claude-code-system-prompts/system-prompts/skill-building-llm-powered-applications-with-claude.md

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.

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 {{OPUS_NAME}}, which you can access via the exact model string {{OPUS_ID}}. 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

⚠️ 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 / Opus 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 4.8/4.7/4.6 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.

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.

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Language Detection

Before reading code examples, determine which language the user is working in:

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.):

   • Suggest cURL/raw HTTP examples from curl/ and note that community SDKs may exist
   • Offer to show Python or TypeScript examples as reference implementations

5. If user needs cURL/raw HTTP examples, read from curl/.

Language-Specific Feature Support

Language	Tool Runner	Managed Agents	Notes
Python	Yes (beta)	Yes (beta)	Full support — @beta_tool decorator
TypeScript	Yes (beta)	Yes (beta)	Full support — betaZodTool + Zod
Java	Yes (beta)	Yes (beta)	Beta tool use with annotated classes
Go	Yes (beta)	Yes (beta)	BetaToolRunner in toolrunner pkg
Ruby	Yes (beta)	Yes (beta)	BaseTool + tool_runner in beta
C#	Yes (beta)	Yes (beta)	BetaToolRunner + raw JSON schema
PHP	Yes (beta)	Yes (beta)	BetaRunnableTool + toolRunner()
cURL	N/A	Yes (beta)	Raw HTTP, no SDK features

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.

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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.

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

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).

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.

Decision Tree

What does your application need?

0. Which provider?
   ├── First-party API or Claude Platform on AWS → continue (full surface available; per-feature exceptions in shared/platform-availability.md).
   └── Amazon Bedrock, Google Vertex AI, or Microsoft Foundry → Claude API (+ tool use for agents); see shared/platform-availability.md for per-feature support.

1. Single LLM call (classification, summarization, extraction, Q&A)
   └── Claude API — one request, one response

2. Do you want Anthropic to run the agent loop and host a per-session
   container where Claude executes tools (bash, file ops, code)?
   └── Yes → Managed Agents — server-managed sessions, persisted agent configs,
       SSE event stream, Skills + MCP, file mounts.
       Examples: "stateful coding agent with a workspace per task",
                 "long-running research agent that streams events to a UI",
                 "agent with persisted, versioned config used across many sessions"

3. Workflow (multi-step, code-orchestrated, with your own tools)
   └── Claude API with tool use — you control the loop

4. Open-ended agent (model decides its own trajectory, your own tools, you host the compute)
   └── Claude API agentic loop (maximum flexibility)

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: 2026-06-04)

Model	Model ID	Context	Input $/1M	Output $/1M
{{FABLE_NAME}}	{{FABLE_ID}}	1M	$10.00	$50.00
{{MYTHOS_NAME}} (Project Glasswing only)	{{MYTHOS_ID}}	1M	$10.00	$50.00
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 4.6	claude-sonnet-4-6	1M	$3.00	$15.00
Claude Haiku 4.5	claude-haiku-4-5	200K	$1.00	$5.00

ALWAYS use {{OPUS_ID}} unless the user explicitly names a different model. This is non-negotiable. Do not use {{SONNET_ID}}, {{PREV_SONNET_ID}}, 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 {{FABLE_ID}} only when the user explicitly asks for {{FABLE_NAME}}, "fable", or Anthropic's most capable model — it has different API behavior than the Opus family (see below) and pricing that exceeds Opus-tier.

{{FABLE_NAME}} ({{FABLE_ID}}) — most capable widely released model

{{FABLE_NAME}} is Anthropic's most capable widely released model, for the most demanding reasoning and long-horizon agentic work. {{MYTHOS_NAME}} ({{MYTHOS_ID}}) 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 {{FABLE_NAME}} 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 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.
• 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.
• 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 {{OPUS_NAME}} 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 {{FABLE_ID}} code, include the server-side fallbacks parameter by default (betas: ["server-side-fallback-2026-06-01"] + fallbacks: [{"model": "{{OPUS_ID}}"}]; 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.
• No assistant prefill — same as the rest of the 4.6+ family.
• 30-day data retention required — {{FABLE_NAME}} is not available under zero data retention; requests from an org whose retention configuration doesn't meet the requirement return 400 invalid_request_error.
• 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 {{FABLE_NAME}} → Behavioral shifts (prompt-tunable) for the recommended prompt snippets (anti-overplanning, no-tidying, grounded progress claims, boundaries, async sub-agents, memory, send_to_user).

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.

A 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.

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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Thinking & Effort (Quick Reference)

Fable 5 / Opus 4.8 / 4.7 — 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 and 4.7, {type: "disabled"} and omitting thinking both work; 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). 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. 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, 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, 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, and Sonnet 4.6. Will error on Sonnet 4.5 / Haiku 4.5. On Fable 5, Opus 4.7 and 4.8, effort matters more than on any prior Opus — 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.

Thinking display — "omitted" by default on Fable 5 / Mythos 5 / Opus 4.8 / 4.7: display: "summarized" returns a readable summary of the reasoning; "omitted" (the default on all four — a silent change from Opus 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.)

Task Budgets (beta, Fable 5 / Opus 4.7 / 4.8): 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.

Sonnet 4.6: Supports adaptive thinking (thinking: {type: "adaptive"}). budget_tokens is deprecated on Sonnet 4.6 — use adaptive thinking instead.

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.

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Compaction (Quick Reference)

Beta, Fable 5, Opus 4.8, Opus 4.7, Opus 4.6, 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.

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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 ({{OPUS_NAME}} 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.

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.

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).

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Fast Mode (Quick Reference)

Research preview, Opus 4.8 / 4.7 only (Opus 4.6 fast mode is deprecated and being removed; after removal, speed: "fast" on 4.6 silently falls back to standard speed instead of erroring — use 4.8 or 4.7). 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-4-8", 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.

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Task Budgets (Quick Reference)

Beta, Fable 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:

with client.beta.messages.stream(
    model="claude-opus-4-8", 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.

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.

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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.{{OPUS_ID}}"). 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 4.6) use the bare first-party ID (e.g. "{{OPUS_ID}}"); 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="{{OPUS_ID}}", 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)

{{OPUS_NAME}} 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.

---

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. 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.

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. 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.

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, keeping credentials host-side via custom tools, etc.

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).

---

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 4.8/4.7/4.6 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_20250825	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_20250825 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 (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.

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.

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 (don't split across multiple messages). 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("{{OPUS_ID}}"). 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: "cyber"|"bio"|null, explanation). It is null for every other stop_reason (end_turn, max_tokens, tool_use, pause_turn, …) — always guard before reading.

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.

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

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 / Opus 4.8 / Opus 4.7 / Opus 4.6 / Sonnet 4.6) or replacing a retired model: → Read shared/model-migration.md Prompting or tuning Fable 5 (long turns, effort, verbosity, autonomous runs, sub-agents): → Read shared/model-migration.md → Migrating to Fable 5 → Behavioral shifts (prompt-tunable) + Long-running agent recommendations Prompt caching / optimize caching / "why is my cache hit rate low": → Read shared/prompt-caching.md + {lang}/claude-api/README.md (Prompt Caching section) Count tokens in a file / prompt / diff ("how many tokens is X"): → Read shared/token-counting.md — use messages.count_tokens, never tiktoken

Function calling / tool use / agents: → Read {lang}/claude-api/README.md + shared/tool-use-concepts.md + {lang}/claude-api/tool-use.md

Agent design (tool surface, context management, caching strategy): → Read shared/agent-design.md

Batch processing (non-latency-sensitive): → Read {lang}/claude-api/README.md + {lang}/claude-api/batches.md

File uploads across multiple requests: → Read {lang}/claude-api/README.md + {lang}/claude-api/files-api.md

Managed Agents (server-managed stateful agents with workspace): → Read shared/managed-agents-overview.md + the rest of the shared/managed-agents-*.md files. 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 — see csharp/claude-api/README.md for details, or curl/managed-agents.md for raw HTTP reference.

Claude API (Full File Reference)

Read the language-specific Claude API source — {language}/claude-api/ for every SDK language, curl/examples.md for cURL:

1. {language}/claude-api/README.md — Read this first. Installation, quick start, common patterns, error handling.
2. shared/tool-use-concepts.md — Read when the user needs function calling, code execution, memory, or structured outputs. Covers conceptual foundations.
3. shared/agent-design.md — Read when designing an agent: bash vs. dedicated tools, programmatic tool calling, tool search/skills, context editing vs. compaction vs. memory, caching principles.
4. {language}/claude-api/tool-use.md — Read for language-specific tool use code examples (tool runner, manual loop, code execution, memory, structured outputs).
5. {language}/claude-api/streaming.md — Read when building chat UIs or interfaces that display responses incrementally.
6. {language}/claude-api/batches.md — Read when processing many requests offline (not latency-sensitive). Runs asynchronously at 50% cost.
7. {language}/claude-api/files-api.md — Read when sending the same file across multiple requests without re-uploading.
8. shared/prompt-caching.md — Read when adding or optimizing prompt caching. Covers prefix-stability design, breakpoint placement, and anti-patterns that silently invalidate cache.
9. shared/error-codes.md — Read when debugging HTTP errors or implementing error handling. Includes the per-SDK typed exception class table and the Go errors.As pattern.
10. shared/model-migration.md — Read when upgrading to newer models, replacing retired models, or translating budget_tokens / prefill patterns to the current API.
11. shared/live-sources.md — WebFetch URLs for fetching the latest official documentation.

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.

Note: For the Managed Agents file reference, see the ## Managed Agents (Beta) section above — it lists every shared/managed-agents-*.md file and the language-specific READMEs.

---

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.
• Fable 5 / Opus 4.8 / 4.7 thinking: Adaptive only. thinking: {type: "enabled", budget_tokens: N} returns 400 — budget_tokens is fully removed (along with temperature, top_p, top_k). Use thinking: {type: "adaptive"}. Opus 4.8 inherits this surface from 4.7 with no new breaking changes; Fable 5 adds one — an explicit thinking: {type: "disabled"} returns a 400 (accepted on 4.7/4.8); omit the param instead.
• Opus 4.6 / Sonnet 4.6 thinking: Use thinking: {type: "adaptive"} — do NOT use budget_tokens for new 4.6 code (deprecated on both Opus 4.6 and Sonnet 4.6; for gradual migration of existing code, see the transitional escape hatch in shared/model-migration.md — note this carve-out does not apply to Fable 5, Opus 4.7 or 4.8). For older models, budget_tokens must be less than max_tokens (minimum 1024). This will throw an error if you get it wrong.
• Prefill removed (Fable 5 and the 4.6/4.7/4.8 family): Assistant message prefills (last-assistant-turn prefills) return a 400 error on Fable 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.)
• Fable 5 refusal stop reason: Safety classifiers may decline a request — a successful HTTP 200 with stop_reason: "refusal" (pre-output: empty content, nothing billed; mid-stream: partial output billed — discard it). Check stop_reason before reading response.content[0], or you'll hit index errors on refused requests. To retry on another model, replay the history as-is — other models drop the refused model's thinking blocks from the prompt, unbilled; no stripping needed (and a fallback-credit redemption must echo the refused body exactly anyway, thinking blocks included). Fallbacks are opt-in — new {{FABLE_ID}} code should include the server-side fallbacks parameter by default so a refusal doesn't fail the request outright; see the {{FABLE_NAME}} section above.
• Fable 5 tokenizer: Same tokenizer as Opus 4.8 — token counts are roughly unchanged when migrating from Opus 4.7/4.8. Coming from Opus 4.6, Sonnet, Haiku, or older, token counts differ (the Opus 4.7 tokenizer uses ~1×–1.35× as many tokens) — re-measure by calling count_tokens once with each model and comparing input_tokens.
• 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).
• 128K output tokens: Fable 5, Opus 4.6, Opus 4.7, and Opus 4.8 support up to 128K max_tokens, but the SDKs require streaming for values that large to avoid HTTP timeouts. Use .stream() with .get_final_message() / .finalMessage().
• Tool call JSON parsing (Fable 5 and the 4.6/4.7/4.8 family): Fable 5, Opus 4.6, Opus 4.7, Opus 4.8, and Sonnet 4.6 may produce different JSON string escaping in tool call input fields (e.g., Unicode or forward-slash escaping). Always parse tool inputs with json.loads() / JSON.parse() — never do raw string matching on the serialized input.
• Structured outputs (all models): Use output_config: {format: {...}} instead of the deprecated output_format parameter on messages.create(). This is a general API change, not 4.6-specific.
• Don't reimplement SDK functionality: The SDK provides high-level helpers — use them instead of building from scratch. Specifically: use stream.finalMessage() instead of wrapping .on() events in new Promise(); use typed exception classes (Anthropic.RateLimitError, etc.) instead of string-matching error messages; use SDK types (Anthropic.MessageParam, Anthropic.Tool, Anthropic.Message, etc.) instead of redefining equivalent interfaces.
• Error handling — catch a chain, not one broad class. A single except APIStatusError / catch (AnthropicServiceException) / rescue APIError loses the distinction between retryable (429, ≥500, network) and non-retryable (400/404) failures. Write a most-specific-first chain — e.g. NotFoundError → RateLimitError → APIStatusError → APIConnectionError (or the Go equivalent: errors.As into *anthropic.Error then switch apierr.StatusCode { case 404: …; case 429: …; default: … }). Per-language class names and namespaces are in shared/error-codes.md.
• Don't research SDK types — write first. If a type name isn't shown in the documentation included in this skill, write the code file from the namespace/package tables in the language-specific doc and let the compiler's error point you to the right name. Do not spend turns on WebFetch, SDK-repo clones, or compiling-and-running a separate reflection program to discover type names before writing — produce the source file first, then fix what the compiler reports. A quick strings / jar tf / javap against the installed SDK is acceptable for locating names (it returns in seconds), but don't escalate beyond that. A file with a wrong type name is recoverable; a session spent on discovery with no file written is not.
• Bash and text editor tools are Anthropic-defined, schema-less. Declare {"type": "bash_20250124", "name": "bash"} / {"type": "text_editor_20250728", "name": "str_replace_based_edit_tool"} — no input_schema. A custom tool with your own schema named "bash" is a different tool. Handler paths and security checks are in shared/tool-use-concepts.md § Client-Side Tools.
• Advisor tool model pairing. The advisor tool's model must be at least as capable as the request's top-level model — e.g. executor claude-sonnet-4-6 → advisor claude-opus-4-8 or claude-opus-4-7. An invalid pair returns 400. Pairing table in shared/tool-use-concepts.md § Advisor. Availability: shared/platform-availability.md.
• Agent Skills ≠ Managed Agents. To have Claude generate a .pptx/.xlsx/etc. via Agent Skills, call client.beta.messages.create with container={"skills": [...]}, the code_execution_20250825 tool, and both code-execution-2025-08-25 + skills-2025-10-02 betas. Do not use client.beta.agents / sessions / environments here — those are the Managed Agents surface, not Agent Skills.
• MCP connector needs both halves. mcp_servers=[{type:"url", url, name}] alone is rejected as a validation error — also add tools=[{type:"mcp_toolset", mcp_server_name:<same name>}] with beta mcp-client-2025-11-20. Availability: shared/platform-availability.md.
• Context editing ≠ compaction. Context editing clears tool results and thinking blocks; compaction summarizes history. For context editing, use context_management.edits with type clear_tool_uses_20250919 (or clear_thinking_20251015) on client.beta.messages.* with beta context-management-2025-06-27 — not the compact_20260112 type or compact-2026-01-12 beta, which are compaction.
• inference_geo is a direct top-level request parameter — client.messages.create(..., inference_geo="us") / .inferenceGeo("us"). Do not put it in extra_body / putAdditionalBodyProperty. Supported on Opus 4.6 / Sonnet 4.6 and later; availability: shared/platform-availability.md. response.usage.inference_geo reports where inference ran.
• Fine-grained tool streaming is not a beta feature. Set eager_input_streaming: true on the tool definition and call the regular client.messages.stream(...). There is no beta header and no client.beta.* path.
• Cache diagnostics is beta. Use client.beta.messages.* with beta cache-diagnosis-2026-04-07. Pass diagnostics: {previous_message_id: null} on the first turn and diagnostics: {previous_message_id: <previous response id>} on subsequent turns; the result is on response.diagnostics. Availability: shared/platform-availability.md.
• Memory tool type is memory_20250818. Declare {"type": "memory_20250818", "name": "memory"}. Go uses the beta-namespace type {OfMemoryTool20250818: &anthropic.BetaMemoryTool20250818Param{}} on client.Beta.Messages.New; Python/TypeScript/Ruby/PHP/C# use the non-beta client.messages.create; Java has both a non-beta MemoryTool20250818 and a beta tool-runner path. Python/TypeScript provide BetaAbstractMemoryTool / betaMemoryTool helpers for implementing the backend.
• Use a model the feature actually supports. Some features are restricted to specific model tiers — fast mode is Opus 4.8 / 4.7 only (Opus 4.6 fast mode is deprecated — after removal it will fall back silently to standard speed), task budgets are Fable 5 / Opus 4.8 / 4.7 only, and the advisor tool requires a valid executor↔advisor pair. If the user's prompt names a model that the feature doesn't support, use a supported model instead and note the substitution in the output.
• Bedrock / Foundry: use the platform client class. For Bedrock use the …BedrockMantle… client (e.g. Python AnthropicBedrockMantle, Java BedrockMantleBackend) with anthropic.-prefixed model IDs; AnthropicBedrock/BedrockBackend without Mantle is the legacy path. For Foundry use AnthropicFoundry / FoundryBackend / AnthropicFoundryClient where the SDK supports it (C#, Java, PHP, Python, TypeScript); Go and Ruby have no Foundry client — Ruby's documented fallback is the first-party client with a custom base_url. Per-language table above.
• Don't define custom types for SDK data structures: The SDK exports types for all API objects. Use Anthropic.MessageParam for messages, Anthropic.Tool for tool definitions, Anthropic.ToolUseBlock / Anthropic.ToolResultBlockParam for tool results, Anthropic.Message for responses. Defining your own interface ChatMessage { role: string; content: unknown } duplicates what the SDK already provides and loses type safety.
• Report and document output: For tasks that produce reports, documents, or visualizations, the code execution sandbox has python-docx, python-pptx, matplotlib, pillow, and pypdf pre-installed. Claude can generate formatted files (DOCX, PDF, charts) and return them via the Files API — consider this for "report" or "document" type requests instead of plain stdout text.
• Server-tool errors don't raise. Web search and web fetch errors return HTTP 200 with a web_search_tool_result / web_fetch_tool_result block whose content is a single error object (e.g. {error_code: "max_uses_exceeded"}) — not a raised exception. For web search, a success content is a list; an error content is an object — branch on that before indexing.
• Code execution output block type: code_execution_20250825 returns bash_code_execution_tool_result (with .content.stdout), not the legacy bare code_execution_tool_result. Iterate response.content and match on the correct type.
• Tool search: never defer everything. The search tool itself must not have defer_loading: true, and at least one tool in tools must be non-deferred, or the API returns 400 All tools have defer_loading set.
• strict: true goes on the tool, not tool_choice. Putting strict on tool_choice does nothing; it's a sibling of name/description/input_schema on the tool definition itself.
• Parallel tool results go in ONE user message. Splitting tool_result blocks across multiple user messages silently trains Claude to stop making parallel calls. One assistant message of tool_use blocks → one user message of tool_result blocks.
• Citations + structured outputs are incompatible. Enabling citations: {enabled: true} on a document while also setting output_config.format returns a 400.
• Batch results are unordered. Match by custom_id, never by position in the results stream.
• Vertex model IDs have no prefix. Unlike Bedrock's anthropic.-prefixed IDs, Vertex takes the bare first-party ID for current-generation models (e.g. "{{OPUS_ID}}"); dated-snapshot models use an @ separator (e.g. claude-haiku-4-5@20251001).
• stop_details is null unless stop_reason == "refusal". For max_tokens, end_turn, etc., stop_details is null — guard before reading .category.
• WIF auth: unset ANTHROPIC_API_KEY, ANTHROPIC_AUTH_TOKEN, and ANTHROPIC_PROFILE. ANTHROPIC_API_KEY and ANTHROPIC_AUTH_TOKEN (even set to "") outrank Workload Identity Federation in the SDK's precedence chain and silently win; a set ANTHROPIC_PROFILE also wins (a missing named profile is an error, not a fall-through). unset them, don't blank them.