I Cut My Claude API Bill 87% With One Open-Source Tool — Code-Review-Graph

If you have shipped a real codebase to Claude, Cursor, or Copilot, you have felt the bill. Every "review this PR" call drags 50,000 tokens of irrelevant context across the wire. Code-Review-Graph — a new open-source CLI from Tirth Patel — kills that overhead by building a persistent, local knowledge graph of your repo and shipping only the files that matter.

The Numbers Are Not Subtle

• Flask: 9.1× token reduction on PR reviews
• Gin: 16.4× reduction
• NextJS monorepo: 49× — narrowing 27,732 files to roughly 15 relevant ones
• Average across 6 real repos and 13 commits: 8.2×
• Impact recall: 100% (it never silently drops a broken dependency)

How It Works (The Five-Second Version)

flowchart LR
    A[Repository] --> B[Tree-sitter Parser
23 languages]
    B --> C[AST nodes + edges]
    C --> D[(SQLite graph
+ FTS5 index)]
    E[git diff / file save] --> F{Changed files?}
    F -->|yes| G[SHA-256 diff]
    G --> H[Re-parse only deltas]
    H --> D
    D --> I[Blast Radius Engine]
    I --> J[Minimal Review Set
~15 files]
    J --> K[Claude / Cursor / Codex
via MCP]
    style D fill:#0ea5e9,stroke:#0369a1,color:#fff
    style J fill:#22c55e,stroke:#15803d,color:#fff
    style K fill:#a855f7,stroke:#7e22ce,color:#fff

What Most "AI Code Review" Tools Do Wrong

The default playbook for most AI coding assistants is brute force: dump the diff, dump nearby files, hope context window absorbs the rest. That works on toy repos. It collapses on monorepos, microservices, and any project where a single function is called from twenty places.

Code-Review-Graph flips the model. Instead of "send more," it sends less, but the right less. The graph knows which functions are callers and callees, which tests cover which modules, which classes inherit from which. When a file changes, the system performs a graph traversal and returns the impact radius — the actual files that need reviewing.

Three Things That Make This Production-Grade

1. Local-first. SQLite stored at .code-review-graph/. No cloud, no telemetry, no exfiltration risk. Your code never leaves your laptop.
2. Incremental. SHA-256 diffs catch changes; only modified files re-parse. A 1,122-file FastAPI repo rebuilds in 128ms.
3. MCP-native. 28 MCP tools surface graph queries to Claude Code, Cursor, Windsurf, Zed, Continue, Codex, Antigravity, OpenCode, and more.

Real Cost Math

At Sonnet 4.6 pricing (~$3 per million input tokens), an average team running 200 PR reviews/month with 50K tokens of unfocused context burns about $30/month per repo. With Code-Review-Graph trimming context 8.2× on average, that drops to ~$3.65. Across a 50-engineer org with 30 active repos, the savings compound into mid-five figures annually — and that is before you count the time saved by faster, more focused reviews.

Bottom Line

The tool installs in one command (pip install code-review-graph), supports 11 AI platforms, and ships open source. If you are paying for AI coding assistants and not running something like this in front of them, you are lighting money on fire.

Repo: github.com/tirth8205/code-review-graph

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