Agent Memory Tools
Complete toolkit for building AI agents with memory. Vector databases, frameworks, and platforms compared.
1. Vector Database
Store embeddings and metadata for semantic search. Choose managed (Pinecone) or self-hosted (Qdrant, Weaviate).
2. Memory Framework
Simplify memory management with LlamaIndex, LangChain, or specialized tools like Mem0.
3. Observability
Monitor and debug with LangSmith to track memory retrieval and agent performance.
Vector Databases
Storage layer for embeddings and metadata. Essential for semantic search and retrieval.
Production apps, zero DevOps
$70+/month
Flexibility, knowledge graphs
Free (self-host)
Speed, advanced filtering
Free (self-host)
Massive scale, GPU acceleration
Free (self-host)
Prototyping, quick starts
Free
Research, custom deployments
Free
Quick Pick: Use ChromaDB for prototyping, Pinecone for production managed, or Qdrant for self-hosted production.
Memory Frameworks
High-level tools that abstract memory management. Handle storage, retrieval, and LLM integration.
Key Features:
- Query engines
- Chat engines
- Memory management
- Data loaders
Key Features:
- Conversation buffers
- Memory types
- Agent workflows
- Tool use
Key Features:
- User memory
- Agent memory
- Session tracking
- Auto-updates
Key Features:
- Unlimited context
- Memory swapping
- OS-inspired design
- Stateful agents
Quick Pick: Use LlamaIndex for RAG-heavy apps, LangChain for agent workflows, Mem0 for user-specific memory, or MemGPT for unlimited context.
Observability & Testing
Debug, monitor, and evaluate your agent memory systems in production.
Recommended Stacks
Pre-configured tool combinations for different use cases.
Perfect for: MVPs, demos, learning agent memory concepts
Perfect for: Enterprise, on-premise, high-scale at lower cost