LLMling
by phil65
Overview
A framework for declarative LLM application development, focusing on structured resource management, prompt templating, and tool execution.
Installation
uvx mcp-server-llmling@latest start path/to/your/config.ymlSecurity Notes
The framework explicitly supports dynamic loading of Python callables for tools and prompts, execution of arbitrary Python code (via `exec` in `register_code_tool`), execution of shell commands (via `subprocess` in CLI resource loaders and OpenAPI dereferencing), and cloning Git repositories. These features provide powerful extensibility but introduce significant security risks if exposed to untrusted input or LLMs without strict sandboxing and capability management. While the configuration (e.g., `llm_capabilities`) allows disabling these high-risk features, their presence in the core functionality requires careful deployment and trust boundaries.
Similar Servers
fastmcp
FastMCP is a Python framework for building and interacting with Model Context Protocol (MCP) servers. It provides client and server capabilities, enabling the creation of AI agents and services through definable tools, resources, and prompts. It supports various transports, authentication methods, logging, and background task execution, with strong integration for OpenAPI specifications.
npcpy
A comprehensive Python library and framework for building, evaluating, and serving LLM-powered agents and multi-agent systems, integrating fine-tuning capabilities, knowledge graphs, and scalable model operations, with a built-in Flask API server for deployment.
arcade-mcp
A framework and collection of toolkits for building and deploying AI agent servers that integrate with various external services.
AgentUp
A developer-first framework for building, deploying, and managing secure, scalable, and configurable AI agents, supporting various agent types (reactive, iterative) and the Model-Context Protocol (MCP) for seamless interactions.