codebase-context indexes team patterns for AI agents

While most RAG tools focus on what code exists, codebase-context focuses on how your team actually writes it, preventing AI from hallucinating "technically correct" but non-idiomatic solutions.

• –Hybrid RRF search combines semantic embeddings with keyword matching to catch specific symbols that vector-only search often misses
• –Automatic pattern detection tracks "Rising" vs. "Declining" conventions based on git recency, steering agents away from legacy debt
• –Local-first architecture using LanceDB and Xenova embeddings ensures zero-latency, private indexing without external API costs
• –The "edit-readiness gate" explicitly tells agents when their context is too thin to trust, reducing the risk of confident but wrong refactors