> ## Documentation Index
> Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LangGraph

> Setting up MemMachine with LangGraph

For a complete, runnable example (including python files), see the official **[LangGraph Example](https://github.com/MemMachine/MemMachine/tree/main/examples/langgraph)** in the repository.

## Overview

MemMachine provides memory tools that can be integrated into LangGraph workflows to enable AI agents with persistent memory capabilities. This allows agents to remember past interactions, user preferences, and context across multiple sessions.

## Configuration

The demo can be configured via environment variables:

| Variable               | Description                           | Default                 |
| ---------------------- | ------------------------------------- | ----------------------- |
| `MEMORY_BACKEND_URL`   | URL of the MemMachine backend service | `http://localhost:8080` |
| `LANGGRAPH_GROUP_ID`   | Group identifier for the demo         | `langgraph_demo`        |
| `LANGGRAPH_AGENT_ID`   | Agent identifier for the demo         | `demo_agent`            |
| `LANGGRAPH_USER_ID`    | User identifier for the demo          | `demo_user`             |
| `LANGGRAPH_SESSION_ID` | Session identifier for the demo       | `demo_session_001`      |

## Usage

### Setting Environment Variables

```bash theme={null}
# Set environment variables (optional)
export MEMORY_BACKEND_URL="http://localhost:8080"
export LANGGRAPH_GROUP_ID="my_group"
export LANGGRAPH_AGENT_ID="my_agent"
export LANGGRAPH_USER_ID="my_user"
export LANGGRAPH_SESSION_ID="my_session"

# Run the demo
python examples/langgraph/demo.py
```

### Running the Demo

```bash theme={null}
cd examples/langgraph
python demo.py
```

## Integration Guide

### 1. Initialize MemMachineTools

```python theme={null}
import os
from tool import MemMachineTools

# Get configuration from environment variables or use defaults
base_url = os.getenv("MEMORY_BACKEND_URL", "http://localhost:8080")
group_id = os.getenv("LANGGRAPH_GROUP_ID", "my_group")
agent_id = os.getenv("LANGGRAPH_AGENT_ID", "my_agent")
user_id = os.getenv("LANGGRAPH_USER_ID", "user123")

tools = MemMachineTools(
    base_url=base_url,
    group_id=group_id,
    agent_id=agent_id,
    user_id=user_id,
)
```

### 2. Create Tool Functions

```python theme={null}
from tool import create_add_memory_tool, create_search_memory_tool

add_memory = create_add_memory_tool(tools)
search_memory = create_search_memory_tool(tools)
```

### 3. Use in LangGraph Nodes

```python theme={null}
from langgraph.graph import StateGraph, END

def memory_node(state: AgentState):
    # Search for relevant memories
    search_result = search_memory(
        query=state["messages"][-1].content,
        user_id=state["user_id"],
    )

    # Add new memory
    add_memory(
        content=state["messages"][-1].content,
        user_id=state["user_id"],
    )

    return {
        "context": search_result.get("summary", ""),
        "memory_tool_results": [search_result],
    }

# Build graph
workflow = StateGraph(AgentState)
workflow.add_node("memory", memory_node)
workflow.set_entry_point("memory")
workflow.add_edge("memory", END)
```

### 4. Run the Workflow

```python theme={null}
app = workflow.compile()
result = app.invoke({
    "messages": [{"content": "I like Python"}],
    "user_id": "user123",
    "context": "",
    "memory_tool_results": [],
})
```

## Requirements

* MemMachine server running (default: [http://localhost:8080](http://localhost:8080))
* Python 3.12+
* LangGraph (for full workflow integration)