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Guides Claude in building LLM-powered applications using the Anthropic SDK, covering language detection, API surface selection (Claude API vs Managed Agents), model defaults, thinking/effort configuration, and language-specific documentation reading
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Instructions for creating dashboard artifacts with KPI tiles, chart specifications, and breakdown tables from the built-in template
Instructions for creating sortable and filterable data-table artifacts from the built-in template
Instructions for creating explainer artifacts with numbered-step or section walkthrough structures from the built-in template
Instructions for creating long-form report artifacts with a masthead, table of contents, structured sections, and optional appendix from the built-in template
Guided setup and customization workflow for auto mode environment context, optional rule carve-outs, and settings updates
Shared /code-review finder-angle block covering line-by-line diff scanning, removed-behavior auditing, and cross-file tracing
| name | building-llm-powered-applications-with-claude |
| description | Guides Claude in building LLM-powered applications using the Anthropic SDK, covering language detection, API surface selection (Claude API vs Managed Agents), model defaults, thinking/effort configuration, and language-specific documentation reading |
| metadata | {"originalName":"Skill: Building LLM-powered applications with Claude","ccVersion":"2.1.203","sourceUrl":"https://github.com/Piebald-AI/claude-code-system-prompts/blob/main/system-prompts/skill-building-llm-powered-applications-with-claude.md","source":{"owner":"Piebald-AI","repo":"claude-code-system-prompts","ref":"main","path":"system-prompts/skill-building-llm-powered-applications-with-claude.md"}} |
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.
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.
When the user asks you to add, modify, or implement a Claude feature, your code must call Claude through one of:
anthropic, @anthropic-ai/sdk, com.anthropic.*, etc.). This is the default whenever a supported SDK exists for the project.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.
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
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 / Sonnet 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, 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. |
The {lang}/ files in this skill are authoritative over recalled patterns.
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. |
Before reading code examples, determine which language the user is working in:
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/If multiple languages detected (e.g., both Python and TypeScript files):
If language can't be inferred (empty project, no source files, or unsupported language):
If unsupported language detected (Rust, Swift, C++, Elixir, etc.):
curl/ and note that community SDKs may existIf user needs cURL/raw HTTP examples, read from curl/.
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.
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.mdfor client setup. For per-feature availability on Claude Platform on AWS, Amazon Bedrock, Google Vertex AI, and Microsoft Foundry, seeshared/platform-availability.md— that table is the single source of truth in this skill; do not infer availability from anywhere else.
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 viaclient.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 overPOST /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 callquery(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.
Before choosing the agent tier, check all four criteria:
If the answer is "no" to any of these, stay at a simpler tier (single call or workflow).
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.
| 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 5 | claude-sonnet-5 | 1M | $3.00 ($2.00 intro through 2026-08-31) | $15.00 ($10.00 intro) |
| 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. 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.
{{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; everything below also applies to {{MYTHOS_NAME}} ({{MYTHOS_ID}}, Project Glasswing — same capabilities, pricing, and API surface; successor to the invitation-only claude-mythos-preview). 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 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).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); details in shared/model-migration.md.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 {{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 — elsewhere, incl. Bedrock/Vertex/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; code examples in {lang}/claude-api/README.md § Refusal Fallbacks.400 invalid_request_error.shared/model-migration.md → Migrating to {{FABLE_NAME}} → Behavioral shifts (prompt-tunable) for the recommended prompt snippets.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.
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:
ant CLI: just run it. The zero-arg client constructor and every ant … subcommand pick up the profile automatically — no env var needed.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.
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 | {type: "adaptive"} or omit; explicit {type: "disabled"} returns 400 — omit the param instead | Runs adaptive (thinking is always on) | Removed — {type: "enabled", budget_tokens: N} returns 400 | Removed — 400 | low/medium/high/xhigh/max |
| 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) | Allowed | low/medium/high/max (xhigh arrived with Opus 4.7) |
| Older (Sonnet 4.5, Haiku 4.5, …) — only if explicitly requested | {type: "enabled", budget_tokens: N} | No thinking | Required for thinking; must be less than max_tokens, minimum 1024 — errors otherwise | Allowed | effort works on Opus 4.5 (low/medium/high only — no xhigh/max); errors on Sonnet 4.5 / Haiku 4.5 |
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).
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)."omitted" by default on Fable 5 / Mythos 5 / Opus 4.8 / 4.7 / Sonnet 5: display: "summarized" returns a readable summary of the reasoning; "omitted" (the default on all five — a silent change from Opus 4.6 and Sonnet 4.6, where it was "summarized") streams thinking blocks with empty text. display controls visibility only — thinking happens and is billed the same under every setting; the raw chain of thought is never exposed on any model. If you stream reasoning to users, the default looks like a long pause before output — set thinking: {type: "adaptive", display: "summarized"} explicitly. (Independent of display, echo thinking blocks back unchanged when continuing on the same model; other models silently ignore them — see the migration guide.)budget_tokens: always use Fable 5, Opus 4.8, 4.7, or 4.6 with thinking: {type: "adaptive"} — the fixed thinking-token-budget concept is deprecated and 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 just because the user mentions it. Gradual-migration carve-out: budget_tokens is still functional on Opus 4.6 and Sonnet 4.6 only, as a transitional escape hatch for existing code that needs a hard token ceiling before you've tuned effort — see shared/model-migration.md → Transitional escape hatch. It is fully removed on Fable 5, Opus 4.7/4.8, and Sonnet 5.Beta, Fable 5, Opus 4.8, Opus 4.7, Opus 4.6, Sonnet 5, and Sonnet 4.6. For long-running conversations that may exceed the 1M context window, enable server-side compaction. The API automatically summarizes earlier context when it approaches the trigger threshold (default: 150K tokens). Requires beta header compact-2026-01-12.
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.
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).
Research preview, Opus 4.8 / 4.7 only. Opus 4.7 fast mode is deprecated — after removal, speed: "fast" on 4.7 returns an error. Opus 4.8 is the durable fast-capable tier. Fast mode runs the same model at up to 2.5x higher output tokens per second, at premium pricing. Three things are required on every request: use the beta messages endpoint (client.beta.messages.…), pass the beta flag fast-mode-2026-02-01, and set speed: "fast" as a top-level request parameter (not a header, not in extra_body).
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.
Beta, Fable 5 / Sonnet 5 / Opus 4.8 / 4.7. A task budget gives Claude a token ceiling for an agentic loop so it paces itself and finishes gracefully instead of being cut off — 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-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.
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.
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.
| 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.
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. "{{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: "…") |
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.
{{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 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. 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 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-side tools run on Anthropic's infrastructure — no client-side execution loop. Declare in tools; results arrive as content blocks in the same response. No beta header unless noted. Prefer the latest type variant your model supports. The _20260209 web search / web fetch variants below (dynamic filtering) require Opus 4.8/4.7/4.6, Sonnet 5, or Sonnet 4.6; the basic variants for older models are listed after the table.
| 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.
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 (returns a 400).
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.
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).
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.
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.
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 / 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
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 (prefix-stability design, breakpoint placement, anti-patterns that silently invalidate cache) + {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 (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
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).
Use WebFetch to get the latest documentation when:
Live documentation URLs are in shared/live-sources.md.
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.)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).max_tokens, but the SDKs require streaming for values that large to avoid HTTP timeouts. Use .stream() with .get_final_message() / .finalMessage().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.output_config: {format: {...}} instead of the deprecated output_format parameter on messages.create(). This is a general API change, not 4.6-specific.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.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.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.{"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.model must be at least as capable as the request's top-level model — e.g. executor claude-sonnet-5 → 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..pptx/.xlsx/etc. via Agent Skills, call client.beta.messages.create with container={"skills": [...]}, the code_execution_20260521 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_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.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.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.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_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.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.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.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_20260521 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.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.