Fine-grained tool streaming

This feature is eligible for Zero Data Retention (ZDR). When your organization has a ZDR arrangement, data sent through this feature is not stored after the API response is returned.

Fine-grained tool streaming is available on all models and all platforms. It enables streaming of tool use parameter values without buffering or JSON validation, reducing the latency to begin receiving large parameters.

When using fine-grained tool streaming, you may potentially receive invalid or partial JSON inputs. Make sure to account for these edge cases in your code.

How to use fine-grained tool streaming

Fine-grained tool streaming is supported on the Claude API, Claude Platform on AWS, Amazon Bedrock, Vertex AI, and Microsoft Foundry. To use it, set eager_input_streaming to true on any user-defined tool where you want fine-grained streaming enabled, and enable streaming on your request.

Here's an example of how to use fine-grained tool streaming with the API:

client = anthropic.Anthropic()

with client.messages.stream(
    max_tokens=65536,
    model="claude-opus-4-7",
    tools=[
        {
            "name": "make_file",
            "description": "Write text to a file",
            "eager_input_streaming": True,
            "input_schema": {
                "type": "object",
                "properties": {
                    "filename": {
                        "type": "string",
                        "description": "The filename to write text to",
                    },
                    "lines_of_text": {
                        "type": "array",
                        "description": "An array of lines of text to write to the file",
                    },
                },
                "required": ["filename", "lines_of_text"],
            },
        }
    ],
    messages=[
        {
            "role": "user",
            "content": "Can you write a long poem and make a file called poem.txt?",
        }
    ],
) as stream:
    for event in stream:
        pass
    final_message = stream.get_final_message()

print(final_message.usage)

In this example, fine-grained tool streaming enables Claude to stream the lines of a long poem into the tool call make_file without buffering to validate if the lines_of_text parameter is valid JSON. This means you can see the parameter stream as it arrives, without having to wait for the entire parameter to buffer and validate.

With fine-grained tool streaming, tool use chunks start streaming faster, and are often longer and contain fewer word breaks. This is because of differences in chunking behavior.

Example:

Without fine-grained streaming (15s delay):

Chunk 1: '{"'
Chunk 2: 'query": "Ty'
Chunk 3: 'peScri'
Chunk 4: 'pt 5.0 5.1 '
Chunk 5: '5.2 5'
Chunk 6: '.3'
Chunk 7: ' new f'
Chunk 8: 'eatur'
...

With fine-grained streaming (3s delay):

Chunk 1: '{"query": "TypeScript 5.0 5.1 5.2 5.3'
Chunk 2: ' new features comparison'

Because fine-grained streaming sends parameters without buffering or JSON validation, there is no guarantee that the resulting stream will complete in a valid JSON string. Particularly, if the stop reason max_tokens is reached, the stream may end midway through a parameter and may be incomplete. You generally have to write specific support to handle when max_tokens is reached.

Accumulating tool input deltas

When a tool_use content block streams, the initial content_block_start event contains input: {} (an empty object). This is a placeholder. The actual input arrives as a series of input_json_delta events, each carrying a partial_json string fragment. Your code must concatenate these fragments and parse the result once the block closes.

The accumulation contract:

On content_block_start with type: "tool_use", initialize an empty string: input_json = ""
For each content_block_delta with type: "input_json_delta", append: input_json += event.delta.partial_json
On content_block_stop, parse the accumulated string: json.loads(input_json)

The type mismatch between the initial input: {} (object) and partial_json (string) is by design. The empty object marks the slot in the content array; the delta strings build the real value.

The Python and TypeScript SDKs provide higher-level stream helpers (stream.get_final_message(), stream.finalMessage()) that perform this accumulation for you. Use the preceding manual pattern only when you need to react to partial input before the block closes, such as rendering a progress indicator or starting a downstream request early.

Handling invalid JSON in tool responses

When using fine-grained tool streaming, you may receive invalid or incomplete JSON from the model. If you need to pass this invalid JSON back to the model in an error response block, you may wrap it in a JSON object to ensure proper handling (with a reasonable key). For example:

{
  "INVALID_JSON": "<your invalid json string>"
}

This approach helps the model understand that the content is invalid JSON while preserving the original malformed data for debugging purposes.

When wrapping invalid JSON, make sure to properly escape any quotes or special characters in the invalid JSON string to maintain valid JSON structure in the wrapper object.

Next steps

Streaming messages

Full reference for server-sent events and stream event types.

Handle tool calls

Execute tools and return results in the required message format.

Tool reference

Full directory of Anthropic-schema tools and their version strings.

import json
import anthropic

client = anthropic.Anthropic()

tool_inputs = {}  # index -> accumulated JSON string

with client.messages.stream(
    model="claude-opus-4-7",
    max_tokens=1024,
    tools=[
        {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "eager_input_streaming": True,
            "input_schema": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
            },
        }
    ],
    messages=[{"role": "user", "content": "Weather in Paris?"}],
) as stream:
    for event in stream:
        if (
            event.type == "content_block_start"
            and event.content_block.type == "tool_use"
        ):
            tool_inputs[event.index] = ""
        elif (
            event.type == "content_block_delta"
            and event.delta.type == "input_json_delta"
        ):
            tool_inputs[event.index] += event.delta.partial_json
        elif event.type == "content_block_stop" and event.index in tool_inputs:
            parsed = json.loads(tool_inputs[event.index])
            print(f"Tool input: {parsed}")