**DeerFlow** (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) is a community-driven Deep Research framework that builds upon the incredible work of the open source community. Our goal is to combine language models with specialized tools for tasks like web search, crawling, and Python code execution, while giving back to the community that made this possible.
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.
- **[`nvm`](https://github.com/nvm-sh/nvm):**
Manage multiple versions of the Node.js runtime effortlessly.
- **[`pnpm`](https://pnpm.io/installation):**
Install and manage dependencies of Node.js project.
### Environment Requirements
Make sure your system meets the following minimum requirements:
- **[Python](https://www.python.org/downloads/):** Version `3.12+`
- **[Node.js](https://nodejs.org/en/download/):** Version `22+`
- It supports the integration of most models through [litellm](https://docs.litellm.ai/docs/providers).
- Support for open source models like Qwen
- OpenAI-compatible API interface
- Multi-tier LLM system for different task complexities
### Tools and MCP Integrations
- 🔍 **Search and Retrieval**
- Web search via Tavily, Brave Search and more
- Crawling with Jina
- Advanced content extraction
- 🔗 **MCP Seamless Integration**
- Expand capabilities for private domain access, knowledge graph, web browsing and more
- Facilitates integration of diverse research tools and methodologies
### Human Collaboration
- 🧠 **Human-in-the-loop**
- Supports interactive modification of research plans using natural language
- Supports auto-acceptance of research plans
- 📝 **Report Post-Editing**
- Supports Notion-like block editing
- Allows AI refinements, including AI-assisted polishing, sentence shortening, and expansion
- Powered by [tiptap](https://tiptap.dev/)
### Content Creation
- 🎙️ **Podcast and Presentation Generation**
- AI-powered podcast script generation and audio synthesis
- Automated creation of simple PowerPoint presentations
- Customizable templates for tailored content
## Architecture
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.
The system employs a streamlined workflow with the following components:
1.**Coordinator**: The entry point that manages the workflow lifecycle
- Initiates the research process based on user input
- Delegates tasks to the planner when appropriate
- Acts as the primary interface between the user and the system
2.**Planner**: Strategic component for task decomposition and planning
- Analyzes research objectives and creates structured execution plans
- Determines if enough context is available or if more research is needed
- Manages the research flow and decides when to generate the final report
3.**Research Team**: A collection of specialized agents that execute the plan:
- **Researcher**: Conducts web searches and information gathering using tools like web search engines, crawling and even MCP services.
- **Coder**: Handles code analysis, execution, and technical tasks using Python REPL tool.
Each agent has access to specific tools optimized for their role and operates within the LangGraph framework
4.**Reporter**: Final stage processor for research outputs
- Aggregates findings from the research team
- Processes and structures the collected information
- Generates comprehensive research reports
## Text-to-Speech Integration
DeerFlow now includes a Text-to-Speech (TTS) feature that allows you to convert research reports to speech. This feature uses the volcengine TTS API to generate high-quality audio from text. Features like speed, volume, and pitch are also customizable.
### Using the TTS API
You can access the TTS functionality through the `/api/tts` endpoint:
```bash
# Example API call using curl
curl --location 'http://localhost:8000/api/tts' \
--header 'Content-Type: application/json' \
--data '{
"text": "This is a test of the text-to-speech functionality.",
DeerFlow uses LangGraph for its workflow architecture. You can use LangGraph Studio to debug and visualize the workflow in real-time.
#### Running LangGraph Studio Locally
DeerFlow includes a `langgraph.json` configuration file that defines the graph structure and dependencies for the LangGraph Studio. This file points to the workflow graphs defined in the project and automatically loads environment variables from the `.env` file.
##### Mac
```bash
# Install uv package manager if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install dependencies and start the LangGraph server
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](https://github.com/langchain-ai/langchain)**: Their exceptional framework powers our LLM interactions and chains, enabling seamless integration and functionality.
- **[LangGraph](https://github.com/langchain-ai/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.
