LangGraph Integration with MemMachine

This directory contains examples and tools for integrating MemMachine with LangGraph workflows.

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

# 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

cd examples/langgraph
python demo.py

Integration Guide

1. Initialize MemMachineTools

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

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)

The generated tools support:

• raw search filter strings via filter='metadata.category = "work"'
• episode_type as either an EpisodeType enum or a string value like "message"

3. Use in LangGraph Nodes

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"],
        filter='metadata.category = "work"',
    )

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

    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

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)
• Python 3.12+
• LangGraph (for full workflow integration)