Streaming Messages

MessagesModel capabilities

Streaming messages

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

Streaming with SDKs

The Python and TypeScript SDKs offer multiple ways of streaming. The PHP SDK provides streaming via createStream(). 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-opus-4-7",
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Get the final message without handling events

If you don't need to process text as it arrives, the SDKs provide a way to use streaming under the hood while returning the complete Message object, identical to what .create() returns. This is especially useful for requests with large max_tokens values, where the SDKs require streaming to avoid HTTP timeouts.

client = anthropic.Anthropic()

with client.messages.stream(
    max_tokens=128000,
    messages=[{"role": "user", "content": "Write a detailed analysis..."}],
    model="claude-opus-4-7",
) as stream:
    message = stream.get_final_message()

print(message.content[0].text)

The .stream() call keeps the HTTP connection alive with server-sent events, then .get_final_message() (Python) or .finalMessage() (TypeScript) accumulates all events and returns the complete Message object. In Go, you call message.Accumulate(event) inside the stream loop to build the same complete Message. In Java, use MessageAccumulator.create() and call accumulator.accumulate(event) on each event. In Ruby, call .accumulated_message on the stream. In the PHP SDK, you iterate over stream events manually to accumulate the response.

Event types

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

Each stream uses the following event flow:

message_start: contains a Message object with empty content.
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 has an index that corresponds to its index in the final Message content array.
One or more message_delta events, indicating top-level changes to the final Message object.
A final message_stop event.

The token counts shown in the usage field of the message_delta event are cumulative.

Ping events

Event streams may also include any number of ping events.

Error events

The API 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:

Example error

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

Other events

In accordance with the versioning policy, new event types may be added, 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:

Text delta

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 the SDKs, which provide helpers to access parsed incremental values.

A tool_use content block delta looks like:

Input JSON delta

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

Note: 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, they are emitted 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.

When display: "omitted" is set on the thinking configuration, no thinking_delta events are sent. The thinking block opens, receives a single signature_delta, and closes. See Controlling thinking display.

A typical thinking delta looks like:

Thinking delta

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "I need to find the GCD of 1071 and 462 using the Euclidean algorithm.\n\n1071 = 2 × 462 + 147"}}

The signature delta looks like:

Signature delta

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

Full HTTP stream response

Use the client SDKs when using streaming mode. However, if you are building a direct API integration, you need to handle these events yourself.

A stream response consists of:

A message_start event
Potentially multiple content blocks, each of which contains:
- A content_block_start event
- Potentially multiple content_block_delta events
- A content_block_stop event
One or more message_delta events
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

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-4-7",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=256,
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Response

event: message_start
data: {"type": "message_start", "message": {"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", "type": "message", "role": "assistant", "content": [], "model": "claude-opus-4-7", "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

Tool use supports fine-grained streaming for parameter values. Enable it per tool with eager_input_streaming.

This request asks Claude to use a tool to report the weather.

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-opus-4-7",
    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)

Response

event: message_start
data: {"type":"message_start","message":{"id":"msg_014p7gG3wDgGV9EUtLvnow3U","type":"message","role":"assistant","model":"claude-opus-4-7","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_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

This request enables extended thinking with streaming. The display: "summarized" setting streams a condensed summary of Claude's reasoning rather than the full chain of thought.

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=20000,
    thinking={"type": "adaptive", "display": "summarized"},
    messages=[
        {
            "role": "user",
            "content": "What is the greatest common divisor of 1071 and 462?",
        }
    ],
) 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)

Response

event: message_start
data: {"type": "message_start", "message": {"id": "msg_01...", "type": "message", "role": "assistant", "content": [], "model": "claude-opus-4-7", "stop_reason": null, "stop_sequence": null}}

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

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "I need to find the GCD of 1071 and 462 using the Euclidean algorithm.\n\n1071 = 2 × 462 + 147"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n462 = 3 × 147 + 21"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n147 = 7 × 21 + 0"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\nThe remainder is 0, so GCD(1071, 462) = 21."}}

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": "The greatest common divisor of 1071 and 462 is **21**."}}

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

This request asks Claude to search the web for current weather information.

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-4-7",
    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)

Response

event: message_start
data: {"type":"message_start","message":{"id":"msg_01G...","type":"message","role":"assistant","model":"claude-opus-4-7","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

Claude 4.5 and earlier

For Claude 4.5 models and earlier, you can recover a streaming request that was interrupted due to network issues, timeouts, or other errors by resuming from where the stream was interrupted. This approach saves you from re-processing the entire response.

The basic recovery strategy involves:

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

Claude 4.6 and later

For Claude 4.6 and later models, the same capture-and-resume strategy applies, but step 2 changes: instead of placing the partial response in an assistant message, add a user message that instructs the model to continue from where it left off.

Capture the partial response: Save all content that was successfully received before the error occurred
Construct a continuation request: Create a new API request with a user message containing the partial response and an instruction to continue, for example:
Sample prompt
```
Your previous response was interrupted and ended with [previous_response]. Continue from where you left off.
```
Resume streaming: Continue receiving the rest of the response from where it was interrupted

Error recovery best practices

Use SDK features: Leverage the SDK's built-in message accumulation and error handling capabilities
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.

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MessagesModel capabilities

Streaming messages

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

Streaming with SDKs

client = anthropic.Anthropic()

with client.messages.stream(
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
    model="claude-opus-4-7",
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Get the final message without handling events

client = anthropic.Anthropic()

with client.messages.stream(
    max_tokens=128000,
    messages=[{"role": "user", "content": "Write a detailed analysis..."}],
    model="claude-opus-4-7",
) as stream:
    message = stream.get_final_message()

print(message.content[0].text)

Event types

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

Each stream uses the following event flow:

message_start: contains a Message object with empty content.
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 has an index that corresponds to its index in the final Message content array.
One or more message_delta events, indicating top-level changes to the final Message object.
A final message_stop event.

The token counts shown in the usage field of the message_delta event are cumulative.

Ping events

Event streams may also include any number of ping events.

Error events

Example error

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

Other events

In accordance with the versioning policy, new event types may be added, 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:

Text delta

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

Input JSON delta

A tool_use content block delta looks like:

Input JSON delta

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

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:

Thinking delta

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "I need to find the GCD of 1071 and 462 using the Euclidean algorithm.\n\n1071 = 2 × 462 + 147"}}

The signature delta looks like:

Signature delta

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

Full HTTP stream response

Use the client SDKs when using streaming mode. However, if you are building a direct API integration, you need to handle these events yourself.

A stream response consists of:

A message_start event
Potentially multiple content blocks, each of which contains:
- A content_block_start event
- Potentially multiple content_block_delta events
- A content_block_stop event
One or more message_delta events
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

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-4-7",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=256,
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Response

event: message_start
data: {"type": "message_start", "message": {"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", "type": "message", "role": "assistant", "content": [], "model": "claude-opus-4-7", "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

Tool use supports fine-grained streaming for parameter values. Enable it per tool with eager_input_streaming.

This request asks Claude to use a tool to report the weather.

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-opus-4-7",
    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)

Response

event: message_start
data: {"type":"message_start","message":{"id":"msg_014p7gG3wDgGV9EUtLvnow3U","type":"message","role":"assistant","model":"claude-opus-4-7","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_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

This request enables extended thinking with streaming. The display: "summarized" setting streams a condensed summary of Claude's reasoning rather than the full chain of thought.

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=20000,
    thinking={"type": "adaptive", "display": "summarized"},
    messages=[
        {
            "role": "user",
            "content": "What is the greatest common divisor of 1071 and 462?",
        }
    ],
) 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)

Response

event: message_start
data: {"type": "message_start", "message": {"id": "msg_01...", "type": "message", "role": "assistant", "content": [], "model": "claude-opus-4-7", "stop_reason": null, "stop_sequence": null}}

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

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "I need to find the GCD of 1071 and 462 using the Euclidean algorithm.\n\n1071 = 2 × 462 + 147"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n462 = 3 × 147 + 21"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n147 = 7 × 21 + 0"}}

event: content_block_delta
data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\nThe remainder is 0, so GCD(1071, 462) = 21."}}

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": "The greatest common divisor of 1071 and 462 is **21**."}}

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

This request asks Claude to search the web for current weather information.

client = anthropic.Anthropic()

with client.messages.stream(
    model="claude-opus-4-7",
    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)

Response

event: message_start
data: {"type":"message_start","message":{"id":"msg_01G...","type":"message","role":"assistant","model":"claude-opus-4-7","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

Claude 4.5 and earlier

The basic recovery strategy involves:

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

Claude 4.6 and later

Capture the partial response: Save all content that was successfully received before the error occurred
Construct a continuation request: Create a new API request with a user message containing the partial response and an instruction to continue, for example:
Sample prompt
```
Your previous response was interrupted and ended with [previous_response]. Continue from where you left off.
```
Resume streaming: Continue receiving the rest of the response from where it was interrupted

Error recovery best practices

Use SDK features: Leverage the SDK's built-in message accumulation and error handling capabilities
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.

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