codeprysm
Graph-based code intelligence with MCP server for AI assistants. Tree-sitter parsing, semantic search, relationship analysis. Your codebase as a searchable knowledge graph.
Install / Use
claude mcp add codeprysm -- npx -y github:codeprysm/codeprysmIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
Development & EngineeringSupported Platforms
Skill content
View source on GitHubCodePrysm
A powerful tool for analyzing code repositories and generating relationship graphs using Tree-sitter abstract syntax trees. CodePrysm transforms source code into a searchable knowledge graph, enabling semantic code search, dependency analysis, and intelligent code navigation.
Overview
CodePrysm builds a comprehensive graph representation of your codebase where:
- Nodes represent code entities using three semantic types: Container (classes, interfaces, structs), Callable (functions, methods), and Data (fields, properties, constants)
- Edges represent three types of relationships: CONTAINS (hierarchy), USES (dependencies), and DEFINES (definitions)
- Embeddings enable semantic search using natural language with full metadata support
Key Features
- Semantic Code Search - Find code using natural language queries with kind/subtype filtering
- AST-Based Analysis - Precise parsing using Tree-sitter with declarative SCM tags
- Rich Dependency Graphs - Three relationship types (CONTAINS, USES, DEFINES) for comprehensive analysis
- Fine-Grained Entities - Distinguish structs from interfaces, async from sync, fields from properties
- Scalable Architecture - Handles codebases with 100K+ files
- MCP Integration - AI-powered code exploration via Model Context Protocol
- Multi-Language - Python, JavaScript/TypeScript, C/C++, C#, Go, Rust
- GPU Acceleration - Metal (macOS) and CUDA (Linux/Windows) support
Installation
From crates.io (Recommended)
cargo install codeprysm-cli
From Source
git clone https://github.com/codeprysm/codeprysm.git
cd codeprysm
cargo build --release
GPU Acceleration (Optional)
For faster embedding generation, install with GPU support:
# macOS (Apple Silicon)
cargo install codeprysm-cli --features metal
# Linux (NVIDIA GPU)
cargo install codeprysm-cli --features cuda
Prerequisites
- Docker (for Qdrant vector database)
- Rust 1.85+ (only if building from source)
Quick Start
-
Start Qdrant (required for semantic search):
docker run -d --name qdrant \ -p 6333:6333 -p 6334:6334 \ -v qdrant_storage:/qdrant/storage \ qdrant/qdrant:latest -
Initialize your codebase:
cd /path/to/your/repo codeprysm init -
Start the MCP server (optional, for AI assistants):
codeprysm mcp
How It Works
graph LR
A[Source Code] --> B[Code Graph]
B --> C[Semantic Index]
C --> D[MCP Server]
D --> E[AI Assistants]
The system operates in three main phases:
- Code Graph Generation - Parse source files into a graph structure using Tree-sitter AST
- Indexing for Search - Create embeddings for semantic search using Qdrant
- MCP Server Integration - Expose capabilities to AI assistants via MCP protocol
Documentation
- Getting Started - Setup guide
- Docker Setup - Running with Docker
- SCM Tag Convention - Query file syntax
- SCM Overlays - Adding scope metadata
CLI Commands
# Generate code graph
codeprysm init --root /path/to/repo
# Start MCP server
codeprysm mcp --root /path/to/repo --qdrant-url http://localhost:6334
# Search codebase
codeprysm search "function that handles authentication"
# Show statistics
codeprysm stats --codeprysm-dir .codeprysm
# Incremental update
codeprysm update --root /path/to/repo
Supported Languages
| Language | Containers | Callables | Data | |----------|------------|-----------|------| | Python | Classes, modules | Functions, methods, async | Fields, constants | | JavaScript/TypeScript | Classes, interfaces, enums | Functions, methods, constructors | Fields, properties | | C/C++ | Structs, classes, enums, namespaces | Functions, methods | Fields, enum constants | | C# | Classes, structs, interfaces, enums | Methods, constructors | Fields, properties | | Go | Structs, interfaces | Functions, methods | Fields | | Rust | Structs, enums, traits | Functions, methods, async | Fields, const values |
Performance & Scalability
| Codebase Size | Files | Processing Time | Memory Usage | |---------------|-------|-----------------|--------------| | Small | <1K | <1 min | <1 GB | | Medium | 1K-10K | 1-5 min | 1-4 GB | | Large | 10K-50K | 5-20 min | 4-16 GB | | Very Large | 50K-100K | 20-60 min | 16-32 GB |
Development
For development, install just command runner:
# Build
just rust-build
# Test
just rust-test
# Lint
just rust-lint
# Format
just rust-fmt
See CONTRIBUTING.md for detailed development guidelines.
Project Structure
codeprysm/
├── crates/
│ ├── codeprysm-core/ # Graph generation, tree-sitter parsing
│ ├── codeprysm-search/ # Vector search, embeddings
│ ├── codeprysm-mcp/ # MCP server
│ ├── codeprysm-cli/ # Command-line interface
│ ├── codeprysm-config/ # Configuration management
│ └── codeprysm-backend/ # Backend abstraction
├── tests/fixtures/ # Test repositories
├── docs/ # Documentation
└── docker/ # Docker configuration
Contributing
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments
- Tree-sitter for powerful parsing capabilities
- Qdrant for vector search
- Candle for ML inference
Related Skills
momen-cursurrules-prompt-file
40.6kCursor rules for building custom frontends with Momen.app as headless BaaS with GraphQL API, actionflows, AI agents, and Stripe integration.
semiotic-react-dataviz-cursorrules-prompt-file
40.6kCursor rules for Semiotic data visualization library with 30+ chart types, MCP server, and AI-assisted chart generation.
Agent-Reach
72.2kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
ruflo
68.0k🌊 The original agent meta-harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, RAG integration, and native Claude Code / Codex / Hermes and many more Integrated
