314e2a3489
* feat(task_tool): introduce read-only preflight validation before delegation side effects Add _preflight_delegation_checks() as a zero-side-effect preflight that runs before any delegation side effects (tool assembly, executor creation, background task registration). Existing inline checks for context_mode, bash host policy, and subagent_type config membership are centralized into this function. A rejected preflight returns immediately with status='failed' and leaves state unchanged, so callers can safely retry with corrected arguments. A reserved resume_task_id parameter is included for a future recovery feature, pointing to issue #7633 (validate before claim/cancel) to avoid the reject-after-claim anti-pattern. * fix(task_tool): return config from preflight, drop dead resume_task_id param, soften side-effect comment Fixes UnboundLocalError on successful delegation paths: the helper now returns (config, error) and callers destructure both. Previously config was scoped inside the helper so the happy-path references at lines 1044/1074/1082 raised UnboundLocalError. Verified against test_task_tool_core_logic.py. Drops the unused resume_task_id parameter and its hardcoded rejection branch — dead code since the only call site never passes it, and a future recovery feature should plug real read-only recoverability checks here (see issue #7633). Narrows the helper-side-effect claim in the caller comment: input_control.child(tool_call_id) already registers a benign retry-reusable child gate before the preflight runs, so the wording now scopes the zero-side-effect promise to the helper itself. * test(task_tool): cover preflight abort and happy paths Adds unit coverage for _preflight_delegation_checks (invalid context_mode, host bash disabled/enabled, subagent outside caller policy, no permitted subagents) plus a rejected-delegation test asserting no executor is created and no tools are assembled. Adds a happy-path regression test that would have caught the UnboundLocalError: it asserts the config resolved by the preflight reaches SubagentExecutor, including the unconditional config.model == 'inherit' branch. * fix(task_tool): resolve catalog inside preflight after guards Copilot review flagged that catalog discovery (get_available_subagent_names) was hoisted out of the helper and ran before any guard, so an invalid context_mode could hit the managed-subagent store instead of returning status='failed'. Now the helper takes only allowed_subagents and performs its own catalog lookup strictly after the context_mode and bash guards. The caller no longer calls get_available_subagent_names ahead of preflight. Adds test_preflight_resolves_catalog_after_guards asserting get_available_subagent_names is never invoked when context_mode is invalid. * test(runtime): use ToolRuntime for native task results * docs(task_tool): translate preflight comments to English Comment-only change; no behavior impact. --------- Co-authored-by: Willem Jiang <willem.jiang@gmail.com> Co-authored-by: AochenShen99 <sac1063467113@hotmail.com>
English | 中文 | 日本語 | Français | Русский | Português | 한국어
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 |
| 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.