chat

What problem does it solve?

Starting a new LLM project or building a basic chat application often involves boilerplate code for different providers and managing conversation history. This Skill provides a unified, simple interface for core chat completions, letting you focus on your application logic.

Core Features & Use Cases

• Unified Chat API: Send messages to OpenAI, Anthropic, Google, or Ollama using a single, consistent LLMRequest and LLMResponse structure.
• Semantic Aliases: Use task-based names (e.g., "summarizer", "chatbot") defined in a lockfile to easily switch models without changing code, adapting to evolving needs.
• Multi-Turn Conversations: Maintain conversation history effortlessly by appending Message objects, enabling natural, ongoing dialogues with your AI.
• Use Case: Quickly build a simple AI assistant that can answer questions, summarize text, or engage in multi-turn conversations, with the flexibility to switch between different LLM providers as needed for cost or performance.

Quick Start

First, initialize your lockfile with 'llmring lock init' and bind 'summarizer' to a model. Then, use the chat skill to send "Hello!" to your 'summarizer' alias.