gpt-researcher: An autonomous agent that conducts deep research on any data using any LLM providers

by assafelovic · MCP Server · ★ 30.0k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install gpt-researcher

npx skills add assafelovic/gpt-researcher
git clone https://github.com/assafelovic/gpt-researcher.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 71/100

Flagged: environment access. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentaiautomationdeepresearchllmsmcpmcp-serverpythonresearchsearch

Quick Facts

Stars30,010
Forks4,099
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score71/100
Open Issues31
Last Updated2026-10-10
Created2023-05-12
Platformsbrowser, mcp, python
Est. Tokens~17k

About gpt-researcher

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Compatible Skills

These tools work well together with gpt-researcher for enhanced workflows:

  • local-deep-research — semantic(0.26)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • miroflow — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)
  • OpenAI_Agent_Swarm — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • Cerno-Agentic-Local-Deep-Research — semantic(0.31)+complementary+rare_topics+same_lang+shared_platform (50%)
  • af-deep-research — semantic(0.40)+complementary+same_lang+shared_platform (49%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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