ToolRAG
Dynamically selects the most relevant tools for each query, providing access to unlimited LLM tools without context window limitations.
About
ToolRAG offers a streamlined solution for leveraging an extensive range of function definitions with Large Language Models (LLMs). It intelligently selects only the most pertinent tools for each user query, bypassing the constraints of context window limitations, reducing costs, and preventing performance degradation. By utilizing vector embeddings for semantic tool search and integrating seamlessly with Model Context Protocol (MCP) compliant servers, ToolRAG enables access to a broad ecosystem of tools, formatted for OpenAI compatibility.
Key Features
- Semantic Tool Search
- MCP Integration
- Cost Optimization
- OpenAI Compatible
- Unlimited Tool Definitions
- Performance Improvement
- 8 GitHub stars
Use Cases
- AI Platforms
- Enterprise Systems
- Multi-tool AI Assistants
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