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William FHandGitHub
a734f5e6ce
Main jtbd here:
a) clarify who/how a graph is being accessed and make the factory aware
of the `context` where relevant (and make it obvious when it is
available)
b) make it more clear when you can bypass / defer resources with
expensive lifespans (like MCp connections)
c) make auth access more type-safe.
Gives us room to add other information, like:
- langsmith distributed tracing information
---- old
Can start doing things like this:
```
def my_graph(runtime: ServerRuntime):
if runtime.ensure_user().permissions not in ("foo"):
raise ValueError("bar")
```
etc.
Points of expected confusion:
- You won't have a stream_writer in this context.
- This won't be an accessible object within the graph, only the graph
factory.
For maintainers, related draft PR int he server
https://github.com/langchain-ai/langgraph/pull/6774
LangGraph Python SDK
This repository contains the Python SDK for interacting with the LangSmith Deployment REST API.
Quick Start
To get started with the Python SDK, install the package
pip install -U langgraph-sdk
You will need a running LangGraph API server. If you're running a server locally using langgraph-cli, SDK will automatically point at http://localhost:8123, otherwise
you would need to specify the server URL when creating a client.
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)