LangGraph Python SDK
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Quick Install
uv add langgraph-sdk
🤔 What is this?
This library provides the Python SDK for interacting with the LangGraph API. Use it to connect to a running LangGraph API server, manage assistants and threads, and stream runs from Python applications.
You will need a running LangGraph API server. If you're running a server locally using langgraph-cli, the SDK will automatically point at http://localhost:8123; otherwise, specify the server URL when creating a client.
📖 Documentation
For full documentation, see the API reference. For conceptual guides and tutorials, see the LangGraph Docs.
Quick Start
from langgraph_sdk import get_client
# If you're using a remote server, initialize the client with `get_client(url=REMOTE_URL)`
client = get_client()
# List all assistants
assistants = await client.assistants.search()
# We auto-create an assistant for each graph you register in config.
agent = assistants[0]
# Start a new thread
thread = await client.threads.create()
# Start a streaming run
input = {"messages": [{"role": "human", "content": "what's the weather in la"}]}
async for chunk in client.runs.stream(
thread["thread_id"], agent["assistant_id"], input=input
):
print(chunk)
Known Limitations
- WebSocket transport requires
websockets>=14and is only available on the async client (AsyncThreadStream). The sync client (SyncThreadStream) uses SSE exclusively. thread.extensions[name]opens a new subscription each time the same name is accessed. Assign the projection to a variable and reuse it within a single session rather than re-indexing across multiple iterations.- Sync streaming drives the lifecycle watcher in a background thread. Long-lived sync sessions will hold that thread open until the context manager exits.
- Reconnect attempts are limited to 5 by default for both the shared SSE fan-out and the lifecycle watcher. Persistent network partitions will surface as
RuntimeErroron in-flight projections.
Thread-Centric Streaming (v3)
client.threads.stream() returns a context manager that owns the SSE session for one thread. Typed projections — values snapshots, message streams, tool calls, custom events — all share the same underlying connection.
from langgraph_sdk import get_client
import asyncio
client = get_client()
async with client.threads.stream(
thread_id="my-thread",
assistant_id="agent",
) as thread:
await thread.run.start(input={"messages": [{"role": "user", "content": "hi"}]})
# Start all consumers concurrently so they share one SSE connection.
async def get_messages():
return [s async for s in thread.messages]
async def get_tool_calls():
return [c async for c in thread.tool_calls]
messages, tool_calls = await asyncio.gather(get_messages(), get_tool_calls())
for stream in messages:
print(await stream.text) # accumulated text
final = await thread.output # terminal state values
📕 Releases & Versioning
See our Releases and Versioning policies.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.
1 maintainer
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