Trail-Code
Provide a shared context layer for coding agents by pairing a deterministic code dependency map with persistent team learnings.
Trail-Code serves as a self-learning context layer and shared memory system for AI coding agents such as Claude Code, Cursor, and Codex. By utilizing tree-sitter, it parses codebases locally into a comprehensive symbol and call graph without requiring LLM keys or external daemons, tracking dependencies and real-time blast radius directly on the machine. Alongside structural mapping, Trail-Code continuously records and surfaces team architectural decisions, past corrections, and guidelines across sessions, helping coding assistants navigate large codebases faster, slash token usage, and prevent conflicting edits.
Key Features
01Seamless integration with Claude Code hooks, Cursor, Codex, and standard MCP environments
02Persistent learnings layer to capture team decisions, rejected approaches, and session habits
03Deterministic code mapping across 24 languages using tree-sitter without external models or daemons
0411,188 GitHub stars
05Real-time blast radius detection showing dependencies immediately after file edits
06Automatic cache freshness checks that parse uncommitted edits within milliseconds
Use Cases
01Detecting potential code breakage and team conflicts before opening pull requests
02Eliminating repetitive exploration and token waste when AI coding agents navigate unfamiliar codebases
03Preserving architectural decisions and constraints across multiple developer and agent sessions