LangGraph

# 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

cd examples/langgraph
python demo.py

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)

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)

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