Automatically extracts architectural decisions, patterns, and insights from Git commits to build a local, structured project memory. It exposes this living context to AI tools via MCP, allowing them to understand the historical reasoning and evolution behind your codebase.
Record development decisions as structured JSON, embed them as vectors via Gemini, and search semantically over MCP. Works with Claude Code, Cursor, Windsurf, and any MCP client.
A local MCP server providing persistent memory for AI coding assistants by storing and searching architectural decisions, patterns, and solutions. It also includes tools for git automation and mapping codebase expertise based on project history.
The decision system for agentic engineering: keeps your project's decisions, rationale, and rejected paths in plain files and surfaces them to AI coding agents before they plan or change code.
The shared AI context engine for git — save, search, and share the reasoning behind code changes. Captures the why behind every commit and slide on PRs for coding agents.
