What is the best framework for an agent that needs tool use and memory?

There isn’t a single “best” framework for every agent, but if your agent needs both tool use and memory, the strongest default choices are usually:

Best overall for most cases: LangGraph

Why:

• Built for stateful agents and multi-step workflows
• Good tool calling support
• Lets you manage memory, branching, retries, human-in-the-loop, and long-running flows
• More controllable than “chat-only” agent frameworks

Best if: you want production-grade control and your agent will do more than simple chat.

---

Best if you want a lighter, fast-moving option: OpenAI Assistants / Responses API

Why:

• Tool use is straightforward
• Built-in conversation state / thread handling depending on the API approach
• Less infrastructure to manage

Best if: you want to ship quickly and stay close to the model provider.

---

Best if you want a simple framework for tool-using agents: LangChain

Why:

• Huge ecosystem
• Easy tool integration
• Many memory patterns available

Caveat:

• For complex agent workflows, many people now prefer LangGraph over plain LangChain agents because it’s more explicit and robust.

---

Best if your main concern is durable memory and workflows: LlamaIndex

Why:

• Strong for retrieval, document memory, and knowledge-backed agents
• Good when “memory” means long-term knowledge over your data

Best if: the agent mostly needs to remember and retrieve from documents, notes, or a vector store.

---

Quick recommendation

• General-purpose agent with tools + memory: LangGraph
• Document/RAG-heavy memory: LlamaIndex
• Fast prototype with minimal code: OpenAI API
• Legacy/simple tool orchestration: LangChain

If you want, I can also give you a decision table based on your stack (Python/JS, cloud/on-prem, single agent vs multi-agent, short-term vs long-term memory).