What should I use to build AI agents with tools and memory?

If you want to build AI agents with tools and memory, the best choice depends on how much control you want and how fast you want to ship.

Good options

• LangChain / LangGraph

  • Best-known ecosystem for agent workflows
  • Lots of integrations for tools, retrievers, vector stores, and memory
  • LangGraph is especially good if you want reliable, stateful agent flows

• OpenAI Responses API / Assistants-style tooling

  • Good if you want a more direct, managed approach
  • Easy tool calling, structured outputs, and memory can be built on top of your own storage
  • Less framework overhead

• LlamaIndex

  • Great if your “memory” is mainly documents, knowledge bases, and retrieval
  • Strong for RAG and data connectors
  • Can also support agent tool use

• CrewAI

  • Good for multi-agent workflows
  • Easy to get started if you want role-based agents collaborating
  • Less flexible than lower-level frameworks in some cases

• AutoGen

  • Useful for multi-agent conversations and orchestration
  • Strong if you want agents to interact with each other

For memory specifically

Most frameworks do not give you “memory” magically. Usually you combine:

• Short-term memory: conversation history/state
• Long-term memory: vector database or structured storage
• Persistent storage: Postgres, Redis, SQLite, etc.

Common memory backends:

• Pinecone
• Weaviate
• Qdrant
• Chroma
• Postgres + pgvector
• Redis for fast session memory

My practical recommendation

• If you want production-grade agent workflows: LangGraph
• If you want document-heavy memory / RAG: LlamaIndex
• If you want simple tool-calling quickly: OpenAI API + your own memory store
• If you want multi-agent collaboration: AutoGen or CrewAI

Best stack for most apps

A solid modern setup is:

• LLM API: OpenAI / Anthropic / etc.
• Agent orchestration: LangGraph
• Memory: Postgres + pgvector or Qdrant
• Tooling: your own functions/APIs
• Observability: LangSmith / OpenTelemetry

If you tell me:

1. your language (Python or JavaScript),
2. whether you want single-agent or multi-agent, and
3. what kind of memory you need (chat history, user preferences, document knowledge, or all three),

I can recommend a specific stack.