Cognitive Codebase Matrix (CCM)

English | Turkce

A code graph for AI coding agents that keeps up with your edits — and tells you when it is not watching.

CCM parses your project with tree-sitter (13 languages), keeps a call and import graph plus an optional semantic index on disk, and serves them to agents such as Claude Code, Codex and Cursor through MCP. The recommended agent entry point is context: give it a symbol, path:line, or a natural- language coding task and CCM chooses structural graph lookup or hybrid search server-side. On Django 5.1 (6,670 files) a saved change shows up in graph results after a median of about 0.6 s (measurement: release build, embedder off). Query responses start with the index state (fresh, stale · … while a registered change is pending, or auto-refresh off · indexed Xs ago), and a failed re-index never replaces the last good graph. In the pre-registered L1 freshness benchmark (one-function edits in Flask 3.0.3 and Django 5.1, probes at fixed times), every graph answer from 0.25 s after a save on matched the edit; in the sub-250 ms window before the watcher applies the event, 6 of 126 probes returned the pre-edit answer still labeled fresh, and 4 more a partial or empty one (benchmarks/).

Status: search quality has a 35-task pilot benchmark. In an LLM-free benchmark of 24 fixed questions on Flask and Django, the 0.4.0 tools answer with 74% fewer response bytes and a third fewer calls than the 0.3 tools (token cost). A 2026-10-02 Claude Code pilot then showed the product-level gap: on three small/medium tasks the agent never called CCM, while merely exposing CCM increased input tokens. The active work therefore moves tool selection into the new one-call context entry point. That change has not yet been shown to reduce end-to-end agent tokens; the pilot and its negative result remain published under benchmarks/results/agent/pilot-claude/.

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Why CCM?

Coding agents find code well with grep. What grep does not give them is