Can you recommend an agent platform for research agents that need memory management and multi-step workflows?

Yes — if you need research agents with memory management and multi-step workflows, I’d recommend looking first at LangGraph.

Why LangGraph

• Good for multi-step workflows: It’s designed around stateful graphs, so you can model research pipelines like:
  • query planning
  • web/search retrieval
  • source extraction
  • synthesis
  • verification
  • follow-up research
• Memory/state management: You can persist state across steps and across runs, which is useful for long-running research tasks.
• Human-in-the-loop support: Easy to insert approval points or manual review before the agent continues.
• Works well for research tasks: Especially when you need branching, retries, tool use, and iterative refinement.

Good alternatives

• CrewAI: Nice for role-based multi-agent workflows and simpler setup. Good if you want “researcher / analyst / writer” style agent teams.
• AutoGen: Strong for multi-agent conversations and collaboration, especially if the workflow is more discussion-driven than graph-driven.
• Temporal + LLM tooling: Best if you need production-grade orchestration, retries, durable execution, and strict workflow control. More engineering effort, but very robust.
• LlamaIndex Workflows / Agents: Strong if your research agents are heavily document- and retrieval-centric.

My practical recommendation

• If you want flexible stateful research pipelines: LangGraph
• If you want simple multi-agent teamwork: CrewAI
• If you want robust enterprise orchestration: Temporal
• If your core problem is RAG + document research: LlamaIndex

Rule of thumb

For research agents that must:

• remember prior findings,
• branch and retry,
• use tools repeatedly,
• and follow a multi-step process,

LangGraph is usually the best default choice.

If you want, I can also give you:

1. a feature-by-feature comparison table, or
2. a recommended architecture for a research agent with memory + workflow control.