Connect your own Python LLM agent to Cognee MCP to give it persistent knowledge graph memory. The mcp Python SDK lets you call all Cognee MCP tools programmatically — no IDE or chat client required.
Prefer the v1.0 memory tools (remember, recall, forget_memory, improve) for new agent integrations. The legacy tools (cognify, search, delete) are still available when you need lower-level control.
Prerequisites
Python 3.10+
LLM_API_KEY environment variable set (OpenAI key or equivalent)mcp package installed: pip install "mcp>=1.12.0"
Connection Options Option 1: stdio
Option 2: HTTP
Launch the MCP server as a subprocess. The mcp SDK handles the process lifecycle and communicates over stdin/stdout. # Install cognee-mcp and its dependencies pip install "mcp>=1.12.0" uv git clone https://github.com/topoteretes/cognee.git
import asyncio import os from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client server_params = StdioServerParameters( command = "uv" , args = [ "--directory" , "/path/to/cognee/cognee-mcp" , "run" , "cognee-mcp" ], env = { ** os.environ, "LLM_API_KEY" : os.environ[ "LLM_API_KEY" ]}, ) async def main (): async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() # Store knowledge await session.call_tool( "remember" , arguments = { "data" : "Acme Corp signed a $1.2M healthcare contract in Q1 2025." , "dataset_name" : "sales" , }) # Retrieve context for your LLM prompt result = await session.call_tool( "recall" , arguments = { "query" : "healthcare contracts" , "search_type" : "GRAPH_COMPLETION" , }) context = result.content[ 0 ].text print (context) asyncio.run(main())
Replace /path/to/cognee/cognee-mcp with the absolute path to the cognee-mcp directory in your cloned repository. Start the MCP server separately (e.g. with Docker or from source ), then connect over HTTP: # Start the server (HTTP transport, port 8000) docker run -e TRANSPORT_MODE=http --env-file .env -p 8000:8000 --rm -it cognee/cognee-mcp:main
import asyncio from mcp.client.streamable_http import streamable_http_client from mcp import ClientSession async def main (): async with streamable_http_client( "http://localhost:8000/mcp" ) as (read, write): async with ClientSession(read, write) as session: await session.initialize() # Store knowledge await session.call_tool( "remember" , arguments = { "data" : "Acme Corp signed a $1.2M healthcare contract in Q1 2025." , "dataset_name" : "sales" , }) # Retrieve context for your LLM prompt result = await session.call_tool( "recall" , arguments = { "query" : "healthcare contracts" , "search_type" : "GRAPH_COMPLETION" , }) context = result.content[ 0 ].text print (context) asyncio.run(main())
For SSE transport (server started with --transport sse), use mcp.client.sse.sse_client and connect to http://localhost:8000/sse instead. Inject context into your LLM calls Once you have the retrieved context string, pass it to your LLM as part of the system or user prompt:
import openai client = openai.AsyncOpenAI() async def answer_with_memory ( session , question : str ) -> str : result = await session.call_tool( "recall" , arguments = { "query" : question, "search_type" : "GRAPH_COMPLETION" , }) context = result.content[ 0 ].text response = await client.chat.completions.create( model = "gpt-4o-mini" , messages = [ { "role" : "system" , "content" : f "Use this context to answer: \n\n { context } " }, { "role" : "user" , "content" : question}, ], ) return response.choices[ 0 ].message.content
Key tools for agent context Tool Purpose rememberv1.0 API — store data with optional session scoping recallv1.0 API — smart retrieval with session awareness forget_memoryv1.0 API — delete a dataset or wipe everything improvev1.0 API — enrich the graph and bridge session memory into permanent memory cognifyLegacy tool — ingest text, files, or URLs into the knowledge graph searchLegacy tool — retrieve context (GRAPH_COMPLETION, RAG_COMPLETION, CHUNKS) cognify_statusPoll background indexing progress pruneReset all memory (useful in tests)
See the Tools Reference for all available tools and parameters.
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