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by sigoden · MCP Server · ★ 766
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LLM Functions This project empowers you to effortlessly build powerful LLM tools and agents using familiar languages like Bash, JavaScript, and Python. Forget complex integrations, harness the power of function calling to connect your LLMs directly to custom code and unlock a world of possibilities. Execute system commands, process data, interact with APIs – the only limit is your imagination. Tools Showcase Agents showcase Prerequisites Make sure you have the following tools installed: argc: A bash command-line framework and command runner jq: A JSON processor Getting Started with AIChat Currently, AIChat is the only CLI tool that supports . We look forward to more tools supporting . Clone the repository Build tools and agents I. Create a file with each tool filename on a new line. Where is the websearch tool? The websearch
| Stars | 766 |
| Forks | 131 |
| Language | Shell |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.9193904535691/100 |
| Open Issues | 11 |
| Last Updated | 2025-06-25 |
| Created | 2024-05-16 |
| Platforms | cli, mcp |
| Est. Tokens | ~14k |
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llm-functions is Easily create LLM tools and agents using plain Bash/JavaScript/Python functions.. It is categorized as a MCP Server with 766 GitHub stars.
llm-functions is primarily written in Shell. It covers topics such as ai, ai-agents, ai-tools.
You can find installation instructions and usage details in the llm-functions GitHub repository at github.com/sigoden/llm-functions. The project has 766 stars and 131 forks, indicating an active community.
llm-functions is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm-functions on Agent Skills Hub include dexto, superglue, octocode-mcp. 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.
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