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DeerFlow

A community-driven Deep Research framework integrating language models with web search and Python tools.

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Information

  • Publisher
    frend guo
  • Websitegithub.com
  • Published date2025/05/20

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Introduction

DeerFlow

DeerFlow (Deep Exploration and Efficient Research Flow) is an innovative, community-driven framework designed for advanced research by integrating language models with specialized tools. Built on the robust foundations of open-source technologies like LangChain and LangGraph, it offers a modular multi-agent system for automated research and code analysis.

Key Features
  • LLM Integration: Supports a wide range of models via LiteLLM, including open-source options like Qwen and OpenAI-compatible APIs, with a multi-tier system for task complexity.
  • Search & Retrieval Tools: Incorporates web search via Tavily, Brave Search, DuckDuckGo, and Arxiv, alongside crawling with Jina for comprehensive data gathering.
  • MCP Seamless Integration: Expands capabilities for private domain access and knowledge graph integration, enhancing research depth.
  • Human-in-the-Loop: Facilitates interactive plan modifications and report post-editing with Notion-like block editing powered by Tiptap.
  • Content Creation: Generates AI-powered podcasts and PowerPoint presentations from research reports with customizable templates.
  • Text-to-Speech: Converts reports to audio using Volcengine TTS API, with adjustable speed, volume, and pitch.
Use Cases
  • Academic Research: Ideal for researchers needing to compile detailed reports on complex topics with access to scientific papers via Arxiv.
  • Technical Analysis: Coders and developers can leverage Python REPL tools for code execution and analysis within research workflows.
  • Content Production: Marketers and creators can produce podcasts and presentations directly from research outputs, streamlining content workflows.
  • Deep Market Insights: Businesses can utilize web search and crawling for competitive analysis and market trend reports.

DeerFlow stands out with its commitment to open-source principles, offering a flexible, state-based workflow through LangGraph, and supporting debugging via LangSmith tracing and LangGraph Studio. It targets researchers, developers, and content creators seeking efficient, AI-driven research solutions.

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