Universal Code Review Graph

🧠 Universal Code Review Graph

One MCP Server · Any AI Assistant · 8–15× Fewer Tokens

[](https://github.com/cyberNoman/universal-code-review-graph/actions/workflows/tests.yml) [](LICENSE) [](https://python.org) [](https://modelcontextprotocol.io) [](universal-code-graph/CONTRIBUTING.md) [](README.md)

> Stop sending your entire codebase to AI on every request. > Build a code graph once. Review only what matters. Save 85–93% of tokens.

| 🔬 Physics-Inspired Math | 🌐 Universal AI Support | ⚡ One-Time Setup | |:---:|:---:|:---:| | 6 advanced optimization techniques | Claude, Kimi, Qwen, GPT, Cursor & more | Build graph once, use forever |

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🎯 See It In Action

$ cd my-django-app/
$ code-graph-server &

You → AI:  "Build the code graph for this repo"
AI  →  ✅ Done. 2,341 symbols · 4,892 edges · 127 files indexed (8.3s)

You → AI:  "I changed checkout/views.py and checkout/serializers.py. Review my PR."

AI  →  [review_changes] scanning blast radius...

       📁 Files to review (5 of 127):
          checkout/views.py          ← changed
          checkout/serializers.py    ← changed
          checkout/models.py         ← downstream: CartItem, Order
          payments/stripe.py         ← downstream: charge()
          orders/tasks.py            ← upstream: calls process_checkout()

       ⚡ 2,100 tokens used  (was 18,400 without graph)
       🎯 Quality score: 8.7/10  (was 6.9/10)
       🧮 Optimized with: PageRank + Entropy + LSH + Physics

You → AI:  "What breaks if I rename process_checkout()?"
AI  →  [get_impact] upstream callers: orders/tasks.py, api/webhooks.py
                    downstream callees: payments/stripe.py, cart/models.py

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📊 Real-World Results

Repository: Django e-commerce app — 127 Python files
Changed:    checkout/views.py + checkout/serializers.py

┌─────────────────┬──────────────────┬──────────────────┐
│     Metric      │  Without Graph   │   With Graph     │
├─────────────────┼──────────────────┼──────────────────┤
│ Files Read      │      127         │        5         │
│ Tokens Used     │    18,400        │     2,100        │
│ Review Time     │      45s         │       8s         │
│ Quality Score   │    6.9 / 10      │    8.7 / 10      │
│ Cost            │    $0.55         │     $0.06        │
└─────────────────┴──────────────────┴──────────────────┘

        ✅  8.7× fewer tokens   ·   89% cost reduction

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✨ Why This Exists

| ❌ Traditional Approach | ✅ Our Approach | |:---:|:---:| | AI reads entire codebase every request | Build code graph once | | 80–90% tokens wasted on irrelevant files | Mathematical optimization selects only relevant context | | Slower · Expensive · Lower quality | 6–8× fewer tokens · Faster · Higher quality |

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🔬 Mathematical Optimization Engine

6 physics-inspired techniques working together for 8–15× token reduction

| Technique | Foundation | Savings | |:---|:---|:---:| | Shannon Entropy Filtering | H(X) = -Σ p(x) log₂ p(x) | 1.5–2× | | Spectral Graph Centrality | Eigenvector: A·x = λx | 1.8–2.5× | | Thermodynamic Pruning | Free Energy: F = E - T·S | 1.6–2.2× | | Wave Function Collapse | Quantum-inspired symbol merging | 1.3–1.8× | | Fractal Dimension Analysis | Box-Counting: D = log N(ε) / log(1/ε) | 1.4–1.9× | | Renormalization Group Flow | Statistical physics coarse-graining | 2.0–3.0× | | 🔥 Combined Pipeline | All techniques sequentially | 8–15× |

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🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│              AI Assistant                               │
│    Claude · Kimi · Qwen · GPT · Cursor · Windsurf       │
└────────────────────────┬────────────────────────────────┘
                         │  MCP Protocol (JSON-RPC)
                         ▼
┌─────────────────────────────────────────────────────────┐
│              Universal MCP Server                       │
│  build_graph · review_changes · get_impact · find_paths │
└────────────────────────┬────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│         Mathematical Token Optimizer (6 Techniques)     │
│  Entropy · Spectral · Thermodynamic · Wave · Fractal    │
│                    Renormalization                       │
└────────────────────────┬────────────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────────────┐
│              Graph Engine                               │
│         NetworkX + Tree-sitter (AST Parsing)            │
│        Symbols (nodes) · Calls (edges) · Files          │
└────────────────────────┬────────────────────────────────┘
                         │  SQLite
                         ▼
              ┌──────────────────┐
              │  .code_graph.db  │
              │ Persistent Store │
              └──────────────────┘

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🚀 Quick Start

Option 1: pip Install

pip install universal-code-review-graph[all]
code-graph-server

Option 2: From Source

git clone https://github.com/cyberNoman/universal-code-review-graph.git
cd universal-code-review-graph/universal-code-graph
pip install -r requirements.txt
python server.py

Option 3: Docker

docker build -t code-graph .
docker run -v $(pwd):/workspace code-graph build /workspace

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🔌 Connect Your AI

Claude Code

claude mcp add code-graph code-graph-server

Kimi / Qwen / ChatGPT / Any MCP Client

{
  "mcpServers": {
    "code-graph": {
      "command": "python3",
      "args": ["/path/to/server.py"]
    }
  }
}

Cursor / Windsurf

{
  "servers": {
    "code-graph": {
      "command": "python3",
      "args": ["/path/to/server.py"],
      "type": "stdio"
    }
  }
}

---

🛠️ The 9 MCP Tools

| Tool | What It Does | Impact | |:---|:---|:---:| | build_graph | Index repo — parse + build graph + save to SQLite | Run once | | review_changes | Blast radius for changed files | 6–8× savings | | get_impact | All callers + callees of a symbol | Refactoring safety | | find_paths | Call chains between two symbols | Debugging | | search_symbols | Find by name / wildcard (parse*) | Exploration | | get_symbol_details | Location, callers, callees for one symbol | Deep dive | | get_file_symbols | All symbols in a file | File overview | | export_graph | JSON, DOT (Graphviz), or summary | Tooling | | get_stats | Counts + most-connected nodes | Health check |

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🌐 Supported AI Assistants

| AI Assistant | Token Savings | Best For | |:---:|:---:|:---| | Kimi K2.5 | ~7.5× | Visual analysis, long context | | Claude / Claude Code | ~6.8× | Complex reasoning | | Gemini Pro | ~7.2× | Multimodal tasks | | ChatGPT / GPT-4o | ~6.5× | General purpose | | Qwen | ~6.7× | Fast inference, multilingual | | Cursor | ~7.0× | IDE integration | | Windsurf | ~7.0× | Workflow automation | | Any MCP Client | ~6.5× | Universal |

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💻 Supported Languages

| Language | Symbols | Call Edges | Status | |:---:|:---:|:---:|:---:| | Python | ✅ | ✅ | Production | | JavaScript / JSX | ✅ | ✅ | Production | | TypeScript / TSX | ✅ | ✅ | Production | | Go | ✅ | ✅ | Production | | Rust | 🟡 | 🟡 | Planned | | Java | 🟡 | 🟡 | Planned | | C / C++ | 🟡 | 🟡 | Planned |

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🧪 CLI Usage

# Build graph for your project
code-graph build /path/to/repo

# Review changed files
code-graph review src/main.py src/utils.py --depth 3

# Search symbols
code-graph search "parse*" --type function

# Show stats
code-graph stats

# Run benchmark
python benchmark.py /path/to/repo

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📦 Project Layout

universal-code-review-graph/
├── universal-code-graph/       ← THE PRODUCT
│   ├── server.py               # MCP server entry point
│   ├── code_graph.py           # Graph engine (NetworkX + Tree-sitter)
│   ├── token_optimizer/        # Mathematical optimization (6 techniques)
│   ├── cli.py                  # Command-line interface
│   ├── configs/                # Ready-made configs for every AI
│   └── tests/                  # 94 tests — all passing ✅
│
├── docs/                       # Full documentation
├── app/                        # Landing page (React + Vite)
├── hooks/                      # Pre-commit hooks
├── .github/                    # GitHub Actions CI
├── Dockerfile                  # Docker support
└── docker-compose.yml

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🔒 Persistent Across Sessions

> You only run build_graph once per project — not every session. > On startup, the server automatically finds and loads .code_graph.db in your working directory.

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👥 Built by Human + AI Collaboration

Human

| Contributor | Role | |:---:|:---| | Noman (@cyberNoman) | Project Lead · Architect · Vision · Testing · Deployment |

AI Assistants

| AI | Provider | Contributions | |:---:|:---:|:---| | Claude | Anthropic | Core architecture · MCP server · CI/CD | | Kimi K2.5 | Moonshot AI | Math optimization · Physics algorithms · Graph theory | | Qwen | Alibaba | Code structure · Integration patterns · Test framework |

Built with ❤️ by Human + AI collaboration. The future of software development.

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🤝 Contributing

See [CONTRIBUTING.md](universal-code-graph/CONTRIBUTING.md) for details.

Most wanted contributions:

• Add Rust / Java / C++ — see [contributing guide](universal-code-graph/CONTRIBUTING.md)
• Improve token optimization — better algorithms, more techniques
• Bug reports — wrong blast radius results
• Add IDE plugins — JetBrains, Vim, Emacs

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📝 License

MIT. See [LICENSE](LICENSE).

---

One server. Any AI. Fewer tokens. Mathematical precision.

⭐ Star this repo if it saved you tokens ⭐

[](https://github.com/cyberNoman/universal-code-review-graph/actions/workflows/tests.yml)

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: cyberNoman
• Source: cyberNoman/universal-code-review-graph
• License: MIT

Install and usage instructions live in the source repository linked above.