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

Use the upstream install or setup path that matches your environment:

• git clone https://github.com/langchain-ai/open_deep_research.git
• uv venv
• uv sync
• uv pip install -r pyproject.toml

Requirements and caveats from upstream:

• uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph dev --allow-blocking
• Open Deep Research supports a wide range of LLM providers via the init_chat_model() API. It uses LLMs for a few different tasks. See the below mo...
• Note: the selected model will need to support structured outputs and tool calling.

Basic usage or getting-started notes:

• cp .env.example .env

• This will open the LangGraph Studio UI in your browser.

• 🚀 API: http://127.0.0.1:2024

• Source: https://github.com/langchain-ai/open_deep_research

• Extracted from upstream docs: https://raw.githubusercontent.com/langchain-ai/open_deep_research/HEAD/README.md

Documentation

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

Source

• Agent Skill Exchange