DeerFlow 2.0 is ByteDance’s open-source super-agent harness for tasks that run minutes to hours. 65.6K stars, 8.7K forks, still on GitHub Trending. It’s a full v2 rewrite on LangChain and LangGraph — a supervisor agent plans the work, spawns sub-agents in parallel, hands them sandboxed tools, and keeps memory across the run.
What it actually does
One agent picks the strategy, others execute in parallel: web search, code execution, document and PPT generation, image and video output, full report writeups. Sandboxed in Docker or Kubernetes for prod, local for dev. LangSmith and Langfuse hooks let you trace where a 30-minute run got stuck.
API surface and integrations
Self-hosted, MIT-licensed, model-agnostic — bring any provider. It speaks MCP, so external tools plug in cleanly. The built-in message gateway wires into Slack, Telegram, Feishu, WeChat, and DingTalk — rare in open-source agent frameworks. Typical builds people are shipping on it: research pipelines, slide decks from a single prompt, automated content ops, internal copilots running off company chat.
Why it matters
Long-horizon agents are the hottest open-source category right now. Most frameworks ship a planner and call it done. DeerFlow ships the boring infra around it — sandboxes, memory, observability, IM channels — which is the actual hard part. Big-tech-backed code with a real maintainer team is what pushed it to 65K stars this fast.
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