Data: Claude API reference — TypeScript

Claude API — TypeScript

Installation

npm install @anthropic-ai/sdk

Client Initialization

import Anthropic from "@anthropic-ai/sdk";

// Default — resolves credentials from the environment:
// ANTHROPIC_API_KEY, or ANTHROPIC_AUTH_TOKEN, or an `ant auth login` profile.
// Prefer this for local dev; don't hardcode a key.
const client = new Anthropic();

// Explicit API key (only when you must inject a specific key)
const client = new Anthropic({ apiKey: "your-api-key" });

Basic Message Request

const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  messages: [{ role: "user", content: "What is the capital of France?" }],
});
// response.content is ContentBlock[] — a discriminated union. Narrow by .type
// before accessing .text (TypeScript will error on content[0].text without this).
for (const block of response.content) {
  if (block.type === "text") {
    console.log(block.text);
  }
}

System Prompts

const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  system:
    "You are a helpful coding assistant. Always provide examples in Python.",
  messages: [{ role: "user", content: "How do I read a JSON file?" }],
});

Mid-conversation system messages (beta, model-gated)

For operator instructions that arrive mid-conversation (mode switches, injected state), append {role: "system", ...} to messages instead of editing top-level system — this preserves the cached prefix and carries operator authority. Must follow a user message; cannot be messages[0]. Unsupported models return a 400 (role 'system' is not supported on this model). See shared/prompt-caching.md for when to use this vs. top-level system.

// SDK types for role:"system" in messages are pending — pass the beta header
// directly until the SDK updates, then switch to client.beta.messages.create
// with betas: ["mid-conversation-system-2026-04-07"].
const response = await client.messages.create(
  {
    model: MODEL_ID, // must support mid-conversation system messages
    max_tokens: 16000,
    system: [
      { type: "text", text: STABLE_SYSTEM, cache_control: { type: "ephemeral" } },
    ],
    messages: [
      ...history,
      { role: "user", content: userMessage },
      // @ts-expect-error — role:"system" pending SDK types
      { role: "system", content: "Terse mode enabled — keep responses under 40 words." },
    ],
  },
  { headers: { "anthropic-beta": "mid-conversation-system-2026-04-07" } },
);

Vision (Images)

URL

const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  messages: [
    {
      role: "user",
      content: [
        {
          type: "image",
          source: { type: "url", url: "https://example.com/image.png" },
        },
        { type: "text", text: "Describe this image" },
      ],
    },
  ],
});

Base64

import fs from "fs";

const imageData = fs.readFileSync("image.png").toString("base64");

const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  messages: [
    {
      role: "user",
      content: [
        {
          type: "image",
          source: { type: "base64", media_type: "image/png", data: imageData },
        },
        { type: "text", text: "What's in this image?" },
      ],
    },
  ],
});

Prompt Caching

Caching is a prefix match — any byte change anywhere in the prefix invalidates everything after it. For placement patterns, architectural guidance (frozen system prompt, deterministic tool order, where to put volatile content), and the silent-invalidator audit checklist, read shared/prompt-caching.md.

Automatic Caching (Recommended)

Use top-level cache_control to automatically cache the last cacheable block in the request:

const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  cache_control: { type: "ephemeral" }, // auto-caches the last cacheable block
  system: "You are an expert on this large document...",
  messages: [{ role: "user", content: "Summarize the key points" }],
});

Manual Cache Control

For fine-grained control, add cache_control to specific content blocks:

const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  system: [
    {
      type: "text",
      text: "You are an expert on this large document...",
      cache_control: { type: "ephemeral" }, // default TTL is 5 minutes
    },
  ],
  messages: [{ role: "user", content: "Summarize the key points" }],
});

// With explicit TTL (time-to-live)
const response2 = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  system: [
    {
      type: "text",
      text: "You are an expert on this large document...",
      cache_control: { type: "ephemeral", ttl: "1h" }, // 1 hour TTL
    },
  ],
  messages: [{ role: "user", content: "Summarize the key points" }],
});

Verifying Cache Hits

console.log(response.usage.cache_creation_input_tokens); // tokens written to cache (~1.25x cost)
console.log(response.usage.cache_read_input_tokens);     // tokens served from cache (~0.1x cost)
console.log(response.usage.input_tokens);                // uncached tokens (full cost)

If cache_read_input_tokens is zero across repeated identical-prefix requests, a silent invalidator is at work — Date.now() or a UUID in the system prompt, non-deterministic key ordering, or a varying tool set. See shared/prompt-caching.md for the full audit table.

Extended Thinking

Opus 4.8, Opus 4.7, Opus 4.6, and Sonnet 4.6: Use adaptive thinking. budget_tokens is removed on Opus 4.8 and 4.7 (400 if sent); deprecated on Opus 4.6 and Sonnet 4.6. Older models: Use thinking: {type: "enabled", budget_tokens: N} (must be < max_tokens, min 1024).

// Opus 4.8 / 4.7 / 4.6: adaptive thinking (recommended)
const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  thinking: { type: "adaptive" },
  output_config: { effort: "high" }, // low | medium | high | max
  messages: [
    { role: "user", content: "Solve this math problem step by step..." },
  ],
});

for (const block of response.content) {
  if (block.type === "thinking") {
    console.log("Thinking:", block.thinking);
  } else if (block.type === "text") {
    console.log("Response:", block.text);
  }
}

Error Handling

Use the SDK's typed exception classes — never check error messages with string matching:

import Anthropic from "@anthropic-ai/sdk";

try {
  const response = await client.messages.create({...});
} catch (error) {
  if (error instanceof Anthropic.BadRequestError) {
    console.error("Bad request:", error.message);
  } else if (error instanceof Anthropic.AuthenticationError) {
    console.error("Invalid API key");
  } else if (error instanceof Anthropic.RateLimitError) {
    console.error("Rate limited - retry later");
  } else if (error instanceof Anthropic.APIError) {
    console.error(`API error ${error.status}:`, error.message);
  }
}

All classes extend Anthropic.APIError with a typed status field. Check from most specific to least specific. See shared/error-codes.md for the full error code reference.

Multi-Turn Conversations

The API is stateless — send the full conversation history each time. Use Anthropic.MessageParam[] to type the messages array:

const messages: Anthropic.MessageParam[] = [
  { role: "user", content: "My name is Alice." },
  { role: "assistant", content: "Hello Alice! Nice to meet you." },
  { role: "user", content: "What's my name?" },
];

const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  messages: messages,
});

Rules:

• Consecutive same-role messages are allowed — the API combines them into a single turn
• First message must be user
• Use SDK types (Anthropic.MessageParam, Anthropic.Message, Anthropic.Tool, etc.) for all API data structures — don't redefine equivalent interfaces

Compaction (long conversations)

Beta, Opus 4.8, Opus 4.7, Opus 4.6, and Sonnet 4.6. When conversations approach the 200K context window, compaction automatically summarizes earlier context server-side. The API returns a compaction block; you must pass it back on subsequent requests — append response.content, not just the text.

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();
const messages: Anthropic.Beta.BetaMessageParam[] = [];

async function chat(userMessage: string): Promise<string> {
  messages.push({ role: "user", content: userMessage });

  const response = await client.beta.messages.create({
    betas: ["compact-2026-01-12"],
    model: "{{OPUS_ID}}",
    max_tokens: 16000,
    messages,
    context_management: {
      edits: [{ type: "compact_20260112" }],
    },
  });

  // Append full content — compaction blocks must be preserved
  messages.push({ role: "assistant", content: response.content });

  const textBlock = response.content.find(
    (b): b is Anthropic.Beta.BetaTextBlock => b.type === "text",
  );
  return textBlock?.text ?? "";
}

// Compaction triggers automatically when context grows large
console.log(await chat("Help me build a Python web scraper"));
console.log(await chat("Add support for JavaScript-rendered pages"));
console.log(await chat("Now add rate limiting and error handling"));

Stop Reasons

The stop_reason field in the response indicates why the model stopped generating:

Structured Stop Details

When stop_reason is "refusal", the response includes a stop_details object with structured information about the refusal:

if (response.stop_reason === "refusal" && response.stop_details) {
  console.log(`Category: ${response.stop_details.category}`); // "cyber" | "bio" | null
  console.log(`Explanation: ${response.stop_details.explanation}`);
}

Cost Optimization Strategies

1. Use Prompt Caching for Repeated Context

// Automatic caching (simplest — caches the last cacheable block)
const response = await client.messages.create({
  model: "{{OPUS_ID}}",
  max_tokens: 16000,
  cache_control: { type: "ephemeral" },
  system: largeDocumentText, // e.g., 50KB of context
  messages: [{ role: "user", content: "Summarize the key points" }],
});

// First request: full cost
// Subsequent requests: ~90% cheaper for cached portion

2. Use Token Counting Before Requests

const countResponse = await client.messages.countTokens({
  model: "{{OPUS_ID}}",
  messages: messages,
  system: system,
});

const estimatedInputCost = countResponse.input_tokens * 0.000005; // $5/1M tokens
console.log(`Estimated input cost: $${estimatedInputCost.toFixed(4)}`);