Verified project record
bytedance/deer-flow
DeerFlow is an open-source agent harness that orchestrates sub-agents, memory, and sandboxes to perform multi-step tasks ranging from research to code generation. It supports local or Docker-based sandbox execution and integrates with external MCP servers and messaging platforms.
Project overview
It orchestrates sub-agents, long-term memory, and sandboxed code execution to address complex tasks that can take minutes to hours.
- Project type
- AI Agent · Workflow · Infrastructure
- Use cases
- Search & Research · Coding & Development · Automation
- Deployment
- Refer to project documentation
- License
- MIT
Best for
- Developers and AI engineers needing a harness to coordinate sub-agents, memory, and sandboxed code execution for long-horizon tasks.
- Enterprise teams looking to trigger automated workflows and interact with agents through IM channels such as Slack, DingTalk, or Feishu.
Key capabilities
- Orchestrates sub-agents, memory, and sandboxes to perform complex long-horizon tasks.
- Supports Local Execution, Docker Execution, and Docker Execution with Kubernetes for sandbox code.
- Supports configurable MCP servers and skills to extend its capabilities, including HTTP/SSE and stdio MCP servers.
- Supports receiving tasks from messaging apps including Telegram, Slack, Feishu, WeChat, WeCom, and DingTalk.
- Ships a skill that lets users interact with a running DeerFlow instance directly from Claude Code.
- Maintains long-term memory for the agent.
- Includes built-in integrations for LangSmith, Langfuse, and Monocle tracing.
Limitations and risks
- Deploying in untrusted environments may introduce security risks such as unauthorized illegal invocation.
- Unauthorized illegal invocation can trigger bulk unauthorized requests and execute high-risk operations.
- If illegally invoked for cyberattacks or data theft, the system poses compliance and legal risks.
Getting started
- Setup is considered medium difficulty. It requires Python 3.12+ and Node.js 22+. The first success path involves cloning the repository, running make setup, running make install to configure the LLM provider and API keys, and starting the development environment with make dev. A starting evaluation hardware profile is 4 vCPU, 8 GB RAM, and 20 GB free SSD.
Alternatives and comparisons
- An autonomous work engine that plans, acts, recovers, and proves the result by operating files, terminal, browser, and APIs while keeping the user in control.
- Orchestrates swarms of CLI-based coding agents in parallel across isolated git worktrees, providing built-in terminal, review, and IDE handoff workflows.
- An open-source operations platform enabling engineers to rapidly build AI Agents, Apps, and Automations that take action across business systems, which can be securely self-hosted.
Project comparisons
Evidence and sources
- GitHub project description: An open-source long-horizon SuperAgent harness that researches, codes, and creates.
- README: DeerFlow (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) is an open-source **super agent harness** that orchestrates **sub-agents**, **memory**, and **sandboxes…
- README: DeerFlow 2.0 is a ground-up rewrite.
- README: DeerFlow supports multiple sandbox execution modes: - **Local Execution** (runs sandbox code directly on the host machine) - **Docker Execution** (runs sandbox code in isolated Do…
- README: DeerFlow supports configurable MCP servers and skills to extend its capabilities.
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