pi-mono

Abstract provider differences across multiple AI services with a unified TypeScript/JavaScript LLM API.

Updated Feb 12, 2026
One-click install
npx skills add https://github.com/wibus-wee/bbot --skill pi-mono
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: pi-mono
Source: https://github.com/wibus-wee/bbot/tree/main/.agents/skills/pi-mono
Command: npx skills add https://github.com/wibus-wee/bbot --skill pi-mono

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies LLM integrations by providing a single, consistent API across numerous AI providers, abstracting away their differences and enabling easy switching.

Core Features & Use Cases

  • Multi-Provider Support: Integrates with OpenAI, Anthropic, Google, Azure, Mistral, Groq, xAI, and more.
  • Tool Calling: Enables agentic workflows with robust function calling capabilities using TypeBox schemas.
  • Streaming & Context: Handles streaming responses, manages conversation context, and supports cross-provider context handoffs.
  • Use Case: Build an AI agent that can dynamically choose the best LLM for a given task (e.g., speed vs. capability) without code changes, or migrate your AI backend without rewriting your integration logic.

Quick Start

Use the pi-mono skill to get a response from the 'gpt-4o' model with the system prompt 'You are a helpful assistant.' and the user message 'Hello!'.

Frequently Asked Questions about pi-mono

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I switch LLM providers in TypeScript without rewriting integration logic?

To switch LLM providers in TypeScript, use a unified LLM API library that abstracts provider differences, enabling you to migrate your AI backend without rewriting integration logic.

What is a unified LLM API and when do I need it for agentic workflows?

A unified LLM API provides a single consistent interface across multiple AI services, needed for agentic workflows when dynamically choosing the best model for speed or capability without code changes.

Does this unified LLM API library support tool calling and streaming responses?

Yes, the unified LLM API library supports tool calling using TypeBox schemas for agentic workflows and handles streaming responses to manage conversation context effectively.

Can I use this library to manage conversation context across different AI providers?

Yes, you can use this library to manage conversation context across different AI providers, as it supports cross-provider context handoffs and handles streaming responses seamlessly.

What are the limitations of using provider abstraction for AI integrations?

The main limitation of provider abstraction is that it may obscure proprietary features unique to specific AI providers, though it simplifies switching between OpenAI, Anthropic, Google, and others.