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## 冲突解决 (Resolve conflicts) - input-box.tsx: 保留 ModeHoverGuide 包裹的模式选择器（PR #26 的 mode-hover-guide） - message-group.tsx: 保留 getCleanContent / hasCitationsBlock / useParsedCitations - message-list-item.tsx: 保留 useParsedCitations，移除重复的 MessageLink（使用 CitationAwareLink） - artifact-file-detail.tsx: 保留 CitationAwareLink、useParsedCitations、contentWithoutCitationsFromParsed - artifacts.py: 保留 path_utils 与 _extract_citation_urls + remove_citations_block 精简实现 - citations/index.ts: 保留并补充 contentWithoutCitationsFromParsed 导出 - en-US.ts: 保留 Ultra 模式描述 "Reasoning, planning and execution with subagents..." - zh-CN.ts: 保留「超级」标签，描述保留「思考、计划并执行，可调用子代理分工协作...」 ## PR #26 代码改动汇总 ### 1. Citations（引用） - lead_agent prompt: 增加 Web search 与子代理合成时的 citation 提示 - general_purpose: 子代理 system prompt 增加 <citations_format> 说明 - frontend utils: 新增 contentWithoutCitationsFromParsed，removeAllCitations 基于单次解析 - frontend artifact: 使用 contentWithoutCitationsFromParsed(parsed) 避免对同一内容解析两次 - backend artifacts: _extract_citation_urls + remove_citations_block，json 提到顶部 ### 2. path_utils（路径解析） - 新增 backend/src/gateway/path_utils.py：resolve_thread_virtual_path，防 path traversal - artifacts.py / skills.py：删除内联路径解析，统一使用 path_utils ### 3. Mode hover guide - input-box: 模式选择器外包 ModeHoverGuide，悬停展示模式说明 ### 4. i18n - en: ultraModeDescription 与 zh: ultraMode / ultraModeDescription 与上游对齐并保留 PR 文案 Co-authored-by: Cursor <cursoragent@cursor.com>
🦌 DeerFlow - v2
Originated from Open Source, give back to Open Source.
A LangGraph-based AI agent backend with sandbox execution capabilities.
Quick Start
Option 1: Docker (Recommended)
The fastest way to get started with a consistent environment:
-
Configure the application:
cp config.example.yaml config.yaml # Edit config.yaml and set your API keys -
Initialize and start:
make docker-init # First time only make docker-dev # Start all services -
Access: http://localhost:2026
See CONTRIBUTING.md for detailed Docker development guide.
Option 2: Local Development
If you prefer running services locally:
-
Check prerequisites:
make check # Verifies Node.js 22+, pnpm, uv, nginx -
Configure and install:
cp config.example.yaml config.yaml make install -
(Optional) Pre-pull sandbox image:
# Recommended if using Docker/Container-based sandbox make setup-sandbox -
Start services:
make dev -
Access: http://localhost:2026
See CONTRIBUTING.md for detailed local development guide.
Features
- 🤖 LangGraph-based Agents - Multi-agent orchestration with sophisticated workflows
- 🧠 Persistent Memory - LLM-powered context retention across conversations with automatic fact extraction
- 🔧 Model Context Protocol (MCP) - Extensible tool integration
- 🎯 Skills System - Reusable agent capabilities
- 🛡️ Sandbox Execution - Safe code execution environment
- 🌐 Unified API Gateway - Single entry point with nginx reverse proxy
- 🔄 Hot Reload - Fast development iteration
- 📊 Real-time Streaming - Server-Sent Events (SSE) support
Documentation
- Contributing Guide - Development environment setup and workflow
- Configuration Guide - Setup and configuration instructions
- Architecture Overview - Technical architecture details
- Backend Architecture - Backend architecture and API reference
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.