deer-flow

bytedance/deer-flow

An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, suba

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Python MITLast pushed Jul 7, 2026

An open-source long-horizon SuperAgent harness that researches, codes, and creates. With the help of sandboxes, memories, tools, skill, subagents and message gateway, it handles different levels of tasks that could take minutes to hours.

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Install

pip install deer-flow

README

🦌 DeerFlow - 2.0

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bytedance%2Fdeer-flow | Trendshift

On February 28th, 2026, DeerFlow claimed the 🏆 #1 spot on GitHub Trending following the launch of version 2. Thanks a million to our incredible community — you made this happen! 💪🔥

DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to do almost anything — powered by extensible skills.

https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18

[!NOTE] DeerFlow 2.0 is a ground-up rewrite. It shares no code with v1. If you're looking for the original Deep Research framework, it's maintained on the 1.x branch — contributions there are still welcome. Active development has moved to 2.0.

Official Website

Learn more and see real demos on our official website.

Coding Plan from ByteDance Volcengine

InfoQuest

DeerFlow has newly integrated the intelligent search and crawling toolset independently developed by BytePlus--InfoQuest (supports free online experience)

InfoQuest_banner

Table of Contents

  • 🦌 DeerFlow - 2.0
    • Official Website
    • Coding Plan from ByteDance Volcengine
    • InfoQuest
    • Table of Contents
    • One-Line Agent Setup
    • Quick Start
      • Configuration
      • Running the Application
        • Deployment Sizing
        • Option 1: Docker (Recommended)
        • Option 2: Local Development
      • Advanced
        • Sandbox Mode
        • MCP Server
        • IM Channels
        • LangSmith Tracing
        • Langfuse Tracing
        • Using Both Providers
    • From Deep Research to Super Agent Harness
    • Core Features
      • Skills & Tools
        • Claude Code Integration
      • Session Goals
      • Manual Context Compaction
      • Sub-Agents
      • Sandbox & File System
      • Context Engineering
      • Long-Term Memory
    • Recommended Models
    • Embedded Python Client
    • Scheduled Tasks
    • Terminal Workbench (TUI)
    • Documentation
    • ⚠️ Security Notice
      • [Improper Deployment May Introduce Security Risks](#improper-deployment-may