Open-source Deep Research
Open-source Deep Research for GPT Researcher
Use AutoSearch when agent hosts need broad cited research without changing their LLM stack.
01
MCP-native for agent hosts
AutoSearch is designed to be called by MCP-capable hosts and agent tools. That makes it useful when GPT Researcher-style work needs to happen inside Claude Code, Cursor, Cline, or another environment where the user already works.
02
Broader channel mix by design
Research tasks often need code discussions, papers, social sentiment, and Chinese-language sources in one pass. AutoSearch focuses on 40 channels and 10+ Chinese sources, helping agents collect varied evidence before synthesis begins.
03
Keeps synthesis model separate
AutoSearch does not require teams to standardize on one synthesis model. The host agent can choose how to summarize, debate, or report findings while AutoSearch concentrates on cited discovery and source coverage.
How it fits
AutoSearch fits in stacks where deep research is a capability called by an existing agent host, not a separate destination. If your workflow already uses GPT Researcher concepts, AutoSearch can provide MCP-native source discovery across 40 channels, then return cited material to whichever host or model is doing synthesis. This is helpful for teams that want open-source deep research while keeping control over agent orchestration and LLM selection.
Try this prompt
Compare GPT Researcher and MCP-native research workflows for developer teams.
Return cited differences in setup, source coverage, and agent integration.