Build intelligent AI agents with persistent, evolving memory using Mem0's hybrid storage architecture. Learn to implement adaptive memory systems that continuously learn from user interactions and self-improve over time.
- Storing long-term knowledge using a vector database for semantic recall
- Mapping concepts into a knowledge graph using Mem0’s graph memory capabilities
- Blending structured and unstructured memory to answer richer research queries
- Teaching agents user-specific preferences and using that context in future sessions
- Retrieving the right information by combining embeddings and graph traversal
- Mem0 (Open Source)
- OpenAI API Key: For LLM reasoning and embeddings
- Qdrant or another vector store supported by Mem0: Vector database for semantic search
- Neo4j or another graph database supported by Mem0: Graph database for relationship mapping
Personal AI Research Assistant that:
- Maintains intelligent memory that automatically extracts and stores research interests
- Maps knowledge relationships between papers, authors, and concepts using graph storage
- Adapts to user preferences through self-improving memory capabilities
- Provides contextual assistance using hybrid memory retrieval
- Learns and evolves from each research conversation