mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-22 13:44:46 +08:00
feat: add docker support (#104)
* init docker support * chore: update Dockerfile and .dockerignore for improved build context and dependency management * feat: add Docker support with Dockerfile, docker-compose, and .dockerignore for web application * feat: update environment configuration and docker-compose for improved API integration * docs: update Japanese and Chinese README files for consistency and clarity
This commit is contained in:
Leo Hui
122
README.md
122
README.md
@@ -3,6 +3,7 @@
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[](https://www.python.org/downloads/)
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[](https://opensource.org/licenses/MIT)
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[](https://deepwiki.com/bytedance/deer-flow)
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<!-- DeepWiki badge generated by https://deepwiki.ryoppippi.com/ -->
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[English](./README.md) | [简体中文](./README_zh.md) | [日本語](./README_ja.md) | [Deutsch](./README_de.md)
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@@ -20,6 +21,7 @@ Please visit [our official website](https://deerflow.tech/) for more details.
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https://github.com/user-attachments/assets/f3786598-1f2a-4d07-919e-8b99dfa1de3e
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In this demo, we showcase how to use DeerFlow to:
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- Seamlessly integrate with MCP services
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- Conduct the Deep Research process and produce a comprehensive report with images
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- Create podcast audio based on the generated report
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---
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## 📑 Table of Contents
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- [🚀 Quick Start](#quick-start)
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- [🌟 Features](#features)
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- [🏗️ Architecture](#architecture)
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- [🛠️ Development](#development)
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- [🐳 Docker](#docker)
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- [🗣️ Text-to-Speech Integration](#text-to-speech-integration)
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- [📚 Examples](#examples)
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- [❓ FAQ](#faq)
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- [💖 Acknowledgments](#acknowledgments)
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- [⭐ Star History](#star-history)
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## Quick Start
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DeerFlow is developed in Python, and comes with a web UI written in Node.js. To ensure a smooth setup process, we recommend using the following tools:
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### Recommended Tools
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- **[`uv`](https://docs.astral.sh/uv/getting-started/installation/):**
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Simplify Python environment and dependency management. `uv` automatically creates a virtual environment in the root directory and installs all required packages for you—no need to manually install Python environments.
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@@ -64,11 +66,14 @@ DeerFlow is developed in Python, and comes with a web UI written in Node.js. To
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Install and manage dependencies of Node.js project.
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### Environment Requirements
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Make sure your system meets the following minimum requirements:
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- **[Python](https://www.python.org/downloads/):** Version `3.12+`
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- **[Node.js](https://nodejs.org/en/download/):** Version `22+`
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### Installation
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```bash
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# Clone the repository
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git clone https://github.com/bytedance/deer-flow.git
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### Web UI
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This project also includes a Web UI, offering a more dynamic and engaging interactive experience.
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> [!NOTE]
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> You need to install the dependencies of web UI first.
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Explore more details in the [`web`](./web/) directory.
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## Supported Search Engines
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DeerFlow supports multiple search engines that can be configured in your `.env` file using the `SEARCH_API` variable:
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- **Tavily** (default): A specialized search API for AI applications
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- Requires `TAVILY_API_KEY` in your `.env` file
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- Sign up at: https://app.tavily.com/home
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- Requires `TAVILY_API_KEY` in your `.env` file
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- Sign up at: https://app.tavily.com/home
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- **DuckDuckGo**: Privacy-focused search engine
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- No API key required
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- No API key required
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- **Brave Search**: Privacy-focused search engine with advanced features
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- Requires `BRAVE_SEARCH_API_KEY` in your `.env` file
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- Sign up at: https://brave.com/search/api/
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- Requires `BRAVE_SEARCH_API_KEY` in your `.env` file
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- Sign up at: https://brave.com/search/api/
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- **Arxiv**: Scientific paper search for academic research
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- No API key required
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- Specialized for scientific and academic papers
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- No API key required
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- Specialized for scientific and academic papers
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To configure your preferred search engine, set the `SEARCH_API` variable in your `.env` file:
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### Core Capabilities
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- 🤖 **LLM Integration**
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- It supports the integration of most models through [litellm](https://docs.litellm.ai/docs/providers).
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- Support for open source models like Qwen
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- OpenAI-compatible API interface
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- Multi-tier LLM system for different task complexities
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- It supports the integration of most models through [litellm](https://docs.litellm.ai/docs/providers).
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- Support for open source models like Qwen
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- OpenAI-compatible API interface
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- Multi-tier LLM system for different task complexities
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### Tools and MCP Integrations
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- 🔍 **Search and Retrieval**
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- Web search via Tavily, Brave Search and more
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- Crawling with Jina
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- Advanced content extraction
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- Web search via Tavily, Brave Search and more
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- Crawling with Jina
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- Advanced content extraction
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- 🔗 **MCP Seamless Integration**
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- Expand capabilities for private domain access, knowledge graph, web browsing and more
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- Facilitates integration of diverse research tools and methodologies
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- Expand capabilities for private domain access, knowledge graph, web browsing and more
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- Facilitates integration of diverse research tools and methodologies
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### Human Collaboration
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- 🧠 **Human-in-the-loop**
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- Supports interactive modification of research plans using natural language
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- Supports auto-acceptance of research plans
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- Supports interactive modification of research plans using natural language
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- Supports auto-acceptance of research plans
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- 📝 **Report Post-Editing**
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- Supports Notion-like block editing
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- Allows AI refinements, including AI-assisted polishing, sentence shortening, and expansion
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- Powered by [tiptap](https://tiptap.dev/)
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- Supports Notion-like block editing
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- Allows AI refinements, including AI-assisted polishing, sentence shortening, and expansion
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- Powered by [tiptap](https://tiptap.dev/)
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### Content Creation
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- 🎙️ **Podcast and Presentation Generation**
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- AI-powered podcast script generation and audio synthesis
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- Automated creation of simple PowerPoint presentations
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- Customizable templates for tailored content
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- AI-powered podcast script generation and audio synthesis
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- Automated creation of simple PowerPoint presentations
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- Customizable templates for tailored content
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## Architecture
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DeerFlow implements a modular multi-agent system architecture designed for automated research and code analysis. The system is built on LangGraph, enabling a flexible state-based workflow where components communicate through a well-defined message passing system.
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> See it live at [deerflow.tech](https://deerflow.tech/#multi-agent-architecture)
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The system employs a streamlined workflow with the following components:
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1. **Coordinator**: The entry point that manages the workflow lifecycle
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- Initiates the research process based on user input
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- Delegates tasks to the planner when appropriate
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- Acts as the primary interface between the user and the system
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2. **Planner**: Strategic component for task decomposition and planning
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- Analyzes research objectives and creates structured execution plans
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- Determines if enough context is available or if more research is needed
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- Manages the research flow and decides when to generate the final report
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3. **Research Team**: A collection of specialized agents that execute the plan:
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- **Researcher**: Conducts web searches and information gathering using tools like web search engines, crawling and even MCP services.
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- **Coder**: Handles code analysis, execution, and technical tasks using Python REPL tool.
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Each agent has access to specific tools optimized for their role and operates within the LangGraph framework
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Each agent has access to specific tools optimized for their role and operates within the LangGraph framework
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4. **Reporter**: Final stage processor for research outputs
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- Aggregates findings from the research team
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--output speech.mp3
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```
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## Development
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### Testing
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```
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After starting the LangGraph server, you'll see several URLs in the terminal:
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- API: http://127.0.0.1:2024
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- Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
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- API Docs: http://127.0.0.1:2024/docs
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5. Provide feedback during the planning phase to refine research plans
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When you submit a research topic in the Studio UI, you'll be able to see the entire workflow execution, including:
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- The planning phase where the research plan is created
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- The feedback loop where you can modify the plan
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- The research and writing phases for each section
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- The final report generation
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## Docker
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You can also run this project with Docker.
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First, you need read the [configuration](#configuration) below. Make sure `.env`, `.conf.yaml` files are ready.
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Second, to build a Docker image of your own web server:
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```bash
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docker build -t deer-flow-api .
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```
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Final, start up a docker container running the web server:
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```bash
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# Replace deer-flow-api-app with your preferred container name
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docker run -d -t -p 8000:8000 --env-file .env --name deer-flow-api-app deer-flow-api
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# stop the server
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docker stop deer-flow-api-app
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```
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### Docker Compose (include both backend and frontend)
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DeerFlow provides a docker-compose setup to easily run both the backend and frontend together:
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```bash
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# building docker image
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docker compose build
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# start the server
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docker compose up
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```
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## Examples
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The following examples demonstrate the capabilities of DeerFlow:
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### Research Reports
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1. **OpenAI Sora Report** - Analysis of OpenAI's Sora AI tool
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- Discusses features, access, prompt engineering, limitations, and ethical considerations
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- [View full report](examples/openai_sora_report.md)
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2. **Google's Agent to Agent Protocol Report** - Overview of Google's Agent to Agent (A2A) protocol
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- Discusses its role in AI agent communication and its relationship with Anthropic's Model Context Protocol (MCP)
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- [View full report](examples/what_is_agent_to_agent_protocol.md)
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3. **What is MCP?** - A comprehensive analysis of the term "MCP" across multiple contexts
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- Explores Model Context Protocol in AI, Monocalcium Phosphate in chemistry, and Micro-channel Plate in electronics
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- [View full report](examples/what_is_mcp.md)
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4. **Bitcoin Price Fluctuations** - Analysis of recent Bitcoin price movements
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- Examines market trends, regulatory influences, and technical indicators
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- Provides recommendations based on historical data
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- [View full report](examples/bitcoin_price_fluctuation.md)
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5. **What is LLM?** - An in-depth exploration of Large Language Models
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- Discusses architecture, training, applications, and ethical considerations
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- [View full report](examples/what_is_llm.md)
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6. **How to Use Claude for Deep Research?** - Best practices and workflows for using Claude in deep research
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- Covers prompt engineering, data analysis, and integration with other tools
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- [View full report](examples/how_to_use_claude_deep_research.md)
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7. **AI Adoption in Healthcare: Influencing Factors** - Analysis of factors driving AI adoption in healthcare
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- Discusses AI technologies, data quality, ethical considerations, economic evaluations, organizational readiness, and digital infrastructure
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- [View full report](examples/AI_adoption_in_healthcare.md)
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8. **Quantum Computing Impact on Cryptography** - Analysis of quantum computing's impact on cryptography
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- Discusses vulnerabilities of classical cryptography, post-quantum cryptography, and quantum-resistant cryptographic solutions
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- [View full report](examples/Quantum_Computing_Impact_on_Cryptography.md)
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The application now supports an interactive mode with built-in questions in both English and Chinese:
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1. Launch the interactive mode:
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```bash
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uv run main.py --interactive
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```
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1. **Plan Review**: When human in the loop is enabled, the system will present the generated research plan for your review before execution
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2. **Providing Feedback**: You can:
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- Accept the plan by responding with `[ACCEPTED]`
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- Edit the plan by providing feedback (e.g., `[EDIT PLAN] Add more steps about technical implementation`)
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- The system will incorporate your feedback and generate a revised plan
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3. **Auto-acceptance**: You can enable auto-acceptance to skip the review process:
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- Via API: Set `auto_accepted_plan: true` in your request
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4. **API Integration**: When using the API, you can provide feedback through the `feedback` parameter:
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```json
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{
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"messages": [{"role": "user", "content": "What is quantum computing?"}],
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"messages": [{ "role": "user", "content": "What is quantum computing?" }],
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"thread_id": "my_thread_id",
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"auto_accepted_plan": false,
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"feedback": "[EDIT PLAN] Include more about quantum algorithms"
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@@ -464,6 +523,7 @@ We would like to extend our sincere appreciation to the following projects for t
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These projects exemplify the transformative power of open-source collaboration, and we are proud to build upon their foundations.
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### Key Contributors
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A heartfelt thank you goes out to the core authors of `DeerFlow`, whose vision, passion, and dedication have brought this project to life:
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- **[Daniel Walnut](https://github.com/hetaoBackend/)**
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