Framework scenario

Best AI Agent Framework for Research Assistants

For research assistants, LangGraph is best when source routing, critique loops, and stateful review matter. AutoGen or CrewAI can be reasonable for exploratory multi-agent research prototypes where speed matters more than production controls.

Best pick

LangGraph

Runner-up

AutoGen

Use when

Source retrieval, Citation checks, Critique pass, Multi-step synthesis, Traceability

Why this recommendation fits

Research workflows often branch by source quality and uncertainty. A graph helps keep retrieval, critique, synthesis, and citation validation separate instead of burying the process inside a single prompt.

AutoGen remains interesting for experimental multi-agent research loops, but it needs careful production review for observability and deterministic stopping conditions.

Decision checklist

  • Source retrieval
  • Citation checks
  • Critique pass
  • Multi-step synthesis
  • Traceability

Frequently asked questions

What framework fits a research assistant?
Use LangGraph when the assistant needs source routing, citation checks, critique loops, and state. Use AutoGen or CrewAI for faster experimental multi-agent prototypes.
Do research agents need graph orchestration?
Graph orchestration helps when source quality, search depth, or critique decisions change the next step. Simple summarizers may not need it.
How should citations be handled?
Citations should be generated from retrieved source metadata and verified before final synthesis. Do not let the model invent source titles or URLs.

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