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Gpt Researcher
An autonomous agent that conducts deep research on any data using any LLM providers
Install
npx skills add assafelovic/gpt-researcherInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →English | 中文 | 日本語 | 한국어 | Русский
🔎 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. GPT Researcher goes well beyond RAG: planner and execution agents break each task into research questions, decision models keep only the context that is actually useful for answering them, multi-agent workflows coordinate specialized agents from planning to publication, and recursive deep research explores a topic in both breadth and depth. Research runs in parallel, which keeps it fast, stable and reliable, and reduces misinformation.
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- LLMs trained on outdated information can hallucinate, becoming irrelevant for current research tasks.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
Install as Claude Skill
Extend Claude's deep research capabilities by installing GPT Researcher as a Claude Skill:
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
Features
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