🦌DeerFlow
bytedance/deer-flow · homepage ↗
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.
⭐ Hugely popular: 80k stars, gaining about 568 a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- Unlike most agent frameworks that expect you to build the rest yourself, DeerFlow ships as a more complete runtime, with filesystem access, skills, persistent memory, and sandboxed code execution out of the box. Kiledjian That lowers the barrier from “interesting research prototype” to “thing I can actually deploy.”.
- DeerFlow remembers across sessions: it builds a persistent memory of your profile, preferences, and accumulated knowledge. The more you use it, the better it knows you, your writing style, your technical stack, your recurring workflows. GitHub.
- Supports three deployment modes: local for dev, Docker for single-server production, and Kubernetes for scale, which tells you ByteDance designed this for real infrastructure.
under the hood
- Built on LangGraph for directed graph orchestration and LangChain for LLM reasoning. Model-agnostic: works with any OpenAI-compatible API endpoint.
- Skills and MCP servers extend its capabilities; OAuth token flows are supported for HTTP/SSE MCP servers. GitHub The community has already pushed it well beyond research into coding, content generation, and automation.
- Human-in-the-loop by design: the agent plans and surfaces its reasoning before executing, so you stay in control of long-horizon tasks.
our take from PR#29, 2026-03-11
star history
- PR#29 25k 2026-03-11
- now 80k + 55k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Output and attention are roughly in balance.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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ByteDance’s Production-Ready Super Agent Harness
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