Open-source Deep Research

Deep Research for LangChain Agents

Equip LangChain workflows with cited research tools that stay separate from your model choice.

MCP access for agent systems

AutoSearch can sit outside a LangChain graph or agent runtime and expose research actions over MCP. That keeps retrieval orchestration separate from chain logic, while still letting the agent ask for cited evidence when a task needs broader context.

Complements custom retrievers cleanly

Many LangChain apps already use private vector stores or structured tools. AutoSearch adds public and community source discovery without replacing those systems, making it useful for market scans, technical validation, and source-backed prompt expansion.

Model choice stays flexible

LangChain teams often swap models, providers, and routing strategies. AutoSearch keeps deep research behavior outside that decision, so the same 40-channel source workflow can support different LLMs, evaluators, and deployment targets.

How it fits

AutoSearch fits next to a LangChain app as an MCP-native research capability. Your LangChain code can continue to manage prompts, memory, tools, and model routing. AutoSearch handles open-source deep research across technical, academic, social, and Chinese sources, then returns cited material for the agent to process. This is useful when private retrieval is not enough and the agent needs fresh external context without hardwiring research behavior into the chain itself.

Try this prompt

Compare recent LangChain agent patterns for tool reliability.
Use docs, GitHub issues, and community posts, then return cited tradeoffs.