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AI Agent Analysis
OR

langchain-ai/open_deep_research

stars
12k
Language
Python
GitHub
Source and compliance noteLast synced: Jul 22, 2026

Git-Stars is independent and not affiliated with GitHub or this project. Analysis may be AI-assisted and based on public repository metadata plus short README-derived summaries. We do not mirror full README files, docs, issues, or social comments.

Original GitHub sourceMethodologyEditorial Policy

Overview

Open Deep Research is an open-source, configurable AI agent framework for automated deep research, supporting multiple LLM providers, search tools, and MCP servers. Its core value proposition is providing a simple, high-performance, and fully customizable alternative to proprietary deep research agents, with performance on par with top solutions on the Deep Research Bench leaderboard.

Installation

Clone the repo, run `uv sync` to install dependencies, copy `.env.example` to `.env`, then launch with `uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph dev --allow-blocking`.

Problem solved

It solves the problem of building a high-quality, transparent deep research agent without vendor lock-in. Unlike proprietary solutions, it offers full configurability of models, search APIs, and MCP tools, enabling researchers and developers to adapt the agent to their specific needs and data sources.

What you can build

Developers can build automated research assistants that conduct multi-step web searches, summarize findings, and generate comprehensive reports on complex topics. Real-world use cases include academic literature reviews, competitive analysis, market research, and fact-checking. The ceiling is high: it can handle PhD-level research tasks across 22 fields, as evidenced by its #6 ranking on the Deep Research Bench leaderboard.

Community sentiment

Positive

No community feedback yet.

Concerns

No concerns documented yet.

Bottom line

This framework is ideal for developers and researchers who need a customizable, open-source deep research agent that can match proprietary performance. It is not for those seeking a plug-and-play solution without configuration effort. The key trade-off is flexibility versus setup complexity: you gain full control over models and search tools but must invest time in configuration and infrastructure.

Analyzed by Git-Stars - 7/22/2026