Claude API

What does your application need?

0. Are you deploying through Amazon Bedrock, Google Vertex AI, or Microsoft Foundry?
    Yes -> Claude API (+ tool use for agents) - Managed Agents is 1P only.
   No -> continue.

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)

# Message Batches API — Python

The Batches API (`POST /v1/messages/batches`) processes Messages API requests asynchronously at 50% of standard prices.

## Key Facts

- Up to 100,000 requests or 256 MB per batch
- Most batches complete within 1 hour; maximum 24 hours
- Results available for 29 days after creation
- 50% cost reduction on all token usage
- All Messages API features supported (vision, tools, caching, etc.)

---

## Create a Batch

```python
import anthropic
from anthropic.types.message_create_params import MessageCreateParamsNonStreaming
from anthropic.types.messages.batch_create_params import Request

client = anthropic.Anthropic()

message_batch = client.messages.batches.create(
    requests=[
        Request(
            custom_id="request-1",
            params=MessageCreateParamsNonStreaming(
                model="claude-opus-4-7",
                max_tokens=16000,
                messages=[{"role": "user", "content": "Summarize climate change impacts"}]
            )
        ),
        Request(
            custom_id="request-2",
            params=MessageCreateParamsNonStreaming(
                model="claude-opus-4-7",
                max_tokens=16000,
                messages=[{"role": "user", "content": "Explain quantum computing basics"}]
            )
        ),
    ]
)

print(f"Batch ID: {message_batch.id}")
print(f"Status: {message_batch.processing_status}")

import time

while True:
    batch = client.messages.batches.retrieve(message_batch.id)
    if batch.processing_status == "ended":
        break
    print(f"Status: {batch.processing_status}, processing: {batch.request_counts.processing}")
    time.sleep(60)

print("Batch complete!")
print(f"Succeeded: {batch.request_counts.succeeded}")
print(f"Errored: {batch.request_counts.errored}")

for result in client.messages.batches.results(message_batch.id):
    match result.result.type:
        case "succeeded":
            msg = result.result.message
            text = next((b.text for b in msg.content if b.type == "text"), "")
            print(f"[{result.custom_id}] {text[:100]}")
        case "errored":
            if result.result.error.type == "invalid_request":
                print(f"[{result.custom_id}] Validation error - fix request and retry")
            else:
                print(f"[{result.custom_id}] Server error - safe to retry")
        case "canceled":
            print(f"[{result.custom_id}] Canceled")
        case "expired":
            print(f"[{result.custom_id}] Expired - resubmit")

cancelled = client.messages.batches.cancel(message_batch.id)
print(f"Status: {cancelled.processing_status}")  # "canceling"

shared_system = [
    {"type": "text", "text": "You are a literary analyst."},
    {
        "type": "text",
        "text": large_document_text,  # Shared across all requests
        "cache_control": {"type": "ephemeral"}
    }
]

message_batch = client.messages.batches.create(
    requests=[
        Request(
            custom_id=f"analysis-{i}",
            params=MessageCreateParamsNonStreaming(
                model="claude-opus-4-7",
                max_tokens=16000,
                system=shared_system,
                messages=[{"role": "user", "content": question}]
            )
        )
        for i, question in enumerate(questions)
    ]
)

import anthropic
import time
from anthropic.types.message_create_params import MessageCreateParamsNonStreaming
from anthropic.types.messages.batch_create_params import Request

client = anthropic.Anthropic()

# 1. Prepare requests
items_to_classify = [
    "The product quality is excellent!",
    "Terrible customer service, never again.",
    "It's okay, nothing special.",
]

requests = [
    Request(
        custom_id=f"classify-{i}",
        params=MessageCreateParamsNonStreaming(
            model="claude-haiku-4-5",
            max_tokens=50,
            messages=[{
                "role": "user",
                "content": f"Classify as positive/negative/neutral (one word): {text}"
            }]
        )
    )
    for i, text in enumerate(items_to_classify)
]

# 2. Create batch
batch = client.messages.batches.create(requests=requests)
print(f"Created batch: {batch.id}")

# 3. Wait for completion
while True:
    batch = client.messages.batches.retrieve(batch.id)
    if batch.processing_status == "ended":
        break
    time.sleep(10)

# 4. Collect results
results = {}
for result in client.messages.batches.results(batch.id):
    if result.result.type == "succeeded":
        msg = result.result.message
        results[result.custom_id] = next((b.text for b in msg.content if b.type == "text"), "")

for custom_id, classification in sorted(results.items()):
    print(f"{custom_id}: {classification}")

### python/claude-api/files-api.md

[Download python/claude-api/files-api.md](/skills/claude-api/python/claude-api/files-api.md)

```markdown
# Files API — Python

The Files API uploads files for use in Messages API requests. Reference files via `file_id` in content blocks, avoiding re-uploads across multiple API calls.

**Beta:** Pass `betas=["files-api-2025-04-14"]` in your API calls (the SDK sets the required header automatically).

## Key Facts

- Maximum file size: 500 MB
- Total storage: 100 GB per organization
- Files persist until deleted
- File operations (upload, list, delete) are free; content used in messages is billed as input tokens
- Not available on Amazon Bedrock or Google Vertex AI

---

## Upload a File

```python
import anthropic

client = anthropic.Anthropic()

uploaded = client.beta.files.upload(
    file=("report.pdf", open("report.pdf", "rb"), "application/pdf"),
)
print(f"File ID: {uploaded.id}")
print(f"Size: {uploaded.size_bytes} bytes")

response = client.beta.messages.create(
    model="claude-opus-4-7",
    max_tokens=16000,
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Summarize the key findings in this report."},
            {
                "type": "document",
                "source": {"type": "file", "file_id": uploaded.id},
                "title": "Q4 Report",           # optional
                "citations": {"enabled": True}   # optional, enables citations
            }
        ]
    }],
    betas=["files-api-2025-04-14"],
)
for block in response.content:
    if block.type == "text":
        print(block.text)

image_file = client.beta.files.upload(
    file=("photo.png", open("photo.png", "rb"), "image/png"),
)

response = client.beta.messages.create(
    model="claude-opus-4-7",
    max_tokens=16000,
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "What's in this image?"},
            {
                "type": "image",
                "source": {"type": "file", "file_id": image_file.id}
            }
        ]
    }],
    betas=["files-api-2025-04-14"],
)

files = client.beta.files.list()
for f in files.data:
    print(f"{f.id}: {f.filename} ({f.size_bytes} bytes)")

file_info = client.beta.files.retrieve_metadata("file_011CNha8iCJcU1wXNR6q4V8w")
print(f"Filename: {file_info.filename}")
print(f"MIME type: {file_info.mime_type}")

client.beta.files.delete("file_011CNha8iCJcU1wXNR6q4V8w")

file_content = client.beta.files.download("file_011CNha8iCJcU1wXNR6q4V8w")
file_content.write_to_file("output.txt")

import anthropic

client = anthropic.Anthropic()

# 1. Upload once
uploaded = client.beta.files.upload(
    file=("contract.pdf", open("contract.pdf", "rb"), "application/pdf"),
)
print(f"Uploaded: {uploaded.id}")

# 2. Ask multiple questions using the same file_id
questions = [
    "What are the key terms and conditions?",
    "What is the termination clause?",
    "Summarize the payment schedule.",
]

for question in questions:
    response = client.beta.messages.create(
        model="claude-opus-4-7",
        max_tokens=16000,
        messages=[{
            "role": "user",
            "content": [
                {"type": "text", "text": question},
                {
                    "type": "document",
                    "source": {"type": "file", "file_id": uploaded.id}
                }
            ]
        }],
        betas=["files-api-2025-04-14"],
    )
    print(f"\nQ: {question}")
    text = next((b.text for b in response.content if b.type == "text"), "")
    print(f"A: {text[:200]}")

# 3. Clean up when done
client.beta.files.delete(uploaded.id)

### python/claude-api/streaming.md

[Download python/claude-api/streaming.md](/skills/claude-api/python/claude-api/streaming.md)

```markdown
# Streaming — Python

## Quick Start

```python
with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=64000,
    messages=[{"role": "user", "content": "Write a story"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

async with async_client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=64000,
    messages=[{"role": "user", "content": "Write a story"}]
) as stream:
    async for text in stream.text_stream:
        print(text, end="", flush=True)

with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=64000,
    thinking={"type": "adaptive"},
    messages=[{"role": "user", "content": "Analyze this problem"}]
) as stream:
    for event in stream:
        if event.type == "content_block_start":
            if event.content_block.type == "thinking":
                print("\n[Thinking...]")
            elif event.content_block.type == "text":
                print("\n[Response:]")

        elif event.type == "content_block_delta":
            if event.delta.type == "thinking_delta":
                print(event.delta.thinking, end="", flush=True)
            elif event.delta.type == "text_delta":
                print(event.delta.text, end="", flush=True)

with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=64000,
    tools=tools,
    messages=messages
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

    response = stream.get_final_message()
    # Continue with tool execution if response.stop_reason == "tool_use"

with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=64000,
    messages=[{"role": "user", "content": "Hello"}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

    # Get full message after streaming
    final_message = stream.get_final_message()
    print(f"\n\nTokens used: {final_message.usage.output_tokens}")

def stream_with_progress(client, **kwargs):
    """Stream a response with progress updates."""
    total_tokens = 0
    content_parts = []

    with client.messages.stream(**kwargs) as stream:
        for event in stream:
            if event.type == "content_block_delta":
                if event.delta.type == "text_delta":
                    text = event.delta.text
                    content_parts.append(text)
                    print(text, end="", flush=True)

            elif event.type == "message_delta":
                if event.usage and event.usage.output_tokens is not None:
                    total_tokens = event.usage.output_tokens

        final_message = stream.get_final_message()

    print(f"\n\n[Tokens used: {total_tokens}]")
    return "".join(content_parts)

try:
    with client.messages.stream(
        model="claude-opus-4-7",
        max_tokens=64000,
        messages=[{"role": "user", "content": "Write a story"}]
    ) as stream:
        for text in stream.text_stream:
            print(text, end="", flush=True)
except anthropic.APIConnectionError:
    print("\nConnection lost. Please retry.")
except anthropic.RateLimitError:
    print("\nRate limited. Please wait and retry.")
except anthropic.APIStatusError as e:
    print(f"\nAPI error: {e.status_code}")

### python/claude-api/tool-use.md

[Download python/claude-api/tool-use.md](/skills/claude-api/python/claude-api/tool-use.md)

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### python/managed-agents/README.md

[Download python/managed-agents/README.md](/skills/claude-api/python/managed-agents/README.md)

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### ruby/claude-api.md

[Download ruby/claude-api.md](/skills/claude-api/ruby/claude-api.md)

```markdown
# Claude API — Ruby

> **Note:** The Ruby SDK supports the Claude API. A tool runner is available in beta via `client.beta.messages.tool_runner()`. Agent SDK is not yet available for Ruby.

## Installation

```bash
gem install anthropic

require "anthropic"

# Default (uses ANTHROPIC_API_KEY env var)
client = Anthropic::Client.new

# Explicit API key
client = Anthropic::Client.new(api_key: "your-api-key")

message = client.messages.create(
  model: :"claude-opus-4-7",
  max_tokens: 16000,
  messages: [
    { role: "user", content: "What is the capital of France?" }
  ]
)
# content is an array of polymorphic block objects (TextBlock, ThinkingBlock,
# ToolUseBlock, ...). .type is a Symbol — compare with :text, not "text".
# .text raises NoMethodError on non-TextBlock entries.
message.content.each do |block|
  puts block.text if block.type == :text
end

stream = client.messages.stream(
  model: :"claude-opus-4-7",
  max_tokens: 64000,
  messages: [{ role: "user", content: "Write a haiku" }]
)

stream.text.each { |text| print(text) }

class GetWeatherInput < Anthropic::BaseModel
  required :location, String, doc: "City and state, e.g. San Francisco, CA"
end

class GetWeather < Anthropic::BaseTool
  doc "Get the current weather for a location"

  input_schema GetWeatherInput

  def call(input)
    "The weather in #{input.location} is sunny and 72°F."
  end
end

client.beta.messages.tool_runner(
  model: :"claude-opus-4-7",
  max_tokens: 16000,
  tools: [GetWeather.new],
  messages: [{ role: "user", content: "What's the weather in San Francisco?" }]
).each_message do |message|
  puts message.content
end

message = client.messages.create(
  model: :"claude-opus-4-7",
  max_tokens: 16000,
  system_: [
    { type: "text", text: long_system_prompt, cache_control: { type: "ephemeral" } }
  ],
  messages: [{ role: "user", content: "Summarize the key points" }]
)

### ruby/managed-agents/README.md

[Download ruby/managed-agents/README.md](/skills/claude-api/ruby/managed-agents/README.md)

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### shared/agent-design.md

[Download shared/agent-design.md](/skills/claude-api/shared/agent-design.md)

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### shared/error-codes.md

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### shared/live-sources.md

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### shared/managed-agents-api-reference.md

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### shared/managed-agents-client-patterns.md

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### shared/managed-agents-core.md

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### shared/managed-agents-environments.md

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### shared/managed-agents-events.md

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### shared/managed-agents-memory.md

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### shared/managed-agents-multiagent.md

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### shared/managed-agents-onboarding.md

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### shared/managed-agents-outcomes.md

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### shared/managed-agents-overview.md

[Download shared/managed-agents-overview.md](/skills/claude-api/shared/managed-agents-overview.md)

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### shared/managed-agents-tools.md

[Download shared/managed-agents-tools.md](/skills/claude-api/shared/managed-agents-tools.md)

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### shared/managed-agents-webhooks.md

[Download shared/managed-agents-webhooks.md](/skills/claude-api/shared/managed-agents-webhooks.md)

```markdown
# Managed Agents — Webhooks

Anthropic can POST to your HTTPS endpoint when a Managed Agents resource changes state — an alternative to holding an SSE stream or polling. Payloads are **thin** (event type + resource IDs only); on receipt, fetch the resource for current state. Every delivery is HMAC-signed.

> **Direction matters.** This page covers *Anthropic → you* notifications about session/vault state. It does **not** cover *third-party → you* webhooks that *trigger* a session (e.g. a GitHub push handler that calls `sessions.create()`) — that's ordinary application code on your side with no Anthropic-specific wire format.

---

## Register an endpoint (Console only)

Console → **Manage → Webhooks**. There is no programmatic endpoint-management API yet. Secret rotation is supported from the same page.

| Field | Constraint |
|---|---|
| URL | HTTPS on port 443, publicly resolvable hostname |
| Event types | Subscribe per `data.type` — you only receive subscribed types (plus test events) |
| Signing secret | `whsec_`-prefixed, 32 bytes, **shown once at creation** — store it |

---

## Verify the signature

Every delivery is HMAC-signed. **Use the SDK's `client.beta.webhooks.unwrap()`** — it verifies the signature, rejects payloads more than ~5 minutes old, and returns the parsed event. It reads the `whsec_` secret from `ANTHROPIC_WEBHOOK_SIGNING_KEY`.

```python
import anthropic
from flask import Flask, request

client = anthropic.Anthropic()  # reads ANTHROPIC_WEBHOOK_SIGNING_KEY from env
app = Flask(__name__)


@app.route("/webhook", methods=["POST"])
def webhook():
    try:
        event = client.beta.webhooks.unwrap(
            request.get_data(as_text=True),
            headers=dict(request.headers),
        )
    except Exception:
        return "invalid signature", 400

    if event.id in seen_event_ids:  # dedupe retries — id is per-event, not per-delivery
        return "", 204
    seen_event_ids.add(event.id)

    match event.data.type:
        case "session.status_idled":
            session = client.beta.sessions.retrieve(event.data.id)
            notify_user(session)
        case "vault_credential.refresh_failed":
            alert_oncall(event.data.id)

    return "", 204

{
  "type": "event",
  "id": "event_01ABC...",
  "created_at": "2026-03-18T14:05:22Z",
  "data": {
    "type": "session.status_idled",
    "id": "session_01XYZ...",
    "organization_id": "8a3d2f1e-...",
    "workspace_id": "c7b0e4d9-..."
  }
}

### shared/model-migration.md

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### shared/models.md

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### shared/prompt-caching.md

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### shared/tool-use-concepts.md

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### typescript/claude-api/README.md

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### typescript/claude-api/batches.md

[Download typescript/claude-api/batches.md](/skills/claude-api/typescript/claude-api/batches.md)

```markdown
# Message Batches API — TypeScript

The Batches API (`POST /v1/messages/batches`) processes Messages API requests asynchronously at 50% of standard prices.

## Key Facts

- Up to 100,000 requests or 256 MB per batch
- Most batches complete within 1 hour; maximum 24 hours
- Results available for 29 days after creation
- 50% cost reduction on all token usage
- All Messages API features supported (vision, tools, caching, etc.)

---

## Create a Batch

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

const client = new Anthropic();

const messageBatch = await client.messages.batches.create({
  requests: [
    {
      custom_id: "request-1",
      params: {
        model: "claude-opus-4-7",
        max_tokens: 16000,
        messages: [
          { role: "user", content: "Summarize climate change impacts" },
        ],
      },
    },
    {
      custom_id: "request-2",
      params: {
        model: "claude-opus-4-7",
        max_tokens: 16000,
        messages: [
          { role: "user", content: "Explain quantum computing basics" },
        ],
      },
    },
  ],
});

console.log(`Batch ID: ${messageBatch.id}`);
console.log(`Status: ${messageBatch.processing_status}`);

let batch;
while (true) {
  batch = await client.messages.batches.retrieve(messageBatch.id);
  if (batch.processing_status === "ended") break;
  console.log(
    `Status: ${batch.processing_status}, processing: ${batch.request_counts.processing}`,
  );
  await new Promise((resolve) => setTimeout(resolve, 60_000));
}

console.log("Batch complete!");
console.log(`Succeeded: ${batch.request_counts.succeeded}`);
console.log(`Errored: ${batch.request_counts.errored}`);

for await (const result of await client.messages.batches.results(
  messageBatch.id,
)) {
  switch (result.result.type) {
    case "succeeded":
      console.log(
        `[${result.custom_id}] ${result.result.message.content[0].text.slice(0, 100)}`,
      );
      break;
    case "errored":
      if (result.result.error.type === "invalid_request") {
        console.log(`[${result.custom_id}] Validation error - fix and retry`);
      } else {
        console.log(`[${result.custom_id}] Server error - safe to retry`);
      }
      break;
    case "expired":
      console.log(`[${result.custom_id}] Expired - resubmit`);
      break;
  }
}

const cancelled = await client.messages.batches.cancel(messageBatch.id);
console.log(`Status: ${cancelled.processing_status}`); // "canceling"

### typescript/claude-api/files-api.md

[Download typescript/claude-api/files-api.md](/skills/claude-api/typescript/claude-api/files-api.md)

```markdown
# Files API — TypeScript

The Files API uploads files for use in Messages API requests. Reference files via `file_id` in content blocks, avoiding re-uploads across multiple API calls.

**Beta:** Pass `betas: ["files-api-2025-04-14"]` in your API calls (the SDK sets the required header automatically).

## Key Facts

- Maximum file size: 500 MB
- Total storage: 100 GB per organization
- Files persist until deleted
- File operations (upload, list, delete) are free; content used in messages is billed as input tokens
- Not available on Amazon Bedrock or Google Vertex AI

---

## Upload a File

```typescript
import Anthropic, { toFile } from "@anthropic-ai/sdk";
import fs from "fs";

const client = new Anthropic();

const uploaded = await client.beta.files.upload({
  file: await toFile(fs.createReadStream("report.pdf"), undefined, {
    type: "application/pdf",
  }),
  betas: ["files-api-2025-04-14"],
});

console.log(`File ID: ${uploaded.id}`);
console.log(`Size: ${uploaded.size_bytes} bytes`);

const response = await client.beta.messages.create({
  model: "claude-opus-4-7",
  max_tokens: 16000,
  messages: [
    {
      role: "user",
      content: [
        { type: "text", text: "Summarize the key findings in this report." },
        {
          type: "document",
          source: { type: "file", file_id: uploaded.id },
          title: "Q4 Report",
          citations: { enabled: true },
        },
      ],
    },
  ],
  betas: ["files-api-2025-04-14"],
});

console.log(response.content[0].text);

const files = await client.beta.files.list({
  betas: ["files-api-2025-04-14"],
});
for (const f of files.data) {
  console.log(`${f.id}: ${f.filename} (${f.size_bytes} bytes)`);
}

await client.beta.files.delete("file_011CNha8iCJcU1wXNR6q4V8w", {
  betas: ["files-api-2025-04-14"],
});

const response = await client.beta.files.download(
  "file_011CNha8iCJcU1wXNR6q4V8w",
  { betas: ["files-api-2025-04-14"] },
);
const content = Buffer.from(await response.arrayBuffer());
await fs.promises.writeFile("output.txt", content);

### typescript/claude-api/streaming.md

[Download typescript/claude-api/streaming.md](/skills/claude-api/typescript/claude-api/streaming.md)

```markdown
# Streaming — TypeScript

## Quick Start

```typescript
const stream = client.messages.stream({
  model: "claude-opus-4-7",
  max_tokens: 64000,
  messages: [{ role: "user", content: "Write a story" }],
});

for await (const event of stream) {
  if (
    event.type === "content_block_delta" &&
    event.delta.type === "text_delta"
  ) {
    process.stdout.write(event.delta.text);
  }
}

const stream = client.messages.stream({
  model: "claude-opus-4-7",
  max_tokens: 64000,
  thinking: { type: "adaptive" },
  messages: [{ role: "user", content: "Analyze this problem" }],
});

for await (const event of stream) {
  switch (event.type) {
    case "content_block_start":
      switch (event.content_block.type) {
        case "thinking":
          console.log("\n[Thinking...]");
          break;
        case "text":
          console.log("\n[Response:]");
          break;
      }
      break;
    case "content_block_delta":
      switch (event.delta.type) {
        case "thinking_delta":
          process.stdout.write(event.delta.thinking);
          break;
        case "text_delta":
          process.stdout.write(event.delta.text);
          break;
      }
      break;
  }
}

import Anthropic from "@anthropic-ai/sdk";
import { betaZodTool } from "@anthropic-ai/sdk/helpers/beta/zod";
import { z } from "zod";

const client = new Anthropic();

const getWeather = betaZodTool({
  name: "get_weather",
  description: "Get current weather for a location",
  inputSchema: z.object({
    location: z.string().describe("City and state, e.g., San Francisco, CA"),
  }),
  run: async ({ location }) => `72°F and sunny in ${location}`,
});

const runner = client.beta.messages.toolRunner({
  model: "claude-opus-4-7",
  max_tokens: 64000,
  tools: [getWeather],
  messages: [
    { role: "user", content: "What's the weather in Paris and London?" },
  ],
  stream: true,
});

// Outer loop: each tool runner iteration
for await (const messageStream of runner) {
  // Inner loop: stream events for this iteration
  for await (const event of messageStream) {
    switch (event.type) {
      case "content_block_delta":
        switch (event.delta.type) {
          case "text_delta":
            process.stdout.write(event.delta.text);
            break;
          case "input_json_delta":
            // Tool input being streamed
            break;
        }
        break;
    }
  }
}

const stream = client.messages.stream({
  model: "claude-opus-4-7",
  max_tokens: 64000,
  messages: [{ role: "user", content: "Hello" }],
});

for await (const event of stream) {
  // Process events...
}

const finalMessage = await stream.finalMessage();
console.log(`Tokens used: ${finalMessage.usage.output_tokens}`);

event: message_start
data: {"type":"message_start","message":{"id":"msg_...","type":"message",...}}

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn"},"usage":{"output_tokens":12}}

event: message_stop
data: {"type":"message_stop"}

### typescript/claude-api/tool-use.md

[Download typescript/claude-api/tool-use.md](/skills/claude-api/typescript/claude-api/tool-use.md)

_Binary resource_

### typescript/managed-agents/README.md

[Download typescript/managed-agents/README.md](/skills/claude-api/typescript/managed-agents/README.md)

_Binary resource_

## See in GitHub

[See in GitHub](https://github.com/anthropics/skills/tree/main/claude-api)