Open Deep Research

Open Deep Research (dzhng/deep-research) is a sub-500-line TypeScript implementation of a deep research agent that combines Firecrawl-powered web search and scraping with an LLM (o3-mini by default, with optional DeepSeek R1 via Fireworks or any OpenAI-compatible endpoint) to iteratively refine its own research direction. You give it a query, a breadth parameter (how many SERP queries per round), and a depth parameter (how many recursive rounds), and it loops: generate queries, extract learnings, propose follow-up directions, and dive deeper until it emits a comprehensive markdown report with sources.

The appeal is deliberate minimalism. Unlike the heavier LangChain/CrewAI-style research stacks, this repo is small enough to read in one sitting and fork as a starting point for a custom agent. It's aimed at developers who want to understand how a deep-research loop actually works, self-host it (Docker compose is provided), point it at a local LLM via OpenAI-compatible endpoints, and swap pieces in or out. There is no hosted product or pricing - you bring your own Firecrawl and model keys.

Python port exists in the community (Finance-LLMs/deep-research-python). MIT licensed, ~19k GitHub stars and active maintenance. Caveat: this is a CLI/library, not a polished web app - expect to read code, manage your own API credits, and handle Firecrawl rate limits on the free tier.