Implements Agentic Context Engineering to create self-improving AI coding assistants that learn from execution feedback and build persistent knowledge playbooks. Reduces token usage by 86.9% while improving code accuracy by 10.6% through incremental context updates.
Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON
A deterministic AST evidence engine that forces AI agents to debug using verified execution facts instead of pattern-matching symptoms, enabling hallucination-free debugging for MCP-compatible agents.
A semantic code retrieval engine for AI agents that enables hybrid search, graph expansion, and token-aware context packing, integrating with MCP to provide precise code context to LLMs.
An MCP code-intelligence server for AI agents with pre-indexed AST cache, 62 MCP tools, and TOON-compressed output, enabling token-efficient code analysis and project health grading entirely locally.
