--- title: "gpt-researcher" type: "tool" slug: "assafelovic-gpt-researcher" canonical_url: "https://www.graphcanon.com/tools/assafelovic-gpt-researcher" github_url: "https://github.com/assafelovic/gpt-researcher" homepage_url: "https://gptr.dev" stars: 28134 forks: 3800 primary_language: "Python" license: "Apache-2.0" categories: ["ai-agents"] tags: ["llms", "mcp-server", "deepresearch", "ai", "python", "automation", "agent", "mcp"] updated_at: "2026-07-07T18:22:23.663668+00:00" --- # gpt-researcher > Autonomous AI agent for deep research on any data using LLM providers GPT Researcher is a Python-based autonomous agent that conducts thorough research tasks by leveraging various Large Language Model (LLM) providers. It aims to generate detailed, factual, and unbiased reports. ## Facts - Repository: https://github.com/assafelovic/gpt-researcher - Homepage: https://gptr.dev - Stars: 28,134 · Forks: 3,800 · Open issues: 210 · Watchers: 169 - Primary language: Python - License: Apache-2.0 - Last pushed: 2026-07-05T14:51:31+00:00 ## Categories - [AI Agents](/categories/ai-agents.md) ## Tags llms, mcp-server, deepresearch, ai, python, automation, agent, mcp ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,070) - [firecrawl](/tools/firecrawl-firecrawl.md) - The API to search, scrape, and interact with the web at scale. (★ 147,117) - [langchain](/tools/langchain-ai-langchain.md) - The agent engineering platform. (★ 141,211) ## README (excerpt) ```text
Logo #### [English](README.md) | [中文](README-zh_CN.md) | [日本語](README-ja_JP.md) | [한국어](README-ko_KR.md)
# 🔎 GPT Researcher **GPT Researcher the first open deep research agent designed for both web and local research on any given task.** The agent produces detailed, factual, and unbiased research reports with citations. GPT Researcher provides a full suite of customization options to create tailor made and domain specific research agents. Inspired by the recent [Plan-and-Solve](https://arxiv.org/abs/2305.04091) and [RAG](https://arxiv.org/abs/2005.11401) papers, GPT Researcher addresses misinformation, speed, determinism, and reliability by offering stable performance and increased speed through parallelized agent work. **Our mission is to empower individuals and organizations with accurate, unbiased, and factual information through AI.** ## Why GPT Researcher? - Objective conclusions for manual research can take weeks, requiring vast resources and time. - LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks. - Current LLMs have token limitations, insufficient for generating long research reports. - Limited web sources in existing services lead to misinformation and shallow results. - Selective web sources can introduce bias into research tasks. ## Demo Demo video ## Install as Claude Skill Extend Claude's deep research capabilities by installing GPT Researcher as a [Claude Skill](https://skills.sh/assafelovic/gpt-researcher/gpt-researcher): ```bash npx skills add assafelovic/gpt-researcher ``` Once installed, Claude can leverage GPT Researcher's deep research capabilities directly within your conversations. ## Architecture The core idea is to utilize 'planner' and 'execution' agents. The planner generates research questions, while the execution agents gather relevant information. The publisher then aggregates all findings into a comprehensive report.
Steps: * Create a task-specific agent based on a research query. * Generate questions that collectively form an objective opinion on the task. * Use a crawler agent for gathering information for each question. * Summarize and source-track each resource. * Filter and aggregate summaries into a final research report. ## Tutorials - [How it Works](https://docs.gptr.dev/blog/building-gpt-researcher) - [How to Install](https://www.loom.com/share/04ebffb6ed2a4520a27c3e3addcdde20?sid=da1848e8-b1f1-42d1-93c3-5b0b9c3b24ea) - [Live Demo](https://www.loom.com/share/6a3385db4e8747a1913dd85a7834846f?sid=a740fd5b-2aa3-457e-8fb7-86976f59f9b8) ## Features - 📝 Generate detailed research reports using web and local documents. - 🖼️ Smart image scraping and filtering for reports. - 🍌 **AI-generated inline images** using Google Gemini (Nano Banana) for visual illustrations. - 📜 Generate detailed reports exceeding 2,000 words. - 🌐 Aggregate over 20 sources for objective conclusions. - 🖥️ Frontend available in lightweight (HTML/CSS/JS) and production-ready (NextJS + Tailwind) versions. - 🔍 JavaScript-enabled web scraping. - 📂 Maintains memory and context throughout research. - 📄 Export reports to PDF, Word, and other formats. ## 📖 Documentation See the [Documentation](https://docs.gptr.dev/docs/gpt-researcher/getting-started) for: - Installation and setup guides - Configuration and customization options - How-To examples - Full API references ## ⚙️ Getting Start ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/assafelovic-gpt-researcher`](/api/graphcanon/tools/assafelovic-gpt-researcher) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_