deep research

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Basic Information

Deep Research is an AI-powered research assistant implemented as a compact Node.js project that performs iterative, deep research on arbitrary topics by combining search engine queries, web scraping, and large language models. The repository aims to be a minimal, easy-to-understand implementation (kept under 500 lines of code) that generates targeted SERP queries, processes and extracts content via the Firecrawl API, and synthesizes findings using OpenAI or local LLM endpoints. It supports configuring breadth and depth parameters, running in a Docker container, and switching to alternative providers such as Fireworks R1 when corresponding API keys are present. The project is designed for users who want a runnable research agent out of the box or a small base to extend for custom research workflows.

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Features
The README documents several concrete features: iterative research that loops through query generation, result processing, and recursive exploration; LLM-driven intelligent query generation and follow-up question creation; configurable breadth and depth controls to manage exploration scope; generation of comprehensive markdown reports that include findings and sources; concurrent processing of multiple searches with an adjustable concurrency limit; support for Firecrawl for web search and extraction and OpenAI or custom endpoints for LLMs; optional use of Fireworks R1 when its API key is provided; and installation and execution options via npm or Docker for reproducible runs.
Use Cases
This repository helps users automate and accelerate multi-step research tasks by producing targeted search queries, extracting and summarizing web content, and iteratively refining the research direction based on prior learnings. It reduces manual search and synthesis work by generating follow-up questions and maintaining context across iterations, and it outputs organized markdown reports with sources for easy review. The tool is configurable for breadth, depth, concurrency and model endpoints, so it can be tuned for faster runs or lower rate limits. Its small codebase and Docker support make it useful both for end users who want a ready research assistant and for developers who want a simple foundation to customize or extend.

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