Best agent frameworks for research agents
Research agents need retrieval context, source tracking, browser automation, and multi-step synthesis boundaries.
LlamaIndex
Data-oriented agent and workflow framework for building LLM agents over private data, tools, retrieval, and workflows.
LangChain / LangGraph
Agent and graph runtime ecosystem for building tool-using, stateful, and observable LLM applications.
AutoGen
Microsoft open-source framework for multi-agent conversation patterns, agent teams, and collaborative task solving.
Hugging Face MCP Server
MCP server entry for Hugging Face Hub model, dataset, and agent integration workflows.
Playwright MCP Server
MCP server from Microsoft for browser automation and testing workflows backed by Playwright.
Ranking signals
| Signal | Weight | Rationale |
|---|---|---|
| Source provenance | 5 | Research workflows need inspectable sources and citation boundaries. |
| Retrieval and browsing | 5 | Research agents often combine retrieval context with browser automation. |
| Multi-step synthesis | 4 | Research outputs need orchestration across collection, filtering, and synthesis steps. |
Source boundary
Scenario recommendation is derived from retrieval, browser automation, multi-agent workflow, and source verification metadata in this graph.
How should teams choose between AutoGen and CrewAI?
Choose AutoGen for conversational multi-agent experiments and Microsoft ecosystem alignment. Choose CrewAI for role-based crews, task delegation, and team-style workflow automation.
What should a research-agent stack verify before implementation?
Verify retrieval sources, browser automation boundaries, citation handling, and whether the selected framework can keep source provenance visible across multi-step synthesis.