langgraph/libs/sdk-py

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>=14 and 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 RuntimeError on 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.