getting the version to pin:** `agents.create()` and `agents.update()` both return `version` in the response. Store it alongside `agent_id`. To fetch the current latest for an existing agent: `GET /v1/agents/{id}` → `.version`.\n\n**When to update vs create new:** Update (`POST /v1/agents/{id}`) when it's conceptually the same agent with tweaked behavior (better prompt, extra tool). Create a new agent when it's a different persona/purpose. Rule of thumb: if you'd give it the same `name`, update.\n\n### Agent Endpoints\n\n| Operation | Method | Path |\n| ---------------- | -------- | ------------------------------------- |\n| Create | `POST` | `/v1/agents` |\n| List | `GET` | `/v1/agents` |\n| Get | `GET` | `/v1/agents/{id}` |\n| Update | `POST` | `/v1/agents/{id}` |\n| Archive | `POST` | `/v1/agents/{id}/archive` |\n\n> ⚠️ **Archive is permanent.** Archiving makes the agent read-only: existing sessions continue to run, but **new sessions cannot reference it**, and there is no unarchive. Since agents have no `delete`, this is the terminal lifecycle state. Never archive a production agent as routine cleanup — confirm with the user first.\n\n### Using an Agent in a Session\n\nReference the agent by string ID (latest version) or by object with an explicit version:\n\n```python\n# String shorthand — uses the agent's latest version\nsession = client.beta.sessions.create(\n agent=agent.id,\n environment_id=environment_id,\n)\n\n# Or pin to a specific version (int)\nsession = client.beta.sessions.create(\n agent={\"type\": \"agent\", \"id\": agent.id, \"version\": agent.version},\n environment_id=environment_id,\n)\n```\n\n### Override agent configuration for a session\n\nThe third `agent` form, `agent_with_overrides`, replaces parts of the agent's configuration for **a single session** — try a different model or grant an extra tool without versioning the agent. Pass `id` (and optionally `version`; omitted = latest, same default as the other two forms) plus any of `model`, `system`, `tools`, `mcp_servers`, `skills`:\n\n```python\nsession = client.beta.sessions.create(\n agent={\n \"type\": \"agent_with_overrides\",\n \"id\": agent.id,\n \"model\": \"claude-opus-4-8\", # replace the agent's model for this session\n \"system\": None, # clear the system prompt for this session\n },\n environment_id=environment_id,\n)\n```\n\nEach overridable field follows tri-state rules:\n- **Omit** → the session inherits the value from the referenced agent version.\n- **`null` (or `[]` for list fields)** → the session runs with that field cleared. Applies in full to `system`, `mcp_servers`, `skills`. Two exceptions: `model` is never clearable (`model: null` → 400 `agent_model_required`); clearing `tools` returns 400 when the session's effective `skills` is non-empty (skills require the `read` tool), otherwise `tools: null` / `tools: []` clears.\n- **A value** → replaces the agent's value **in full**. Overrides never merge — a `tools` override must list every tool the session should have.\n\nOverrides are session-local: they do **not** modify the agent resource or create a new agent version. The response's `agent` object reflects the post-override configuration, while its `id` and `version` still identify the base agent — so you can trace a session back to its base. In multiagent sessions, overrides apply to the coordinator and its `{type: \"self\"}` copies; roster agents referenced by ID always use their own as-created configuration (see `shared/managed-agents-multiagent.md`).\n\n### Updating the agent configuration mid-session\n\n`sessions.update()` can change `agent.tools`, `agent.mcp_servers` (including permission policies), and `vault_ids` on an **existing** session. This is a **session-local override** — it does not create a new agent version and does not propagate back to the agent object. The provided arrays are **full replacements**; to append one tool, `GET` the session, modify, and `POST` back. The session must be `idle` — interrupt first if running.\n\nOnly `tools` and `mcp_servers` can change after a session is created — to run with a `model`, `system`, or `skills` other than the agent's values, use `agent_with_overrides` at create time (above). The agent's configured `system` field is fixed for the session's lifetime; you can still **replace the effective system prompt between turns** by sending a `system.message` event (see `shared/managed-agents-events.md` § Updating the system prompt mid-session).\n\n```python\nclient.beta.sessions.update(\n session.id,\n agent={\n \"tools\": [\n {\"type\": \"agent_toolset_20260401\"},\n {\"type\": \"mcp_toolset\", \"mcp_server_name\": \"linear\"},\n ],\n \"mcp_servers\": [{\"type\": \"url\", \"name\": \"linear\", \"url\": \"https://mcp.linear.app/sse\"}],\n },\n vault_ids=[\"vlt_...\"],\n)\n```\n\n\n\n# Managed Agents — Environments & Resources\n\n## Environments\n\nCreating a session requires an `environment_id`. Environments are **reusable configuration templates** for spinning up containers in Anthropic's infrastructure — you might create different environments for different use cases (e.g. data visualization vs web development, with different package sets). Anthropic handles scaling, container lifecycle, and work orchestration.\n\n**Environment names must be unique.** Creating an environment with an existing name returns 409.\n\n### Networking\n\n| Network Policy | Description |\n| ---------------- | ------------------------------------------------------------- |\n| `unrestricted` | Full egress (except legal blocklist) |\n| `limited` | Deny-by-default; opt in via `allowed_hosts` / `allow_package_managers` / `allow_mcp_servers` |\n\n```json\n{\n \"networking\": {\n \"type\": \"limited\",\n \"allow_package_managers\": true,\n \"allow_mcp_servers\": true,\n \"allowed_hosts\": [\"api.example.com\"]\n }\n}\n```\n\nAll three `limited` fields are optional. `allow_package_managers` (default `false`) permits PyPI/npm/etc.; `allow_mcp_servers` (default `false`) permits the agent's configured MCP server endpoints without listing them in `allowed_hosts`.\n\n**MCP caveat:** Under `limited` networking, either set `allow_mcp_servers: true` or add each MCP server domain to `allowed_hosts`. Otherwise the container can't reach them and tools silently fail.\n\n### Creating an environment\n\nThe SDK adds `managed-agents-2026-04-01` automatically. TypeScript:\n\n```ts\nconst env = await client.beta.environments.create({\n name: \"my_env\",\n config: {\n type: \"cloud\",\n networking: { type: \"unrestricted\" },\n },\n});\n```\n\n### Self-hosted sandboxes\n\nTo run tool execution in **your own infrastructure** instead of Anthropic's, set `config: {type: \"self_hosted\"}` — the agent loop stays on Anthropic's side, but `bash` / file ops / code execute in a container you control via an outbound-polling worker. The `networking` block does not apply (you control egress). Resource mounting (`file`, `github_repository`) and memory stores behave differently — see `shared/managed-agents-self-hosted-sandboxes.md` for the worker, credentials, and cloud-vs-self-hosted comparison.\n\n### Environment CRUD\n\n| Operation | Method | Path | Notes |\n| ---------------- | -------- | ------------------------------------------ | ----- |\n| Create | `POST` | `/v1/environments` | |\n| List | `GET` | `/v1/environments` | Paginated (`limit`, `after_id`, `before_id`) |\n| Get | `GET` | `/v1/environments/{id}` | |\n| Update | `POST` | `/v1/environments/{id}` | Changes apply only to **new** containers; existing sessions keep their original config |\n| Delete | `DELETE` | `/v1/environments/{id}` | Returns 204. |\n| Archive | `POST` | `/v1/environments/{id}/archive` | Makes it **read-only**; existing sessions continue, new sessions cannot reference it. No unarchive — terminal state. |\n\n---\n\n## Resources\n\nAttach files, GitHub repositories, and memory stores to a session. **Session creation blocks until all resources are mounted** — the container won't go `running` until every file and repo is in place. Max **999 file resources** per session. Multiple GitHub repositories per session are supported. For `type: \"memory_store\"` resources (persistent cross-session memory — max 8 per session), see `shared/managed-agents-memory.md`.\n\n### File Uploads (input — host → agent)\n\nUpload a file first via the Files API, then reference by `file_id` + `mount_path`:\n\n```ts\n// 1. Upload\nconst file = await client.beta.files.upload({\n file: fs.createReadStream(\"data.csv\"),\n purpose: \"agent\",\n});\n\n// 2. Attach as a session resource\nconst session = await client.beta.sessions.create({\n agent: agent.id,\n environment_id: envId,\n resources: [\n { type: \"file\", file_id: file.id, mount_path: \"/workspace/data.csv\" }\n ],\n});\n```\n\n**`mount_path` is required** and must be absolute. Parent directories are created automatically. Agent working directory defaults to `/workspace`. Files are mounted read-only — the agent writes modified versions to new paths.\n\n### Session outputs (output — agent → host)\n\nThe agent can write files to `/mnt/session/outputs/` during a session. These are automatically captured by the Files API and can be listed and downloaded afterwards:\n\n```ts\n// After the turn completes, list output files scoped to this session:\nfor await (const f of client.beta.files.list({\n scope_id: session.id,\n betas: [\"managed-agents-2026-04-01\"],\n})) {\n console.log(f.filename, f.size_bytes);\n const resp = await client.beta.files.download(f.id);\n const text = await resp.text();\n}\n```\n\n**Requirements:**\n- The `write` tool (or `bash`) must be enabled for the agent to create output files.\n- Session-scoped `files.list` / `files.download` captures outputs written to `/mnt/session/outputs/`.\n- The filter parameter is **`scope_id`** (REST query param `?scope_id=`). The SDK's files resource auto-adds only the `files-api-2025-04-14` header, so pass `betas: [\"managed-agents-2026-04-01\"]` explicitly (or both headers on raw HTTP) — without it the API may reject `scope_id` as an unknown field. Requires `@anthropic-ai/sdk` ≥ 0.88.0 / `anthropic` (Python) ≥ 0.92.0 — older versions don't type `scope_id`. The `ant` CLI does **not** expose this flag yet; use the SDK or curl.\n- Pass the session ID returned by `sessions.create()` verbatim (e.g. `sesn_011CZx...`) — the API validates the prefix.\n- There's a brief indexing lag (~1–3s) between `session.status_idle` and output files appearing in `files.list`. Retry once or twice if empty.\n\n> **Fallback when `scope_id` filtering is unavailable** (older SDK, or endpoint returns an error): send a follow-up `user.message` asking the agent to `read` each file under `/mnt/session/outputs/` and return the contents. The agent streams the file bodies back as `agent.message` text. This works for text files only and costs output tokens — use it to unblock, not as the primary path.\n\nThis gives you a bidirectional file bridge: upload reference data in, download agent artifacts out.\n\n### GitHub Repositories\n\nClones a GitHub repository into the session container during initialization, before the agent begins execution. The agent can read, edit, commit, and push via `bash` (`git`). Multiple repositories per session are supported — add one `resources` entry per repo. Repositories are cached, so future sessions that use the same repository start faster.\n\nRepositories are attached for the lifetime of the session — to change which repositories are mounted, create a new session. You **can** rotate a repository's `authorization_token` on a running session via `client.beta.sessions.resources.update(resource_id, {session_id, authorization_token})`; the resource `id` is returned at session creation and by `resources.list()`.\n\n**Fields:**\n\n| Field | Required | Notes |\n|---|---|---|\n| `type` | ✅ | `\"github_repository\"` |\n| `url` | ✅ | The GitHub repository URL |\n| `authorization_token` | ✅ | GitHub Personal Access Token with repository access. **Never echoed in API responses.** |\n| `mount_path` | ❌ | Path where the repository will be cloned. Defaults to `/workspace/`. |\n| `checkout` | ❌ | `{type: \"branch\", name: \"...\"}` or `{type: \"commit\", sha: \"...\"}`. Defaults to the repo's default branch. |\n\n**Token permission levels** (fine-grained PATs):\n- `Contents: Read` — clone only\n- `Contents: Read and write` — push changes and create pull requests\n\n**How auth works:** `authorization_token` is never placed inside the container. `git pull` / `git push` and GitHub REST calls against the attached repository are routed through an Anthropic-side git proxy that injects the token after the request leaves the sandbox. Code running in the container — including anything the agent writes — cannot read or exfiltrate it.\n\n> ‼️ **To generate pull requests** you also need GitHub **MCP server** access — the `github_repository` resource gives filesystem + git access only. See `shared/managed-agents-tools.md` → MCP Servers. The PR workflow is: edit files in the mounted repo → push branch via `bash` (authenticated via the git proxy using `authorization_token`) → create PR via the MCP `create_pull_request` tool (authenticated via the vault).\n\n**TypeScript:**\n\n```ts\n// 1. Create the agent — declare GitHub MCP (no auth here)\nconst agent = await client.beta.agents.create(\n {\n name: 'GitHub Agent',\n model: 'claude-opus-4-8',\n mcp_servers: [\n { type: 'url', name: 'github', url: 'https://api.githubcopilot.com/mcp/' },\n ],\n tools: [\n { type: 'agent_toolset_20260401', default_config: { enabled: true } },\n { type: 'mcp_toolset', mcp_server_name: 'github' },\n ],\n },\n);\n\n// 2. Start a session — attach vault for MCP auth + mount the repo\nconst session = await client.beta.sessions.create({\n agent: agent.id,\n environment_id: envId,\n vault_ids: [vaultId], // vault contains the GitHub MCP OAuth credential\n resources: [\n {\n type: 'github_repository',\n url: 'https://github.com/owner/repo',\n authorization_token: process.env.GITHUB_TOKEN, // repo clone token (≠ MCP auth)\n checkout: { type: 'branch', name: 'main' },\n },\n ],\n});\n```\n\n**Python:**\n\n```python\nimport os\n\nagent = client.beta.agents.create(\n name=\"GitHub Agent\",\n model=\"claude-opus-4-8\",\n mcp_servers=[{\n \"type\": \"url\",\n \"name\": \"github\",\n \"url\": \"https://api.githubcopilot.com/mcp/\",\n }],\n tools=[\n {\"type\": \"agent_toolset_20260401\", \"default_config\": {\"enabled\": True}},\n {\"type\": \"mcp_toolset\", \"mcp_server_name\": \"github\"},\n ],\n)\n\nsession = client.beta.sessions.create(\n agent=agent.id,\n environment_id=env_id,\n vault_ids=[vault_id], # vault contains the GitHub MCP OAuth credential\n resources=[{\n \"type\": \"github_repository\",\n \"url\": \"https://github.com/owner/repo\",\n \"authorization_token\": os.environ[\"GITHUB_TOKEN\"], # repo clone token (≠ MCP auth)\n \"checkout\": {\"type\": \"branch\", \"name\": \"main\"},\n }],\n)\n```\n\n---\n\n## Files API\n\nUpload and manage files for use as session resources, and download files the agent wrote to `/mnt/session/outputs/`.\n\n| Operation | Method | Path | SDK |\n| ---------------- | -------- | ------------------------------------- | --- |\n| Upload | `POST` | `/v1/files` | `client.beta.files.upload({ file })` |\n| List | `GET` | `/v1/files?scope_id=...` | `client.beta.files.list({ scope_id, betas: [\"managed-agents-2026-04-01\"] })` |\n| Get Metadata | `GET` | `/v1/files/{id}` | `client.beta.files.retrieveMetadata(id)` |\n| Download | `GET` | `/v1/files/{id}/content` | `client.beta.files.download(id)` → `Response` |\n| Delete | `DELETE` | `/v1/files/{id}` | `client.beta.files.delete(id)` |\n\nThe `scope_id` filter on List scopes the results to files written to `/mnt/session/outputs/` by that session. Without the filter, you get all files uploaded to your account.\n\n\n\n# Managed Agents — Events & Steering\n\n## Events\n\n### Sending Events\n\nSend events to a session via `POST /v1/sessions/{id}/events`.\n\n| Event Type | When to Send |\n| ------------------------- | --------------------------------------------------- |\n| `user.message` | Send a user message |\n| `user.interrupt` | Interrupt the agent while it's running |\n| `user.tool_confirmation` | Approve/deny a tool call (when `always_ask` policy) |\n| `user.custom_tool_result` | Provide result for a custom tool call |\n| `user.define_outcome` | Start a rubric-graded iterate loop — see `shared/managed-agents-outcomes.md` |\n| `system.message` | Update the agent's system prompt between turns — **Claude Opus 4.8 only**; see § Updating the system prompt mid-session |\n\n#### Updating the system prompt mid-session (`system.message`)\n\nUnlike the `system` field on the agent definition (fixed at session creation), a `system.message` event changes the system prompt **as the session progresses** — a different persona, revised constraints, or runtime-fetched context that should shape behavior going forward:\n\n```python\nclient.beta.sessions.events.send(\n session.id,\n events=[\n {\n \"type\": \"system.message\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"The user's current timezone is America/New_York.\"},\n ],\n },\n ],\n)\n```\n\nConstraints:\n\n- **Claude Opus 4.8 only.** If any model configured on the agent does not support mid-conversation system injection, the event is rejected with a `model_does_not_support_mid_conversation_system` validation error.\n- **Cannot be sent while the session is idle with `stop_reason: requires_action`** (blocked on `user.custom_tool_result` / `user.tool_confirmation`).\n- `content` accepts 1–1000 text items.\n\n### Receiving Events\n\nThree methods:\n\n1. **Streaming (SSE)**: `GET /v1/sessions/{id}/events/stream` — real-time Server-Sent Events. **Long-lived** — the server sends periodic heartbeats to keep the connection alive.\n2. **Polling**: `GET /v1/sessions/{id}/events` — paginated event list (query params: `limit` default 1000, `page`). **Returns immediately** — this is a plain paginated GET, not a long-poll.\n3. **Webhooks**: Anthropic POSTs session state transitions to your HTTPS endpoint — thin payloads (IDs only), HMAC-signed, Console-registered. See `shared/managed-agents-webhooks.md`.\n\nAll **persisted** events carry `id`, `type`, and `processed_at` (ISO 8601; `null` if not yet processed by the agent). The stream-only `event_start` / `event_delta` preview events (see § Live previews) carry only the `id` of the event they preview.\n\n> ⚠️ **Robust polling (raw HTTP).** If you bypass the SDK and roll your own poll loop, don't rely on `requests` or `httpx` timeouts as wall-clock caps — they're **per-chunk** read timeouts, reset every time a byte arrives. A trickling response (heartbeats, a wedged chunked-encoding body, a misbehaving proxy) can keep the call blocked indefinitely even with `timeout=(5, 60)` or `httpx.Timeout(120)`. Neither library has a \"total wall-clock\" timeout built in. For a hard deadline: track `time.monotonic()` at the loop level and break/cancel if a single request exceeds your budget (e.g. via a watchdog thread, or `asyncio.wait_for()` around async httpx). **Prefer the SDK** — `client.beta.sessions.events.stream()` and `client.beta.sessions.events.list()` handle timeout + retry sanely.\n>\n> If `GET /v1/sessions/{id}/events` (paginated) ever hangs after headers, you've likely hit `GET /v1/sessions/{id}/events` by mistake or a server-side stall — report it; don't treat it as a client-config problem.\n\n### Event Types (Received)\n\nEvent types use dot notation, grouped by namespace:\n\n| Event Type | Description |\n| --- | --- |\n| `agent.message` | Agent text output |\n| `agent.thinking` | Extended thinking blocks |\n| `agent.tool_use` | Agent used a built-in tool (`agent_toolset_20260401`) |\n| `agent.tool_result` | Result from a built-in tool |\n| `agent.mcp_tool_use` | Agent used an MCP tool |\n| `agent.mcp_tool_result` | Result from an MCP tool |\n| `agent.custom_tool_use` | Agent invoked a custom tool — session goes idle, you respond with `user.custom_tool_result` |\n| `agent.thread_context_compacted` | Conversation context was compacted |\n| `session.status_idle` | Agent has finished the current task, and is awaiting input. It's either waiting for input to continue working via a `user.message` or blocked awaiting a `user.custom_tool_result` or `user.tool_confirmation`. The `stop_reason` attached contains more information about why the Agent has stopped working. |\n| `session.status_running` | Session has starting running, and the Agent is actively doing work. |\n| `session.status_rescheduled` | Session is (re)scheduling after a retryable error has occurred, ready to be picked up by the orchestration system. |\n| `session.status_terminated` | Session has terminated, entering an irreversible and unusable state. |\n| `session.error` | Error occurred during processing |\n| `span.model_request_start` | Model inference started |\n| `span.model_request_end` | Model inference completed |\n| `span.outcome_evaluation_start` / `_ongoing` / `_end` | Grader progress for outcome-oriented sessions — see `shared/managed-agents-outcomes.md` |\n| `session.thread_created` | Subagent thread spawned (multiagent) — see `shared/managed-agents-multiagent.md` |\n| `session.thread_status_running` / `_idle` / `_rescheduled` / `_terminated` | Subagent thread status transitions (multiagent). `_idle` carries `stop_reason`. |\n| `agent.thread_message_sent` / `_received` | Cross-thread message, carries `to_session_thread_id` / `from_session_thread_id` (multiagent) |\n\nThe stream also echoes back user-sent events (`user.message`, `user.interrupt`, `user.tool_confirmation`, `user.custom_tool_result`, `user.define_outcome`).\n\nStream-only delta preview events (`event_start`, `event_delta`) are the one exception to the `{domain}.{action}` naming convention — see § Live previews below; they never appear in `GET /v1/sessions/{id}/events`.\n\n---\n\n## Live previews\n\nBy default, assistant text reaches the stream as buffered `agent.message` events — emitted only after the model request that produced them finishes. **Live previews** let you render that text incrementally while the model is still generating. The buffered `agent.message` is always the authoritative record; a client that ignores previews still receives a complete, correct stream. The wire format is **not** Messages-API streaming: the delta type is `content_delta`, not `content_block_delta`, so Messages-API accumulator code does not carry over unchanged.\n\n**Opt in per stream connection** by adding the `event_deltas[]` query parameter to `GET /v1/sessions/{id}/events/stream`, repeated once per event type to preview. Accepted values: `agent.message`, `agent.thinking` (any other value → 400). Only the session-level stream supports it — per-thread streams (`/threads/{tid}/stream`) reject the parameter.\n\n```python\nstream = client.beta.sessions.events.stream(\n session_id=session.id,\n event_deltas=[\"agent.message\"],\n)\n```\n\nWhen a previewed event begins, the stream emits an `event_start` carrying the upcoming event's `type` and `id`; for `agent.message` it's followed by `event_delta` events carrying incremental text:\n\n```json\n{\"type\": \"event_start\", \"event\": {\"type\": \"agent.message\", \"id\": \"sevt_01abc...\"}}\n{\"type\": \"event_delta\", \"event_id\": \"sevt_01abc...\", \"delta\": {\"type\": \"content_delta\", \"index\": 0, \"content\": {\"type\": \"text\", \"text\": \"Here is the summary\"}}}\n```\n\n`event_start` and `event_delta` have no `id` or `processed_at` of their own — the only identifier they carry is the `id` of the event they preview. For `agent.thinking`, **only** the `event_start` is emitted (a \"thinking has started\" signal) — no deltas follow; read content from the buffered `agent.thinking` event.\n\n**Accumulate-and-reconcile pattern.** Treat the preview as a scratch buffer keyed by `(event_id, index)`. On `event_start`, create an empty entry for the announced `id`. On each `event_delta`, append `delta.content.text` to `(event_id, delta.index)` and render the running text. When the buffered `agent.message` arrives, match it by `id`, **discard the accumulated preview**, and render the message's content instead. The identifiers always line up: `event_start.event.id`, every `event_delta.event_id`, and the buffered event's `id` are the same value. On a normal turn the order is fixed: `session.status_running` → `span.model_request_start` → `event_start` → `event_delta`* → buffered `agent.message` → `span.model_request_end`. If the turn errors or is interrupted the buffered event may never arrive, but `span.model_request_end` still does — close any unreconciled preview when you see it. Python/TypeScript/Go SDKs ship an accumulator helper that implements this; in other SDKs apply the manual pattern to the generated event types.\n\n**Limitations:**\n- **Best effort** — under load the server may shed deltas for an event; you receive a contiguous prefix and then no further deltas for that event. The buffered `agent.message` still arrives complete. Never treat an accumulated preview as final.\n- **No replay on reconnect** — deltas are delivered only to the connection that opted in, while it's open. After a drop, follow the consolidation pattern in § Reconnecting after a dropped stream — the history fetch returns any buffered events emitted during the gap; missed deltas cannot be re-requested.\n- **Primary thread, text only** — tool use, tool results, MCP results, and subagent-thread activity are never previewed.\n- **Never persisted** — `event_start` / `event_delta` exist only on the live SSE stream, never in `GET /v1/sessions/{id}/events`.\n\n---\n\n## Steering Patterns\n\nPractical patterns for driving a session via the events surface.\n\n### Stream-first ordering\n\n**Open the stream before sending events.** The stream only delivers events that occur *after* it's opened — it does not replay current state or historical events. If you send a message first and open the stream second, early events (including fast status transitions) arrive buffered in a single batch and you lose the ability to react to them in real time.\n\n```ts\n// ✅ Correct — stream and send concurrently\nconst [response] = await Promise.all([\n streamEvents(sessionId), // opens SSE connection\n sendMessage(sessionId, text),\n]);\n\n// ❌ Wrong — events before stream opens arrive as a single buffered batch\nawait sendMessage(sessionId, text);\nconst response = await streamEvents(sessionId);\n```\n\n**For full history,** use `GET /v1/sessions/{id}/events` (paginated list) — the stream only gives you live events from connection onward.\n\n### Reconnecting after a dropped stream\n\n**The SSE stream has no replay.** If your connection drops (httpx read timeout, network blip) and you reconnect, you only get events emitted *after* reconnection. Any events emitted during the gap are lost from the stream.\n\n**The consolidation pattern:** on every (re)connect, overlap the stream with a history fetch and dedupe by event ID:\n\n```python\ndef connect_with_consolidation(client, session_id):\n # 1. Open the SSE stream first\n stream = client.beta.sessions.events.stream(session_id=session_id)\n\n # 2. Fetch history to cover any gap\n history = client.beta.sessions.events.list(\n session_id=session_id,\n )\n\n # 3. Yield history first, then stream — dedupe by event.id\n seen = set()\n for ev in history.data:\n seen.add(ev.id)\n yield ev\n for ev in stream:\n if ev.id not in seen:\n seen.add(ev.id)\n yield ev\n```\n\n### Message queuing\n\n**You don't have to wait for a response before sending the next message.** User events are queued server-side and processed in order. This is useful for chat bridges where the user sends rapid follow-ups:\n\n```ts\n// All three go into one session; agent processes them in order\nawait sendMessage(sessionId, \"Summarize the README\");\nawait sendMessage(sessionId, \"Actually also check the CONTRIBUTING guide\");\nawait sendMessage(sessionId, \"And compare the two\");\n// Stream once — agent responds to all three as a coherent turn\n```\n\nEvents can be sent up to the Session at any time. There is no need to wait on a specific session status to enqueue new events via `client.beta.sessions.events.send()`\n\n### Interrupt\n\nAn `interrupt` event **jumps the queue** (ahead of any pending user messages) and forces the session into `idle`. Use this for \"stop\" / \"nevermind\" / \"cancel\" commands:\n\n```ts\nawait client.beta.sessions.events.send(sessionId, {\n events: [{ type: 'interrupt' }],\n});\n```\n\nThe agent stops mid-task. It does not see the interrupt as a message — it just halts. Send a follow-up `user` event to explain what to do instead. If an outcome is active, the interrupt also marks `span.outcome_evaluation_end.result: \"interrupted\"` (see `shared/managed-agents-outcomes.md`).\n\n> **Note**: Interrupt events may have empty IDs in the current implementation. When troubleshooting, use the `processed_at` timestamp along with surrounding event IDs.\n\n### Event payloads\n\nsome events carry useful metadata beyond the status change itself:\n\n`session.status_idle` — includes a `stop_reason` field which elaborates on why the session stopped and what type of further action is required by the user.\n```json\n{\n \"id\": \"sevt_456\",\n \"processed_at\": \"2026-04-07T04:27:43.197Z\",\n \"stop_reason\": {\n \"event_ids\": [\n \"sevt_123\"\n ],\n \"type\": \"requires_action\"\n },\n \"type\": \"status_idle\"\n}\n```\n\n`span.model_request_end` contains a `model_usage` field for cost tracking and efficiency analysis:\n\n```json\n{\n \"type\": \"span.model_request_end\",\n \"id\": \"sevt_456\",\n \"is_error\": false,\n \"model_request_start_id\": \"sevt_123\",\n \"model_usage\": {\n \"cache_creation_input_tokens\": 0,\n \"cache_read_input_tokens\": 6656,\n \"input_tokens\": 3571,\n \"output_tokens\": 727\n },\n \"processed_at\": \"2026-04-07T04:11:32.189Z\"\n}\n```\n\n**`agent.thread_context_compacted`** — emitted when the conversation history was summarized to fit context. Includes `pre_compaction_tokens` so you know how much was squeezed:\n\n```json\n{\n \"id\": \"sevt_abc123\",\n \"processed_at\": \"2026-03-24T14:05:15.787Z\",\n \"type\": \"agent.thread_context_compacted\"\n}\n```\n\n### Archive\n\nWhen done with a session, archive it to free resources:\n\n```ts\nawait client.beta.sessions.archive(sessionId);\n```\n\n> Archiving a **session** is routine cleanup — sessions are per-run and disposable. **Do not generalize this to agents or environments**: those are persistent, reusable resources, and archiving them is permanent (no unarchive; new sessions cannot reference them). See `shared/managed-agents-overview.md` → Common Pitfalls.\n\n\n\n# Managed Agents — Memory Stores\n\n> **Public beta.** Memory stores ship under the `managed-agents-2026-04-01` beta header; the SDK sets it automatically on all `client.beta.memory_stores.*` calls. If `client.beta.memory_stores` is missing, upgrade to the latest SDK release.\n\nSessions are ephemeral by default — when one ends, anything the agent learned is gone. A **memory store** is a workspace-scoped collection of small text documents that persists across sessions. When a store is attached to a session (via `resources[]`), it is mounted into the container as a filesystem directory; the agent reads and writes it with the ordinary file tools, and a system-prompt note tells it the mount is there.\n\nEvery mutation to a memory produces an immutable **memory version** (`memver_...`), giving you an audit trail and point-in-time rollback/redact.\n\n## Object model\n\n| Object | ID prefix | Scope | Notes |\n| --- | --- | --- | --- |\n| Memory store | `memstore_...` | Workspace | Attach to sessions via `resources[]` |\n| Memory | `mem_...` | Store | One text file, addressed by `path` (≤ 100KB each — prefer many small files) |\n| Memory version | `memver_...` | Memory | Immutable snapshot per mutation; `operation` ∈ `created` / `modified` / `deleted` |\n\n## Create a store\n\n`description` is passed to the agent so it knows what the store contains — write it for the model, not for humans.\n\n```python\nstore = client.beta.memory_stores.create(\n name=\"User Preferences\",\n description=\"Per-user preferences and project context.\",\n)\nprint(store.id) # memstore_01Hx...\n```\n\nOther SDKs: TypeScript `client.beta.memoryStores.create({...})`; Go `client.Beta.MemoryStores.New(ctx, ...)`. See `shared/managed-agents-api-reference.md` → SDK Method Reference for the full per-language table.\n\nStores support `retrieve` / `update` / `list` (with `include_archived`, `created_at_{gte,lte}` filters) / `delete` / **`archive`**. Archive makes the store read-only — existing session attachments continue, new sessions cannot reference it; no unarchive.\n\n### Seed with content (optional)\n\nPre-load reference material before any session runs. `memories.create` creates a memory at the given `path`; if a memory already exists there the call returns `409` (`memory_path_conflict_error`, with the `conflicting_memory_id`). The store ID is the first positional argument.\n\n```python\nclient.beta.memory_stores.memories.create(\n store.id,\n path=\"/formatting_standards.md\",\n content=\"All reports use GAAP formatting. Dates are ISO-8601...\",\n)\n```\n\n## Attach to a session\n\nMemory stores go in the session's `resources[]` array alongside `file` and `github_repository` resources (see `shared/managed-agents-environments.md` → Resources). Memory stores attach at **session create time only** — `sessions.resources.add()` does not accept `memory_store`.\n\n```python\nsession = client.beta.sessions.create(\n agent=agent.id,\n environment_id=environment.id,\n resources=[\n {\n \"type\": \"memory_store\",\n \"memory_store_id\": store.id,\n \"access\": \"read_write\", # or \"read_only\"; default is \"read_write\"\n \"instructions\": \"User preferences and project context. Check before starting any task.\",\n }\n ],\n)\n```\n\n| Field | Required | Notes |\n| --- | --- | --- |\n| `type` | ✅ | `\"memory_store\"` |\n| `memory_store_id` | ✅ | `memstore_...` |\n| `access` | — | `\"read_write\"` (default) or `\"read_only\"` — enforced at the filesystem level on the mount |\n| `instructions` | — | Session-specific guidance for this store, in addition to the store's `name`/`description`. ≤ 4,096 chars. |\n\n**Max 8 memory stores per session.** Attach multiple when different slices of memory have different owners or lifecycles — e.g. one read-only shared-reference store plus one read-write per-user store, or one store per end-user/team/project sharing a single agent config.\n\n### How the agent sees it (FUSE mount)\n\nEach attached store is mounted in the session container at `/mnt/memory//`. The agent interacts with it using the standard file tools (`bash`, `read`, `write`, `edit`, `glob`, `grep`) — there are no dedicated memory tools. `access: \"read_only\"` makes the mount read-only at the filesystem level; `\"read_write\"` allows the agent to create, edit, and delete files under it. A short description of each mount (name, path, `instructions`, access) is automatically injected into the system prompt so the agent knows the store exists without you having to mention it.\n\nWrites the agent makes under the mount are persisted back to the store and produce memory versions just like host-side `memories.update` calls.\n\n## Manage memories directly (host-side)\n\nUse these for review workflows, correcting bad memories, or seeding stores out-of-band.\n\n### List\n\nReturns `Memory | MemoryPrefix` entries — a `MemoryPrefix` (`type: \"memory_prefix\"`, just a `path`) is a directory-like node when listing hierarchically. Use `path_prefix` to scope (include a trailing slash: `\"/notes/\"` matches `/notes/a.md` but not `/notes_backup/old.md`) and `depth` to bound the tree walk. `order_by` / `order` sort the result. Pass `view=\"full\"` to include `content` in each item; the default `\"basic\"` returns metadata only.\n\n```python\nfor m in client.beta.memory_stores.memories.list(store.id, path_prefix=\"/\"):\n if m.type == \"memory\":\n print(f\"{m.path} ({m.content_size_bytes} bytes, sha={m.content_sha256[:8]})\")\n else: # \"memory_prefix\"\n print(f\"{m.path}/\")\n```\n\n### Read\n\n```python\nmem = client.beta.memory_stores.memories.retrieve(memory_id, memory_store_id=store.id)\nprint(mem.content)\n```\n\n`retrieve` defaults to `view=\"full\"` (content included); `view` matters mainly on list endpoints.\n\n### Create vs. update\n\n| Operation | Addressed by | Semantics |\n| --- | --- | --- |\n| `memories.create(store_id, path=..., content=...)` | **Path** | Create at `path`. `409` (`memory_path_conflict_error`, includes `conflicting_memory_id`) if the path is already occupied. |\n| `memories.update(mem_id, memory_store_id=..., path=..., content=...)` | **`mem_...` ID** | Mutate existing memory. Change `content`, `path` (rename), or both. Renaming onto an occupied path returns the same `409 memory_path_conflict_error`. |\n\n```python\nmem = client.beta.memory_stores.memories.create(\n store.id,\n path=\"/preferences/formatting.md\",\n content=\"Always use tabs, not spaces.\",\n)\n\nclient.beta.memory_stores.memories.update(\n mem.id,\n memory_store_id=store.id,\n path=\"/archive/2026_q1_formatting.md\", # rename\n)\n```\n\n### Optimistic concurrency (precondition on `update`)\n\n`memories.update` accepts a `precondition` so you can read → modify → write back without clobbering a concurrent writer. The only supported type is `content_sha256`. On mismatch the API returns `409` (`memory_precondition_failed_error`) — re-read and retry against fresh state.\n\n```python\nclient.beta.memory_stores.memories.update(\n mem.id,\n memory_store_id=store.id,\n content=\"CORRECTED: Always use 2-space indentation.\",\n precondition={\"type\": \"content_sha256\", \"content_sha256\": mem.content_sha256},\n)\n```\n\n### Delete\n\n```python\nclient.beta.memory_stores.memories.delete(mem.id, memory_store_id=store.id)\n```\n\nPass `expected_content_sha256` for a conditional delete.\n\n## Audit and rollback — memory versions\n\nEvery mutation creates an immutable `memver_...` snapshot. Versions accumulate for the lifetime of the parent memory; `memories.retrieve` always returns the current head, the version endpoints give you history.\n\n| Operation that triggers it | `operation` field on the version |\n| --- | --- |\n| `memories.create` at a new path | `\"created\"` |\n| `memories.update` changing `content`, `path`, or both (or an agent-side write to the mount) | `\"modified\"` |\n| `memories.delete` | `\"deleted\"` |\n\nEach version also records `created_by` — an actor object with `type` ∈ `session_actor` / `api_actor` / `user_actor` — and, after redaction, `redacted_at` + `redacted_by`.\n\n### List versions\n\nNewest-first, paginated. Filter by `memory_id`, `operation`, `session_id`, `api_key_id`, or `created_at_gte` / `created_at_lte`. Pass `view=\"full\"` to include `content`; default is metadata-only.\n\n```python\nfor v in client.beta.memory_stores.memory_versions.list(store.id, memory_id=mem.id):\n print(f\"{v.id}: {v.operation}\")\n```\n\n### Retrieve a version\n\n```python\nversion = client.beta.memory_stores.memory_versions.retrieve(\n version_id, memory_store_id=store.id\n)\nprint(version.content)\n```\n\n### Redact a version\n\nScrubs content from a historical version while preserving the audit trail (actor + timestamps). Clears `content`, `content_sha256`, `content_size_bytes`, and `path`; everything else stays. Use for leaked secrets, PII, or user-deletion requests.\n\n```python\nclient.beta.memory_stores.memory_versions.redact(version_id, memory_store_id=store.id)\n```\n\n## Endpoint reference\n\nSee `shared/managed-agents-api-reference.md` → Memory Stores / Memories / Memory Versions for the full HTTP method/path tables. Raw HTTP base path:\n\n```\nPOST /v1/memory_stores\nPOST /v1/memory_stores/{memory_store_id}/archive\nGET /v1/memory_stores/{memory_store_id}/memories\nPATCH /v1/memory_stores/{memory_store_id}/memories/{memory_id}\nGET /v1/memory_stores/{memory_store_id}/memory_versions\nPOST /v1/memory_stores/{memory_store_id}/memory_versions/{version_id}/redact\n```\n\nFor cURL examples and the CLI (`ant beta:memory-stores ...`), WebFetch the Memory URL in `shared/live-sources.md` → Managed Agents.\n\n\n\n# Managed Agents — Multiagent Sessions\n\nA coordinator agent can delegate to other agents within one session. All agents **share the container and filesystem**; each runs in its own **thread** — a context-isolated event stream with its own conversation history, model, system prompt, tools, MCP servers, and skills (from that agent's own config). Threads are persistent: the coordinator can send a follow-up to a subagent it called earlier and that subagent retains its prior turns.\n\nThe SDK sets the `managed-agents-2026-04-01` beta header automatically on all `client.beta.{agents,sessions}.*` calls; no additional header is required for multiagent.\n\n---\n\n## Declare the roster on the coordinator\n\n`multiagent` is a **top-level field** on `agents.create()` / `agents.update()` — **not** a `tools[]` entry. `agents` lists 1–20 roster entries. Nothing changes on `sessions.create()` — the roster is resolved from the coordinator's config.\n\n```python\norchestrator = client.beta.agents.create(\n name=\"Engineering Lead\",\n model=\"claude-opus-4-8\",\n system=\"You coordinate engineering work. Delegate code review to the reviewer and test writing to the test agent.\",\n tools=[{\"type\": \"agent_toolset_20260401\"}],\n multiagent={\n \"type\": \"coordinator\",\n \"agents\": [\n reviewer.id, # bare string — latest version\n {\"type\": \"agent\", \"id\": test_writer.id, \"version\": 4}, # pinned version\n {\"type\": \"self\"}, # the coordinator itself\n ],\n },\n)\n\nsession = client.beta.sessions.create(agent=orchestrator.id, environment_id=env.id)\n```\n\n| Roster entry | Shape | Notes |\n|---|---|---|\n| String shorthand | `\"agent_abc123\"` | References the latest version of a stored agent. |\n| Agent reference | `{type: \"agent\", id, version?}` | Omit `version` to pin the latest at coordinator save time. |\n| Self | `{type: \"self\"}` | The coordinator can spawn copies of itself. |\n\nIf the session was created with `agent_with_overrides` (see `shared/managed-agents-core.md` → Override agent configuration for a session), those overrides apply to the **coordinator and its `self` copies**. Roster agents referenced by ID always use their own as-created configuration — overrides do not propagate to them.\n\nUp to **20 unique agents** in the roster; the coordinator may spawn **multiple copies** of each. **One level of delegation only** — depth > 1 is ignored.\n\n---\n\n## Threads\n\nThe session-level event stream is the **primary thread** — it shows the coordinator's trace plus a condensed view of subagent activity (thread status transitions and cross-thread messages, not every subagent tool call). Drill into a specific subagent via the per-thread endpoints:\n\n| Operation | HTTP | SDK (`client.beta.sessions.threads.*`) |\n|---|---|---|\n| List threads | `GET /v1/sessions/{sid}/threads` | `.list(session_id)` |\n| Retrieve one | `GET /v1/sessions/{sid}/threads/{tid}` | `.retrieve(thread_id, session_id=...)` |\n| Archive | `POST /v1/sessions/{sid}/threads/{tid}/archive` | `.archive(thread_id, session_id=...)` |\n| List thread events | `GET /v1/sessions/{sid}/threads/{tid}/events` | `.events.list(thread_id, session_id=...)` |\n| Stream thread events | `GET /v1/sessions/{sid}/threads/{tid}/stream` | `.events.stream(thread_id, session_id=...)` |\n\nEach `SessionThread` carries `id`, `status` (`running` | `idle` | `rescheduling` | `terminated`), `agent` (a resolved snapshot of the agent config — `id`, `name`, `model`, `system`, `tools`, `skills`, `mcp_servers`, `version`), `parent_thread_id` (null for the primary thread, which is included in the list), `archived_at`, and optional `stats`/`usage`. **Session status aggregates thread statuses** — if any thread is `running`, `session.status` is `running`. Max **25 concurrent threads**. When draining a per-thread stream, break on `session.thread_status_idle` (and check its `stop_reason` as you would for the session-level idle).\n\n---\n\n## Multiagent events (on the session stream)\n\n| Event | Payload highlights | Meaning |\n|---|---|---|\n| `session.thread_created` | `session_thread_id`, `agent_name` | A new thread was created. |\n| `session.thread_status_running` | `session_thread_id`, `agent_name` | Thread started activity. |\n| `session.thread_status_idle` | `session_thread_id`, `agent_name`, **`stop_reason`** | Thread is awaiting input. Inspect `stop_reason` (same shape as `session.status_idle.stop_reason`). |\n| `session.thread_status_rescheduled` | `session_thread_id`, `agent_name` | Thread is rescheduling after a retryable error. |\n| `session.thread_status_terminated` | `session_thread_id`, `agent_name` | Thread was archived or hit a terminal error. |\n| `agent.thread_message_sent` | `to_session_thread_id`, `to_agent_name`, `content` | Coordinator sent a follow-up to another thread. |\n| `agent.thread_message_received` | `from_session_thread_id`, `from_agent_name`, `content` | An agent delivered its result to the coordinator. |\n\n---\n\n## Tool permissions and custom tools from subagent threads\n\nWhen a subagent needs your client (an `always_ask` confirmation, or a custom tool result), the request is **cross-posted to the primary thread** with `session_thread_id` identifying the originating thread — so you only need to watch the session stream. Reply with `user.tool_confirmation` (carrying `tool_use_id`) or `user.custom_tool_result` (carrying `custom_tool_use_id`), and **echo the `session_thread_id` from the originating event** (the SDK param type and docstring expect it). The server also routes by the tool-use ID, so the echo is belt-and-suspenders rather than load-bearing — but include it.\n\n```python\nfor event_id in stop.event_ids:\n pending = events_by_id[event_id]\n confirmation = {\n \"type\": \"user.tool_confirmation\",\n \"tool_use_id\": event_id,\n \"result\": \"allow\",\n }\n if pending.session_thread_id is not None:\n confirmation[\"session_thread_id\"] = pending.session_thread_id\n client.beta.sessions.events.send(session.id, events=[confirmation])\n```\n\nThe same pattern applies to `user.custom_tool_result`.\n\n---\n\n## Pitfalls\n\n- **Don't put the roster on `sessions.create()` or in `tools[]`.** `multiagent` is a top-level agent field; update the coordinator, then start a session that references it.\n- **Don't assume shared context.** Threads share the filesystem but not conversation history or tools. If the coordinator needs a subagent to act on something, it must say so in the delegated message (or write it to disk).\n- **Depth > 1 is ignored.** A subagent's own `multiagent` roster (if any) doesn't cascade — only the session's coordinator delegates.\n\nFor per-language bindings beyond Python, WebFetch `https://platform.claude.com/docs/en/managed-agents/multi-agent.md` (see `shared/live-sources.md`).\n\n\n\n# Managed Agents — Onboarding Flow\n\n> **Invoked via `/claude-api managed-agents-onboard`?** You're in the right place. Run the interview below — don't summarize it back to the user, ask the questions.\n\nClaude Managed Agents is a hosted agent: Anthropic runs the agent loop and provisions a sandboxed container per session where the agent's tools execute (or your own worker, with a `self_hosted` environment — see `shared/managed-agents-self-hosted-sandboxes.md`). You supply an **agent config** (tools, skills, model, system prompt — reusable, versioned) and an **environment config** (the sandbox — reusable across agents). Each run is a **session**.\n\nThe flow is four beats — **describe → agent → environment → session** — the same arc as the Console quickstart, and the same philosophy: **value before credentials**. The user goes from idea to a runnable session before any auth ask; each credential is *flagged* at the moment the design makes it relevant (§2) and *collected* once, at session setup (§4), where it binds (`sessions.create()`) and gets exercised (smoke-test). Read `shared/managed-agents-core.md` alongside this — it has full detail for each knob; this doc is the interview script.\n\n---\n\n## 1. Describe the task\n\n**Open with a one-breath signpost and a single open prompt — don't guess, don't questionnaire.** In your own words:\n\n> Managed Agents is hosted — Anthropic runs the agent loop, the sandbox, and the infrastructure; you just define the agent. We'll do this in three moves: the agent, the environment it runs in, then a live test session. So: describe the agent you want — what should it do, and what kicks it off (a person, an event, a schedule)?\n\nLet them answer in full before configuring anything.\n\n## 2. Configure the agent — propose, don't interrogate\n\nTheir description does the interview's work. Draft the agent config from it and **present it as a proposal with your suggestions inline** — the user reacts to a concrete config instead of answering a question list. At most one batched follow-up for true gaps. Suggest where the description gives you an opening:\n\n- **Tools** — enable the full prebuilt toolset by default (`agent_toolset_20260401`: `bash`, `read`, `write`, `edit`, `glob`, `grep`, `web_fetch`, `web_search`). **Suggest MCP servers** for any third-party service the job names (GitHub, Linear, Slack, …) — and flag the credential each one implies as you suggest it (\"Linear MCP → you'll need a Linear API token at kickoff\"), so §4's auth step is a formality, not a surprise. Collection itself waits for §4. Custom tools only if the user's own app must answer calls (name, description, input schema — their handler code is theirs; don't generate it).\n- **Skills** — **suggest** prebuilt `xlsx`/`docx`/`pptx`/`pdf` when the job produces those artifacts; custom by `skill_id` (max 20 total per agent, prebuilt + custom combined).\n- **Outcome** — if the description implies checkable \"done\" criteria (or you can elicit them in the follow-up: not \"a good report\" but \"a CSV with a numeric `price` column per SKU\"), **suggest an Outcome kickoff** — the harness grades and iterates against a rubric (`shared/managed-agents-outcomes.md`).\n- **On-hand resources** — repos on disk (`github_repository`: URL, optional `mount_path`/`checkout`; token comes in §4), files to seed (Files API upload → `{type: \"file\", file_id, mount_path}`; read-only), if the job references them.\n- **Model** — default `claude-opus-4-8`; `claude-fable-5` for the hardest long-horizon work (`shared/model-migration.md` → Migrating to Claude Fable 5).\n\n> ‼️ **PR creation needs the GitHub MCP server too** — a `github_repository` mount is filesystem-only. Edit in the mount → push branch via `bash` → open the PR via the MCP `create_pull_request` tool.\n\nFull detail per knob: `shared/managed-agents-tools.md` (toolset, MCP, custom tools, skills), `shared/managed-agents-environments.md` (repos, files).\n\n## 3. Environment\n\nUsually zero or one question:\n\n- **Reuse or create?** Environments are shared across agents — check for an existing one first.\n- **Networking** — default unrestricted egress. Switch to `limited` only if the user wants egress control — then set `allow_mcp_servers: true` or list every MCP server domain in `allowed_hosts`, or those tools fail silently.\n- **Suggest `self_hosted`** when the signals are there: tools must run on their own infra, secrets can't leave it, or they need binaries/data the cloud container won't have (`shared/managed-agents-self-hosted-sandboxes.md`; not available on Claude Platform on AWS). Otherwise `cloud` — don't raise it unprompted for simple jobs.\n\n## 4. Session — auth, then test run\n\n**Auth happens here — collect the credentials flagged in §2, now that the config is settled:** a vault (existing or `vaults.create()`) + `vaults.credentials.create()` for each MCP server declared in §2, `environment_variable` credentials for API keys the job uses (substituted at egress; the sandbox sees a placeholder), and the `authorization_token` for each repo mount. Credentials are write-only; MCP credentials match servers by URL and auto-refresh. See `shared/managed-agents-tools.md` → Vaults.\n\n**Silent viability gate — run this yourself before emitting anything; surface only the gaps.** Walk the job clause by clause: every verb maps to an enabled tool or MCP server (\"open a PR\" → GitHub MCP, not just the mount); every MCP server and repo mount has its credential from the auth step; every external host is reachable under the networking choice; every file/repo/dataset the job references is mounted; \"done\" is checkable. If something's missing, say so and resolve it — don't emit a config you already know is under-resourced.\n\n**Kickoff — pick one, never both:**\n- `user.message` — conversational.\n- `user.define_outcome` + rubric — when §2 settled on an Outcome; the harness iterates and grades until the rubric passes.\n- **Scheduled shape?** Skip per-session kickoff entirely — create a **deployment** (`deployments.create()` with `schedule` + `initial_events`); each firing creates the session autonomously. See `shared/managed-agents-scheduled-deployments.md`.\n\nMechanics to bake into the runtime code: session creation blocks until resources mount (bad mounts surface there, before tokens); open the event stream *before* sending the kickoff; break on `session.status_terminated`, or `session.status_idle` with a terminal `stop_reason` — anything except `requires_action` (`shared/managed-agents-client-patterns.md` Pattern 5); usage lands on `span.model_request_end`; artifacts land in `/mnt/session/outputs/` (`files.list({scope_id: session.id, ...})`).\n\n## 5. Integrate — emit the code\n\nGo straight from the last answer to the code — no preamble, no lecture about setup-vs-runtime; the two-block structure shows it. Generate **two clearly-separated blocks**:\n\n**Block 1 — Setup (run once, store the IDs).** Prefer **YAML files + `ant` CLI** — agents and environments are version-controlled definitions users should check in and apply from CI:\n\n1. `.agent.yaml` (flat: `name`, `model`, `system`, `tools`, `mcp_servers`, `skills`) and `.environment.yaml`\n2. ```sh\n AGENT_ID=$(ant beta:agents create < .agent.yaml --transform id -r)\n ENV_ID=$(ant beta:environments create < .environment.yaml --transform id -r)\n # CI sync: ant beta:agents update --agent-id \"$AGENT_ID\" --version N < .agent.yaml\n ```\n\nSDK fallback if the user asks — and **required on Claude Platform on AWS**, where auth is SigV4 and the `ant` CLI has no SigV4 mode (use the platform client from `shared/claude-platform-on-aws.md`): label it `# ONE-TIME SETUP — run once, save the IDs` and call `environments.create()` → `agents.create()`.\n\n> ⚠️ **Deployments are newer than the rest of the MA surface.** Before emitting `ant beta:deployments …` or `client.beta.deployments` / `client.beta.deployment_runs` calls, verify the user's installed CLI/SDK exposes them (`ant beta:deployments --help`; `hasattr(client.beta, \"deployments\")`). If not, emit raw HTTP against `POST /v1/deployments` with the `managed-agents-2026-04-01` beta header (plus `oauth-2025-04-20` when authenticating with a Bearer token from `ant auth print-credentials`), and leave an upgrade note marking what simplifies to SDK calls.\n\n**Scheduled shape? The deployment is setup, not runtime.** Create it in Block 1, after the agent/environment IDs exist (`deployments.create()` with `schedule` + `initial_events`). Block 2 is then **not** a session loop — there is no per-run kickoff to send. Emit instead: a manual-run trigger (`POST /v1/deployments/{id}/run`) so the user can test now rather than wait for the first firing — the manual run doubles as the smoke test — plus a fetch helper (latest `deployment_runs` entry → `session_id` → Console URL + `files.list(scope_id=session_id)` for the artifacts).\n\n**Block 2 — Runtime (every invocation; conversational and Outcome shapes).** SDK code in the detected language (Python/TS/cURL — SKILL.md → Language Detection); don't emit shell loops here:\n\n1. Load `agent_id` + `env_id` from config/env\n2. `sessions.create(agent=AGENT_ID, environment_id=ENV_ID, resources=[...], vault_ids=[...])`, then print the Console URL so the user can watch live: `https://platform.claude.com/workspaces/default/sessions/{session.id}` (swap `default` for their workspace slug)\n3. **Smoke-test when the job depends on MCP servers, credentials, or locked-down hosts** — those failures don't surface at `sessions.create()`, only on first use. One cheap probe turn (\"Confirm you can reach and list 1–2 items; don't start the task\"), verify, then send the real kickoff. Skip when there are no external dependencies.\n4. Open stream → send the §4 kickoff → loop with the terminal gate from §4.\n\n> ⚠️ **Never emit `agents.create()` and `sessions.create()` in the same unguarded block** — that teaches creating a new agent per run, the #1 anti-pattern. Single-script requests: wrap creation in `if not os.getenv(\"AGENT_ID\"):`.\n\nPull exact syntax from `{lang}/managed-agents/README.md` for your detected language (cURL and C#: use `curl/managed-agents.md` as the wire-level reference). Don't invent field names.\n\n\n\n# Managed Agents — Outcomes\n\nAn **outcome** elevates a session from *conversation* to *work*: you state what \"done\" looks like, and the harness runs an iterate → grade → revise loop until the artifact meets the rubric, hits `max_iterations`, or is interrupted. A separate **grader** (independent context window) scores each iteration against your rubric and feeds per-criterion gaps back to the agent.\n\nThe SDK sets the `managed-agents-2026-04-01` beta header automatically on all `client.beta.sessions.*` calls; no additional header is required for outcomes.\n\n---\n\n## The `user.define_outcome` event\n\nOutcomes are not a field on `sessions.create()`. You create a normal session, then send a `user.define_outcome` event. The agent starts working on receipt — **do not also send a `user.message`** to kick it off.\n\n```python\nsession = client.beta.sessions.create(\n agent=AGENT_ID,\n environment_id=ENVIRONMENT_ID,\n title=\"Financial analysis on Costco\",\n)\n\nclient.beta.sessions.events.send(\n session_id=session.id,\n events=[\n {\n \"type\": \"user.define_outcome\",\n \"description\": \"Build a DCF model for Costco in .xlsx\",\n \"rubric\": {\"type\": \"text\", \"content\": RUBRIC_MD},\n # or: \"rubric\": {\"type\": \"file\", \"file_id\": rubric.id}\n \"max_iterations\": 5, # optional; default 3, max 20\n }\n ],\n)\n```\n\n| Field | Type | Notes |\n|---|---|---|\n| `type` | `\"user.define_outcome\"` | |\n| `description` | string | The task. This is what the agent works toward — no separate `user.message` needed. |\n| `rubric` | `{type: \"text\", content}` \\| `{type: \"file\", file_id}` | **Required.** Markdown with explicit, independently gradeable criteria. Upload once via `client.beta.files.upload(...)` (beta `files-api-2025-04-14`) to reuse across sessions. |\n| `max_iterations` | int | Optional. Default **3**, max **20**. |\n\nThe event is echoed back on the stream with a server-assigned `outcome_id` and `processed_at`.\n\n> **Writing rubrics.** Use explicit, gradeable criteria (\"CSV has a numeric `price` column\"), not vibes (\"data looks good\") — the grader scores each criterion independently, so vague criteria produce noisy loops. If you don't have a rubric, have Claude analyze a known-good artifact and turn that analysis into one.\n\n---\n\n## Outcome-specific events\n\nThese appear on the standard event stream (`sessions.events.stream` / `.list`) alongside the usual `agent.*` / `session.*` events.\n\n| Event | Payload highlights | Meaning |\n|---|---|---|\n| `span.outcome_evaluation_start` | `outcome_id`, `iteration` (0-indexed) | Grader began scoring iteration *N*. |\n| `span.outcome_evaluation_ongoing` | `outcome_id` | Heartbeat while the grader runs. Grader reasoning is opaque — you see *that* it's working, not *what* it's thinking. |\n| `span.outcome_evaluation_end` | `outcome_evaluation_start_id`, `outcome_id`, `iteration`, `result`, `explanation`, `usage` | Grader finished one iteration. `result` drives what happens next (table below). |\n\n### `span.outcome_evaluation_end.result`\n\n| `result` | Next |\n|---|---|\n| `satisfied` | Session → `idle`. Terminal for this outcome. |\n| `needs_revision` | Agent starts another iteration. |\n| `max_iterations_reached` | No further grader cycles. Agent may run one final revision, then session → `idle`. |\n| `failed` | Session → `idle`. Rubric fundamentally doesn't match the task (e.g. description and rubric contradict). |\n| `interrupted` | Only emitted if `_start` had already fired before a `user.interrupt` arrived. |\n\n```json\n{\n \"type\": \"span.outcome_evaluation_end\",\n \"id\": \"sevt_01jkl...\",\n \"outcome_evaluation_start_id\": \"sevt_01def...\",\n \"outcome_id\": \"outc_01a...\",\n \"result\": \"satisfied\",\n \"explanation\": \"All 12 criteria met: revenue projections use 5 years of historical data, ...\",\n \"iteration\": 0,\n \"usage\": { \"input_tokens\": 2400, \"output_tokens\": 350, \"cache_creation_input_tokens\": 0, \"cache_read_input_tokens\": 1800 },\n \"processed_at\": \"2026-03-25T14:03:00Z\"\n}\n```\n\n---\n\n## Checking status & retrieving deliverables\n\n**Status** — either watch the stream for `span.outcome_evaluation_end`, or poll the session and read `outcome_evaluations`:\n\n```python\nsession = client.beta.sessions.retrieve(session.id)\nfor ev in session.outcome_evaluations:\n print(f\"{ev.outcome_id}: {ev.result}\") # outc_01a...: satisfied\n```\n\n**Deliverables** — the agent writes to `/mnt/session/outputs/`. Once idle, fetch via the Files API with `scope_id=session.id`. This is the same session-outputs mechanism documented in `shared/managed-agents-environments.md` → Session outputs (including the dual-beta-header requirement on `files.list`).\n\n---\n\n## Interaction rules & pitfalls\n\n- **One outcome at a time.** Chain by sending the next `user.define_outcome` only after the previous one's terminal `span.outcome_evaluation_end` (`satisfied` / `max_iterations_reached` / `failed` / `interrupted`). The session retains history across chained outcomes.\n- **Steering is allowed but optional.** You *may* send `user.message` events mid-outcome to nudge direction, but the agent already knows to keep working until terminal — don't send \"keep going\" prompts.\n- **`user.interrupt` pauses the current outcome** — it marks `result: \"interrupted\"` and leaves the session `idle`, ready for a new outcome or conversational turn.\n- **After terminal, the session is reusable** — continue conversationally or define a new outcome.\n- **Outcome ≠ session-create field.** Don't put `outcome`, `rubric`, or `description` on `sessions.create()` — outcomes are always sent as a `user.define_outcome` event.\n- **Idle-break gate is unchanged.** In your drain loop, keep using `event.type === 'session.status_idle' && event.stop_reason?.type !== 'requires_action'` — do **not** gate on `span.outcome_evaluation_end` alone (on `needs_revision` the session keeps running). See `shared/managed-agents-client-patterns.md` Pattern 5.\n\nFor the raw HTTP shapes and per-language SDK bindings beyond Python, WebFetch `https://platform.claude.com/docs/en/managed-agents/define-outcomes.md` (see `shared/live-sources.md`).\n\n\n\n# Managed Agents — Overview\n\nManaged Agents provisions a container per session as the agent's workspace. The agent loop runs on Anthropic's orchestration layer; the container is where the agent's *tools* execute — bash commands, file operations, code. You create a persisted **Agent** config (model, system prompt, tools, MCP servers, skills), then start **Sessions** that reference it. The session streams events back to you; you send user messages and tool results in.\n\n## ⚠️ THE MANDATORY FLOW: Agent (once) → Session (every run)\n\n**Why agents are separate objects: versioning.** An agent is a persisted, versioned config — every update creates a new immutable version, and sessions pin to a version at creation time. This lets you iterate on the agent (tweak the prompt, add a tool) without breaking sessions already running, roll back if a change regresses, and A/B test versions side-by-side. None of that works if you `agents.create()` fresh on every run.\n\nEvery session references a pre-created `/v1/agents` object. Create the agent once, store the ID, and reuse it across runs.\n\n| Step | Call | Frequency |\n|---|---|---|\n| 1 | `POST /v1/agents` — `model`, `system`, `tools`, `mcp_servers`, `skills` live here | **ONCE.** Store `agent.id` **and** `agent.version`. |\n| 2 | `POST /v1/sessions` — `agent: \"agent_abc123\"` or `{type: \"agent\", id, version}` | **Every run.** String shorthand uses latest version. |\n\nIf you're about to write `sessions.create()` with `model`, `system`, or `tools` on the session body — **stop**. Those fields live on `agents.create()`. The session takes a *pointer* only.\n\n**When generating code, separate setup from runtime.** `agents.create()` belongs in a setup script (or a guarded `if agent_id is None:` block), not at the top of the hot path. If the user's code calls `agents.create()` on every invocation, they're accumulating orphaned agents and paying the create latency for nothing. The correct shape is: create once → persist the ID (config file, env var, secrets manager) → every run loads the ID and calls `sessions.create()`.\n\n**To change the agent's behavior, use `POST /v1/agents/{id}` — don't create a new one.** Each update bumps the version; running sessions keep their pinned version, new sessions get the latest (or pin explicitly via `{type: \"agent\", id, version}`). See `shared/managed-agents-core.md` → Agents → Versioning. To change `tools`/`mcp_servers`/`vault_ids` on **one running session** without touching the agent object, use `sessions.update()` — see `shared/managed-agents-core.md` → Updating the agent configuration mid-session.\n\n## Beta Headers\n\nManaged Agents is in beta. The SDK sets required beta headers automatically:\n\n| Beta Header | What it enables |\n| ------------------------------ | ---------------------------------------------------- |\n| `managed-agents-2026-04-01` | Agents, Environments, Sessions, Events, Session Resources, Session Threads, Outcomes, Multiagent, Vaults, Credentials, Memory Stores, Deployments |\n| `skills-2025-10-02` | Skills API (for managing custom skill definitions) |\n| `files-api-2025-04-14` | Files API for file uploads |\n\n**Which beta header goes where:** The SDK sets `managed-agents-2026-04-01` automatically on `client.beta.{agents,environments,sessions,vaults,memory_stores,deployments,deployment_runs}.*` calls, and `files-api-2025-04-14` / `skills-2025-10-02` automatically on `client.beta.files.*` / `client.beta.skills.*` calls. You do NOT need to add the Skills or Files beta header when calling Managed Agents endpoints. **Exception — session-scoped file listing:** `client.beta.files.list({scope_id: session.id})` is a Files endpoint that takes a Managed Agents parameter, so it needs **both** headers. Pass `betas: [\"managed-agents-2026-04-01\"]` explicitly on that call (the SDK adds the Files header; you add the Managed Agents one). See `shared/managed-agents-environments.md` → Session outputs.\n\n\n## Reading Guide\n\n| User wants to... | Read these files |\n| -------------------------------------- | ------------------------------------------------------- |\n| **Get started from scratch / \"help me set up an agent\"** | `shared/managed-agents-onboarding.md` — guided interview (WHERE→WHO→WHAT→WATCH), then emit code |\n| Understand how the API works | `shared/managed-agents-core.md` |\n| See the full endpoint reference | `shared/managed-agents-api-reference.md` |\n| **Create an agent** (required first step) | `shared/managed-agents-core.md` (Agents section) + language file |\n| Update/version an agent | `shared/managed-agents-core.md` (Agents → Versioning) — update, don't re-create |\n| Create a session | `shared/managed-agents-core.md` + `{lang}/managed-agents/README.md` (cURL/C#: `curl/managed-agents.md`) |\n| Configure tools and permissions | `shared/managed-agents-tools.md` |\n| Set up MCP servers | `shared/managed-agents-tools.md` (MCP Servers section) |\n| Stream events / handle tool_use | `shared/managed-agents-events.md` + language file |\n| Get notified of session state changes via webhook (no polling) | `shared/managed-agents-webhooks.md` — Console-registered endpoint, HMAC verify, thin payload + fetch |\n| Define an outcome / rubric-graded iterate loop | `shared/managed-agents-outcomes.md` — `user.define_outcome` event, grader, `span.outcome_evaluation_*` events |\n| Coordinate multiple agents / subagents / threads | `shared/managed-agents-multiagent.md` — `multiagent: {type: \"coordinator\", agents: [...]}` on the agent, session threads, cross-posted tool confirmations |\n| Set up environments | `shared/managed-agents-environments.md` + language file |\n| Run tool execution in your own infra / VPC (self-hosted sandbox) | `shared/managed-agents-self-hosted-sandboxes.md` — `config:{type:\"self_hosted\"}`, `ANTHROPIC_ENVIRONMENT_KEY`, `EnvironmentWorker.run()` / `ant beta:worker poll` |\n| Upload files / attach repos | `shared/managed-agents-environments.md` (Resources) |\n| Give agents persistent memory across sessions | `shared/managed-agents-memory.md` — memory stores, `memory_store` session resource, preconditions, versions/redact |\n| Define agents/environments as version-controlled YAML; drive the API from the shell | `shared/anthropic-cli.md` — `ant beta:agents create < agent.yaml`, `--transform`, `@file` inlining |\n| Store credentials (MCP auth, API keys for CLIs/SDKs) | `shared/managed-agents-tools.md` (Vaults section) — `mcp_oauth` / `static_bearer` / `environment_variable` |\n| Call a non-MCP API / CLI that needs a secret | `shared/managed-agents-tools.md` (Vaults section) — `environment_variable` credential, substituted at egress. If that doesn't fit (e.g. self-hosted sandboxes), `shared/managed-agents-client-patterns.md` Pattern 9 keeps the secret host-side via a custom tool |\n| Run an agent on a recurring cron schedule | `shared/managed-agents-scheduled-deployments.md` — deployments, deployment runs, pause/auto-pause |\n\n## Common Pitfalls\n\n- **Agent FIRST, then session — NO EXCEPTIONS** — the session's `agent` field accepts **only** a string ID or `{type: \"agent\", id, version}`. `model`, `system`, `tools`, `mcp_servers`, `skills` are **top-level fields on `POST /v1/agents`**, never on `sessions.create()`. If the user hasn't created an agent, that is step zero of every example.\n- **Agent ONCE, not every run** — `agents.create()` is a setup step. Store the returned `agent_id` and reuse it; don't call `agents.create()` at the top of your hot path. If the agent's config needs to change, `POST /v1/agents/{id}` — each update creates a new version, and sessions can pin to a specific version for reproducibility.\n- **MCP auth goes through vaults** — the agent's `mcp_servers` array declares `{type, name, url}` only (no auth). Credentials live in vaults (`client.beta.vaults.credentials.create`) and attach to sessions via `vault_ids`. Anthropic auto-refreshes OAuth tokens using the stored refresh token. Vaults also hold `environment_variable` credentials for non-MCP services (CLIs, SDKs, direct API calls) — substituted at egress, never visible in the sandbox.\n- **Reconcile resources before the first run** — a session with a clear ask but a missing tool, credential, data mount, or context will discover the gap mid-run, then flail and give up. Before creating the session, check that every action in the task maps to a configured tool/MCP server, every MCP server has a vault credential, and every referenced file/host is mounted/reachable. When helping a user set one up, run the reconciliation in `shared/managed-agents-onboarding.md` → §3 Pre-flight viability check.\n- **Stream to get events** — `GET /v1/sessions/{id}/events/stream` is the primary way to receive agent output in real-time.\n- **SSE stream has no replay — reconnect with consolidation** — if the stream drops while a `agent.tool_use`, `agent.mcp_tool_use`, or `agent.custom_tool_use` is pending resolution (`user.tool_confirmation` for the first two, `user.custom_tool_result` for the last one), the session deadlocks (client disconnects → session idles → reconnect happens → no client resolution happens). On every (re)connect: open stream with `GET /v1/sessions/{id}/events/stream` , fetch `GET /v1/sessions/{id}/events`, dedupe by event ID, then proceed. See `shared/managed-agents-events.md` → Reconnecting after a dropped stream.\n- **Don't trust HTTP-library timeouts as wall-clock caps** — `requests` `timeout=(c, r)` and `httpx.Timeout(n)` are *per-chunk* read timeouts; they reset every byte, so a trickling connection can block indefinitely. For a hard deadline on raw-HTTP polling, track `time.monotonic()` at the loop level and bail explicitly. Prefer the SDK's `sessions.events.stream()` / `session.events.list()` over hand-rolled HTTP. See `shared/managed-agents-events.md` → Receiving Events.\n- **Messages queue** — you can send events while the session is `running` or `idle`; they're processed in order. No need to wait for a response before sending the next message.\n- **Environment `config.type` is `\"cloud\"` or `\"self_hosted\"`** — `cloud` runs the container on Anthropic's infrastructure; `self_hosted` moves tool execution to your own (see `shared/managed-agents-self-hosted-sandboxes.md`).\n- **Archive is permanent on every resource** — archiving an agent, environment, session, vault, credential, or memory store makes it read-only with no unarchive. For agents, environments, and memory stores specifically, archived resources cannot be referenced by new sessions (existing sessions continue). Do not call `.archive()` on a production agent, environment, or memory store as cleanup — **always confirm with the user before archiving**.\n\n\n\n# Managed Agents — Scheduled Deployments\n\nA **scheduled deployment** runs an agent on a recurring cron schedule — each firing creates a session autonomously. Use it for predictable-cadence work: nightly triage, weekly compliance scans, hourly monitors.\n\nRequires the `managed-agents-2026-04-01` beta header (the SDK sets it automatically for `client.beta.deployments.*` / `client.beta.deployment_runs.*` calls).\n\n## Create a deployment\n\nA deployment bundles everything a session needs (agent, environment, optional files / GitHub / memory stores / vaults) plus a `schedule` and the `initial_events` that kick off each run:\n\n- `agent` and `environment_id` are required — same shapes as `sessions.create` (see `shared/managed-agents-core.md`).\n- `initial_events` must contain the starting `user.message`.\n- `schedule` takes a cron `expression` and an IANA `timezone`. Minute-level granularity is the maximum.\n\n```bash\ncurl -fsSL https://api.anthropic.com/v1/deployments \\\n -H \"x-api-key: $ANTHROPIC_API_KEY\" \\\n -H \"anthropic-version: 2023-06-01\" \\\n -H \"anthropic-beta: managed-agents-2026-04-01\" \\\n -H \"content-type: application/json\" \\\n -d @- < ⚠️ **DST edge:** wall-clock times that don't exist on a spring-forward day (e.g. 2AM) are **skipped**; times that occur twice on a fall-back day **fire twice**. Schedule outside the 1–3AM local window, or use UTC, when missed or duplicate executions are unacceptable.\n\n## Deployment runs\n\nEvery trigger attempt — successful or not — writes a **deployment run** record (`drun_` prefix), so you can audit failures independent of the session lifecycle. A successful run carries the created `session_id`; follow that session via the event stream (`shared/managed-agents-events.md`) or webhooks (`shared/managed-agents-webhooks.md`) as usual. A failed run carries an `error` whose `type` explains why session creation was rejected.\n\n```python\n# All runs for a deployment\nfor run in client.beta.deployment_runs.list(deployment_id=deployment.id):\n print(run.created_at, run.session_id or run.error.type)\n\n# Failures only\nfor run in client.beta.deployment_runs.list(deployment_id=deployment.id, has_error=True):\n print(run.created_at, run.error.type, run.error.message)\n```\n\n```typescript\nfor await (const run of client.beta.deploymentRuns.list({\n deployment_id: deployment.id,\n has_error: true,\n})) {\n console.log(run.created_at, run.error?.type, run.error?.message);\n}\n```\n\nRaw HTTP: `GET /v1/deployment_runs?deployment_id=...&has_error=true`. To retrieve a single run by ID, `GET /v1/deployment_runs/{deployment_run_id}` (SDK: `client.beta.deployment_runs.retrieve(run_id)`) — a `deployment_run.*` webhook event carries the run ID as its `data.id`.\n\nA failed run looks like:\n\n```json\n{\n \"type\": \"deployment_run\",\n \"id\": \"drun_01abc124\",\n \"deployment_id\": \"depl_01xyz\",\n \"trigger_context\": { \"type\": \"schedule\", \"scheduled_at\": \"2026-05-09T00:00:00Z\" },\n \"session_id\": null,\n \"error\": { \"type\": \"environment_archived\", \"message\": \"environment `env_01abc` is archived\" },\n \"agent\": { \"type\": \"agent\", \"id\": \"agent_01ghi789\", \"version\": 3 },\n \"created_at\": \"2026-05-09T00:00:01Z\"\n}\n```\n\nError types include `environment_archived`, `agent_archived`, `vault_not_found`, `session_rate_limited`, and `service_unavailable`.\n\nThe outcome of each **scheduled** run (started/succeeded/failed) and each deployment lifecycle change (created/updated/paused/unpaused/archived/deleted) is also delivered as a webhook event — see `shared/managed-agents-webhooks.md` for the `deployment.*` and `deployment_run.*` event types — so you can react without polling. Manual runs do **not** emit `deployment_run.*` webhook events.\n\n## Lifecycle: pause / unpause / archive\n\n| Operation | SDK | Effect |\n|---|---|---|\n| Pause | `client.beta.deployments.pause(id)` | Suppresses scheduled triggers go-forward. Sessions already running continue. **Manual runs are still permitted while paused.** Sets `paused_reason: {\"type\": \"manual\"}`. |\n| Unpause | `client.beta.deployments.unpause(id)` | Resumes from the next scheduled occurrence. **Missed triggers are not backfilled.** Clears `paused_reason`. |\n| Archive | `client.beta.deployments.archive(id)` | **Terminal** — the schedule stops and the deployment can no longer be modified. Use pause for anything reversible. |\n\nRaw HTTP: `POST /v1/deployments/{deployment_id}/pause` (likewise `/unpause`, `/archive`).\n\n### Failure behavior\n\n- **Rate-limited:** recorded immediately as a `session_rate_limited` run, **no retry** — the schedule simply tries again at the next occurrence. (Rate limits on API calls *inside* a session are handled by the session itself.)\n- **Other failed runs** (e.g. `environment_archived`, `vault_not_found`, `service_unavailable`): the run records the `error.type` — monitor runs and fix the referenced resource, or pause the deployment.\n- **Agent archived or deleted:** the deployment is automatically **archived** (terminal) and no further sessions are created.\n\n## Manual runs\n\n`POST /v1/deployments/{deployment_id}/run` (SDK: `client.beta.deployments.run(id)`) creates a session immediately and writes a run with `trigger_context.type: \"manual\"`. Use it to **test a deployment before committing to the schedule** — and remember it works even while the deployment is paused.\n\n\n\n# Managed Agents — Self-Hosted Sandboxes\n\nWith `config.type: \"self_hosted\"`, the **agent loop stays on Anthropic's orchestration layer** but **tool execution moves to infrastructure you control** — bash, file ops, and code run inside your container, so filesystem contents and network egress never leave your environment. Contrast with `config.type: \"cloud\"`, where Anthropic runs the container. Connectivity is **outbound-only**: your worker long-polls Anthropic's work queue; Anthropic never dials into your network.\n\n## Flow\n\n```\n1. Create environment: config: {type: \"self_hosted\"} → env_...\n2. Generate environment key (Console, on the environment page) → sk-ant-oat01-... as ANTHROPIC_ENVIRONMENT_KEY\n3. Run a worker: EnvironmentWorker.run() or ant beta:worker poll\n4. Sessions reference environment_id=env_... exactly as for cloud\n```\n\n## Create the environment\n\n```python\nclient = anthropic.Anthropic()\n\nenvironment = client.beta.environments.create(\n name=\"self-hosted\", config={\"type\": \"self_hosted\"}\n)\n```\n\n`{\"type\": \"self_hosted\"}` is the entire config — there are no pool, capacity, or networking sub-fields; you control those on your side.\n\n## Run a worker — SDK (primary path)\n\n`EnvironmentWorker` wraps the poll → dispatch → tool-execute loop. `.run()` is the always-on loop; `.run_one()` / `.runOne()` handles one work item (for webhook-driven wake).\n\n**Python — always-on:**\n\n```python\nimport asyncio\nimport os\nfrom anthropic import AsyncAnthropic\nfrom anthropic.lib.environments import EnvironmentWorker\n\n\nasync def main() -> None:\n environment_key = os.environ[\"ANTHROPIC_ENVIRONMENT_KEY\"]\n environment_id = os.environ[\"ANTHROPIC_ENVIRONMENT_ID\"]\n async with AsyncAnthropic(auth_token=environment_key) as client:\n await EnvironmentWorker(\n client,\n environment_id=environment_id,\n environment_key=environment_key,\n workdir=\"/workspace\",\n ).run()\n\n\nasyncio.run(main())\n```\n\n**TypeScript — always-on:**\n\n```typescript\nimport Anthropic from \"@anthropic-ai/sdk\";\nimport { EnvironmentWorker } from \"@anthropic-ai/sdk/helpers/beta/environments\";\n\nconst environmentKey = process.env.ANTHROPIC_ENVIRONMENT_KEY!;\nconst environmentId = process.env.ANTHROPIC_ENVIRONMENT_ID!;\nconst client = new Anthropic({ authToken: environmentKey });\nconst ctrl = new AbortController();\nprocess.once(\"SIGTERM\", () => ctrl.abort());\n\nawait new EnvironmentWorker({\n client,\n environmentId,\n environmentKey,\n workdir: \"/workspace\",\n signal: ctrl.signal\n}).run();\n```\n\n**Customizing tools.** `EnvironmentWorker` runs the built-in toolset by default. To add or replace tools, use `AgentToolContext(workdir=, client=, session_id=)` with `beta_agent_toolset(env)` / `betaAgentToolset(env)` and pass the resulting tools to the lower-level `tool_runner()`. Skills attached to the agent are downloaded into `{workdir}/skills//` before tool calls begin (`AgentToolContext` handles this when given `client` and `session_id`). Downloaded skill files are marked executable automatically by the CLI and SDK; if you implement skills download yourself, you set permissions.\n\n> **Runtime deps:** the SDK helpers require `/bin/bash` at that exact path. The TypeScript SDK additionally requires `unzip`, `tar`, and Node.js 22+. These are resolved at fixed paths and do **not** respect `PATH` overrides.\n\n## Run a worker — `ant` CLI (fixed tools)\n\nThe `ant` CLI ships a worker with the fixed built-in toolset (`bash`, `read`, `write`, `edit`, `glob`, `grep`). 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