mirror of
https://github.com/wavetermdev/waveterm.git
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6e3554407bWorking on AI SDK compatible backends for OpenAI and Anthropic. Thinking + ToolUse etc. For use with AI SDK useChat on frontend. Still needs more testing, WIP, but this is a good start. Want to get this committed to so I can work on more integrations.
631 lines
23 KiB
Markdown
631 lines
23 KiB
Markdown
# Streaming Messages
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When creating a Message, you can set `"stream": true` to incrementally stream the response using [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent%5Fevents/Using%5Fserver-sent%5Fevents) (SSE).
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## Streaming with SDKs
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Our [Python](https://github.com/anthropics/anthropic-sdk-python) and [TypeScript](https://github.com/anthropics/anthropic-sdk-typescript) SDKs offer multiple ways of streaming. The Python SDK allows both sync and async streams. See the documentation in each SDK for details.
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<CodeGroup>
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```Python Python
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import anthropic
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client = anthropic.Anthropic()
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with client.messages.stream(
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max_tokens=1024,
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messages=[{"role": "user", "content": "Hello"}],
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model="claude-opus-4-1-20250805",
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) as stream:
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for text in stream.text_stream:
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print(text, end="", flush=True)
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````
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```TypeScript TypeScript
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import Anthropic from '@anthropic-ai/sdk';
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const client = new Anthropic();
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await client.messages.stream({
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messages: [{role: 'user', content: "Hello"}],
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model: 'claude-opus-4-1-20250805',
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max_tokens: 1024,
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}).on('text', (text) => {
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console.log(text);
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});
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````
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</CodeGroup>
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## Event types
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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.
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Each stream uses the following event flow:
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1. `message_start`: contains a `Message` object with empty `content`.
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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.
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3. One or more `message_delta` events, indicating top-level changes to the final `Message` object.
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4. A final `message_stop` event.
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<Warning>
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The token counts shown in the `usage` field of the `message_delta` event are *cumulative*.
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</Warning>
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### Ping events
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Event streams may also include any number of `ping` events.
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### Error events
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We may occasionally send [errors](/en/api/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:
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```json Example error
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event: error
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data: {"type": "error", "error": {"type": "overloaded_error", "message": "Overloaded"}}
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```
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### Other events
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In accordance with our [versioning policy](/en/api/versioning), we may add new event types, and your code should handle unknown event types gracefully.
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## Content block delta types
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Each `content_block_delta` event contains a `delta` of a type that updates the `content` block at a given `index`.
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### Text delta
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A `text` content block delta looks like:
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```JSON Text delta
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event: content_block_delta
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data: {"type": "content_block_delta","index": 0,"delta": {"type": "text_delta", "text": "ello frien"}}
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```
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### Input JSON delta
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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_.
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You can accumulate the string deltas and parse the JSON once you receive a `content_block_stop` event, by using a library like [Pydantic](https://docs.pydantic.dev/latest/concepts/json/#partial-json-parsing) to do partial JSON parsing, or by using our [SDKs](https://docs.anthropic.com/en/api/client-sdks), which provide helpers to access parsed incremental values.
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A `tool_use` content block delta looks like:
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```JSON Input JSON delta
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event: content_block_delta
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data: {"type": "content_block_delta","index": 1,"delta": {"type": "input_json_delta","partial_json": "{\"location\": \"San Fra"}}}
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```
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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.
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### Thinking delta
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When using [extended thinking](/en/docs/build-with-claude/extended-thinking#streaming-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.
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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.
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A typical thinking delta looks like:
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```JSON Thinking delta
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event: content_block_delta
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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"}}
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```
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The signature delta looks like:
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```JSON Signature delta
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}}
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```
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## Full HTTP Stream response
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We strongly recommend that you use our [client SDKs](/en/api/client-sdks) when using streaming mode. However, if you are building a direct API integration, you will need to handle these events yourself.
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A stream response is comprised of:
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1. A `message_start` event
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2. Potentially multiple content blocks, each of which contains:
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- A `content_block_start` event
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- Potentially multiple `content_block_delta` events
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- A `content_block_stop` event
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3. A `message_delta` event
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4. A `message_stop` event
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There may be `ping` events dispersed throughout the response as well. See [Event types](#event-types) for more details on the format.
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### Basic streaming request
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<CodeGroup>
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```bash Shell
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curl https://api.anthropic.com/v1/messages \
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--header "anthropic-version: 2023-06-01" \
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--header "content-type: application/json" \
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--header "x-api-key: $ANTHROPIC_API_KEY" \
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--data \
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'{
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"model": "claude-opus-4-1-20250805",
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"messages": [{"role": "user", "content": "Hello"}],
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"max_tokens": 256,
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"stream": true
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}'
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```
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```python Python
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import anthropic
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client = anthropic.Anthropic()
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with client.messages.stream(
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model="claude-opus-4-1-20250805",
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messages=[{"role": "user", "content": "Hello"}],
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max_tokens=256,
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) as stream:
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for text in stream.text_stream:
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print(text, end="", flush=True)
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```
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</CodeGroup>
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```json Response
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event: message_start
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data: {"type": "message_start", "message": {"id": "msg_1nZdL29xx5MUA1yADyHTEsnR8uuvGzszyY", "type": "message", "role": "assistant", "content": [], "model": "claude-opus-4-1-20250805", "stop_reason": null, "stop_sequence": null, "usage": {"input_tokens": 25, "output_tokens": 1}}}
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event: content_block_start
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data: {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}
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event: ping
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data: {"type": "ping"}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "Hello"}}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "!"}}
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event: content_block_stop
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data: {"type": "content_block_stop", "index": 0}
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event: message_delta
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data: {"type": "message_delta", "delta": {"stop_reason": "end_turn", "stop_sequence":null}, "usage": {"output_tokens": 15}}
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event: message_stop
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data: {"type": "message_stop"}
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```
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### Streaming request with tool use
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<Tip>
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Tool use now supports fine-grained streaming for parameter values as a beta feature. For more details, see [Fine-grained tool streaming](/en/docs/agents-and-tools/tool-use/fine-grained-tool-streaming).
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</Tip>
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In this request, we ask Claude to use a tool to tell us the weather.
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<CodeGroup>
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```bash Shell
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curl https://api.anthropic.com/v1/messages \
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-H "content-type: application/json" \
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-H "x-api-key: $ANTHROPIC_API_KEY" \
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-H "anthropic-version: 2023-06-01" \
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-d '{
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"model": "claude-opus-4-1-20250805",
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"max_tokens": 1024,
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"tools": [
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{
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"name": "get_weather",
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"description": "Get the current weather in a given location",
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"input_schema": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA"
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}
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},
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"required": ["location"]
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}
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}
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],
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"tool_choice": {"type": "any"},
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"messages": [
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{
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"role": "user",
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"content": "What is the weather like in San Francisco?"
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}
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],
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"stream": true
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}'
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```
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```python Python
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import anthropic
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client = anthropic.Anthropic()
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tools = [
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{
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"name": "get_weather",
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"description": "Get the current weather in a given location",
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"input_schema": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA"
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}
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},
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"required": ["location"]
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}
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}
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]
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with client.messages.stream(
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model="claude-opus-4-1-20250805",
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max_tokens=1024,
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tools=tools,
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tool_choice={"type": "any"},
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messages=[
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{
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"role": "user",
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"content": "What is the weather like in San Francisco?"
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}
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],
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) as stream:
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for text in stream.text_stream:
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print(text, end="", flush=True)
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```
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</CodeGroup>
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```json Response
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event: message_start
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data: {"type":"message_start","message":{"id":"msg_014p7gG3wDgGV9EUtLvnow3U","type":"message","role":"assistant","model":"claude-opus-4-1-20250805","stop_sequence":null,"usage":{"input_tokens":472,"output_tokens":2},"content":[],"stop_reason":null}}
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event: content_block_start
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data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}
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event: ping
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data: {"type": "ping"}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Okay"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":","}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" let"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"'s"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" check"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" the"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" weather"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" for"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" San"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" Francisco"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":","}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" CA"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":":"}}
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event: content_block_stop
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data: {"type":"content_block_stop","index":0}
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event: content_block_start
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data: {"type":"content_block_start","index":1,"content_block":{"type":"tool_use","id":"toolu_01T1x1fJ34qAmk2tNTrN7Up6","name":"get_weather","input":{}}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":""}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"{\"location\":"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" \"San"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" Francisc"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"o,"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":" CA\""}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":", "}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"\"unit\": \"fah"}}
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event: content_block_delta
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data: {"type":"content_block_delta","index":1,"delta":{"type":"input_json_delta","partial_json":"renheit\"}"}}
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event: content_block_stop
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data: {"type":"content_block_stop","index":1}
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event: message_delta
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data: {"type":"message_delta","delta":{"stop_reason":"tool_use","stop_sequence":null},"usage":{"output_tokens":89}}
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event: message_stop
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data: {"type":"message_stop"}
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```
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### Streaming request with extended thinking
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In this request, we enable extended thinking with streaming to see Claude's step-by-step reasoning.
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<CodeGroup>
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```bash Shell
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curl https://api.anthropic.com/v1/messages \
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--header "x-api-key: $ANTHROPIC_API_KEY" \
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--header "anthropic-version: 2023-06-01" \
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--header "content-type: application/json" \
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--data \
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'{
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"model": "claude-opus-4-1-20250805",
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"max_tokens": 20000,
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"stream": true,
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"thinking": {
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"type": "enabled",
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"budget_tokens": 16000
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},
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"messages": [
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{
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"role": "user",
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"content": "What is 27 * 453?"
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}
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]
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}'
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```
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```python Python
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import anthropic
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client = anthropic.Anthropic()
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with client.messages.stream(
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model="claude-opus-4-1-20250805",
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max_tokens=20000,
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thinking={
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"type": "enabled",
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"budget_tokens": 16000
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},
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messages=[
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{
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"role": "user",
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"content": "What is 27 * 453?"
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}
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],
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) as stream:
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for event in stream:
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if event.type == "content_block_delta":
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if event.delta.type == "thinking_delta":
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print(event.delta.thinking, end="", flush=True)
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elif event.delta.type == "text_delta":
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print(event.delta.text, end="", flush=True)
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```
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</CodeGroup>
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```json Response
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event: message_start
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data: {"type": "message_start", "message": {"id": "msg_01...", "type": "message", "role": "assistant", "content": [], "model": "claude-opus-4-1-20250805", "stop_reason": null, "stop_sequence": null}}
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event: content_block_start
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data: {"type": "content_block_start", "index": 0, "content_block": {"type": "thinking", "thinking": ""}}
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event: content_block_delta
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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"}}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n2. 453 = 400 + 50 + 3"}}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n3. 27 * 400 = 10,800"}}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n4. 27 * 50 = 1,350"}}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n5. 27 * 3 = 81"}}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "thinking_delta", "thinking": "\n6. 10,800 + 1,350 + 81 = 12,231"}}
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event: content_block_delta
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data: {"type": "content_block_delta", "index": 0, "delta": {"type": "signature_delta", "signature": "EqQBCgIYAhIM1gbcDa9GJwZA2b3hGgxBdjrkzLoky3dl1pkiMOYds..."}}
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event: content_block_stop
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data: {"type": "content_block_stop", "index": 0}
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event: content_block_start
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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.
|
|
|
|
<CodeGroup>
|
|
```bash Shell
|
|
curl https://api.anthropic.com/v1/messages \
|
|
--header "x-api-key: $ANTHROPIC_API_KEY" \
|
|
--header "anthropic-version: 2023-06-01" \
|
|
--header "content-type: application/json" \
|
|
--data \
|
|
'{
|
|
"model": "claude-opus-4-1-20250805",
|
|
"max_tokens": 1024,
|
|
"stream": true,
|
|
"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?"
|
|
}
|
|
]
|
|
}'
|
|
```
|
|
|
|
```python Python
|
|
import anthropic
|
|
|
|
client = anthropic.Anthropic()
|
|
|
|
with client.messages.stream(
|
|
model="claude-opus-4-1-20250805",
|
|
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)
|
|
```
|
|
|
|
</CodeGroup>
|
|
|
|
```json Response
|
|
event: message_start
|
|
data: {"type":"message_start","message":{"id":"msg_01G...","type":"message","role":"assistant","model":"claude-opus-4-1-20250805","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.
|