ToolRAG

<div align="center"><strong>ToolRAG</strong></div> <div align="center">Infinity LLM tools, zero context conntraints<br />Context-aware tool retrieval for large language models.</div>

ToolRAG provides a seamless solution for using an unlimited number of function definitions with Large Language Models (LLMs), without worrying about context window limitations, costs, or performance degradation.

• Unlimited Tool Definitions: Say goodbye to context window constraints. ToolRAG dynamically selects only the most relevant tools for each query.
• Semantic Tool Search: Uses vector embeddings to find the most contextually relevant tools for a given user query.
• Cost Optimization: Reduces token usage by only including the most relevant function definitions.
• Performance Improvement: Prevents performance degradation that occurs when overwhelming LLMs with too many function definitions.
• MCP Integration: Works with any Model Context Protocol (MCP) compliant servers, enabling access to a wide ecosystem of tools.
• OpenAI Compatible: Format tools as OpenAI function definitions for seamless integration.

1. Tool Registration: ToolRAG connects to MCP servers and registers available tools.
2. Embedding Generation: Tool descriptions and parameters are embedded using vector embeddings (OpenAI or Google).
3. Query Analysis: When a user query comes in, ToolRAG finds the most relevant tools via semantic search.
4. Tool Execution: Execute selected tools against the appropriate MCP servers.

npm install @antl3x/toolrag
# or
yarn add @antl3x/toolrag
# or
pnpm add @antl3x/toolrag

import { ToolRAG } from "@antl3x/toolrag";
import OpenAI from "openai";

// Initialize ToolRAG with MCP servers
const toolRag = await ToolRAG.init({
  mcpServ