bytedance/deer-flow

🦌 DeerFlow - 2.0 English | 中文 | 日本語 | Français | Русский 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 ( D eep E xploration and E fficient R esearch Flow ) is an open-source super agent harness that orchestrates sub-agents , memory , and sandboxes to do almost anything — powered by extensible skills . deer-flow-720p.mp4 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.x branch — 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 中國大陸地區的開發者請點選這裡 InfoQuest InfoQuest reader, web search, and image search use a 30-second HTTP connect/read inactivity timeout. The crawl timeout and navigation_timeout settings remain separate server-side options; they do not control the local HTTP timeout. DeerFlow has newly integrated the intelligent search and crawling toolset independently developed by BytePlus-- InfoQuest (supports free online experience) Table of Contents 🦌 DeerFlow - 2.0 Official Website Coding Plan from ByteDance Volcengine InfoQuest Table of Contents One-Line Agent Setup Quick Start Configuration Running the Application Deployment Sizing Option 1: Docker (Recommended) Upgrading an existing checkout Option 2: Local Development Advanced Sandbox Mode MCP Server IM Channels LangSmith Tracing Langfuse Tracing Monocle Tracing Using Multiple Providers Personal Access Tokens From Deep Research to Super Agent Harness Core Features Skills & Tools Claude Code Integration Session Goals Manual Context Compaction Sub-Agents Sandbox & File System Context Engineering Long-Term Memory Recommended Models Embedded Python Client Projects Scheduled Tasks Terminal Workbench (TUI) Documentation ⚠️ Security Notice Improper Deployment May Introduce Security Risks Security Recommendations Contributing License Acknowledgments Key Contributors Star History 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 Configuration Operators can extend lead-agent, subagent, and DeerMem extraction prompts with literal prepend/append configuration without editing source templates. See prompt overlays . Optional per-model request_admission paces requests to help stay within provider request-per-minute limits. It is disabled by default; see the linked guide to enable it. Clone the DeerFlow repository git clone https://github.com/bytedance/deer-flow.git cd deer-flow Run the setup wizard From the project root directory ( deer-flow/ ), run: make setup This launches an interactive wizard that guides you through choosing an LLM provider, optional web search, and execution/safety preferences such as sandbox mode, bash access, and file-write tools. It generates a minimal config.yaml and writes your keys to .env . Takes about 2 minutes. The wizard also lets you configure an optional web search provider, or skip it for now. Jina, Browserless, and InfoQuest web fetches resolve relative links and image sources using the requested page URL (or a usable HTML base URL), so returned Markdown includes complete destinations. Link resolution preserves the surrounding HTML source, including malformed-page formatting. Run make doctor at any time to verify your setup and get actionable fix hints. If you are opening a GitHub issue about a local setup or runtime problem, run make support-bundle . The command prints reporter next steps, writes a *-issue-summary.md file to paste into the issue, a *-issue-draft.md file for AI-assisted issue filing, and an optional evidence zip under .deer-flow/support-bundles/ . If an AI assistant files the issue, start from the draft and replace every REQUIRED placeholder instead of inventing missing facts. Attach the zip only if a maintainer asks for it, or if the summary alone is not enough. Maintainers and AI triage tools can start with triage.json ; the bundle includes redacted diagnostics and file manifests only, and does not include .env , raw conversation messages, or user file contents. Advanced / manual configuration : If you prefer to edit config.yaml directly, run make config instead to copy the full template. Optional dependency auto-detection accepts UTF-8 configuration files with or without a byte-order mark (BOM). See config.example.yaml for the complete reference including CLI-backed providers (Codex CLI, Claude Code OAuth), OpenRouter, Responses API, subagent runtime caps such as subagents.max_total_per_run , and more. Optional per-model pricing must use one currency across all priced models. DeerFlow disables Console cost estimates when currencies are mixed rather than presenting an invalid aggregate. Administrators can also open Settings → Models to add, edit, test, and enable/disable shared OpenAI-compatible Chat Completions models without editing config.yaml . Enter a unique name, base URL, model ID, and optional API key; saving refreshes the chat model list. Connection testing sends a short streaming tool-call request and may incur provider charges. It does not save the draft or verify image support; set image support and token limits from provider documentation. Official DeepSeek models at https://api.deepseek.com or https://api.deepseek.com/v1 (default HTTPS port) automatically use DeerFlow's DeepSeek adapter, preserving reasoning content across tool calls and honoring output token limits. Chat uses the selected thinking mode; the connection test temporarily disables thinking because DeepSeek rejects forced tool selection in thinking mode. The test checks streaming tool connectivity, not every agent workflow or thinking-mode behavior. Existing saved DeepSeek profiles receive this adapter without re-entering credentials. DeepSeek-specific settings for third-party proxies, other native adapters, and advanced reasoning settings remain YAML-configured. DeepSeek regression tests run offline with the normal backend suite. To verify the real provider explicitly, set DEEPSEEK_TEST_API_KEY in your environment and run from backend/ : DEER_FLOW_RUN_LIVE_TESTS=1 uv run --no-sync pytest tests/test_managed_deepseek_live.py -q These opt-in tests send short requests to DeepSeek and may incur charges; they use temporary state, never save credentials to the deployment catalog, and are skipped in CI. DEEPSEEK_TEST_MODEL optionally selects a different DeepSeek model ID (default: deepseek-flash ). The same tests can be run on unfixed and fixed revisions; success is always the expected result. YAML models remain read-only in this page and take precedence on name conflicts. Managed models are appended after YAML models; edits apply to new configuration snapshots, while active runs retain their existing snapshot. Disabling a model removes it from future selection/resolution, so update any custom-agent or scheduled task definitions that explicitly reference it before disabling it.