b4410f64d4
* fix(agents): count each delegation once when a provider reuses its call id Tool-call ids are unique per response, not per conversation. Some providers number every response's calls from call_0, so two task calls in one run can share an id. The delegation ledger used (run_id, tool_call_id) as a delegation's identity: - extract_delegations built one id -> entry map over the whole run, so a later call_0 replaced the earlier delegation's description and took its result while it was still running. - merge_delegations replaced the same (run_id, id), keeping one entry. - SubagentLimitMiddleware counted distinct ids, so the per-run cap (subagents.max_total_per_run) never advanced past the first call_0 and a run could launch any number of subagents one response at a time. Each entry now also records dispatch_message_id, the AIMessage that issued the call, and results pair through pair_tool_call_results, which answers a call only from its own response. The reducer, DurableContext's change filter and the cap use the extended identity. An entry without a dispatch id still updates its run's entry for that call id, and a dispatch-scoped entry adopts a ledger entry written before the field existed, so runs in flight across the upgrade are not counted twice. The steer path names the dispatching AIMessage from the tool request's state, so its delegation update and the graph's own capture stay one entry. * fix(agents): close an earlier run's dangling dispatch by its own response _close_delegations_left_by_earlier_runs decided "answered" by (run_id, tool_call_id). When a stopped run had dispatched call_0 twice, the first reply kept the second, dangling entry in_progress, so the lead kept hearing "already delegated" for work nothing would finish. Entries that carry dispatch_message_id are now decided by pairing that response's calls through pair_tool_call_results. Legacy entries and dispatches no longer in history keep the id-based inference. Also pin that two runs reusing call_0 stay two ledger entries, and cross-reference the three delegation identity helpers. * fix(agents): settle an approval-gated task delegation in the ledger With `task` in `hitl_approval.tools`, the paused run records the dispatch as in_progress and the resume run executes it. The resume run opens with no HumanMessage, so its capture window starts after the pre-existing messages and never contains the dispatching AIMessage: the result was dropped and the entry stayed in_progress with no result_brief, forever. DurableContext now pairs results the resumed window receives with the last pre-existing dispatch and settles the issuing run's entry, keeping its run_id instead of re-tagging it. HITL denials of `task` carry `cancelled` subagent status, so a rejected or denied delegation settles too. * fix(agents): address review on the delegation ledger - Steer path: hold each wait's dispatch AIMessage locally and pass it to the observer, so a later response reusing the call id while the first call is still observed cannot take over its args or dispatch id. - New-turn close: a dispatch whose reply carries subagent status settles from that reply instead of staying in_progress, covering a crash between the tools checkpoint and the next capture. - Reuse thread_state.delegation_key instead of a duplicate identity helper in DurableContext. - CHANGELOG: reference #8128. * docs(changelog): drop the #8128 entry per review
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On February 28th, 2026, DeerFlow claimed the 🏆 #1 spot on GitHub Trending following the launch of version 2. Thanks a million to our incredible community — you made this happen! 💪🔥
DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source super agent harness that orchestrates sub-agents, memory, and sandboxes to do almost anything — powered by extensible skills.
https://github.com/user-attachments/assets/a8bcadc4-e040-4cf2-8fda-dd768b999c18
Note
DeerFlow 2.0 is a ground-up rewrite. It shares no code with v1. If you're looking for the original Deep Research framework, it's maintained on the
1.xbranch — contributions there are still welcome. Active development has moved to 2.0.
Official Website
Learn more and see real demos on our official website. The landing-page case studies open as allowlisted, read-only showcases without requiring a sign-in.
Sister Projects
- LLM Space - Meet our secret weapon behind DeerFlow — one desktop tool to prototype agent ideas, inspect each harness step, replay failures, and benchmark performance.
Coding Plan from ByteDance Volcengine
- We strongly recommend using Doubao-Seed-2.0-Code, DeepSeek v3.2 and Kimi 2.5 to run DeerFlow
- Learn more
- 中国大陆地区的开发者请点击这里
InfoQuest
DeerFlow has newly integrated the intelligent search and crawling toolset independently developed by BytePlus — InfoQuest (supports free online experience)
One-Line Agent Setup
If you use Claude Code, Codex, Cursor, Windsurf, or another coding agent, you can hand it the setup instructions in one sentence:
Help me clone DeerFlow if needed, then bootstrap it for local development by following https://raw.githubusercontent.com/bytedance/deer-flow/main/Install.md
That prompt is intended for coding agents. It tells the agent to clone the repo if needed, choose Docker when available, and stop with the exact next command plus any missing config the user still needs to provide.
Quick Start
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow
make setup # interactive wizard: writes config.yaml and .env (about 2 minutes)
Then start DeerFlow with Docker (recommended) or run it locally:
# Docker
make docker-init # pull the sandbox image (once, or when the image updates)
make docker-start # start services
# Local development
make check # verifies Node.js 22+, pnpm, uv, nginx
make install
make dev
Open http://localhost:2026.
- All configuration options, providers and manual setup: Configuration
- Docker details, sizing, upgrades, startup modes and production deployment: Running the Application
- Running on a server or exposing it beyond localhost: read the Security Notice first
From Deep Research to Super Agent Harness
DeerFlow started as a Deep Research framework — and the community ran with it. Since launch, developers have pushed it far beyond research: building data pipelines, generating slide decks, spinning up dashboards, automating content workflows. Things we never anticipated.
That told us something important: DeerFlow wasn't just a research tool. It was a harness — a runtime that gives agents the infrastructure to actually get work done.
So we rebuilt it from scratch.
DeerFlow 2.0 is no longer a framework you wire together. It's a super agent harness — batteries included, fully extensible. Built on LangGraph and LangChain, it ships with everything an agent needs out of the box: a filesystem, memory, skills, sandbox-aware execution, and the ability to plan and spawn sub-agents for complex, multi-step tasks.
Use it as-is. Or tear it apart and make it yours.
Guides
Each guide is the full reference for its topic. Open the one you need.
| Topic | Guide |
|---|---|
Models, providers, config.yaml, environment variables |
Configuration |
| Docker, local development, upgrades, production deployment | Running the Application |
| Sandbox modes, file system, uploads and outputs | Sandbox |
| Skills, built-in tools, script workflows, Claude Code integration | Skills & Tools |
| MCP servers | MCP Server |
| Sub-agents and task delegation | Sub-Agents, Tool restrictions |
| Long-term memory | Long-Term Memory |
| Session goals, context compaction, task notes, evidence-based decisions | Context, Goals and Agent Decisions |
| Private knowledge retrieval (RAGFlow) | Private Knowledge Retrieval |
| Agentic browser control | Agentic Browser Control |
| Projects, instructions, document shelf | Projects |
| Scheduled tasks | Scheduled Tasks |
| Feishu, Slack, Telegram, Discord, DingTalk and other IM channels | IM Channels |
| Token usage dashboard, workspace, chat archive | Workspace UI |
| Terminal workbench | Terminal Workbench (TUI) |
| Personal access tokens, stream actions, embedded Python client | API Access and Python Client |
| Trace correlation, LangSmith, Langfuse, Monocle | Tracing and Observability |
| Deployment risks, admin model, security recommendations | Security Notice |
Recommended Models
DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. That said, it performs best with models that support:
- Long context windows (100k+ tokens) for deep research and multi-step tasks
- Reasoning capabilities for adaptive planning and complex decomposition
- Multimodal inputs for image understanding and video comprehension
- Strong tool-use for reliable function calling and structured outputs
More Documentation
- Install.md - Setup instructions written for coding agents
- Configuration Reference - Every
config.yamloption - Backend Architecture - Backend architecture and API reference
- Architecture Overview - How the services fit together
- Contributing Guide - Development environment, workflow and tests
- Changelog - Release notes
⚠️ Security Notice
DeerFlow can execute commands and read and write files. It is designed to run in a local trusted environment, and a Gateway admin account is equivalent to code execution on the host. Before you deploy it on a server or expose it to a network, read the full Security Notice and SECURITY.md.
Contributing
We welcome contributions! Please see CONTRIBUTING.md for development setup, workflow, and guidelines.
License
This project is open source and available under the MIT License.
Acknowledgments
DeerFlow is built upon the incredible work of the open-source community. We are deeply grateful to all the projects and contributors whose efforts have made DeerFlow possible. Truly, we stand on the shoulders of giants.
We would like to extend our sincere appreciation to the following projects for their invaluable contributions:
- LangChain: Their exceptional framework powers our LLM interactions and chains, enabling seamless integration and functionality.
- LangGraph: Their innovative approach to multi-agent orchestration has been instrumental in enabling DeerFlow's sophisticated workflows.
These projects exemplify the transformative power of open-source collaboration, and we are proud to build upon their foundations.
Key Contributors
A heartfelt thank you goes out to the core authors of DeerFlow, whose vision, passion, and dedication have brought this project to life:
Your unwavering commitment and expertise have been the driving force behind DeerFlow's success. We are honored to have you at the helm of this journey.