langgraph-api-inmem 0.0.5rc1

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LangGraph API (In-Memory)

This package implements a local version of the LangGraph API for rapid development and testing. Build and iterate on LangGraph agents with a tight feedback loop. The sesrver is backed by a predominently in-memory data store that is persisted to local disk when the server is restarted.

For production use, see the various deployment options for the LangGraph API, which are backed by a production-grade database.

Installation

Install the langgraph-cli package with the inmem extra. Your CLI version must be no lower than 0.1.55.

pip install -U langgraph-cli[inmem]

Quickstart

1. (Optional) Clone a starter template:

   langgraph new --template new-langgraph-project-python ./my-project
   cd my-project
   

   (Recommended) Use a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate
   python -m pip install -e .
   python -m pip install -U langgraph-cli[inmem]
   

2. Start the development server:

   langgraph dev --config ./langgraph.json
   

3. The server will launch, opening a browser window with the graph UI. Interact with your graph or make code edits; the server automatically reloads on changes.

Usage

Start the development server:

langgraph dev

Your agent's state (threads, runs, assistants) persists in memory while the server is running - perfect for development and testing. Each run's state is tracked and can be inspected, making it easy to debug and improve your agent's behavior.

How-To

Attaching a debugger

Debug mode lets you attach your IDE's debugger to the LangGraph API server to set breakpoints and step through your code line-by-line.

1. Install debugpy:

   pip install debugpy
   

2. Start the server in debug mode:

   langgraph dev --debug-port 5678
   

3. Configure your IDE:

   • VS Code: Add this launch configuration:

     {
       "name": "Attach to LangGraph",
       "type": "debugpy",
       "request": "attach",
       "connect": {
                "host": "0.0.0.0",
                "port": 5678
            },
     }
     

   • PyCharm: Use "Attach to Process" and select the langgraph process

4. Set breakpoints in your graph code and start debugging.

CLI options

langgraph dev [OPTIONS]

Options:
  --debug-port INTEGER         Enable remote debugging on specified port
  --no-browser                 Skip opening browser on startup
  --n-jobs-per-worker INTEGER  Maximum concurrent jobs per worker process
  --config PATH               Custom configuration file path
  --no-reload                 Disable code hot reloading
  --port INTEGER              HTTP server port (default: 8000)
  --host TEXT                 HTTP server host (default: localhost)

License

This project is licensed under the Elastic License 2.0 - see the LICENSE file for details.