Ambiance
Provides intelligent code context and analysis for modern IDEs by leveraging semantic analysis, AST parsing, and token-efficient compression for AI assistants.
About
Ambiance operates as a Model Context Protocol (MCP) server, designed to significantly enhance how AI assistants interact with codebases. It eliminates the need for manual file exploration by offering instant, accurate understanding through intelligent semantic search, Abstract Syntax Tree (AST) parsing, and an impressive 60-80% token reduction via semantic compaction. This multi-tier intelligence solution works offline for core functions, progressively enhancing capabilities with OpenAI integration for advanced AI summarization and analysis, and future cloud services for integrated GitHub repository insights, making code context highly efficient and accessible.
Key Features
- Semantic Compaction: Achieves 60-80% token reduction while preserving code meaning.
- Multi-Language Support: Analyzes TypeScript, JavaScript, Python, Go, Rust, Java, and more.
- Multi-tier Intelligence: Seamlessly integrates local processing, OpenAI, and future cloud services.
- Offline Core Functionality: Basic semantic analysis and AST parsing work without internet.
- AST-Grep Search: Provides structural code pattern searching capabilities.
- 0 GitHub stars
Use Cases
- Empowering AI assistants with deep, token-efficient codebase understanding.
- Reducing LLM inference costs and improving context window efficiency.
- Performing offline semantic code search and AST-based analysis within projects.
