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.
For a complete, runnable example (including python files), see the official LangGraph Example 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
# 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
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,
)
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
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
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)
