No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by guy-hartstein · Agent Tool · ★ 2.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is company-research-agent safe to install? View the security audit →
Agentic Company Researcher 🔍 A multi-agent tool that generates comprehensive company research reports. The platform uses a pipeline of AI agents to gather, curate, and synthesize information about any company. ✨Check it out online! https://companyresearcher.tavily.com ✨ https://github.com/user-attachments/assets/0e373146-26a7-4391-b973-224ded3182a9 Features Multi-Source Research: Gathers data from various sources, including company websites, news articles, financial reports, and industry analyses AI-Powered Content Filtering: Uses Tavily's relevance scoring for content curation Asynchronous Processing: Efficient polli
| Stars | 2,284 |
| Forks | 317 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 73.4452561873879/100 |
| Last Updated | 2026-09-21 |
| Created | 2025-02-12 |
| Platforms | gemini, python |
| Est. Tokens | ~16k |
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company-research-agent is An agentic company research tool powered by LangGraph and Tavily that conducts deep diligence on companies using a multi-agent framework. It leverages Google's Gemini 2.5 Flash and OpenAI's GPT-5.1 on. It is categorized as a Agent Tool with 2.3k GitHub stars.
company-research-agent is primarily written in Python. It covers topics such as agents, ai, company.
You can find installation instructions and usage details in the company-research-agent GitHub repository at github.com/guy-hartstein/company-research-agent. The project has 2.3k stars and 317 forks, indicating an active community.
company-research-agent is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: