Run a Local Server
Status: ACTIVE (pulled from docs.langchain.com) Source: https://docs.langchain.com/oss/python/langgraph/local-server Timestamp: 2026-05-11
Run a LangGraph application locally for development and testing.
Steps
1. Install the LangGraph CLI
pip install -U "langgraph-cli[inmem]"
2. Create a LangGraph App
langgraph new path/to/your/app --template new-langgraph-project-python
3. Install Dependencies
cd path/to/your/app
pip install -e .
4. Create a .env File
LANGSMITH_API_KEY=lsv2...
Optionally set LANGSMITH_TRACING=false to prevent data from leaving your machine.
5. Launch Agent Server
langgraph dev
Output:
- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs
This in-memory server is for development. For production, use LangSmith Deployment.
6. Test the API
Python SDK (async):
from langgraph_sdk import get_client
client = get_client(url="http://localhost:2024")
async def main():
async for chunk in client.runs.stream(
None, "agent",
input={"messages": [{"role": "human", "content": "What is LangGraph?"}]},
):
print(chunk.data)
asyncio.run(main())
Python SDK (sync):
from langgraph_sdk import get_sync_client
client = get_sync_client(url="http://localhost:2024")
for chunk in client.runs.stream(
None, "agent",
input={"messages": [{"role": "human", "content": "What is LangGraph?"}]},
stream_mode="messages-tuple",
):
print(chunk.data)
REST API:
curl -s --request POST \
--url "http://localhost:2024/runs/stream" \
--header 'Content-Type: application/json' \
--data '{
"assistant_id": "agent",
"input": {"messages": [{"role": "human", "content": "What is LangGraph?"}]},
"stream_mode": "messages-tuple"
}'