gpt-researcher
GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks.
Decision gist · record as of 2026-08-14
Yes, if you need to automate research report generation and have API keys for OpenAI and Tavily (or compatible services). The project is actively maintained, has no known vulnerabilities, and the MIT license imposes no restrictions. The main commitment is managing 140 dependencies and ensuring Python 3.12+ availability. Start with the pip package and async API examples in the documentation.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later.
- API keys for OpenAI and Tavily (or compatible alternatives) must be set as environment variables before use.
- Installation is straightforward with low friction—a pure Python wheel with no compiled dependencies.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and places no restrictions on use, modification, or distribution in commercial or private projects.
last release 2026-07-18 (27 days) · last repo commit 2026-07-18 · 28,978 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 95,200 downloads/mo, #13,280 on PyPI
Alternatives
Verify before relying
pip install gpt-researcher
from gpt_researcher import GPTResearcher
import asyncio
async def research():
researcher = GPTResearcher(query="your research topic")
result = await researcher.conduct_research()
report = await researcher.write_report()
return report- Whether the 2,000+ word report length claim and 20+ source aggregation are typical or best-case outcomes.
- Performance characteristics and cost implications of the deep research feature mentioned in the description.
- Whether MCP integration and inline image generation features are stable or experimental.
- Real-world latency and reliability of the parallelized agent execution model.
What it is and what it does
GPT Researcher is an autonomous agent framework that automates the research process by decomposing a research query into sub-questions, gathering information from multiple web sources in parallel, and synthesizing findings into a comprehensive report with citations. It addresses common LLM limitations—hallucination, outdated training data, token constraints, and shallow web coverage—by combining planning agents, execution agents, and a publisher component that aggregates results.
The package is designed for both programmatic use (via pip install and async Python API) and interactive deployment (with web frontends in HTML/CSS/JS and NextJS variants). It supports customization through environment variables and configuration objects, including optional features like AI-generated inline images, MCP integration for specialized data sources, and a deep research mode for recursive topic exploration. With 140 runtime dependencies, it brings a full ecosystem of async HTTP clients, web scrapers, LLM integrations, and document processing tools.
Use it for
- Generate objective research reports on current events or technical topics without manual source gathering and synthesis.
- Automate competitive analysis or market research by aggregating information from multiple sources into a structured report.
- Build domain-specific research agents by customizing the planner and execution logic for specialized tasks.
- Integrate research capabilities into chatbots or assistants (e.g., Claude via the published Skill) to answer complex queries with citations.
- Export research findings to PDF or Word for stakeholder communication or documentation.
- Conduct deep exploratory research on complex topics using the recursive tree-like exploration mode.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to automate research report generation and have API keys for OpenAI and Tavily (or compatible services).
The project is actively maintained, has no known vulnerabilities, and the MIT license imposes no restrictions. The main commitment is managing 140 dependencies and ensuring Python 3.12+ availability. Start with the pip package and async API examples in the documentation.
Install
gpt-researcher on PyPI
Before you install
Installation is straightforward with low friction—a pure Python wheel with no compiled dependencies. The project is actively maintained with recent commits and steady releases. Requires Python 3.12 or later and 140 runtime dependencies, which is substantial but typical for a complex agent framework.
Requires Python 3.12 or later. API keys for OpenAI and Tavily (or compatible alternatives) must be set as environment variables before use.
License in practice
MIT license is permissive and places no restrictions on use, modification, or distribution in commercial or private projects.
Quickstart
pip install gpt-researcher
from gpt_researcher import GPTResearcher
import asyncio
async def research():
researcher = GPTResearcher(query="your research topic")
result = await researcher.conduct_research()
report = await researcher.write_report()
return reportVerify before relying
- Whether the 2,000+ word report length claim and 20+ source aggregation are typical or best-case outcomes.
- Performance characteristics and cost implications of the deep research feature mentioned in the description.
- Whether MCP integration and inline image generation features are stable or experimental.
- Real-world latency and reliability of the parallelized agent execution model.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 140 packagesaiofilesaiohappyeyeballsaiohttpaiosignalannotated-typesanyioarxivattrsbackoffbeautifulsoup4brotlicertificffichardetcharset-normalizerclickcoloramacryptographycssselect2dataclasses-jsondistrodocoptduckduckgo-searchemojifastapifeedparserfilelockfiletypefonttoolsfrozenlist |
| Maintenance | Actively maintained 27 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 95,200 / month, #13,280 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: gpt_researcher-0.16.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “autonomous research agent”
- gpt-researcherGPT Researcher is an autonomous agent that conducts comprehensive…
- praisonaiagentsPraisonAI agents is a Python framework for building and running…
- crewaiCrewAI is a Python framework for building multi-agent AI systems…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also praisonaiagents · duckduckgo-mcp · cuga · deepagents · cognee · PraisonAI · web-forager · stashai · genagent · pyod