---
title: "Run configurable multi-source deep research passes with Open Deep Research"
description: "Use Open Deep Research when an agent should run a configurable research job that searches, compresses, synthesizes, and writes a cited report across multiple model and search backends."
verification: "listed"
source: "https://github.com/langchain-ai/open_deep_research"
author: "LangChain"
publisher_type: "organization"
category:
  - "Research & Scraping"
framework:
  - "Multi-Framework"
tool_ecosystem:
  github_repo: "langchain-ai/open_deep_research"
  github_stars: 11125
---

# Run configurable multi-source deep research passes with Open Deep Research

Use Open Deep Research when an agent should run a configurable research job that searches, compresses, synthesizes, and writes a cited report across multiple model and search backends.

## Prerequisites

Python, uv, model API credentials, one or more supported search tools

## Installation

Choose whichever fits your setup:

1. Copy this skill folder into your local skills directory.
2. Clone the repo and symlink or copy the skill into your agent workspace.
3. Add the repo as a git submodule if you manage shared skills centrally.
4. Install it through your internal provisioning or packaging workflow.
5. Download the folder directly from GitHub and place it in your skills collection.

Install command or upstream instructions:

```
Clone the repository, create a virtual environment, install dependencies with uv sync, configure the .env file for model and search providers, then start the LangGraph development server to run research jobs.
```

## Documentation

- https://github.com/langchain-ai/open_deep_research#readme

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/run-configurable-multi-source-deep-research-passes-with-open-deep-research/)