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feat: add langgraph support (MemMachine#491)
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‎examples/langgraph/README.md‎

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# LangGraph Integration with MemMachine
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This directory contains examples and tools for integrating MemMachine with LangGraph workflows.
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## Overview
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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.
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## Configuration
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The demo can be configured via environment variables:
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| Variable | Description | Default |
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|----------|-------------|---------|
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| `MEMORY_BACKEND_URL` | URL of the MemMachine backend service | `http://localhost:8080` |
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| `LANGGRAPH_GROUP_ID` | Group identifier for the demo | `langgraph_demo` |
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| `LANGGRAPH_AGENT_ID` | Agent identifier for the demo | `demo_agent` |
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| `LANGGRAPH_USER_ID` | User identifier for the demo | `demo_user` |
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| `LANGGRAPH_SESSION_ID` | Session identifier for the demo | `demo_session_001` |
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## Usage
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### Setting Environment Variables
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```bash
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# Set environment variables (optional)
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export MEMORY_BACKEND_URL="http://localhost:8080"
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export LANGGRAPH_GROUP_ID="my_group"
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export LANGGRAPH_AGENT_ID="my_agent"
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export LANGGRAPH_USER_ID="my_user"
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export LANGGRAPH_SESSION_ID="my_session"
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# Run the demo
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python examples/langgraph/demo.py
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```
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### Running the Demo
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```bash
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cd examples/langgraph
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python demo.py
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```
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## Integration Guide
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### 1. Initialize MemMachineTools
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```python
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import os
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from tool import MemMachineTools
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# Get configuration from environment variables or use defaults
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base_url = os.getenv("MEMORY_BACKEND_URL", "http://localhost:8080")
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group_id = os.getenv("LANGGRAPH_GROUP_ID", "my_group")
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agent_id = os.getenv("LANGGRAPH_AGENT_ID", "my_agent")
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user_id = os.getenv("LANGGRAPH_USER_ID", "user123")
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tools = MemMachineTools(
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base_url=base_url,
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group_id=group_id,
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agent_id=agent_id,
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user_id=user_id,
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)
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```
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### 2. Create Tool Functions
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```python
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from tool import create_add_memory_tool, create_search_memory_tool
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add_memory = create_add_memory_tool(tools)
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search_memory = create_search_memory_tool(tools)
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```
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### 3. Use in LangGraph Nodes
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```python
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from langgraph.graph import StateGraph, END
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def memory_node(state: AgentState):
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# Search for relevant memories
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search_result = search_memory(
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query=state["messages"][-1].content,
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user_id=state["user_id"],
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)
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# Add new memory
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add_memory(
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content=state["messages"][-1].content,
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user_id=state["user_id"],
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)
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return {
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"context": search_result.get("summary", ""),
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"memory_tool_results": [search_result],
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}
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# Build graph
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workflow = StateGraph(AgentState)
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workflow.add_node("memory", memory_node)
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workflow.set_entry_point("memory")
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workflow.add_edge("memory", END)
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```
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### 4. Run the Workflow
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```python
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app = workflow.compile()
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result = app.invoke({
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"messages": [{"content": "I like Python"}],
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"user_id": "user123",
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"context": "",
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"memory_tool_results": [],
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})
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```
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## Requirements
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- MemMachine server running (default: http://localhost:8080)
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- Python 3.12+
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- LangGraph (for full workflow integration)

‎examples/langgraph/demo.py‎

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import json
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import os
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from typing import Annotated, TypedDict
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from tool import MemMachineTools, create_add_memory_tool, create_search_memory_tool
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# ============================================================================
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# Configuration
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# ============================================================================
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# Configuration values can be set via environment variables or use defaults
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MEMORY_BACKEND_URL = os.getenv("MEMORY_BACKEND_URL", "http://localhost:8080")
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LANGGRAPH_GROUP_ID = os.getenv("LANGGRAPH_GROUP_ID", "langgraph_demo")
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LANGGRAPH_AGENT_ID = os.getenv("LANGGRAPH_AGENT_ID", "demo_agent")
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LANGGRAPH_USER_ID = os.getenv("LANGGRAPH_USER_ID", "demo_user")
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LANGGRAPH_SESSION_ID = os.getenv("LANGGRAPH_SESSION_ID", "demo_session_001")
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# State definition for LangGraph workflow
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class AgentState(TypedDict):
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"""State for the agent workflow."""
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messages: Annotated[list, "List of messages in the conversation"]
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user_id: str
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context: str
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memory_tool_results: Annotated[list, "Results from memory tool calls"]
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def simple_memory_workflow_demo():
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"""
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Simple demo showing basic memory operations without LangGraph dependency.
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This demonstrates the MemMachine tools functionality that can be integrated
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into LangGraph workflows.
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"""
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print("=" * 60)
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print("MemMachine LangGraph Tools Demo")
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print("=" * 60)
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# Initialize tools
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print("\n1. Initializing MemMachine tools...")
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print(" Configuration:")
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print(f" - Backend URL: {MEMORY_BACKEND_URL}")
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print(f" - Group ID: {LANGGRAPH_GROUP_ID}")
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print(f" - Agent ID: {LANGGRAPH_AGENT_ID}")
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print(f" - User ID: {LANGGRAPH_USER_ID}")
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print(f" - Session ID: {LANGGRAPH_SESSION_ID}")
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tools = MemMachineTools(
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base_url=MEMORY_BACKEND_URL,
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group_id=LANGGRAPH_GROUP_ID,
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agent_id=LANGGRAPH_AGENT_ID,
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user_id=LANGGRAPH_USER_ID,
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session_id=LANGGRAPH_SESSION_ID,
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)
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# Check if server is available
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try:
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health = tools.client.health_check()
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print(f"✅ MemMachine server is healthy: {health.get('status', 'ok')}")
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except Exception as e:
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print(f"❌ MemMachine server not available: {e}")
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print(f" Please start MemMachine server on {MEMORY_BACKEND_URL}")
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return
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# Create tool functions
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add_memory = create_add_memory_tool(tools)
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search_memory = create_search_memory_tool(tools)
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print("\n2. Adding memories...")
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# Add some memories
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memories_to_add = [
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{
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"content": "User prefers working with Python for backend development",
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"metadata": {"category": "preference", "technology": "Python"},
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},
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{
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"content": "User mentioned they are working on a project deadline this Friday",
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"metadata": {"category": "task", "urgency": "high"},
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},
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{
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"content": "User enjoys hiking on weekends and lives in San Francisco",
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"metadata": {"category": "personal", "hobby": "hiking"},
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},
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{
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"content": "User is interested in machine learning and AI agents",
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"metadata": {"category": "interest", "field": "AI"},
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},
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]
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for mem in memories_to_add:
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result = add_memory(
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content=mem["content"],
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metadata=mem["metadata"],
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)
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if result["status"] == "success":
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print(f" ✅ Added: {mem['content'][:50]}...")
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else:
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print(f" ❌ Failed: {result.get('message', 'Unknown error')}")
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print("\n3. Searching memories...")
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# Search for memories
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search_queries = [
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"What does the user prefer for development?",
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"What are the user's upcoming deadlines?",
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"What are the user's hobbies?",
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"What is the user interested in?",
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]
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for query in search_queries:
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print(f"\n 🔍 Query: {query}")
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result = search_memory(query=query, limit=3)
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if result["status"] == "success":
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results = result["results"]
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episodic = results.get("episodic_memory", [])
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if episodic:
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print(f" Found {len(episodic)} relevant memories:")
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for i, mem in enumerate(episodic[:3], 1):
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content = (
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mem.get("content", "") if isinstance(mem, dict) else str(mem)
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)
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print(f" {i}. {content[:80]}...")
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else:
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print(" No memories found")
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else:
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print(f" ❌ Search failed: {result.get('message', 'Unknown error')}")
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print("\n4. Getting context...")
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context = tools.get_context()
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print(f" Context: {json.dumps(context, indent=2)}")
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# Cleanup
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tools.close()
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def main():
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"""Main demo function."""
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try:
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# Run simple demo
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simple_memory_workflow_demo()
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except KeyboardInterrupt:
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print("\n\nDemo interrupted by user.")
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except Exception as e:
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print(f"\n❌ Demo failed with error: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()

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