Streaming Messages

Streaming Messages

When creating a Message, you can set "stream": true to incrementally stream the response using server-sent events (SSE).

Streaming with SDKs

Our Python and TypeScript SDKs offer multiple ways of streaming. The Python SDK allows both sync and async streams. See the documentation in each SDK for details.

client = anthropic.Anthropic()

with client.messages.stream(
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
    model="claude-sonnet-4-5",
) as stream:
  for text in stream.text_stream:
      print(text, end="", flush=True)
```

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

const client = new Anthropic();

await client.messages.stream({
    messages: [{role: 'user', content: "Hello"}],
    model: 'claude-sonnet-4-5',
    max_tokens: 1024,
}).on('text', (text) => {
    console.log(text);
});
```

Event types

Each server-sent event includes a named event type and associated JSON data. Each event will use an SSE event name (e.g. event: message_stop), and include the matching event type in its data.

Each stream uses the following event flow:

1. message_start: contains a Message object with empty content.
2. A series of content blocks, each of which have a content_block_start, one or more content_block_delta events, and a content_block_stop event. Each content block will have an index that corresponds to its index in the final Message content array.
3. One or more message_delta events, indicating top-level changes to the final Message object.
4. A final message_stop event.

Ping events

Event streams may also include any number of ping events.

Error events

We may occasionally send errors in the event stream. For example, during periods of high usage, you may receive an overloaded_error, which would normally correspond to an HTTP 529 in a non-streaming context:

event: error
data: {"type": "error", "error": {"type": "overloaded_error", "message": "Overloaded"}}

Other events

In accordance with our versioning policy, we may add new event types, and your code should handle unknown event types gracefully.

Content block delta types

Each content_block_delta event contains a delta of a type that updates the content block at a given index.

Text delta

A text content block delta looks like:

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

Input JSON delta

The deltas for tool_use content blocks correspond to updates for the input field of the block. To support maximum granularity, the deltas are partial JSON strings, whereas the final tool_use.input is always an object.

You can accumulate the string deltas and parse the JSON once you receive a content_block_stop event, by using a library like Pydantic to do partial JSON parsing, or by using our SDKs, which provide helpers to access parsed incremental values.

A tool_use content block delta looks like:

event: content_block_delta
data: {"type": "content_block_delta","index": 1,"delta": {"type": "input_json_delta","partial_json": "{\"location\": \"San Fra"}}}

Note: Our current models only support emitting one complete key and value property from input at a time. As such, when using tools, there may be delays between streaming events while the model is working. Once an input key and value are accumulated, we emit them as multiple content_block_delta events with chunked partial json so that the format can automatically support finer granularity in future models.

Thinking delta

When using extended thinking with streaming enabled, you'll receive thinking content via thinking_delta events. These deltas correspond to the thinking field of the thinking content blocks.

For thinking content, a special signature_delta event is sent just before the content_block_stop event. This signature is used to verify the integrity of the thinking block.

A typical thinking delta looks like:

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "Let me solve this step by step:\n\n1. First break down 27 * 453"}}

The signature delta looks like:

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}}

Full HTTP Stream response

We strongly recommend that you use our client SDKs when using streaming mode. However, if you are building a direct API integration, you will need to handle these events yourself.

A stream response is comprised of:

1. A message_start event
2. Potentially multiple content blocks, each of which contains:
   • A content_block_start event
   • Potentially multiple content_block_delta events
   • A content_block_stop event
3. A message_delta event
4. A message_stop event

There may be ping events dispersed throughout the response as well. See Event types for more details on the format.

Basic streaming request

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-sonnet-4-5",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=256,
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

event: message_start
data: {"type": "message_start", "message": {"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", "type": "message", "role": "assistant", "content": [], "model": "claude-sonnet-4-5-20250929", "stop_reason": null, "stop_sequence": null, "usage": {"input_tokens": 25, "output_tokens": 1}}}

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

event: ping
data: {"type": "ping"}

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

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

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

event: message_delta
data: {"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence":null}, "usage": {"output_tokens": 15}}

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

Streaming request with tool use

In this request, we ask Claude to use a tool to tell us the weather.

import anthropic

client = anthropic.Anthropic()

tools = [
    {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "input_schema": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                }
            },
            "required": ["location"]
        }
    }
]

with client.messages.stream(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    tools=tools,
    tool_choice={"type": "any"},
    messages=[
        {
            "role": "user",
            "content": "What is the weather like in San Francisco?"
        }
    ],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

event: message_start
data: {"type":"message_start","message":{"id":"msg_014p7gG3wDgGV9EUtLvnow3U","type":"message","role":"assistant","model":"claude-sonnet-4-5-20250929","stop_sequence":null,"usage":{"input_tokens":472,"output_tokens":2},"content":[],"stop_reason":null}}

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

event: ping
data: {"type": "ping"}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

event: content_block_start
data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01T1x1fJ34qAmk2tNTrN7Up6","name":"get_weather","input":{}}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"{\"location\":"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" \"San"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" Francisc"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"o,"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" CA\""}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":", "}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"\"unit\": \"fah"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"renheit\"}"}}

event: content_block_stop
data: {"type":"content_block_stop","index":1}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"tool_use","stop_sequence":null},"usage":{"output_tokens":89}}

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

Streaming request with extended thinking

In this request, we enable extended thinking with streaming to see Claude's step-by-step reasoning.

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-sonnet-4-5",
    max_tokens=20000,
    thinking={
        "type": "enabled",
        "budget_tokens": 16000
    },
    messages=[
        {
            "role": "user",
            "content": "What is 27 * 453?"
        }
    ],
) as stream:
    for event in stream:
        if 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)

event: message_start
data: {"type": "message_start", "message": {"id": "msg_01...", "type": "message", "role": "assistant", "content": [], "model": "claude-sonnet-4-5-20250929", "stop_reason": null, "stop_sequence": null}}

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

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "Let me solve this step by step:\n\n1. First break down 27 * 453"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n2. 453 = 400 + 50 + 3"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n3. 27 * 400 = 10,800"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n4. 27 * 50 = 1,350"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n5. 27 * 3 = 81"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n6. 10,800 + 1,350 + 81 = 12,231"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}}

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

event: content_block_start
data: {"type": "content_block_start", "index": 1, "content_block": {"type": "text", "text": ""}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 1, "delta": {"type": "text_delta", "text": "27 * 453 = 12,231"}}

event: content_block_stop
data: {"type": "content_block_stop", "index": 1}

event: message_delta
data: {"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence": null}}

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

Streaming request with web search tool use

In this request, we ask Claude to search the web for current weather information.

import anthropic

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-sonnet-4-5",
    max_tokens=1024,
    tools=[
        {
            "type": "web_search_20250305",
            "name": "web_search",
            "max_uses": 5
        }
    ],
    messages=[
        {
            "role": "user",
            "content": "What is the weather like in New York City today?"
        }
    ],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

event: message_start
data: {"type":"message_start","message":{"id":"msg_01G...","type":"message","role":"assistant","model":"claude-sonnet-4-5-20250929","content":[],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2679,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":3}}}

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":"I'll check"}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" the current weather in New York City for you"}}

event: ping
data: {"type": "ping"}

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

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

event: content_block_start
data: {"type":"content_block_start","index":1,"content_block":{"type":"server_tool_use","id":"srvtoolu_014hJH82Qum7Td6UV8gDXThB","name":"web_search","input":{}}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"{\"query"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"\":"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" \"weather"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" NY"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"C to"}}

event: content_block_delta
data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"day\"}"}}

event: content_block_stop
data: {"type":"content_block_stop","index":1 }

event: content_block_start
data: {"type":"content_block_start","index":2,"content_block":{"type":"web_search_tool_result","tool_use_id":"srvtoolu_014hJH82Qum7Td6UV8gDXThB","content":[{"type":"web_search_result","title":"Weather in New York City in May 2025 (New York) - detailed Weather Forecast for a month","url":"https://world-weather.info/forecast/usa/new_york/may-2025/","encrypted_content":"Ev0DCioIAxgCIiQ3NmU4ZmI4OC1k...","page_age":null},...]}}

event: content_block_stop
data: {"type":"content_block_stop","index":2}

event: content_block_start
data: {"type":"content_block_start","index":3,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":3,"delta":{"type":"text_delta","text":"Here's the current weather information for New York"}}

event: content_block_delta
data: {"type":"content_block_delta","index":3,"delta":{"type":"text_delta","text":" City:\n\n# Weather"}}

event: content_block_delta
data: {"type":"content_block_delta","index":3,"delta":{"type":"text_delta","text":" in New York City"}}

event: content_block_delta
data: {"type":"content_block_delta","index":3,"delta":{"type":"text_delta","text":"\n\n"}}

...

event: content_block_stop
data: {"type":"content_block_stop","index":17}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn","stop_sequence":null},"usage":{"input_tokens":10682,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"output_tokens":510,"server_tool_use":{"web_search_requests":1}}}

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

Error recovery

When a streaming request is interrupted due to network issues, timeouts, or other errors, you can recover by resuming from where the stream was interrupted. This approach saves you from re-processing the entire response.

The basic recovery strategy involves:

1. Capture the partial response: Save all content that was successfully received before the error occurred
2. Construct a continuation request: Create a new API request that includes the partial assistant response as the beginning of a new assistant message
3. Resume streaming: Continue receiving the rest of the response from where it was interrupted

Error recovery best practices

1. Use SDK features: Leverage the SDK's built-in message accumulation and error handling capabilities
2. Handle content types: Be aware that messages can contain multiple content blocks (text, tool_use, thinking). Tool use and extended thinking blocks cannot be partially recovered. You can resume streaming from the most recent text block.