LangGraph Integration for assistant-stream#

This document describes the LangGraph integration for the assistant-stream package.

Installation#

To use the LangGraph integration, install the assistant-stream package with the langgraph extra:

pip install assistant-stream[langgraph]

This will install the required dependencies including langchain-core.

Usage#

The integration exposes append_langgraph_event, which folds the events produced by LangGraph's native streaming into the state managed by a RunController. It is meant to consume graph.astream(..., stream_mode=["messages", "updates"]) output directly: the event type is the LangGraph stream mode name, and the payload is the raw chunk LangGraph yields for that mode.

Function Signature#

def append_langgraph_event(
    state: Any,
    _namespace: Any,
    type: str,
    payload: Any,
) -> None

Parameters#

• state: The state to mutate, normally controller.state. It is read and written with dictionary-style access.
• _namespace: The LangGraph namespace for the event. It is accepted for forward compatibility and is currently unused.
• type: The LangGraph stream mode that produced the event, either "messages" or "updates".
• payload: The raw chunk LangGraph yields for that stream mode, described below.

Event Types#

Message events (type="messages")#

The payload is the (message, metadata) tuple that LangGraph yields in "messages" mode, where message is a single BaseMessage (often an AIMessageChunk) and metadata is currently unused.

The function will:

• Create a messages list in the state if it does not exist.
• Convert the message to a plain dict with model_dump().
• Merge into an existing message when the id (or tool_call_id) matches: for an AIMessageChunk the message is merged with add_ai_message_chunks, then patched into state with granular set / append-text operations where possible. This lets streaming text and tool-call argument chunks update only the field that changed instead of sending the whole message again. If the shape cannot be represented safely as object-stream operations, the helper falls back to replacing the message.
• Append the message when no existing id matches.

from langchain_core.messages import AIMessageChunk

# one chunk yielded by stream_mode="messages"
chunk = (AIMessageChunk(content="Hello", id="msg1"), {})
append_langgraph_event(controller.state, namespace, "messages", chunk)

Updates events (type="updates")#

The payload is the {node_name: {channel: value}} dict that LangGraph yields in "updates" mode.

The function will:

• Write each channel value directly onto the state (state[channel] = value), so the node name is not retained.
• Skip the messages channel, since messages are handled by message events.
• Skip a node whose value is not a dict.

updates = {"weather_agent": {"status": "completed", "temperature": 72}}
append_langgraph_event(controller.state, namespace, "updates", updates)
# state now contains {"status": "completed", "temperature": 72}

Notes#

• The state holds plain JSON values (lists, dicts, str, int, bool, None); LangChain messages are converted with model_dump() before they are stored.
• Event types other than "messages" and "updates" are ignored.

Example Integration#

The events come straight from graph.astream, so a run callback forwards each one to append_langgraph_event:

from assistant_stream import RunController, create_run
from assistant_stream.modules.langgraph import append_langgraph_event
from assistant_stream.serialization import DataStreamResponse

async def run_callback(controller: RunController):
    async for namespace, event_type, chunk in graph.astream(
        {"messages": input_messages},
        stream_mode=["messages", "updates"],
        subgraphs=True,
    ):
        append_langgraph_event(controller.state, namespace, event_type, chunk)

stream = create_run(run_callback, state={})
response = DataStreamResponse(stream)

As the assistant message streams in, its chunks merge by id, so controller.state["messages"] ends with a single assistant message:

# controller.state
# {
# "messages": [
# {"type": "human", "content": "What is the weather?", "id": "user1"},
# {"type": "ai", "content": "The weather is sunny today.", "id": "ai1"},
# ],
# }

See python/assistant-transport-backend-langgraph for a complete server built on this pattern, including subgraph state via get_tool_call_subgraph_state.

Testing#

The integration is covered by unit tests that exercise append_langgraph_event against a real state proxy:

uv run pytest tests/test_langgraph.py