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 fetchai · MCP Server · ★ 1.1k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is innovation-lab-examples safe to install? View the security audit →
Fetch.ai Innovation Lab Examples 80+ production-ready AI agent examples in Python Build autonomous AI agents, multi-agent systems and agentic AI workflows with uAgents, ASI:One, Agentverse, MCP, the A2A protocol, LangChain, CrewAI, Gemini, Claude and OpenAI. Quickstart · Examples · Structure · Docker · Contributing · FAQ 📈 Star history <img alt="GitHub stars" src="https://img.shields.io/github/stars/fet
| Stars | 1,147 |
| Forks | 83 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.8760648070211/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-01 |
| Created | 2025-06-16 |
| Platforms | claude-code, gemini, mcp, python |
| Est. Tokens | ~18k |
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innovation-lab-examples is 80+ production-ready AI agent examples in Python — build autonomous agents, multi-agent systems and agentic AI with uAgents, ASI:One, MCP, A2A, LangChain, CrewAI, Gemini, Claude and OpenAI.. It is categorized as a MCP Server with 1.1k GitHub stars.
innovation-lab-examples is primarily written in Python. It covers topics such as a2a-protocol, agentic-ai, agentverse.
You can find installation instructions and usage details in the innovation-lab-examples GitHub repository at github.com/fetchai/innovation-lab-examples. The project has 1.1k stars and 83 forks, indicating an active community.
innovation-lab-examples is released under the MIT 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: