Open-source Deep Research

Deep Research for AutoGen Agents

Let AutoGen teams gather source-backed evidence before agents debate, plan, or write.

Shared research for agent teams

AutoGen workflows often split tasks across several agents. AutoSearch gives those agents a common MCP-native research capability, so planner, critic, and writer roles can refer to the same cited evidence instead of relying on separate browser notes.

External context before consensus

Multi-agent systems can converge on weak assumptions when outside evidence is thin. AutoSearch helps each loop bring in docs, papers, GitHub discussions, social feedback, and Chinese sources before agents finalize recommendations or implementation plans.

Provider-independent research behavior

AutoGen deployments may use different models for different roles. AutoSearch keeps the research capability LLM-decoupled, allowing the same source discovery process to serve heterogeneous agent teams without being bound to one model provider.

How it fits

AutoSearch works as an MCP-native research service around an AutoGen setup. AutoGen can keep coordinating agents, role prompts, handoffs, and decisions. AutoSearch handles the open-source deep research step when any role needs current evidence from 40 channels. The result is a shared, cited evidence packet that agents can critique, summarize, or transform while source gathering remains separate from the multi-agent orchestration.

Try this prompt

Have the researcher agent compare current AutoGen tool-use patterns.
Use docs, GitHub issues, and community examples, then brief the critic with citations.