gollem: Go framework for agentic AI app with MCP and built-in tools

by gollem-dev · MCP Server · ★ 203

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

Security audit verdict: SAFE · quality 73/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

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.

ai-agentsgollm

Quick Facts

Stars203
Forks14
LanguageGo
CategoryMCP Server
LicenseApache-2.0
Quality Score73/100
Open Issues4
Last Updated2026-10-28
Created2025-04-19
Platformsgo, mcp
Est. Tokens~17k

About gollem

🤖 gollem GO for Large LanguagE Model (GOLLEM) provides: Common interface to query prompt to Large Language Model (LLM) services Generate / Stream: Generate text content from prompt (with per-call option overrides) 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...

Compatible Skills

These tools work well together with gollem for enhanced workflows:

  • gollem — semantic(1.00)+same_lang+similar_pop+shared_platform (60%)
  • goclaw — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • aweb — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • orloj — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)

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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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