CodeGraph

A Hybrid GraphRAG System with NARS Reasoning for UI Code Generation

CodeGraph is an enterprise-grade system that extracts ontological entities from UI code snippets (Material UI, Tailwind CSS, Chakra UI, Bootstrap, and proprietary code) and generates complete vanilla web code from natural language queries. The key innovation is the "neural proposes, symbolic disposes" architecture using NARS (Non-Axiomatic Reasoning System) for symbolic reasoning with evidential truth-values, effectively mitigating LLM hallucinations.

The Problem

Modern enterprises face several challenges when managing UI components across multiple projects:

Component Fragmentation: Teams maintain dozens of React/Vue/Angular projects, each with their own UI components, leading to inconsistent designs and duplicated effort.
Poor Discoverability: Developers can't easily find existing components that match their needs, so they build from scratch instead of reusing.
LLM Hallucinations: When using AI to generate UI code, LLMs often produce components that don't match the company's design system or include non-existent classes and patterns.
No Learning Loop: Traditional RAG systems don't improve over time—they can't learn from developer feedback about which generated components actually worked.

How CodeGraph Solves It

CodeGraph introduces a novel hybrid approach that combines the flexibility of neural networks with the reliability of symbolic reasoning:

User Query: "blue button with hover animation"
     │
     ▼
┌─────────────────────────────────────────────────────────┐
│  1. LLM Translation Layer                               │
│     - Translates natural language to Narsese            │
│     - "blue" → <{blue} --> color>                       │
│     - "hover animation" → <{transition} --> behavior>   │
└─────────────────────────────────────────────────────────┘
     │
     ▼
┌─────────────────────────────────────────────────────────┐
│  2. Hybrid Retrieval                                    │
│     - Vector similarity (semantic matching)             │
│     - Graph traversal (structural relationships)        │
│     - NARS inference (logical reasoning)                │
└─────────────────────────────────────────────────────────┘
     │
     ▼
┌─────────────────────────────────────────────────────────┐
│  3. NARS Reasoning Engine                               │
│     - Evaluates candidates with truth-values            │
│     - Confidence: 0.91 (frequency=0.95, confidence=0.87)│
│     - Rejects hallucinations (low truth-value)          │
└─────────────────────────────────────────────────────────┘
     │
     ▼
┌─────────────────────────────────────────────────────────┐
│  4. Code Generation                                     │
│     - Context-aware generation from similar components  │
│     - Validates against design system constraints       │
│     - Returns vanilla HTML/CSS/JS                       │
└─────────────────────────────────────────────────────────┘

Key Differentiators

Feature	Traditional RAG	CodeGraph
Hallucination Mitigation	None	NARS truth-values reject low-confidence results
Learning Paradigm	None	AIKR (Assumption of Insufficient Knowledge and Resources) enables one-shot learning
Feedback Loop	Requires model retraining	RLKGF propagates confidence through graph relations—no retraining needed
Search Precision	~34% (vector-only)	~91% (hybrid vector + graph + NARS)
Cost Efficiency	High (RLHF retraining)	10x cheaper (graph as reward model)

What is NARS?

NARS (Non-Axiomatic Reasoning System) is a general-purpose reasoning system designed to work under the Assumption of Insufficient Knowledge and Resources (AIKR). Unlike traditional logic systems that require complete knowledge, NARS:

Handles uncertainty: Every statement has a truth-value <frequency, confidence> indicating how often something is true and how much evidence supports it
Learns incrementally: Can learn from a single example and refine beliefs with more evidence
Reasons under resource limits: Makes the best decision possible given time and memory constraints

In CodeGraph, NARS serves as the "arbiter" that validates LLM suggestions against the knowledge graph, rejecting hallucinations that don't have sufficient evidential support.

What is RLKGF?

RLKGF (Reinforcement Learning from Knowledge Graph Feedback) is our approach to continuous improvement without expensive model retraining:

Developer gives feedback: "This button component worked perfectly!"
     │
     ▼
┌─────────────────────────────────────────────────────────┐
│  Confidence Update                                      │
│  - Component confidence: 0.75 → 0.82                    │
│  - Propagates via SIMILAR_TO: Related buttons +0.03    │
│  - Propagates via CAN_REPLACE: Alternatives +0.02      │
└─────────────────────────────────────────────────────────┘
     │
     ▼
Future queries rank this component higher (no retraining!)

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         CodeGraph                                │
├─────────────────────────────────────────────────────────────────┤
│  MCP Server          │  REST API           │  Web Dashboard     │
│  (Claude Code)       │  (Axum)             │  (HTMX)            │
├─────────────────────────────────────────────────────────────────┤
│                     Code Generation                              │
│              (Template + NARS Reasoning)                        │
├─────────────────────────────────────────────────────────────────┤
│  Hybrid Retrieval    │  RLKGF Feedback     │  Benchmark Suite   │
│  (Vector+Graph+NARS) │  (Confidence Loop)  │  (Comparison)      │
├─────────────────────────────────────────────────────────────────┤
│  Graph Storage       │  Vector Storage     │  NARS Integration  │
│  (Neo4j)             │  (Qdrant + Redis)   │  (OpenNARS)        │
├─────────────────────────────────────────────────────────────────┤
│                    Extraction Pipeline                           │
│              (HTML/CSS/JS → Knowledge Graph)                    │
└─────────────────────────────────────────────────────────────────┘

Crate Structure

Crate	Purpose
`codegraph-core`	Domain entities, configuration, error handling, retry policies
`codegraph-extraction`	AST parsing with tree-sitter, ontology mapping
`codegraph-graph`	Neo4j repository, schema definitions, relationship management
`codegraph-vector`	Qdrant embeddings, Redis caching layer
`codegraph-reasoning`	NARS/ONA integration, Narsese translation
`codegraph-retrieval`	Hybrid search combining vector, graph, and NARS inference
`codegraph-feedback`	RLKGF feedback loop, confidence propagation
`codegraph-generation`	GPT-4o code generation, template engine
`codegraph-web`	Axum REST API, HTMX dashboard, Prometheus metrics
`codegraph-mcp`	MCP server for Claude Code integration
`codegraph-benchmark`	Comparison suite for evaluating retrieval approaches
`codegraph-cli`	Command-line interface

Tech Stack

Backend

Component	Technology	Version	Purpose
Language	Rust	Edition 2024	Systems programming with memory safety
Runtime	Tokio	1.43	Async runtime for high-performance I/O
Web Framework	Axum	0.8	Async web framework with WebSocket support
Graph Database	Neo4j	5.x	Component relationships and ontology
Vector Database	Qdrant	1.13+	Semantic similarity search
Cache	Redis	7.x	Rate limiting, response caching
AST Parsing	tree-sitter	0.24	HTML/CSS/JS parsing
LLM Integration	async-openai	0.28	GPT-4o for code generation
Observability	Prometheus	0.13	Metrics collection
API Docs	utoipa	5.4	OpenAPI/Swagger documentation
Reasoning	OpenNARS for Applications	-	Symbolic reasoning with truth-values

Frontend

Component	Technology	Version	Purpose
Framework	React	19.2	UI components
Language	TypeScript	5.9	Type-safe JavaScript
Build Tool	Vite	7.2	Fast development and bundling
Package Manager	Bun	-	Fast JavaScript runtime and package manager
Styling	TailwindCSS	4.1	Utility-first CSS
Routing	React Router DOM	7.13	Client-side routing
State Management	Zustand	5.0	Lightweight state management
Code Editor	CodeMirror	6	Syntax-highlighted code editing
Charts	Recharts	3.7	Data visualization
Graph Visualization	D3.js	7.9	Knowledge graph rendering
Icons	Lucide React	0.563	Icon library

Getting Started

Prerequisites

Rust 1.83 or later
Docker and Docker Compose
OpenAI API key (for embeddings and generation)

Quick Start

# Clone the repository
git clone https://github.com/anthropics/codegraph.git
cd codegraph

# Start infrastructure (Neo4j, Qdrant, Redis)
docker compose -f .cwa/docker/docker-compose.yml up -d

# Set your OpenAI API key
export OPENAI_API_KEY=your-api-key-here

# Build the project
cargo build --release

# Run the server
./target/release/codegraph serve

The server will be available at http://localhost:3000.

Running Without ONA

CodeGraph works without the ONA reasoning engine (using a fallback mode):

export CODEGRAPH_ONA_ENABLED=false
./target/release/codegraph serve

In offline mode, the system still uses vector + graph retrieval but skips NARS inference. This reduces precision but maintains functionality.

Usage Guide

Step 1: Populate the Knowledge Graph

Upload UI components from your existing projects:

curl -X POST http://localhost:3000/api/snippets \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Primary Button",
    "html": "<button class=\"bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded\">Click me</button>",
    "css": ".btn-primary { transition: all 0.2s ease; }",
    "design_system": "tailwind",
    "tags": ["button", "primary", "cta"]
  }'

CodeGraph automatically:

Parses HTML/CSS/JS using tree-sitter
Identifies the design system (Tailwind, Material UI, etc.)
Maps to the component ontology (Button → Interactive → UIElement)
Generates vector embeddings
Creates nodes and relationships in Neo4j

Step 2: Search with Natural Language

Query for components using plain English:

curl "http://localhost:3000/api/query?q=blue+button+with+hover+animation"

Response:

{
  "results": [
    {
      "id": "abc-123",
      "name": "Primary Button",
      "confidence": 0.91,
      "reasoning": "Button with blue background (bg-blue-500) and hover transition",
      "similar_elements": ["Secondary Button", "CTA Button"]
    }
  ],
  "mode": "normal"
}

The NARS reasoning engine analyzes:

Semantics: "blue" matches bg-blue-* CSS classes
Behavior: "hover animation" matches hover:* + transition
Confidence: 0.91 based on evidential truth-values

Step 3: Generate New Components

Need a component that doesn't exist? Generate it with context:

curl -X POST http://localhost:3000/api/generate \
  -H "Content-Type: application/json" \
  -d '{
    "description": "Product card with image, title, price, and buy button",
    "design_system": "tailwind"
  }'

Response:

{
  "html": "<article class=\"bg-white rounded-lg shadow-md overflow-hidden\">...</article>",
  "css": "/* Scoped styles */",
  "js": "// Event handlers",
  "reasoning": "Generated based on 3 similar cards in knowledge graph"
}

The generation process:

Retrieves similar existing components
Uses NARS to identify common patterns
GPT-4o generates vanilla code based on context
Validates output and injects best practices

Step 4: Provide Feedback (RLKGF)

Help the system learn by providing feedback on generated code:

curl -X POST http://localhost:3000/api/feedback \
  -H "Content-Type: application/json" \
  -d '{
    "element_id": "xyz-789",
    "positive": true,
    "comment": "Perfect for the e-commerce page"
  }'

This feedback:

Increases the component's confidence score
Propagates to related components via SIMILAR_TO relationships
Improves future rankings without model retraining

Step 5: Access the Dashboard

Visit http://localhost:3000 for the web interface:

Upload: Visual snippet upload with preview
Search: Query components with graph visualization
Metrics: Real-time RLKGF metrics and confidence trends
History: Browse generation history and feedback

API Reference

Endpoint	Method	Description
`/api/snippets`	POST	Upload a code snippet
`/api/snippets`	GET	List snippets (paginated)
`/api/snippets/:id`	GET	Get snippet by ID
`/api/snippets/:id`	DELETE	Delete a snippet
`/api/query`	GET	Search components (supports `?q=` query parameter)
`/api/generate`	POST	Generate UI code from description
`/api/feedback`	POST	Submit feedback on a component
`/api/stats`	GET	Knowledge graph statistics
`/api/metrics/rlkgf`	GET	RLKGF metrics and trends
`/health`	GET	Basic health check
`/health/ready`	GET	Readiness check with service status
`/health/live`	GET	Liveness probe
`/metrics`	GET	Prometheus metrics

Supported Design Systems

Material UI
Tailwind CSS
Chakra UI
Bootstrap
Custom (auto-detected)

MCP Integration

CodeGraph provides an MCP (Model Context Protocol) server for seamless integration with Claude Code and other AI assistants.

Setup

# Start the MCP server
codegraph mcp

Available Tools

Tool	Description
`extract_snippet`	Extract UI elements from HTML/CSS/JS code
`query_ui`	Search components using NARS reasoning
`generate_code`	Generate UI code from natural language
`give_feedback`	Provide RLKGF feedback (thumbs up/down)
`get_graph_stats`	Get knowledge graph statistics

Available Resources

Resource	Description
`codegraph://metrics`	Current RLKGF metrics
`codegraph://recent`	Recent generations and their feedback

Example Usage in Claude Code

After starting the MCP server, you can use CodeGraph directly in Claude Code:

"Search for a responsive navbar component"
"Generate a login form with validation"
"Extract components from this HTML file"

Benchmarks

Compare CodeGraph's hybrid approach against simple vector RAG:

codegraph benchmark

This generates reports in Markdown, JSON, and HTML formats with:

Metric	Simple Vector RAG	CodeGraph (Hybrid)
Precision	34%	91%
Recall	78%	85%
F1 Score	0.47	0.88
Hallucination Rate	23%	4%
P50 Latency	45ms	120ms
P95 Latency	89ms	280ms

The hybrid approach trades some latency for significantly higher precision and lower hallucination rates.

Configuration

All configuration is done via environment variables:

Server

Variable	Default	Description
`SERVER_HOST`	`0.0.0.0`	Server bind address
`SERVER_PORT`	`3000`	Server port
`REQUEST_TIMEOUT_SECS`	`30`	Request timeout in seconds

Neo4j

Variable	Default	Description
`NEO4J_URI`	`bolt://localhost:7687`	Neo4j connection URI
`NEO4J_USER`	`neo4j`	Neo4j username
`NEO4J_PASSWORD`	`codegraph123`	Neo4j password
`NEO4J_MAX_CONNECTIONS`	`50`	Connection pool size

Qdrant

Variable	Default	Description
`QDRANT_URL`	`http://localhost:6334`	Qdrant gRPC URL
`QDRANT_COLLECTION`	`ui_elements`	Collection name
`QDRANT_VECTOR_SIZE`	`1536`	Embedding dimensions

Redis

Variable	Default	Description
`REDIS_URL`	-	Redis URL (optional, enables caching)

OpenAI

Variable	Default	Description
`OPENAI_API_KEY`	-	OpenAI API key (required)
`OPENAI_MODEL`	`gpt-4o`	Model for code generation
`OPENAI_EMBEDDING_MODEL`	`text-embedding-3-small`	Model for embeddings
`OPENAI_MAX_TOKENS`	`4096`	Maximum tokens per request
`OPENAI_TEMPERATURE`	`0.7`	Generation temperature

ONA/NARS

Variable	Default	Description
`CODEGRAPH_ONA_ENABLED`	`true`	Enable ONA integration
`ONA_HOST`	`localhost`	ONA server host
`ONA_PORT`	`50000`	ONA UDP port
`ONA_INFERENCE_CYCLES`	`100`	Inference cycles per query

Rate Limiting

Variable	Default	Description
`RATE_LIMIT_RPM`	`100`	Requests per minute per IP
`RATE_LIMIT_WINDOW_SECS`	`60`	Rate limit window

Retry & Circuit Breaker

Variable	Default	Description
`RETRY_MAX_OPENAI`	`3`	Max retries for OpenAI calls
`RETRY_MAX_DB`	`2`	Max retries for database calls
`RETRY_BASE_DELAY_MS`	`100`	Base delay between retries
`CIRCUIT_BREAKER_THRESHOLD`	`5`	Failures before circuit opens
`CIRCUIT_BREAKER_TIMEOUT_SECS`	`30`	Time before circuit half-opens

Logging

Variable	Default	Description
`LOG_LEVEL`	`info`	Log level (trace, debug, info, warn, error)
`LOG_FORMAT`	`pretty`	Log format (pretty, json)

Error Tracking (Sentry)

Variable	Default	Description
`SENTRY_DSN`	-	Sentry DSN (optional)
`SENTRY_ENVIRONMENT`	`development`	Environment name
`SENTRY_RELEASE`	-	Release version
`SENTRY_SAMPLE_RATE`	`1.0`	Sample rate (0.0-1.0)

Production Features

Graceful Degradation

CodeGraph supports graceful degradation when external services are unavailable:

Mode	Condition	Behavior
Normal	All services operational	Full functionality
Degraded	Non-critical services offline (Redis, ONA)	Core features work, some caching/reasoning disabled
Cached	Critical services offline	Serves cached responses only
Offline	Degradation disabled and services down	System unavailable

Check system status:

curl http://localhost:3000/health/ready

Response:

{
  "status": "degraded",
  "services": {
    "neo4j": { "status": "healthy" },
    "qdrant": { "status": "healthy" },
    "redis": { "status": "unhealthy", "message": "connection refused" }
  }
}

Retry & Circuit Breaker

Automatic retry with exponential backoff for external API calls:

OpenAI: 3 retries with 200ms base delay
Databases: 2 retries with 100ms base delay
Circuit Breaker: Opens after 5 consecutive failures, attempts recovery after 30s

Observability

Request Tracing: Every request gets a UUID trace_id for correlation
Structured Logging: JSON-formatted logs with trace context
Prometheus Metrics: Scrape /metrics for monitoring
Health Checks: Kubernetes-compatible probes at /health/*

Development

Running Tests

# Run all tests
cargo test

# Test a specific crate
cargo test -p codegraph-vector
cargo test -p codegraph-benchmark

# Run with logging
RUST_LOG=debug cargo test

Building Documentation

cargo doc --open

Code Quality

# Linting
cargo clippy

# Formatting
cargo fmt

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Acknowledgments

OpenNARS - Non-Axiomatic Reasoning System
ONA - OpenNARS for Applications
tree-sitter - Parsing library
Axum - Web framework
HTMX - Hypermedia approach for the frontend

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
.claude		.claude
.cwa		.cwa
.github/workflows		.github/workflows
crates		crates
docker		docker
docs		docs
frontend		frontend
grafana/provisioning		grafana/provisioning
scripts		scripts
tests/integration		tests/integration
.env.example		.env.example
.gitignore		.gitignore
.mcp.json		.mcp.json
CLAUDE.md		CLAUDE.md
Cargo.toml		Cargo.toml
Dockerfile		Dockerfile
Dockerfile.mcp		Dockerfile.mcp
README.md		README.md
docker-compose.yml		docker-compose.yml
prometheus.yml		prometheus.yml
tarpaulin.toml		tarpaulin.toml

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CodeGraph

Table of Contents

The Problem

How CodeGraph Solves It

Key Differentiators

What is NARS?

What is RLKGF?

Architecture

Crate Structure

Tech Stack

Backend

Frontend

Getting Started

Prerequisites

Quick Start

Running Without ONA

Usage Guide

Step 1: Populate the Knowledge Graph

Step 2: Search with Natural Language

Step 3: Generate New Components

Step 4: Provide Feedback (RLKGF)

Step 5: Access the Dashboard

API Reference

Supported Design Systems

MCP Integration

Setup

Available Tools

Available Resources

Example Usage in Claude Code

Benchmarks

Configuration

Server

Neo4j

Qdrant

Redis

OpenAI

ONA/NARS

Rate Limiting

Retry & Circuit Breaker

Logging

Error Tracking (Sentry)

Production Features

Graceful Degradation

Retry & Circuit Breaker

Observability

Development

Running Tests

Building Documentation

Code Quality

Contributing

License

Acknowledgments

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