PruvaGraph: MCP and IDE to slash AI API costs

An MCP server and IDE extension that cuts Cursor and Claude API costs by 99% using local AST knowledge graphs.

PruvaGraph: Slash AI API Costs with Local Knowledge Graphs

PruvaGraph is an innovative MCP server and IDE extension designed to drastically reduce your AI API expenditures. By leveraging local Abstract Syntax Tree (AST) knowledge graphs, PruvaGraph enables significant cost savings, potentially up to 99%, on services like Cursor and Claude. This tool is built for developers seeking to optimize their AI development workflows and manage operational costs effectively.

What PruvaGraph Does

PruvaGraph acts as an intermediary between your development environment and AI APIs. It intercepts code analysis requests and processes them locally using a sophisticated knowledge graph built from your project's AST. Instead of sending your entire codebase or complex queries to external APIs for tasks like code understanding, refactoring suggestions, or bug detection, PruvaGraph utilizes its local representation. This dramatically reduces the amount of data sent to the API, thereby cutting down on token usage and associated costs. The extension integrates seamlessly into your IDE, providing AI-powered assistance without the hefty price tag.

Key Features

Massive Cost Reduction: Achieve up to 99% savings on AI API calls by processing code context locally.
Local AST Knowledge Graphs: Builds and maintains a detailed understanding of your codebase's structure and relationships.
IDE Integration: Works directly within your Integrated Development Environment for a streamlined workflow.
MCP Server Component: Provides the backend infrastructure for local AI processing.
Reduced Latency: Local processing can lead to faster response times for AI-driven code assistance.
Enhanced Privacy: Keeps sensitive code analysis within your local environment.

Who PruvaGraph is For

PruvaGraph is an essential tool for AI builders , software engineers , and development teams who are actively using AI-powered tools within their IDEs, such as Cursor or other platforms that rely on large language models for code analysis and generation. It is particularly beneficial for projects with large codebases or for developers who frequently utilize AI for tasks like code completion, refactoring, and debugging, and are looking for a cost-effective solution to manage their API expenses without sacrificing functionality.