DeerFlow

Active
GitHub Python MIT

Description

An open-source long-horizon SuperAgent harness by ByteDance that researches, codes, and creates with sandboxes, memories, tools, skills, subagents and message gateway for complex tasks.

Key Features

  • Super Agent harness - orchestrates sub-agents, memory and sandboxes for complex long-horizon tasks from minutes to hours
  • Extensible skills system - modular skill-based architecture for extending agent capabilities with custom skills
  • Sandboxed code execution - built-in secure sandbox for agents to safely execute code and file operations in isolation
  • Long-term memory - cross-session memory persistence for maintaining context coherence across interactions
  • Context engineering - sophisticated context management and orchestration for information flow in long tasks
  • Multi-model support - supports OpenAI, Anthropic, Gemini, OpenRouter, vLLM and multiple LLM providers

Use Cases

💡 Deep research automation where agents autonomously perform multi-round search, reading, analysis and report writing
💡 Complex coding projects with agents writing, testing and iterating code in sandboxes until features are complete
💡 Content creation workflows orchestrating multiple sub-agents for end-to-end research-to-draft production
💡 Enterprise knowledge management building organizational knowledge agents with continuous learning via long-term memory
💡 Data analysis report generation with agents autonomously performing data processing, statistical analysis and visualization

Quick Start

# Clone the project
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow

# Run the interactive setup wizard
make setup

# Start with Docker
make docker-up
# Or local development mode
make dev

# Verify setup
make doctor

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