Open-source Deep Research
AutoSearch vs Exa — Open-Source Deep Research Alternative
Pick AutoSearch when Exa's neural search is great but you still need Chinese sources, MCP, and self-host.
01
Source breadth vs neural-only
Exa specializes in semantic / embedding-based web discovery — strong for finding ideas and analogies. AutoSearch combines lexical and structured access across web, GitHub, arxiv, Reddit, Hacker News, dev.to, plus 10+ Chinese sources. Use Exa for ideation, AutoSearch for cited multi-channel evidence.
02
MCP-native + open source
Exa ships SDKs but is a managed API. AutoSearch is MIT-licensed and ships an MCP server, so any MCP host plugs in with one config. Self-host keeps research data inside your network; agents stay portable across LLMs.
03
Chinese sources + agent host coverage
Exa indexes English-dominant web. AutoSearch covers Zhihu, WeChat, Xiaohongshu, Weibo, Bilibili, 36Kr, and Linux.do alongside global sources — useful when product research, market analysis, or compliance work touches Chinese ecosystems.
How it fits
AutoSearch and Exa solve different layers — they often work better together than as substitutes. Use Exa for embedding-based novelty discovery (find papers/posts similar to a seed). Use AutoSearch as the MCP retrieval tier that fans queries across channels and returns structured citations. Where they overlap (web search), AutoSearch wins on price (open source) and breadth (multi-channel + Chinese).
Try this prompt
Compare Exa neural search and AutoSearch MCP retrieval for a research task on agentic search architecture.
Return the strengths of each, with cited examples.