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 m-mizutani · MCP Server · ★ 188
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gollem safe to install? View the security audit →
🤖 gollem GO for Large LanguagE Model (GOLLEM) provides: Common interface to query prompt to Large Language Model (LLM) services GenerateContent: Generate text content from prompt GenerateEmbedding: Generate embedding vector from text (OpenAI and Gemini) Framework for building agentic applications of LLMs with Tools by MCP (Model Context Protocol) server and your built-in tools Automatic session management for continuous conversations Portable conversational memory with history for stateless/distributed applications Intelligent memory management with automatic history compaction Middleware system for monitoring, logging, and contro
| Stars | 188 |
| Forks | 11 |
| Language | Go |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 74.0196841171081/100 |
| Open Issues | 6 |
| Last Updated | 2026-06-06 |
| Created | 2025-04-19 |
| Platforms | go, mcp |
| Est. Tokens | ~164k |
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gollem is Go framework for agentic AI app with MCP and built-in tools. It is categorized as a MCP Server with 188 GitHub stars.
gollem is primarily written in Go. It covers topics such as ai-agents, go, llm.
You can find installation instructions and usage details in the gollem GitHub repository at github.com/m-mizutani/gollem. The project has 188 stars and 11 forks, indicating an active community.
gollem is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to gollem on Agent Skills Hub include skylos, maverick-mcp, claude-code-mastery. 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: