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 omerbsezer · MCP Server · ★ 90
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Fast-LLM-Agent-MCP safe to install? View the security audit →
Fast LLM & Agents & MCPs This repo covers LLM, Agents, MCP Tools, Skills concepts both theoretically and practically: LLM Architectures, RAG, Fine Tuning, Agents, Tools, MCP, Agent Frameworks, Reference Documents. Agent Sample Codes with LangChain & LangGraph ( v1.0.0, prod. version). Agent Sample Codes with AWS Strands Agents. Agent Sample Codes with Google Agent Development Kit (ADK). LangChain & LangGraph (v1.0.0) - Agent Sample Code & Projects Sample-00: Basic Agent Sample-01: Agent With Static Tools Sample-02: Agent With Tools Structured Output Sample-03: Agent Short Term Memory Sample-04: Agent Messages Sample-05: Agent PII Middleware Guardrail [Sample-06: Agent Subagents as Tool](https://github.com/omerbsezer/Fast-LLM-Agent-MCP/tree/mai
| Stars | 90 |
| Forks | 34 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 56.0636102121144/100 |
| Last Updated | 2026-09-13 |
| Created | 2025-04-07 |
| Platforms | aws, gemini, mcp, python |
| Est. Tokens | ~19k |
These tools work well together with Fast-LLM-Agent-MCP for enhanced workflows:
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Fast-LLM-Agent-MCP is This repo covers LLM, Agents, MCP Tools, Skills concepts with sample codes: LangChain & LangGraph, AWS Strands Agents, Google Agent Development Kit, Fundamentals.. It is categorized as a MCP Server with 90 GitHub stars.
Fast-LLM-Agent-MCP is primarily written in Python. It covers topics such as agent-development, agents, ai.
You can find installation instructions and usage details in the Fast-LLM-Agent-MCP GitHub repository at github.com/omerbsezer/Fast-LLM-Agent-MCP. The project has 90 stars and 34 forks, indicating an active community.
The top alternatives to Fast-LLM-Agent-MCP on Agent Skills Hub include corpusos, beever-atlas, oreilly-ai-agents. 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: