Building LLM-Powered Applications with Claude

This 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.

Before You Start

Scan 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. (Exception: the prompt-audit subcommand is non-interactive and does not stop here - it records non-Anthropic provider markers in its report's stated assumptions and never proposes switching a non-Anthropic file to the Anthropic SDK.)

Output Requirement

When the user asks you to add, modify, or implement a Claude feature, your code must call Claude through one of:

1. 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.
2. 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.

Never 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.

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.

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.

Defaults

Unless the user requests otherwise:

For the Claude model version, please use Claude Opus 5, which you can access via the exact model string claude-opus-5. 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

Warning: API Drift - Your Training Prior May Be Stale

Several 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:

Area	Stale prior	Current API
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 5/5.1 / Sonnet 5 / Opus 5 / 4.8 / 4.7. Pre-4.6 models still use budget_tokens.
Web search / web fetch tool type	web_search_20250305, web_fetch_20250910	web_search_20260209, web_fetch_20260209 (dynamic filtering) on Opus 5/4.8/4.7/4.6, 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.
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.
Managed Agents credentials	Keep secrets host-side via custom tools (the only option before vaults shipped)	Vault environment_variable credentials - stored by Anthropic, substituted at egress, never visible in the sandbox (shared/managed-agents-tools.md -> Vaults). Host-side custom tools remain the fallback for self-hosted sandboxes.
Files API / Skills	client.beta.files.* / client.beta.skills.* with beta files-api-2025-04-14 / skills-2025-10-02	Out of beta: client.files.* / client.skills.*, no beta header. In current SDKs client.beta.files / client.beta.skills have breaking shape changes from previous versions, matching the stable namespaces - migrate per shared/live-sources.md -> Files API / Skills Guide.

The {lang}/ files in this skill are authoritative over recalled patterns.

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Subcommands

If 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 <subcommand>. If no table in the document matches, treat the request as normal prose.

Subcommand	Action
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. After the per-target changes are applied, audit the in-scope prompt text, tool descriptions, and request code against shared/prompt-audit.md - prompting written for the source model is part of every migration, and it does not announce itself.
prompt-audit	Audit existing prompts, skills, and tool descriptions for dated patterns ("cruft") written for older models. Read shared/prompt-audit.md immediately and follow it in order: Step 0 (establish scope and target model from the request and the repository - state the assumptions in the report, do not stop to ask), inventory, provenance, then the pattern scan. Produce both deliverables in full - the audit report (findings with file:line, pattern, why it's obsolete for the target model, confidence) and a proposed diff - without pausing for confirmation; apply edits only if the request explicitly asked for them. Do not summarize the guide - execute it.
upgrade	Upgrade the project's Anthropic SDK dependency across a major version - currently the Python SDK, anthropic 0.x -> 1.x. Trailing words may name the language and/or a scope (upgrade python, upgrade python sdk src/). Read python/claude-api/sdk-upgrade.md immediately and follow it in order: Step 0 (confirm scope, then establish the current and target versions - a published 1.x must exist before you write a pin), the Step 1 inventory, each numbered section, then verification and the report. Do not summarize the guide - execute it. If the detected or named language has no sdk-upgrade.md in this skill, say that no major-version upgrade guide is bundled for that SDK yet and point the user at that SDK's CHANGELOG (repositories in shared/live-sources.md); do not improvise one from the Python guide. This is not model migration - to move code to a newer Claude model, use migrate.
cost-optimize	Reduce what existing Claude API code costs to run, without sacrificing output quality. Read shared/cost-optimization.md immediately and follow it in order: Step 0 (establish scope, quality bar, and baseline), the token profile - measured through the Usage and Cost Admin API when the user has an Admin API key, from the app's own logs when it has those (ask), or estimated from the code otherwise - then a savings-ranked shortlist of levers (quoted in dollars, % of bill, or relative buckets depending on which of those data sources you have), free wins (caching, input-token hygiene, loop hygiene, output-token hygiene, batch) before tradeoffs (budgets, effort, model choice, multi-model); any lever that earns a place becomes its own diff - proposed by default, applied and measured against the eval covering the traffic it touches when the user asks and approves - and "no changes recommended" is a valid outcome. Two standing rules: every run that exercises the model spends real money, so get the user's approval first; and when context for a lever is missing, work through it interactively with the user - this workflow is not expected to one-shot the audit. Do not summarize the guide - execute it; presenting the profile and the ranked plan to the user is part of executing it.

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Language Detection

Before reading code examples, determine which language the user is working in (exception: for the prompt-audit subcommand, skip this section's ask steps - the audit is non-interactive and its inventory is language-agnostic; when no language is inferable, proceed without asking and state the assumption in the report):

1. Look at project files to infer the language:

   • *.py, requirements.txt, pyproject.toml, setup.py, Pipfile -> Python - read from python/
   • *.ts, *.tsx, package.json, tsconfig.json -> TypeScript - read from typescript/
   • *.js, *.jsx (no .ts files present) -> TypeScript - JS uses the same SDK, read from typescript/
   • *.java, pom.xml, build.gradle -> Java - read from java/
   • *.kt, *.kts, build.gradle.kts -> Java - Kotlin uses the Java SDK, read from java/
   • *.scala, build.sbt -> Java - Scala uses the Java SDK, read from java/
   • *.go, go.mod -> Go - read from go/
   • *.rb, Gemfile -> Ruby - read from ruby/
   • *.cs, *.csproj -> C# - read from csharp/
   • *.php, composer.json -> PHP - read from php/

2. If multiple languages detected (e.g., both Python and TypeScript files):

   • Check which language the user's current file or question relates to
   • If still ambiguous, ask: "I detected both Python and TypeScript files. Which language are you using for the Claude API integration?"

3. If language can't be inferred (empty project, no source files, or unsupported language):

   • Use AskUserQuestion with options: Python, TypeScript, Java, Go, Ruby, cURL/raw HTTP, C#, PHP
   • If AskUserQuestion is unavailable, default to Python examples and note: "Showing Python examples. Let me know if you need a different language."

4. If unsupported language detected (Rust, Swift, C++, Elixir, etc.):

Language-Specific Feature Support

Every SDK language above supports both the beta Tool Runner and Managed Agents (beta) - Python (@beta_tool decorator), TypeScript (betaZodTool + Zod), Java (annotated classes), Go (BetaToolRunner in the toolrunner pkg), Ruby (BaseTool + tool_runner), C# (BetaToolRunner + raw JSON schema), PHP (BetaRunnableTool + toolRunner()); code entry points are in the Tool Use Patterns quick reference below. cURL is raw HTTP (no SDK features) and supports Managed Agents.

Managed Agents code examples: see the reading guide in the ## Managed Agents (Beta) section below.

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Which Surface Should I Use?

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. "Simplest" means the least code you own: for a hosted, scheduled, or memory-backed agent, Managed Agents is usually the simplest option (no loop code, no state files, no scheduler), even though it's a bigger platform.

Use Case	Tier	Recommended Surface	Why
Classification, summarization, extraction, Q&A	Single LLM call	Claude API	One request, one response
Batch processing or embeddings	Single LLM call	Claude API	Specialized endpoints
Multi-step pipelines with code-controlled logic	Workflow	Claude API + tool use	You orchestrate the loop
Custom agent with your own tools	Agent	Claude API + tool use	Maximum flexibility
Server-managed stateful agent with workspace	Agent	Managed Agents	Anthropic runs the loop and hosts the tool-execution sandbox
Persisted, versioned agent configs	Agent	Managed Agents	Agents are stored objects; sessions pin to a version
Long-running multi-turn agent with file mounts	Agent	Managed Agents	Per-session containers, SSE event stream, Skills + MCP
Agent that runs on a schedule (cron, "every night")	Agent	Managed Agents - scheduled deployments	Deployments fire sessions autonomously; no client-side scheduler

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 the agentic loop - its per-turn hooks still give you approval gates, logging, error interception, and conditional execution (see shared/tool-use-concepts.md) - or the manual loop when you want to own the entire loop yourself.

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.

Building an Agent: Four Approaches

Once you've decided you actually need an agent (open-ended, model-driven tool use), there are four distinct ways to build one. Two independent questions separate them: who supplies the harness (the agent loop + context management) and who supplies the deployment (the infra the agent runs on). The Tool Runner and the Claude Agent SDK both supply a harness only - you still host and deploy them yourself - which is why they're easy to conflate. Managed Agents (CMA) is the only option that supplies both the harness and managed deployment; the manual loop supplies neither.

#	Approach	You write	Harness & deployment	Tools available	Use when
1	Claude API - manual loop	The while stop_reason == "tool_use" loop yourself	You build the harness; you host	Only tools you define	You want to own the entire loop - no beta dependency, or a control flow the Tool Runner's per-turn hooks don't fit
2	Claude API - Tool Runner (client.beta.messages.tool_runner + @beta_tool / betaZodTool)	Just the tool functions	SDK supplies the loop (harness only); you host	Only tools you define	A custom-tool agent without hand-writing the loop (most cases). Per-turn hooks still give you approval gates, error interception, result modification (e.g. cache_control), retries, streaming, and compaction
3	Managed Agents (REST, beta)	Agent config + your tool results	Anthropic supplies the harness and hosts a per-session sandbox (harness + deployment)	Anthropic-hosted sandbox (bash, files, code exec) + Skills/MCP + your tools	You want Anthropic to run the loop and host the per-session workspace; persisted/versioned configs; long-running sessions
4	Claude Agent SDK - separate product (claude-agent-sdk / @anthropic-ai/claude-agent-sdk)	A prompt + options	SDK supplies the Claude Code harness + built-in tools (harness only); you host	Built-in Read/Write/Edit/Bash/Glob/Grep/WebSearch/WebFetch + MCP + subagents	You want a batteries-included coding/filesystem agent running on your own infra

The harness/deployment split is the key mental model: options 1, 2, and 4 all leave deployment to you; only option 3 (CMA) adds managed deployment. Options 1-3 are what this skill generates; option 4 is a different library with its own docs - see the disambiguation below.

Tool Runner != Claude Agent SDK. These sound alike but are different packages:

• Tool Runner is part of the regular Anthropic API SDK (anthropic / @anthropic-ai/sdk), reached via client.beta.messages.tool_runner. It automates the request -> execute -> loop cycle for tools you define. No built-in tools, no filesystem access, no sandbox - you supply every tool and host the compute. It is option 2 above, a thin helper over POST /v1/messages.
• Claude Agent SDK (claude-agent-sdk / @anthropic-ai/claude-agent-sdk) is Claude Code packaged as a library. It ships built-in tools (file read/write/edit, bash, grep, web search), the full agent loop, context management, hooks, subagents, permissions, and sessions. You call query(prompt, options) and it drives everything.

Both are harness-only - you host and deploy them. The difference is scope of harness: the Tool Runner loops over tools you define (with per-turn hooks for approval, interception, result modification, and retries - but no built-in tools); the Agent SDK is the full Claude Code harness with built-in tools. Neither provides managed deployment - that's what Managed Agents (CMA) adds (Anthropic hosts the loop and a per-session sandbox).

This skill covers the Claude API and Managed Agents (options 1-3); it does not generate Claude Agent SDK code. If the user actually wants the Claude Agent SDK, point them to its docs (code.claude.com/docs/en/agent-sdk) - don't substitute the API Tool Runner for it, or vice-versa.

Should I Build an Agent?

Before choosing the agent tier, check all four criteria:

• 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")
• Value - Does the outcome justify higher cost and latency?
• Viability - Is Claude capable at this task type?
• Cost of error - Can errors be caught and recovered from? (tests, review, rollback)

If the answer is "no" to any of these, stay at a simpler tier (single call or workflow).

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Architecture

Everything goes through POST /v1/messages. Tools and output constraints are features of this single endpoint - not separate APIs.

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.

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.

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().

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.

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Current Models (cached: 2026-06-24)

Model	Model ID	Context	Input $/1M	Output $/1M
Claude Fable 5.1	claude-fable-5-1	1M	$10.00	$50.00
Claude Mythos 5.1 (Project Glasswing only)	claude-mythos-5-1	1M	$10.00	$50.00
Claude Fable 5	claude-fable-5	1M	$10.00	$50.00
Claude Opus 5	claude-opus-5	1M	$5.00	$25.00
Claude Opus 4.8	claude-opus-4-8	1M	$5.00	$25.00
Claude Opus 4.7	claude-opus-4-7	1M	$5.00	$25.00
Claude Opus 4.6	claude-opus-4-6	1M	$5.00	$25.00
Claude Sonnet 5	claude-sonnet-5	1M	$2.00	$10.00
Claude Sonnet 4.6	claude-sonnet-4-6	1M	$3.00	$15.00
Claude Haiku 4.5	claude-haiku-4-5	200K	$1.00	$5.00

Partner pricing: The prices above are Anthropic first-party API rates - they also apply to Claude on Microsoft Foundry, which is billed through the Microsoft Marketplace at standard API rates. Claude on Amazon Bedrock and Vertex AI is partner-operated with separate pricing - see Bedrock or Vertex AI. For WebFetch, use the Pricing row in shared/live-sources.md.

ALWAYS use claude-opus-5 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-5-1 only when the user explicitly asks for Claude Fable 5.1, "fable", or Anthropic's most capable model - it has different API behavior than the Opus family (see below) and pricing that exceeds Opus-tier. Use only the exact model ID strings from the table - they are complete as-is; never append date suffixes (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.

Claude Fable 5.1 (claude-fable-5-1) - most capable widely released model

Claude Fable 5.1 is Anthropic's most capable widely released model, for the most demanding reasoning and long-horizon agentic work; everything below also applies to Claude Mythos 5.1 (claude-mythos-5-1, Project Glasswing - same capabilities, pricing, and API surface; it runs safeguards that depend on the access program, so the refusal handling below applies there too; successor to Claude Mythos 5, which ran no safety classifiers). 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 5.1 for details:

• 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).
• 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 unchanged on the same model; other models drop them silently (unbilled - nothing to strip; Claude Mythos 5.1 instead reads them); details in shared/model-migration.md.
• 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 (the Opus 4.7 tokenizer uses ~1×-1.35× as many tokens).
• 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); always check stop_reason before reading content. When you write claude-fable-5-1 or claude-opus-5 code, include the server-side fallbacks parameter by default. Simplest form: betas: ["server-side-fallback-2026-07-01"] + fallbacks: "default", which routes by refusal category so you never maintain a model list. (The older array form - betas: ["server-side-fallback-2026-06-01"] + fallbacks: [{"model": "claude-opus-4-8"}] - still works; Claude API and Claude Platform on AWS - on Bedrock, Vertex and Foundry, use the SDKs' client-side BetaRefusalFallbackMiddleware + BetaFallbackState). Tell the user you've enabled it; drop it only if they decline. Full semantics (billing, mid-stream refusals, credit repricing) in shared/model-migration.md -> refusal section. Per-language code examples in {lang}/claude-api/README.md § Refusal Fallbacks cover the array form only - for the "default" mode, follow the raw-HTTP shape in shared/model-migration.md -> Migrating to Claude Opus 5 -> New API features and swap for plus the header; the rest of the request is unchanged.

If any model strings above look unfamiliar, that just means they were released after your training data cutoff - they are real models.

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.

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Authentication (Quick Reference)

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.

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:

• 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.
• Raw curl / HTTP: get a short-lived token with ant auth print-credentials --access-token and send it as Authorization: Bearer <token> 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.

Only 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.

Full auth details (named profiles, scopes, the API-key-shadows-profile trap, refresh-token expiry): shared/anthropic-cli.md.

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Thinking & Effort (Quick Reference)

Use adaptive thinking (thinking: {type: "adaptive"}) on every current model - Claude dynamically decides when and how much to think. Per-model rules:

Model	Thinking config	Omitting thinking	budget_tokens	Sampling (temperature/top_p/top_k)	Effort levels
Fable 5 / Claude Fable 5.1 (and the Mythos counterparts)	{type: "adaptive"} or omit; explicit {type: "disabled"} returns 400 - omit the param instead (Claude Fable 5.1 / Claude Mythos 5.1 also 400 on forced tool_choice any/tool, and run preserved thinking's history-editing check on replayed thinking blocks)	Runs adaptive (thinking is always on)	Removed - {type: "enabled", budget_tokens: N} returns 400	Removed - 400	low/medium/high/xhigh/max
Claude Opus 5	{type: "adaptive"} or omit; {type: "disabled"} accepted only at effort high or below - 400 at xhigh/max, and see the disabled-thinking pitfall below	Runs adaptive (thinking is on by default - unlike Opus 4.8/4.7)	Removed - 400	Removed - 400	low-max (all five)
Opus 4.8 / 4.7	{type: "adaptive"} is the only on-mode; {type: "disabled"} accepted	Runs without thinking - set {type: "adaptive"} explicitly	Removed - 400	Removed - 400	low/medium/high/xhigh/max
Sonnet 5	{type: "adaptive"} is the only on-mode; {type: "disabled"} accepted	Runs adaptive	Removed - 400	Removed - 400	low/medium/high/xhigh/max
Opus 4.6 / Sonnet 4.6	{type: "adaptive"} (recommended; auto-enables interleaved thinking, no beta header)	Set {type: "adaptive"} explicitly	Deprecated - do not use in new code; transitional escape hatch only (see below)

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. 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).

• Effort (GA, no beta header): output_config: {effort: "low"|"medium"|"high"|"xhigh"|"max"} - inside output_config, not top-level; default high (equivalent to omitting it). Controls thinking depth and overall token spend; combine with adaptive thinking for the best cost-quality tradeoffs. xhigh (added on Opus 4.7, between high and max) is 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; effort matters more on those models 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. Use a minimum of high for intelligence-sensitive work, max when correctness matters more than cost, and low for subagents or simple tasks - 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).
• Choosing an effort level (cost tuning): Effort is the first quality-trading lever, after the free wins (caching first) - it trades thoroughness against token spend within one model, and the top of the range earns its cost only on hard problems (raise to max only when measurement shows headroom at the level below). Which workloads repay higher effort is a property of the workload: coding and long-horizon agentic work respond strongly; chat, classification, and high-volume or latency-sensitive routes often don't and do well at low, with medium as the cost-saving step-down where quality holds (the per-level defaults above cover the rest). Measure on a sample of real requests before raising a default, and tune per route rather than globally. Before building a multi-model cost cascade, measure the simpler alternative first - the most capable model at lower effort on the same tasks: lower effort on the newest models often matches or exceeds prior-generation performance at high effort (on Fable 5, lower effort often exceeds xhigh on prior models), and one model means one cache namespace (caches are model-scoped, so a cascade forfeits cache reuse across its models; a mid-conversation top-level effort change still invalidates the messages cache, though the per-message effort system message avoids that on Claude Fable 5.1 / Claude Mythos 5.1 / Claude Opus 5 - shared/prompt-caching.md § Invalidation hierarchy). Judge cost per completed task, not per request - a cheaper request that needs more turns or retries to finish the job isn't cheaper. For the measured effort/cost tradeoffs by workload and the full lever order, shared/cost-optimization.md § 2.6.

---

Compaction (Quick Reference)

Beta, Fable 5/5.1, Opus 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.

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.

See {lang}/claude-api/README.md (Compaction section) for code examples. Full docs via WebFetch in shared/live-sources.md.

---

Prompt Caching (Quick Reference)

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.

Mid-conversation operator instructions (Claude Opus 5, Claude Opus 4.8, Claude Fable 5, Claude Fable 5.1, Claude Mythos 5, Claude Mythos 5.1; not Claude Sonnet 5; 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.

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 model-dependent (512-4096 tokens - see shared/prompt-caching.md § API reference) - shorter prefixes silently won't cache.

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).

For 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).

---

Fast Mode (Quick Reference)

Research preview, Claude Opus 5 / Opus 4.8 only - Claude API and Managed Agents, not Bedrock / Google Cloud / Foundry. Opus 4.7 fast mode has been removed: speed: "fast" on 4.7 returns an error. Fast mode on Claude Opus 5 is priced at $10 / $50 per MTok. 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).

client.beta.messages.create(
    model="claude-opus-5", max_tokens=4096,
    speed="fast", betas=["fast-mode-2026-02-01"],
    messages=[...],
)

Language	Beta flag	Speed parameter
Python	betas=["fast-mode-2026-02-01"]	speed="fast"
TypeScript / Ruby	betas: ["fast-mode-2026-02-01"]	speed: "fast"
Go	[]anthropic.AnthropicBeta{anthropic.AnthropicBetaFastMode2026_02_01}	Speed: anthropic.BetaMessageNewParamsSpeedFast
Java	.addBeta(AnthropicBeta.FAST_MODE_2026_02_01)	.speed(MessageCreateParams.Speed.FAST)
C#	Betas = ["fast-mode-2026-02-01"]	Speed = Speed.Fast (Anthropic.Models.Beta.Messages)
PHP	betas: ['fast-mode-2026-02-01']	speed: 'fast'
cURL	anthropic-beta: fast-mode-2026-02-01 header	"speed": "fast" in body

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.

Priority Tier is not supported on every current model. It is supported on Claude Fable 5, Opus 4.8, and the older current models, but Claude Opus 5, Claude Sonnet 5, Claude Fable 5.1, Claude Mythos 5.1, Claude Mythos 5, and Mythos Preview are excluded - a Priority Tier request naming one of them fails validation.

---

Task Budgets (Quick Reference)

Beta, Claude Opus 5 / Fable 5 / Claude Fable 5.1 (confirm at launch) / 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 - distinct from max_tokens, which is an enforced per-response ceiling the model is not aware of. Minimum total: 20,000. 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 (full details: shared/model-migration.md -> Task Budgets):

with client.beta.messages.stream(
    model="claude-opus-5", max_tokens=128000,
    output_config={"effort": "high", "task_budget": {"type": "tokens", "total": 64000}},
    betas=["task-budgets-2026-03-13"],
    messages=[...], tools=[...],
) as stream:
    response = stream.get_final_message()

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. Not the same thing as Managed Agents session budgets - those are hard, dollar-denominated, platform-enforced caps on one CMA session (shared/managed-agents-core.md § Session budgets); a task budget is advisory and token-denominated.

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.

---

Provider Clients (Quick Reference)

When 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.

Amazon Bedrock

Use the Mantle client (Messages-API Bedrock endpoint). Bedrock model IDs take an anthropic. prefix (e.g. "anthropic.claude-opus-5"). Region is required.

Language	Client
Python	from anthropic import AnthropicBedrockMantle -> AnthropicBedrockMantle(aws_region="...")
TypeScript	import { AnthropicBedrockMantle } from "@anthropic-ai/bedrock-sdk" -> new AnthropicBedrockMantle({ awsRegion: "..." })
Go	bedrock.NewMantleClient(ctx, bedrock.MantleClientConfig{ AWSRegion: "..." })
Java	AnthropicOkHttpClient.builder().backend(BedrockMantleBackend.fromEnv()).build() (from com.anthropic.bedrock.backends)
C#	new AnthropicBedrockMantleClient(new() { AwsRegion = "..." }) (package Anthropic.Bedrock)
PHP	use Anthropic\Bedrock\MantleClient; -> new MantleClient(awsRegion: '...')
Ruby	Anthropic::BedrockMantleClient.new(aws_region: "...")

AnthropicBedrock / BedrockClient / BedrockBackend (without Mantle) are the legacy bedrock-runtime InvokeModel path - prefer the Mantle client for new code.

Microsoft Foundry

Language	Client
Python	from anthropic import AnthropicFoundry -> AnthropicFoundry(api_key=..., resource="...")
TypeScript	import AnthropicFoundry from "@anthropic-ai/foundry-sdk" -> new AnthropicFoundry({ ... })
Java	AnthropicOkHttpClient.builder().backend(FoundryBackend.fromEnv()).build() (from com.anthropic.foundry.backends)
C#	new AnthropicFoundryClient(new AnthropicFoundryApiKeyCredentials(...)) (package Anthropic.Foundry)
PHP	Foundry\Client::withCredentials(...)

The Go and Ruby SDKs do not currently support Foundry. For Ruby, use the standard Anthropic::Client.new(base_url: "<foundry endpoint>") as a fallback (Entra ID auth is not built in). For Claude Platform on AWS, see shared/claude-platform-on-aws.md.

Google Cloud Vertex AI

Two 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-5"); 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.

Language	Client
Python	from anthropic import AnthropicVertex -> AnthropicVertex(project_id="...", region="...") (install "anthropic[vertex]")
TypeScript	import { AnthropicVertex } from "@anthropic-ai/vertex-sdk" -> new AnthropicVertex({ projectId, region })
Go	import "github.com/anthropics/anthropic-sdk-go/vertex" -> anthropic.NewClient(vertex.WithGoogleAuth(ctx, region, projectID))
Java	AnthropicOkHttpClient.builder().backend(VertexBackend.builder().region("...").project("...").build()).build() (from com.anthropic.vertex.backends)
C#	new AnthropicClient { Backend = new VertexBackend(projectId, region) } (package Anthropic.Vertex)
PHP	use Anthropic\Vertex; -> Vertex\Client::fromEnvironment(location: '...', projectId: '...') - note location, not region
Ruby	Anthropic::VertexClient.new(region: "...", project_id: "...")

---

Context Editing (Quick Reference)

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:

client.beta.messages.create(
    model="claude-opus-5", max_tokens=4096,
    betas=["context-management-2025-06-27"],
    context_management={"edits": [{"type": "clear_tool_uses_20250919"}]},
    tools=[...], messages=[...],
)

Strategy 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.

---

Mid-Conversation System Messages (Quick Reference)

Claude Opus 5, Claude Opus 4.8, Claude Fable 5, Claude Fable 5.1, Claude Mythos 5, and Claude Mythos 5.1; not Claude Sonnet 5; 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. A beta extension shipped with Claude Fable 5.1: output_config: {effort: ...} with content: [] changes effort from that point on without a cache reset (beta mid-conversation-output-config-2026-07-01; Claude Fable 5.1, Claude Mythos 5.1, Claude Opus 5; Claude API). An effort-only message (empty content) is exempt from the placement rules above - it can sit anywhere in messages, including first or between an assistant turn and the next user turn; the rules apply to text and clear_at messages. For a per-turn reminder, give the message clear_at: "next_user_message" (beta mid-conversation-system-clear-at-2026-08-21): it renders for one turn, then stays in the transcript cleared - never delete earlier copies (on Claude Fable 5.1 deleting one invalidates later thinking blocks); without the beta, a text block after the tool results, earlier copies kept. See shared/model-migration.md -> Migrating to Claude Fable 5.1 from Claude Fable 5 -> New API features.

---

Managed Agents (Beta)

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.

Availability: shared/platform-availability.md. For agents on Bedrock / Vertex / Foundry (where Managed Agents is unsupported), use Claude API + tool use.

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.

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. Files API and Skills API are out of beta - no beta header needed (see the API Drift table above for the migration guides).

Subcommands - invoke directly with /claude-api <subcommand>:

Subcommand

Action

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.

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. Define agents and environments as version-controlled YAML applied with the ant CLI - this is the recommended flow (see shared/anthropic-cli.md): the CLI owns the control plane (creating and updating agents), your code owns the data plane (sessions.create with the stored agent ID). Call agents.create() in code only when you must provision programmatically; either way, store the returned agent ID and pass it to every subsequent sessions.create; never call agents.create() in the request path. 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 - see csharp/claude-api/README.md for details, or curl/managed-agents.md for raw HTTP reference.

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.

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, etc. For credentials, lead with vault environment_variable credentials - the first-class mechanism; secrets are substituted at egress and never enter the sandbox (shared/managed-agents-tools.md -> Vaults). Keeping credentials host-side via custom tools is the fallback where vault credentials don't fit (e.g. self-hosted sandboxes).

When the user asks about tool approvals, permission policies, or "auto mode" (which tool calls need a human, letting the server evaluate calls, evaluated_permission / evaluation on tool-use events): read shared/managed-agents-tools.md § Permission Policies - always_allow / always_ask / auto and the three auto outcomes (runs, denied as high-risk, pauses when indeterminate). For attaching a terminal to a live session (ant beta:sessions connect): shared/anthropic-cli.md.

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).

When the agent's work fans out (research across several sources, per-file or per-record work, "look into N things, then summarize") or one loop would fill its context with reading: read shared/managed-agents-multiagent.md and recommend a multiagent session - start with just {"type": "self"} in the roster so the agent can delegate to copies of itself, then move reading-heavy sub-tasks to a cheaper worker agent (e.g. Claude Haiku 4.5) referenced by ID.

---

Server Tools (Quick Reference)

Server-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 5/4.8/4.7/4.6, Sonnet 5, or Sonnet 4.6; the basic variants for older models are listed after the table.

Tool	type	name	Key optional params	Result block type
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
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
Code execution	code_execution_20260521	code_execution	none	bash_code_execution_tool_result -> .content.stdout / .stderr / .return_code
Tool search (regex)	tool_search_tool_regex_20251119	tool_search_tool_regex	mark other tools defer_loading: true	tool_search_tool_result
Tool search (BM25)	tool_search_tool_bm25_20251119	tool_search_tool_bm25	mark other tools defer_loading: true	tool_search_tool_result

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.

Document & File Input (Quick Reference)

PDF (base64, no beta): {"type": "document", "source": {"type": "base64", "media_type": "application/pdf", "data": <b64 string>}} 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(...)).

Files API (no beta): upload via client.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. To migrate code off files-api-2025-04-14, WebFetch the Files API row in shared/live-sources.md. Availability: shared/platform-availability.md.

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 (returns a 400).

Tool Use Patterns (Quick Reference)

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)).

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 - splitting them across multiple messages silently trains Claude to stop making parallel calls. For a failed tool, return tool_result with is_error: true - don't drop it.

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(...).

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.

Other API Surfaces (Quick Reference)

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.

Models API (no beta; availability: shared/platform-availability.md): client.models.list() (auto-paginates) and client.models.retrieve("claude-opus-5"). 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.

Stop details (GA, Opus 4.7+): response.stop_details is populated only when stop_reason == "refusal" (fields: type: "refusal", category - an open set, e.g. "cyber", "bio", "reasoning_extraction", "frontier_llm", or null; see the docs for the full list - and explanation). It is null for every other stop_reason (end_turn, max_tokens, tool_use, pause_turn, ...) - always guard before reading.

Admin API (beta, since 2026-08-26): organization management - members, invites, workspaces and workspace members, API keys, rate limit reports, service accounts, federation issuers/rules, CMEK external keys - under client.beta.organization in all seven SDKs and ant beta:organization in the CLI. Requires an admin credential: an Admin API key (sk-ant-admin..., read from ANTHROPIC_API_KEY) or an org:admin OAuth token (ANTHROPIC_AUTH_TOKEN); regular API keys are rejected. Usage and cost reports and the Claude Enterprise user-management/analytics endpoints are not in the SDKs - raw HTTP only. See shared/admin-api.md.

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).

Workload Identity Federation (Quick Reference)

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.

---

Reading Guide

After detecting the language, read the relevant files based on what the user needs. Every {lang}/..., shared/..., and curl/... path cited in this document is relative to this skill's base directory, and none of those files' content is included above - Read each one on demand before relying on what it covers.

All SDK languages use the same multi-file layout - directory {lang}/claude-api/ containing README.md (install, client init, basic request, thinking, caching, stop details, misc), tool-use.md (tool definitions, agentic loop, Anthropic-defined tools, structured outputs), streaming.md, batches.md, files-api.md. Not every language has every file (e.g., Ruby has no batches.md); 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 from shared/live-sources.md. cURL -> curl/examples.md.

The Quick Task Reference below uses the {lang}/claude-api/FILE.md path notation for all languages.

Quick Task Reference

Single text classification/summarization/extraction/Q&A: -> Read only {lang}/claude-api/README.md - always read the README first for any task (installation, quick start, common patterns, error handling)

Chat UI or real-time response display: -> Read {lang}/claude-api/README.md + {lang}/claude-api/streaming.md

Long-running conversations (may exceed context window): -> Read {lang}/claude-api/README.md - see Compaction section Migrating to a newer model (Fable 5.1 / Fable 5 / Opus 5 / Opus 4.8 / Opus 4.7 / Opus 4.6 / Sonnet 5 / Sonnet 4.6), replacing a retired model, or translating budget_tokens / prefill patterns to the current API: -> Read shared/model-migration.md Upgrading the Anthropic SDK package itself across a major version (anthropic 0.x -> 1.x: httpx2, awaited async .with_raw_response, removed deprecated parameters / aliases / Text Completions, Python >= 3.10) - or writing new code against a project already on 1.x: -> Read {lang}/claude-api/sdk-upgrade.md (currently Python only; other SDKs have no bundled major-version guide yet - use that SDK's CHANGELOG via shared/live-sources.md) Prompting or tuning Fable 5/5.1 (long turns, effort, verbosity, autonomous runs, sub-agents): -> Read shared/model-migration.md -> Migrating to Claude Fable 5.1 -> Behavioral shifts (prompt-tunable) + Long-running agent recommendations Prompting or tuning Claude Fable 5.1 (progress updates, parallel tool calls, writing density / formatting, autonomy, test sprawl, whole-file rewrites) or making a harness compatible with preserved thinking's history-editing check (history edits, compaction, per-turn reminders): -> Read shared/model-migration.md -> Migrating to Claude Fable 5.1 from Claude Fable 5 -> New API features + Behavioral shifts (prompt-tunable); for the history-editing check itself (the three-step check, the append-only edit table, compaction shapes), Breaking change 3 in the same section Prompt caching / optimize caching / "why is my cache hit rate low": -> Read shared/prompt-caching.md (prefix-stability design, breakpoint placement, anti-patterns that silently invalidate cache) + {lang}/claude-api/README.md (Prompt Caching section) Auditing or cleaning up prompts, skills, or tool descriptions ("is this prompt outdated", "remove the cruft", "this was written for an older model"): -> Read shared/prompt-audit.md - dated-pattern tables with greppable signals, the keep list (what NOT to delete), and the report + proposed-diff output contract Count tokens in a file / prompt / diff ("how many tokens is X"): -> Read shared/token-counting.md - use messages.count_tokens, never tiktoken Reducing or reviewing API spend ("the bill is too high", "make this cheaper", "am I overspending", cost per completed task, cheapest model or effort that holds quality): -> Read shared/cost-optimization.md - baseline and token profile first, then the levers in order (free wins before tradeoffs) with measured expectations, and a workload-shape -> lever mapping table

Function calling / tool use / agents: -> Read {lang}/claude-api/README.md + shared/tool-use-concepts.md (conceptual foundations: function calling, code execution, memory, structured outputs) + {lang}/claude-api/tool-use.md (language-specific code examples: tool runner, manual loop, code execution, memory, structured outputs)

Agent design (tool surface, context management, caching strategy): -> Read shared/agent-design.md (bash vs. dedicated tools, programmatic tool calling, tool search/skills, context editing vs. compaction vs. memory, caching principles)

Batch processing (non-latency-sensitive; runs asynchronously at 50% cost): -> Read {lang}/claude-api/README.md + {lang}/claude-api/batches.md

File uploads across multiple requests (same file without re-uploading): -> Read {lang}/claude-api/README.md + {lang}/claude-api/files-api.md

Organization administration (members, invites, workspaces, API keys, rate limit reports, service accounts, WIF resources, CMEK): -> Read shared/admin-api.md - client.beta.organization endpoint/method table, admin credentials, per-language naming and pagination, what stays curl-only

Debugging HTTP errors or implementing error handling: -> Read shared/error-codes.md - per-SDK typed exception class table and the Go errors.As pattern

Latest official documentation: -> WebFetch the URLs in shared/live-sources.md

Managed Agents (server-managed stateful agents with workspace): -> See the reading guide in the ## Managed Agents (Beta) section above - it lists every shared/managed-agents-*.md file and the language-specific READMEs ({lang}/managed-agents/README.md, curl/managed-agents.md).

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When to Use WebFetch

Use WebFetch to get the latest documentation when:

• User asks for "latest" or "current" information
• Cached data seems incorrect
• User asks about features not covered here

Live documentation URLs are in shared/live-sources.md.

Common Pitfalls

• Don't truncate inputs when passing files or content to the API. If the content is too long to fit in the context window, notify the user and discuss options (chunking, summarization, etc.) rather than silently truncating.
• Prefill removed (Fable 5, Claude Fable 5.1, Opus 5, Sonnet 5, and the 4.6/4.7/4.8 family): Assistant message prefills (last-assistant-turn prefills) return a 400 error on Fable 5, Claude Fable 5.1, Opus 5, Sonnet 5, Opus 4.6, Opus 4.7, Opus 4.8, and Sonnet 4.6. Use structured outputs (output_config.format) or system prompt instructions to control response format instead. (One exception: the fallback-credit prefill claim - when redeeming a credit with fallback_has_prefill_claim: true, the server accepts the echoed assistant message; see the migration guide's refusal section.)
• Confirm migration scope before editing: When a user asks to migrate code to a newer Claude model without naming a specific file, directory, or file list, ask which scope to apply first - the entire working directory, a specific subdirectory, or a specific set of files. Do not start editing until the user confirms. Imperative phrasings like "migrate my codebase", "move my project to X", "upgrade to Sonnet 4.6", or bare "migrate to Opus 4.8" are still ambiguous - they tell you what to do but not where, so ask. Proceed without asking only when the prompt names an exact file, a specific directory, or an explicit file list ("migrate app.py", "migrate everything under services/", "update a.py and b.py"). See shared/model-migration.md Step 0.
• max_tokens defaults: Don't lowball max_tokens - hitting the cap truncates output mid-thought and requires a retry. For non-streaming requests, default to ~16000 (keeps responses under SDK HTTP timeouts). For streaming requests, default to ~64000 (timeouts aren't a concern, so give the model room). Only go lower when you have a hard reason: classification (~256), cost caps, deliberately short outputs, or max_tokens: 0 for cache pre-warming (see shared/prompt-caching.md -> Pre-warming).
• Disabling thinking on Claude Opus 5 has two failure modes - prefer low/medium effort instead. Only affects code that explicitly opts out; thinking is on by default, so watch for a disabled-thinking setting carried forward from Opus 4.8. With thinking: {type: "disabled"}, the model occasionally writes a tool call into its visible text instead of a tool_use block: the turn succeeds, the call never runs, no error is raised, and in an agentic loop that text pollutes later turns. It can also leak tags into the response. Turning thinking on and lowering fixes both and still cuts cost. If a route must stay thinking-off: any don't-think/don't-reason rule (it makes tag leakage worse), don't name thinking tags, and add the combined instruction Details: -> Two failure modes when thinking is disabled.