Dspy streaming

Stream LM Output with dspy.streamify and StreamListener

Guide the user through streaming DSPy module outputs token-by-token to frontends, APIs, or CLI interfaces using dspy.streamify() and StreamListener.

What is streaming in DSPy

dspy.streamify() wraps any DSPy module and returns an async (or sync) generator that yields chunks as the LM produces tokens. StreamListener lets you target specific output fields. The stream yields three chunk types -- StreamResponse (partial tokens), StatusMessage (progress updates), and Prediction (final result).

When to stream

Stream when...	Do NOT stream when...
Building a chat UI that shows typing	Batch processing many inputs
SSE/WebSocket endpoint for real-time display	Running optimization or evaluation
Long-form generation where users wait	Output is short (< 50 tokens)
ReAct agent loop where you show each step	Pipeline stages that feed into each other

Step 1: Basic streaming setup

import dspy
from dspy.streaming import streamify, StreamListener

lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

# Your existing module
qa = dspy.ChainOfThought("question -> answer")

# Create a listener for a specific output field
answer_listener = StreamListener(signature_field_name="answer")

# Wrap the module for streaming
streaming_qa = streamify(
    qa,
    stream_listeners=[answer_listener],
)

# Consume the async generator
async for chunk in streaming_qa(question="What is DSPy?"):
    if hasattr(chunk, "answer"):
        print(chunk.answer, end="", flush=True)

Step 2: Understanding chunk types

The stream yields three distinct types:

async for chunk in streaming_qa(question="..."):
    if isinstance(chunk, dspy.streaming.StreamResponse):
        # Partial tokens for a field -- the main content you display
        print(chunk.answer, end="", flush=True)

    elif isinstance(chunk, dspy.streaming.StatusMessage):
        # Progress updates -- useful for UI status indicators
        print(f"[Status: {chunk.message}]")

    elif isinstance(chunk, dspy.Prediction):
        # Final complete result -- same as non-streaming output
        final_answer = chunk.answer

Key behavior:

• StreamResponse chunks contain partial text for the field(s) you are listening to
• StatusMessage appears during tool calls, retries, or multi-step processing
• The final Prediction appears last (unless include_final_prediction_in_output_stream=False)

Step 3: Multi-field streaming

Stream multiple output fields simultaneously:

class Summarize(dspy.Signature):
    """Summarize the document and extract key points."""
    document: str = dspy.InputField()
    summary: str = dspy.OutputField()
    key_points: str = dspy.OutputField()

summarizer = dspy.Predict(Summarize)

# Listen to both fields
summary_listener = StreamListener(signature_field_name="summary")
points_listener = StreamListener(signature_field_name="key_points")

streaming_summarizer = streamify(
    summarizer,
    stream_listeners=[summary_listener, points_listener],
)

async for chunk in streaming_summarizer(document="..."):
    if hasattr(chunk, "summary"):
        # Tokens for the summary field
        display_summary(chunk.summary)
    elif hasattr(chunk, "key_points"):
        # Tokens for the key_points field
        display_points(chunk.key_points)

Note: Fields stream sequentially in declaration order. The LM generates summary fully before starting key_points.

Step 4: Streaming ReAct agent loops

For agents, use allow_reuse=True so the listener captures output across multiple reasoning iterations:

from dspy.streaming import StatusMessageProvider

agent = dspy.ReAct("question -> answer", tools=[search, lookup])

# allow_reuse=True is critical for agents -- the agent generates
# multiple "answer" attempts across iterations
answer_listener = StreamListener(
    signature_field_name="answer",
    allow_reuse=True,
)

# status_message_provider must be a StatusMessageProvider subclass
# INSTANCE that overrides status hooks -- not a lambda
class AgentStatus(StatusMessageProvider):
    def tool_start_status_message(self, instance, inputs):
        return f"Calling {instance.name}..."

streaming_agent = streamify(
    agent,
    stream_listeners=[answer_listener],
    status_message_provider=AgentStatus(),
)

async for chunk in streaming_agent(question="..."):
    if isinstance(chunk, dspy.streaming.StatusMessage):
        show_progress(chunk.message)
    elif hasattr(chunk, "answer"):
        show_answer_token(chunk.answer)

Step 5: Disambiguating duplicate fields with predict_name

When a module has multiple predictors with the same output field name, use predict_name to target a specific one:

class Pipeline(dspy.Module):
    def __init__(self):
        self.draft = dspy.ChainOfThought("topic -> answer")
        self.refine = dspy.ChainOfThought("draft_answer -> answer")

    def forward(self, topic):
        draft = self.draft(topic=topic)
        return self.refine(draft_answer=draft.answer)

pipeline = Pipeline()

# Only stream the final refinement step, not the draft
final_listener = StreamListener(
    signature_field_name="answer",
    predict_name="refine",  # matches self.refine attribute name
)

streaming_pipeline = streamify(pipeline, stream_listeners=[final_listener])

Step 6: FastAPI SSE integration

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
import dspy
from dspy.streaming import streamify, StreamListener

app = FastAPI()

qa = dspy.ChainOfThought("question -> answer")
answer_listener = StreamListener(signature_field_name="answer")
streaming_qa = streamify(qa, stream_listeners=[answer_listener])

@app.get("/ask")
async def ask(question: str):
    async def event_stream():
        async for chunk in streaming_qa(question=question):
            if hasattr(chunk, "answer"):
                yield f"data: {chunk.answer}\n\n"
            elif isinstance(chunk, dspy.Prediction):
                yield f"data: [DONE]\n\n"
    return StreamingResponse(event_stream(), media_type="text/event-stream")

Step 7: Sync streaming fallback

For non-async contexts (scripts, notebooks), set async_streaming=False:

streaming_qa = streamify(
    qa,
    stream_listeners=[answer_listener],
    async_streaming=False,  # Returns a sync generator
)

# Use regular for loop instead of async for
for chunk in streaming_qa(question="What is DSPy?"):
    if hasattr(chunk, "answer"):
        print(chunk.answer, end="", flush=True)

Gotchas

1. Claude omits async_streaming=False in non-async code. If you use a regular for loop with an async generator, you get TypeError: 'async_generator' object is not iterable. Always set async_streaming=False for sync contexts.
2. The listener buffers ~10 tokens before yielding. This is by design -- ChatAdapter needs to detect field boundary delimiters ([[ ## field_name ## ]]) before it can emit tokens. First chunk arrives slightly delayed.
3. Claude forgets allow_reuse=True for agents. Without it, StreamListener only captures the first iteration. ReAct and CodeAct agents need allow_reuse=True to stream across multiple reasoning cycles.
4. include_final_prediction_in_output_stream=True is the default. The last item in the stream is always a Prediction object unless you explicitly disable it. Check for both StreamResponse and Prediction types.
5. Claude wraps streamify in an extra async function. streamify() already returns a callable that produces a generator. Do not wrap it in another async function -- just call it directly and iterate.

Additional resources

• dspy.ai/api/streaming
• For API details, see reference.md
• For worked examples, see examples.md

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Serving streaming endpoints -- see /ai-serving-apis
• Building chat UIs with streaming -- see /ai-building-chatbots
• ReAct agents that benefit from streaming -- see /dspy-react
• Async execution patterns -- see /dspy-async
• General DSPy utilities (caching, debugging) -- see /dspy-utils
• Install /ai-do if you do not have it -- it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do