(https://pyup.io/repos/github/phil65/llmling/) A framework for declarative LLM application development focused on resource management, prompt templates, and tool execution. This package provides the backend for two consumers: [A MCP server](https://github.com/phil65/mcp-server-llmling) and [a pydantic-AI based Agent](https://github.com/phil65/llmling-agent) ## Core Concepts LLMLing provides a YAML-based configuration system for LLM applications. It allows to set up custom MPC servers serving content defined in YAML files. - **Static Declaration**: Define your LLM's environment in YAML - no code required - **MCP Protocol**: Built on the Machine Chat Protocol (MCP) for standardized LLM interaction - **Component Types**: - **Resources**: Content providers (files, text, CLI output, etc.) - **Prompts**: Message templates with arguments - **Tools**: Python functions callable by the LLM The YAML configuration creates a complete environment that provides the LLM with: - Access to content via resources - Structured prompts for consistent interaction - Tools for extending capabilities - Written from ground up in modern python (minimum 3.12 required) - 100% typed - pydantic(-ai) based An overview about the whole system: ```mermaid graph TB subgraph LLMling[LLMling Core Package] RT[RuntimeConfig] subgraph Core_Components[Core Components] Resources[Resource Management
- Load files/URLs
- Process content
- Watch changes] Tools[Tool System
- Execute functions
- Register new tools
- OpenAPI integration] Prompts[Prompt System
- Static/Dynamic prompts
- Template rendering
- Completion support] end CLI[Core CLI
- config add/remove/list
- resource list/load
- tool list/execute
- prompt list/render] Core_Components -->|YAML configuration| RT RT -->|All components| CLI CLI -->|modify| Core_Components end subgraph Direct_Access[mcp-server-llmling
Direct Component Access] MCP[HTTP/SSE Server
- Start/Stop server] MCP_CLI[Server CLI
- Start/Stop server] Injection[Injection Server
- Inject components
during runtime] end subgraph Function_Access[llmling-agent
Access via Function Calling] LLM[LLM Integration
- Function calling
- Resource access
- Tool execution
- Structured output] Agent_CLI[Agent CLI
- One-shot execution
- Batch processing] Agent_Web[Agent Web UI
- Interactive chat] end RT -->|All components| MCP RT -->|Resources & Tools
via function calling| LLM MCP_CLI --> CLI Agent_CLI --> CLI classDef core fill:#e1f5fe,stroke:#01579b classDef comp fill:#e3f2fd,stroke:#1565c0 classDef cli fill:#fff3e0,stroke:#e65100 classDef mcp fill:#f3e5f5,stroke:#4a148c classDef agent fill:#e8f5e9,stroke:#1b5e20 classDef access fill:#e8eaf6,stroke:#666 classDef serverBox fill:#7986cb,stroke:#3949ab classDef agentBox fill:#81c784,stroke:#2e7d32 class RT core class Resources,Tools,Prompts comp class CLI,MCP_CLI,Agent_CLI cli class MCP,Injection mcp class LLM,Agent_Web agent class Direct_Access serverBox class Function_Access agentBox ``` ## Usage ### 1. CLI Usage Create a basic configuration file: ```bash # Create a new config file with basic settings llmling config init my_config.yml # Add it to your stored configs llmling config add myconfig my_config.yml llmling config set myconfig # Make it active ``` Basic CLI commands: ```bash # List available resources llmling resource list # Load a resource llmling resource load python_files # Execute a tool llmling tool call open_url url=https://github.com # Show a prompt llmling prompt show greet # Many more commands. The CLI will get extended when installing # llmling-agent and mcp-server-llmling ``` ### 2. Agent Usage (powered by pydantic-AI) Create a configuration file (`config.yml`): ```yaml tools: open_url: import_path: "webbrow

#context-api

LLMling