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 and a pydantic-AI based 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:

graph TB
    subgraph LLMling[LLMling Core Package]
        RT[RuntimeConfig]
subgraph Core_Components[Core Components]

            Resources[Resource Management<br/>- Load files/URLs<br/>- Process content<br/>- Watch changes]

            Tools[Tool System<br/>- Execute functions<br/>- Register new tools<br/>- OpenAPI integration]

            Prompts[Prompt System<br/>- Static/Dynamic prompts<br/>- Template rendering<br/>- Completion support]

        end
CLI[Core CLI<br/>- config add/remove/list<br/>- resource list/load<br/>- tool list/execute<br/>- prompt list/render]
Core_Components -->|YAML configuration| RT

        RT -->|All components| CLI

        CLI -->|modify| Core_Components

    end
subgraph Direct_Access[mcp-server-llmling<br/>Direct Component Access]

        MCP[HTTP/SSE Server<br/>- Start/Stop server]

        MCP_CLI[Server CLI<br/>- Start/Stop server]

        Injection[Injection Server<br/>- Inject components<br/>during runtime]

    end
subgraph Function_Access[llmling-agent<br/>Access via Function Calling]

        LLM[LLM Integration<br/>- Function calling<br/>- Resource access<br/>- Tool execution<br/>- Structured output]

        Agent_CLI[Agent CLI<br/>- One-shot execution<br/>- Batch processing]

        Agent_Web[Agent Web UI<br/>- Interactive chat]

    end
RT -->|All components| MCP

    RT -->|Resources & Tools<br/>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