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Open Deep Research Repository Overview

Project Description

Open Deep Research is a configurable, fully open-source deep research agent that works across multiple model providers, search tools, and MCP (Model Context Protocol) servers. It enables automated research with parallel processing and comprehensive report generation.

Repository Structure

Root Directory

  • README.md - Comprehensive project documentation with quickstart guide
  • pyproject.toml - Python project configuration and dependencies
  • langgraph.json - LangGraph configuration defining the main graph entry point
  • uv.lock - UV package manager lock file
  • LICENSE - MIT license
  • .env.example - Environment variables template (not tracked)

Core Implementation (src/open_deep_research/)

  • deep_researcher.py - Main LangGraph implementation (entry point: deep_researcher)
  • configuration.py - Configuration management and settings
  • state.py - Graph state definitions and data structures
  • prompts.py - System prompts and prompt templates
  • utils.py - Utility functions and helpers
  • files/ - Research output and example files

Legacy Implementations (src/legacy/)

Contains two earlier research implementations:

  • graph.py - Plan-and-execute workflow with human-in-the-loop
  • multi_agent.py - Supervisor-researcher multi-agent architecture
  • legacy.md - Documentation for legacy implementations
  • CLAUDE.md - Legacy-specific Claude instructions
  • tests/ - Legacy-specific tests

Security (src/security/)

  • auth.py - Authentication handler for LangGraph deployment

Testing (tests/)

  • run_evaluate.py - Main evaluation script configured to run on deep research bench
  • evaluators.py - Specialized evaluation functions
  • prompts.py - Evaluation prompts and criteria
  • pairwise_evaluation.py - Comparative evaluation tools
  • supervisor_parallel_evaluation.py - Multi-threaded evaluation

Examples (examples/)

  • arxiv.md - ArXiv research example
  • pubmed.md - PubMed research example
  • inference-market.md - Inference market analysis examples

Key Technologies

  • LangGraph - Workflow orchestration and graph execution
  • LangChain - LLM integration and tool calling
  • Multiple LLM Providers - OpenAI, Anthropic, Google, Groq, DeepSeek support
  • Search APIs - Tavily, OpenAI/Anthropic native search, DuckDuckGo, Exa
  • MCP Servers - Model Context Protocol for extended capabilities

Development Commands

  • uvx langgraph dev - Start development server with LangGraph Studio
  • python tests/run_evaluate.py - Run comprehensive evaluations
  • ruff check - Code linting
  • mypy - Type checking

Configuration

All settings configurable via:

  • Environment variables (.env file)
  • Web UI in LangGraph Studio
  • Direct configuration modification

Key settings include model selection, search API choice, concurrency limits, and MCP server configurations.