DeerFlow 1.x (Deep Research)

Description

DeerFlow (Deep Exploration and Efficient Research Flow) is ByteDance's MIT-licensed open-source deep-research framework, first released around May 2025. The architecture catalogued here is the classic DeerFlow 1.x graph (the `main-1.x` branch), built on LangGraph and LangChain as a stateful multi-agent workflow. A coordinator node gates incoming requests and routes research questions onward; an optional background-investigator runs a preliminary web search to ground planning; a planner decomposes the question into a typed, multi-step plan; a human-feedback interrupt lets the user accept, edit in natural language, or auto-accept the plan; a research-team node then supervises the plan, dispatching each step to a ReAct researcher (web search, crawling, retrieval, MCP tools) or a coder (Python REPL); and a reporter synthesises the findings into a cited final report. Iteration is bounded by max_plan_iterations and max_step_num. The project has since moved to a separate DeerFlow 2.0 "SuperAgent harness" on the `main` branch (a lead-agent plus dynamic subagents in Docker sandboxes), so the node names and file paths referenced here apply to `main-1.x`.

Solution

LangGraph state graph: coordinator -> (background_investigator) -> planner -> human_feedback -> research_team, which dispatches each plan step to researcher or coder (ReAct loops) and collects results back into shared state; the planner may re-plan up to max_plan_iterations before control passes to reporter, which writes the final report.

Primary use cases

• automated deep research over a single natural-language question
• long-form, cited report generation from web and private-KB sources
• plan-then-execute research with a human plan-review gate
• multi-agent workflows combining web search, retrieval, and Python analysis