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 eracle · Agent Tool · ★ 2.9k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenOutreach safe to install? View the security audit →
Describe your product. Define your target market. The AI finds the leads and emails them for you. Demo: 🚀 What is OpenOutreach? OpenOutreach is a self-hosted, open-source, email-first AI sales agent for B2B lead generation. It discovers leads from a licensed data provider, qualifies them on your own machine, and runs agentic email outreach from a mailbox you own — with zero platform-ToS surface: it is browserless, uses no social-network account, and does no scraping. Unlike other tools, you don't need a list of profiles to contact — you describe your product and your target market, and the system autonomously discovers, qualifies, and emails the right people. How it works: You provide a pro
| Stars | 2,859 |
| Forks | 536 |
| Language | Python |
| Category | Agent Tool |
| License | GPL-3.0 |
| Quality Score | 67.489661982253/100 |
| Last Updated | 2026-08-31 |
| Created | 2016-10-07 |
| Platforms | cli, python |
| Est. Tokens | ~17k |
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OpenOutreach is Open-source AI agent for B2B lead generation — describe your product, it finds the people who fit and explains why each one does, then exports a CSV your cold-email tool can send. Self-hosted CLI.. It is categorized as a Agent Tool with 2.9k GitHub stars.
OpenOutreach is primarily written in Python. It covers topics such as agent, agentic, agentic-marketing.
You can find installation instructions and usage details in the OpenOutreach GitHub repository at github.com/eracle/OpenOutreach. The project has 2.9k stars and 536 forks, indicating an active community.
OpenOutreach is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to OpenOutreach on Agent Skills Hub include cursor-talk-to-figma-mcp, bytebot, hive. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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: