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nickscamara avatar

nickscamara/open-deep-research

0
View on GitHub↗
6,173 stars·739 forks·TypeScript·other·30 viewsfirecrawl.dev/extract↗

Open Deep Research

Open Deep Research is an AI-powered web research agent that combines a reasoning model with live web search and data extraction to perform deep, multi-source investigations on any topic. It operates through a dual interface, offering both a command-line tool and a Model Context Protocol server, allowing developers to integrate web capabilities directly into AI agents and coding assistants.

The project distinguishes itself by orchestrating an iterative research loop where a reasoning model plans steps, interprets search results, and guides subsequent web interactions. It uses Firecrawl for scraping and extracting structured data from multiple pages simultaneously, converting content into LLM-ready formats like Markdown, JSON, or screenshots. Playwright-driven browser automation enables programmatic interaction with dynamic web pages, including clicking, typing, scrolling, and waiting for content to load.

Beyond research, the tool provides capabilities for parsing documents from URLs, extracting text from PDFs and DOCX files, and deploying a full research chatbot application to Vercel with one click, including database, file storage, and authentication.

Features

  • Web Research Agents - Uses a reasoning model with web search and data extraction for deep, multi-source research.
  • Deep Research Execution - Uses search and scrape to gather comprehensive information from multiple sources for in-depth research tasks.
  • Customizable Reasoning Loops - Iteratively plans research steps, interprets search results, and guides web interactions through a reasoning model.
  • AI Agent Capabilities - Gives AI agents and coding assistants web search and scrape capabilities through a single CLI or MCP command.
  • Agentic Web Interaction - Links any AI agent or MCP client to web search, scrape, and interaction tools using a single CLI command.
  • Research Step Planners - Leverages a reasoning AI model to plan research steps, interpret results, and guide web interactions.
  • Web Research Tools - Conducts deep, multi-source research by combining a reasoning model with live web search and data extraction.
  • Web Content Scrapers - Extracts clean, LLM-ready data from web pages using Firecrawl's API for scraping and structured data extraction.
  • Web Search APIs - Queries live web search engines and returns full page content for AI consumption.
  • Web Content Indexing - Searches the live web and retrieves complete page content from matching results.
  • Web Data Extraction - Scrapes web pages and returns structured data in LLM-ready formats like Markdown and JSON.
  • Web Data Extraction Tools - Pulls structured information from multiple web pages simultaneously using automated scraping and parsing.
  • CLI and MCP Interfaces - Provides both a CLI and an MCP server for integrating web capabilities into AI agents.
  • MCP Server Interfaces - Ships both a CLI and an MCP server for connecting AI agents to web search and scrape tools.
  • Playwright Scripts - Controls web pages programmatically through Playwright for clicking, typing, scrolling, and waiting on dynamic content.
  • Programmatic Page Interactions - Perform actions on a live webpage—clicking, typing, scrolling, or waiting—via AI prompts or code.
  • Structured Data Extraction - Pulls structured information from multiple web pages simultaneously using automated extraction capabilities.
  • API-Based Extractions - Uses Firecrawl's API to extract structured data from multiple web pages simultaneously for LLM consumption.
  • Research Chatbot Deployments - Deploys a complete research chatbot to Vercel with one click, including database and authentication.
  • Web Scraping - Extracts clean, LLM-ready data from any webpage and returns it as markdown, JSON, or a screenshot.
  • AI-Optimized Content Converters - Converts scraped web content into Markdown, JSON, or screenshots optimized for language model ingestion.
  • Browser Automation - Controls web pages programmatically via Playwright for clicking, typing, scrolling, and waiting on dynamic content.
  • JavaScript-Rendered Content Extractors - Waits for JavaScript-rendered content to fully load before extracting data from single-page applications.
  • Web Page Customizations - Performs actions on live webpages—clicking, typing, scrolling, or waiting—via AI prompts or code.
  • Programmatic Page Interactions - Performs actions on web pages—clicking, typing, scrolling, waiting—via AI prompts or code commands.

Star history

Star history chart for nickscamara/open-deep-researchStar history chart for nickscamara/open-deep-research

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Open Deep Research

These projects share indexed features with Open Deep Research. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • camel-ai/camelcamel-ai avatar

    camel-ai/camel

    17,253View on GitHub↗

    This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

    Pythonagentai-societiesartificial-intelligence
    View on GitHub↗17,253
  • mendableai/firecrawlmendableai avatar

    mendableai/firecrawl

    139,399View on GitHub↗

    Firecrawl is a headless browser automation tool and web crawling engine designed to extract structured data from the web. It functions as an API that transforms raw website content and documents into clean markdown and JSON formats to serve as context for large language models. The project distinguishes itself by using natural language prompts to translate human instructions into targeted data extraction tasks and browser actions. It can execute interactive page navigation, such as clicking and scrolling, and perform automated web research to retrieve structured data without manual interventi

    TypeScript
    View on GitHub↗139,399
  • firecrawl/firecrawl-mcp-serverfirecrawl avatar

    firecrawl/firecrawl-mcp-server

    5,542View on GitHub↗

    Firecrawl MCP Server is a Model Context Protocol tool server that exposes the full suite of Firecrawl’s web scraping, crawling, and automation capabilities as tools that large language models can invoke directly. It acts as a proxy to the Firecrawl cloud platform, which manages headless browser orchestration, async job queues, and rate limiting behind the scenes. The server distinguishes itself by packaging autonomous web agents — both a research agent that browses and collects structured data from multiple pages, and a general web agent that performs multi-step browsing and extraction tasks

    JavaScriptbatch-processingclaudecontent-extraction
    View on GitHub↗5,542
  • apify/crawleeapify avatar

    apify/crawlee

    24,002View on GitHub↗

    Crawlee is a web scraping framework designed for building scalable, reliable, and distributed data extraction pipelines. It provides a unified interface for managing headless browser automation and lightweight HTTP requests, allowing developers to handle complex web navigation, dynamic content rendering, and large-scale data collection within a single, modular architecture. The project distinguishes itself through its resource-aware concurrency controller, which dynamically scales task execution based on real-time CPU and memory usage to prevent host machine exhaustion. It also features a rob

    TypeScriptapifyautomationcrawler
    View on GitHub↗24,002
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Frequently asked questions

What does nickscamara/open-deep-research do?

Open Deep Research is an AI-powered web research agent that combines a reasoning model with live web search and data extraction to perform deep, multi-source investigations on any topic. It operates through a dual interface, offering both a command-line tool and a Model Context Protocol server, allowing developers to integrate web capabilities directly into AI agents and coding assistants.

What are the main features of nickscamara/open-deep-research?

The main features of nickscamara/open-deep-research are: Web Research Agents, Deep Research Execution, Customizable Reasoning Loops, AI Agent Capabilities, Agentic Web Interaction, Research Step Planners, Web Research Tools, Web Content Scrapers.

Which projects share features with nickscamara/open-deep-research?

Projects with overlapping indexed features include: camel-ai/camel — This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified… mendableai/firecrawl — Firecrawl is a headless browser automation tool and web crawling engine designed to extract structured data from the… firecrawl/firecrawl-mcp-server — Firecrawl MCP Server is a Model Context Protocol tool server that exposes the full suite of Firecrawl’s web scraping,… apify/crawlee — Crawlee is a web scraping framework designed for building scalable, reliable, and distributed data extraction… nanmicoder/crawlertutorial — CrawlerTutorial is a comprehensive Python web scraping tutorial and framework designed for extracting data from static… lorien/web-scraping — This project is a comprehensive resource directory for web data extraction, providing a curated collection of tools…