Privacy and advertising choices
Git-Stars uses essential storage for site operation. Optional analytics and ad-measurement scripts stay disabled unless you accept them; partners such as Google may then use cookies or similar identifiers where required. Privacy Policy
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.
This repository page has enough original analysis, source evidence, and comparison context to be treated as an indexable Git-Stars review.
100
review score
Problem solved
DeerFlow solves the challenge of building reliable long-horizon AI agents that can maintain context, use tools, and execute multi-step tasks without losing track. It provides a production-ready harness with built-in memory, sandboxing, and sub-agent orchestration, reducing the complexity of assembling these components from scratch.
Deployment reality
The available setup signal starts with: make setup. Treat this as a starting point, then ask Codex or Claude to inspect the README, environment variables, runtime version, and deployment target before production use.
Commercial use
The recorded license is MIT. This is a useful commercial-use signal, but teams should still verify license text, dependencies, model/API terms, and trademark constraints.
Capability ceiling
Developers can build autonomous research assistants, coding agents, and creative content generators that operate over extended periods. Real-world use cases include automated deep research reports, software development tasks, and complex data analysis. The ceiling is high: with sub-agents, memory, and sandboxing, it can handle tasks that require planning, tool use, and iterative refinement, potentially replacing human effort in many knowledge-work scenarios.
Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.
bytedance/deer-flow is tracked as a Python project in the AI Agent, LLM Tool, Automation area. This evaluation combines public GitHub metadata, category signals, short source summaries, and Git-Stars editorial rules rather than copying project documentation.
Momentum check: the repository has 79k total stars, with +209 today, +0 this week, and +0 this month. These growth windows help distinguish durable adoption from short-lived visibility spikes.
Maintenance check: current activity is Active; the latest push was 22 days ago, and the open issue queue is 939, about 1.19% of total stars. Treat this as an adoption signal, not a substitute for engineering due diligence.
Adoption check: 11k forks and 79k watchers suggest how often the project is reused or followed. License signal: MIT. Always verify license compatibility before commercial or internal use.
Practical fit: this project is most relevant when you need AI prototypes, LLM workflows, and agent-style applications. Be cautious when you need a legal review, security audit, or production SLA.
Source check: Git-Stars currently has 2 explicit source reference(s) for this report and a recent growth signal of 209. Follow the original GitHub repository for final install, security, and release information.
Analysis
ReadyOriginal problem, capability, and verdict guidance are available.
Sources
ReadyRepository metadata and README/source references are attached.
License
ReadyMIT is recorded for review.
Maintenance
ReadyRecent activity is visible in repository metadata.
Alternatives
ReadyEnough nearby projects exist for comparison.
Momentum
79k Stars
Reuse
11k Forks
Attention
79k Watchers
Maintenance
active
License
MIT
Open issues
939
DeerFlow is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to perform a wide range of tasks, powered by extensible skills. It is a ground-up rewrite of the original Deep Research framework, offering advanced features like context engineering, long-term memory, and integration with coding agents.
Key Features
- Orchestrates sub-agents, memory, and sandboxes for versatile task execution - Extensible skills system, including Claude Code integration - Includes a setup wizard, Docker deployment, and support for multiple LLM providers and tracing tools
AI Agent
Agent frameworks, autonomous workflows, and tool-use systems
LLM Tool
Libraries and tools for LLM apps, RAG, prompts, and evals
Automation
Workflow automation, integration glue, and orchestration
make setup90
Health Score
Active
Commit Activity
May 7, 2025
Created
Aug 1, 2026
Last push
+0
Today's growth
+0
7-day growth
+0
30-day growth
Forks
Open
Watchers
bytedance
GitHub profileobra/superpowers
An agentic skills framework & software development methodology that works.
affaan-m/ECC
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
NousResearch/hermes-agent
The agent that grows with you
mattpocock/skills
Skills for Real Engineers. Straight from my .agents directory.
multica-ai/andrej-karpathy-skills
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
✓
License
✓
Forked
✓ Active
Maintained
Problem Solved
DeerFlow solves the challenge of building reliable long-horizon AI agents that can maintain context, use tools, and execute multi-step tasks without losing track. It provides a production-ready harness with built-in memory, sandboxing, and sub-agent orchestration, reducing the complexity of assembling these components from scratch.
Capabilities
Developers can build autonomous research assistants, coding agents, and creative content generators that operate over extended periods. Real-world use cases include automated deep research reports, software development tasks, and complex data analysis. The ceiling is high: with sub-agents, memory, and sandboxing, it can handle tasks that require planning, tool use, and iterative refinement, potentially replacing human effort in many knowledge-work scenarios.
Bottom Line
DeerFlow is ideal for developers and organizations seeking a robust, open-source framework for building long-horizon AI agents. It offers a comprehensive feature set but requires careful deployment and resource planning. The key trade-off is between its powerful automation capabilities and the need for responsible use and infrastructure investment.