Build AI Agents That Actually Remember: Cognee MCP Server

Your AI agents forget everything between conversations. You're rebuilding context from scratch every time, losing valuable insights, and watching your context windows explode with redundant information. Traditional RAG helps, but it's still just sophisticated keyword matching that misses the rich relationships in your data.

Cognee changes this. It's a memory system that gives your AI agents persistent, interconnected knowledge – combining vector similarity with graph relationships to understand not just what information exists, but how it all connects together.

Why Your Current Memory Strategy Isn't Working

Most developers default to vector databases for AI memory. You embed documents, store them, and retrieve similar chunks when needed. It works for basic Q&A, but falls apart when you need:

• Relationship understanding: Vector search finds "Paris" and "France" but doesn't understand that Paris is the capital of France
• Multi-hop reasoning: Connecting facts across multiple documents requires understanding how concepts relate
• Contextual memory: Your agent needs to remember not just facts, but the context of previous conversations and decisions

Cognee solves this with its ECL (Extract-Cognify-Load) pipeline that builds knowledge graphs alongside vector indexes, creating memory that actually understands relationships.

What You Get Out of the Box

5-line integration that immediately gives your agents persistent memory:

import cognee

# Add information
await cognee.add("Our Q4 revenue target is $2M, with $500K from new enterprise deals")
await cognee.cognify()  # Build the knowledge graph

# Query with understanding
results = await cognee.search("What are our enterprise revenue goals?")
# Returns contextually relevant information, not just keyword matches

Hybrid retrieval that combines vector similarity with graph traversal. When you ask about "enterprise goals," it doesn't just find documents containing those words – it follows the relationships to connect revenue targets, deal types, and time periods.

Modular storage backends – start with SQLite for development, scale to Neo4j for production knowledge graphs with millions of relationships.

Real-World Applications That Actually Work

Multi-session customer support agents that remember previous interactions, understand customer history, and can connect issues across multiple touchpoints. Instead of asking customers to repeat their setup every time, your agent builds cumulative understanding.

Research assistants that maintain context across multiple documents and can answer complex questions like "How do the findings in paper X relate to the methodology criticism in paper Y?" The graph structure lets it trace connections that pure vector search would miss.

Code analysis agents that understand your codebase relationships – not just finding similar functions, but understanding dependencies, call patterns, and architectural decisions over time.

The MCP Server Integration

The included MCP server wrapper exposes Cognee's memory capabilities as standardized tools:

• AddMemory: Store new information with automatic relationship extraction
• SearchMemory: Query with hybrid vector+graph retrieval
• SummarizeMemory: Generate contextual summaries to fit context windows
• GraphRAG: Perform multi-hop reasoning across your knowledge graph

Deploy it as a microservice and connect it to any AI system that supports MCP. Your agents get persistent, intelligent memory without changing their core architecture.

Getting Started

Install and configure in under 5 minutes:

pip install cognee[neo4j]  # Include graph database support
export OPENAI_API_KEY="your-key"
export NEO4J_URI="bolt://localhost:7687"  # Or use SQLite for local dev

The MCP server runs with:

python examples/server.py --host 0.0.0.0 --port 8080

Now your agents can POST to /memory/add to store information and /memory/query to retrieve with full relationship understanding.

The Graph Advantage

Traditional RAG treats your data as isolated chunks. Cognee builds a knowledge graph where every piece of information connects to related concepts, creating memory that mirrors how humans actually think and remember.

When you ask about "Q4 targets," it doesn't just return documents containing those words. It follows the graph to understand that Q4 connects to revenue goals, which connect to enterprise deals, which connect to specific customer segments – giving you comprehensive, contextual answers.

This isn't theoretical. The research paper shows significant improvements in multi-hop question answering and reasoning tasks compared to traditional RAG approaches.

Ready to give your AI agents memory that actually works? The 5.8k stars and active community suggest you're not alone in needing this capability.