ContextPrune — LLM Context Window Optimizer

Stop burning $200/month on redundant tokens in your AI coding workflow

ContextPrune sits between your IDE and Claude/GPT APIs, analyzing every prompt for semantic redundancy before transmission. Using embedding-based similarity detection, it removes duplicate code context, repetitive file imports, and redundant documentation while maintaining 95% semantic accuracy—cutting your token bills by 60-80% without changing a single line of your existing AI assistant setup.

Key Benefits:

- Automatic token reduction: Deploy as FastAPI middleware in 5 minutes—no code changes to existing AI workflows, immediate 60-80% cost savings on Claude/GPT API bills

- Context-aware pruning: Embedding-based analyzer identifies truly redundant imports, duplicate function definitions, and repetitive documentation while preserving critical semantic relationships

- Real-time analytics dashboard: PostgreSQL-backed metrics show exactly which files/contexts are wasting tokens, with Redis caching for sub-50ms pruning decisions on repeated prompts

MVP Scope: Build a context window optimizer that analyzes incoming prompts, identifies semantic redundancy using embedding-based similarity detection, and intelligently prunes non-critical tokens while maintaining 95% semantic retention. MVP includes a REST API for prompt preprocessing, integration with Claude/GPT APIs, basic analytics dashboard showing token savings, and CLI tool for developers. Target 60-80% token reduction on typical enterprise codebases.

Tech Stack: Python, FastAPI, PostgreSQL, Redis, OpenAI API, Anthropic API, React, Docker

Components:

- Context Analyzer Engine

- Pruning Decision Engine

- Token Budget Manager

- API Integration Layer

- Analytics Dashboard

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Quality assessment: Strong market-fit concept with concrete value metrics (60-80% savings, 95% accuracy) and solid technical architecture, but lacks originality—context optimization and token pruning are well-explored problems—and the artifact is incomplete (truncated pitch/scope), preventing assessment of depth and differentiation.