llama-cpp-agent — security grade SAFE, quality 72/100

Security audit verdict: SAFE · quality 72/100

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 Maximilian-Winter · Agent Tool · ★ 658

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

🔒 Is llama-cpp-agent safe to install? View the security audit →

About llama-cpp-agent

llama-cpp-agent Not Longer Maintained. Use instead ToolAgents or other Python agentic frameworks. [] Introduction The llama-cpp-agent framework is a tool designed to simplify interactions with Large Language Models (LLMs). It provides an interface for chatting with LLMs, executing function calls, generating structured output, performing retrieval augmented generation, and processing text using agentic chains with tools. The framework uses guided sampling to constrain the model output to the user defined structures. This way also models not fine-tuned to do function calling and JSON output will be able to do it. The framework is compatible with the llama.cpp server, llama-cpp-python and its server, and with TGI and vllm servers. Key Features Simple Chat Interface: Engage in seamless conversations with LLMs. Structured Output: Generate structured output (objects) from LLMs. Singl

agentsfunction-callingllamacppllmllm-agentllm-frameworkllmsparallel-function-call

Quick Facts

Stars658
Forks74
LanguagePython
CategoryAgent Tool
Quality Score71.6139043472405/100
Open Issues25
Last Updated2026-03-09
Created2023-12-29
Platformspython
Est. Tokens~383k

Compatible Skills

These tools work well together with llama-cpp-agent for enhanced workflows:

  • SimplerLLM — semantic(0.30)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • SimpleLLMFunc — semantic(0.28)+complementary+rare_topics+same_lang+similar_pop+shared_platform (59%)
  • LLMCompiler — semantic(0.51)+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • Agently — semantic(0.20)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)
  • ai-microcore — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

llama-cpp-agent alternative? Top 6 similar tools

Looking for a llama-cpp-agent alternative? If you're comparing llama-cpp-agent with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • LLMCompiler by SqueezeAILab · ⭐ 1.9k

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Frequently Asked Questions

What is llama-cpp-agent?

llama-cpp-agent is The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). Allowing users to chat with LLM models, execute structured function calls and get structured ou. It is categorized as a Agent Tool with 658 GitHub stars.

What programming language is llama-cpp-agent written in?

llama-cpp-agent is primarily written in Python. It covers topics such as agents, function-calling, llamacpp.

How do I install or use llama-cpp-agent?

You can find installation instructions and usage details in the llama-cpp-agent GitHub repository at github.com/Maximilian-Winter/llama-cpp-agent. The project has 658 stars and 74 forks, indicating an active community.

What are the best alternatives to llama-cpp-agent?

The top alternatives to llama-cpp-agent on Agent Skills Hub include LLMCompiler, Awesome-AI-For-Security, superglue. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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