docs.rs failed to build pmat-2.213.1
Please check the build logs for more information.
See Builds for ideas on how to fix a failed build, or Metadata for how to configure docs.rs builds.
If you believe this is docs.rs' fault, open an issue.

Visit the last successful build: pmat-2.8.0

PAIML MCP Agent Toolkit (pmat)

Zero-configuration AI context generation system with extreme quality enforcement and Toyota Way standards. Analyze any codebase instantly through CLI, MCP, or HTTP interfaces. Built by Pragmatic AI Labs.

🧬 v2.174.0 Release: Mutation Testing CLI (Sprint 61)! AST-based test quality measurement:

🧪 pmat mutate Command: Test your tests with AST-based mutation testing (no source recompilation)

⚡ Parallel Execution: Configurable worker threads (default: CPU core count) with real-time progress bars

📊 Three Output Formats: Text (terminal), JSON (CI/CD), Markdown (PR comments with "Survived Mutants" section)

🎯 Threshold Enforcement: Fail builds if mutation score below threshold (--threshold 80.0)

🔍 Test Gap Detection: Markdown reports highlight survived mutants for test improvement

🦀 Rust Support: Fully functional for Rust (Python, TypeScript, Go, C++ planned for v2.175.0+)

⏱️ Smart Timeouts: Per-mutant timeout control (default: 30s, configurable via --timeout)

✅ Production Ready: Tested on real files (239+ mutants), progress indicators, execution timing

🧠 v2.158.0 Release: Semantic Code Search System! AI-powered code discovery with natural language:

🔍 Hybrid Search: Combines keyword matching (ripgrep) + vector similarity with RRF algorithm

🧬 AST-Aware Chunking: Semantic code extraction for 5 languages (Rust, TypeScript, Python, C/C++, Go)

🌐 OpenAI Embeddings: 1536-dimensional vectors with incremental updates (optional, for pmat semantic search)

📊 Code Analytics: K-means, DBSCAN, hierarchical clustering + LDA topics via aprender (no API keys!)

🤖 MCP Integration: 4 new tools - semantic_search, find_similar_code, cluster_code, analyze_topics

⚡ Production Ready: 149 tests, <150ms search, pure Rust ML for clustering/topics

💻 CLI Commands: pmat embed sync, pmat semantic search, pmat analyze cluster/topics

🎯 v2.88.0 Release: Technical Debt Grading (TDG) System! Complete code quality scoring with 6 orthogonal metrics:

📊 Comprehensive Scoring: Structural complexity, semantic complexity, code duplication, coupling analysis

📚 Documentation Coverage: Language-specific documentation pattern detection and scoring

🎨 Consistency Analysis: Naming conventions, indentation patterns, and code style consistency

🏆 Grade Classification: A+ through F grading system with detailed component breakdowns

🌍 Multi-Language Support: 10+ languages including Rust, Python, JavaScript, TypeScript, Go, Java, C/C++

🛠️ CLI & MCP Integration: pmat analyze tdg command and MCP tools for programmatic access

📈 Project Analysis: Directory-level analysis with language distribution and aggregated scoring

🚀 v2.10.0: Claude Code Agent Mode - "Always Working" Achievement! Transform PMAT into a persistent background quality agent:

🤖 Claude Code Integration: Native MCP server for seamless Claude Code integration

💾 Persistent State: Monitoring state maintained across restarts with auto-save

⚙️ Production Ready: Environment-specific configs for dev, prod, and CI/CD

📊 Real-time Monitoring: Continuous quality tracking with file system watching

🏗️ Service Architecture: Systemd deployment with health checks and auto-restart

🎯 v2.9.0: Universal Demo "Just Works" Achievement! Complete AI-powered repository intelligence with multi-language analysis:

🤖 AI-Powered Recommendations: Framework-aware repository recommendations with complexity-based learning tiers

🌍 Multi-Language Intelligence: Advanced polyglot analysis with cross-language dependency detection

🏛️ Architecture Pattern Recognition: Microservices, Layered, Event-driven pattern detection with confidence scoring

📚 Repository Showcase Gallery: Curated collection of 8+ repositories across languages and complexity levels

⚡ Universal Demo: Any GitHub repository URL → Complete analysis with AI recommendations

🌐 Enhanced Web Demo: Interactive visualizations with 3 new API endpoints (/api/recommendations, /api/polyglot, /api/showcase)

Toyota Way Excellence: Zero compilation defects maintained throughout development

🚀 Quick Start

Installation

Choose your preferred installation method - PMAT is available across all major package ecosystems:

🦀 Rust (Recommended)

cargo install pmat

📦 Package Managers

# macOS/Linux - Homebrew
brew install pmat

# Windows - Chocolatey  
choco install pmat

# Ubuntu/Debian - APT
sudo apt install pmat                    # (via PPA - coming soon)

# Arch Linux - AUR
yay -S pmat

# Node.js - npm (global)
npm install -g pmat-agent

🐳 Docker

# Latest version
docker run --rm -v $(pwd):/workspace paiml/pmat:latest pmat --version

# Interactive analysis
docker run --rm -v $(pwd):/workspace -w /workspace paiml/pmat:latest pmat context

🔧 From Source

git clone https://github.com/paiml/paiml-mcp-agent-toolkit
cd paiml-mcp-agent-toolkit
make build

📥 Direct Download

# Linux/macOS Quick Install
curl -sSfL https://raw.githubusercontent.com/paiml/paiml-mcp-agent-toolkit/master/scripts/install.sh | sh

# Windows PowerShell
# Download from: https://github.com/paiml/paiml-mcp-agent-toolkit/releases

Basic Usage

# Analyze current directory
pmat context

# Semantic Code Search - NEW!
pmat embed sync ./src                           # Sync embeddings for codebase
pmat semantic search "error handling patterns"  # Natural language search
pmat semantic similar src/main.rs               # Find similar code
pmat analyze cluster --method kmeans --k 10     # Code clustering
pmat analyze topics --num-topics 15             # Topic modeling

# Technical Debt Grading (TDG)
pmat analyze tdg --path . --include-components

# Get complexity metrics
pmat analyze complexity --top-files 10

# Find technical debt
pmat analyze satd

# Mutation Testing - NEW! (v2.174.0 - Sprint 61)
pmat mutate --target src/file.rs                      # Basic mutation testing
pmat mutate --target src/ --output-format json        # JSON output for CI/CD
pmat mutate --target src/ --output-format markdown    # Markdown report
pmat mutate --target src/ --threshold 80.0            # Fail if score < 80%
pmat mutate --target src/ --timeout 60 --jobs 4       # Custom timeout and parallelism

# Validate documentation links
pmat validate-docs

# Validate README for hallucinations - NEW! (Sprint 38)
pmat context --output deep_context.md --format llm-optimized
pmat validate-readme --targets README.md --deep-context deep_context.md

# Run quality gates
pmat quality-gate --strict

# Start MCP server
pmat mcp

Universal Demo - "Just Works" Analysis

# Analyze any GitHub repository with AI recommendations
cargo run --example analyze_github_repo -- --url https://github.com/rust-lang/rust-clippy

# Compare multiple repositories across languages
cargo run --example compare_repos

# Run quality gates on GitHub repositories
cargo run --example quality_gate_github -- https://github.com/owner/repo

# Start interactive web demo
pmat demo --serve
# Then visit http://localhost:8080 for:
# • AI-powered repository recommendations
# • Multi-language project intelligence
# • Repository showcase gallery
# • Interactive analysis visualizations

Toyota Way Development (NEW)

# Setup quality enforcement (one-time)
make setup-quality

# Start development with quality checks
make dev

# Create quality-enforced commit
make commit

# Verify sprint quality
make sprint-close

🎯 Core Capabilities

🧠 Semantic Search Engine (NEW!)

Hybrid Search: Combines keyword (ripgrep) + vector similarity with Reciprocal Rank Fusion (RRF)
AST-Aware Chunking: Semantic code extraction for 5 languages using tree-sitter
OpenAI Embeddings: 1536-dimensional vectors with incremental SHA256-based updates (optional, for pmat semantic search)
Vector Database: Local SQLite storage with cosine similarity search (<100ms)
Code Analytics: K-means, hierarchical, DBSCAN clustering + LDA topic modeling via aprender (pure Rust, no API keys required)
Natural Language Queries: Find "error handling patterns" across different terminology
Similarity Detection: Discover code duplicates and refactoring opportunities
Architecture Insights: Cluster-based pattern discovery and topic extraction

Analysis Engine

Documentation Link Validator: Validate markdown links (internal & external) with HTTP retry logic
Technical Debt Grading (TDG): 6-metric orthogonal code quality scoring with A+ through F grading
Complexity Analysis: McCabe cyclomatic & cognitive complexity with AST precision
Dead Code Detection: Graph-based reachability analysis across 30+ languages
SATD Detection: Self-admitted technical debt with severity classification
Documentation Coverage: Language-specific pattern detection with scoring algorithms
Consistency Analysis: Naming conventions and code style consistency measurement
Deep Context Generation: Multi-dimensional analysis optimized for AI agents

🤖 AI-Powered Intelligence (NEW)

Smart Recommendations: Framework-aware repository suggestions with complexity matching
Polyglot Analysis: Cross-language dependency detection and architecture pattern recognition
Repository Showcase: Curated gallery with learning pathways from beginner to expert
Integration Points: Risk assessment of multi-language project coupling with mitigation strategies

Quality Systems

Quality Gates: Zero-tolerance enforcement (complexity ≤20, SATD=0, coverage >80%)
Quality Proxy: AI code interceptor with 7-stage validation pipeline
PDMT Integration: Deterministic todo generation with embedded quality requirements
Refactoring Engine: State machine-based code transformation with ACID snapshots

🏗️ Agent System (Enterprise)

Distributed Architecture: Actix actor system with zero-copy messaging and Raft consensus
Sub-Agent Types: AnalyzerActor, TransformerActor, ValidatorActor, OrchestratorActor
Workflow Engine: DSL-based automation with step dependencies and parallel execution
Resource Management: CPU/Memory/GPU/Network/IO control with enterprise-grade fault tolerance
MCP Integration: Full Model Context Protocol server with multiple transport modes

Integration Protocols

MCP Protocol: 22 tools via unified pmcp SDK 1.2.0 server (includes TDG + 4 semantic search tools)
- New: semantic_search, find_similar_code, cluster_code, analyze_topics
HTTP API: RESTful with Server-Sent Events streaming
CLI Interface: 50+ commands with POSIX-compliant exit semantics

📖 Documentation

Core Documentation

Complete Specification - Unified source of truth (36 sections)
TDG Guide - NEW! Technical Debt Grading system documentation
Agent Architecture - NEW! Sub-agent system and distributed computing capabilities
API Reference - Service APIs and integration patterns
CLI Reference - Complete command documentation

Quality & Development

Toyota Way Guide - Development workflow and standards
Sprint Management - Task tracking and execution DAG
Quality Gates - Enforcement mechanisms

Language Support

Cross-Language Analysis - NEW! Polyglot codebase analysis
Polyglot AST Feature Flags - NEW! Language feature configuration
Language Support Details - Supported language capabilities

Integration Guides

MCP Integration - Model Context Protocol setup
PDMT Guide - Deterministic todo generation
CI/CD Integration - Pipeline integration

🏗️ Architecture

PMAT implements Toyota Production System principles through rigorous static analysis:

Kaizen (改善): Iterative file-by-file improvement with measurable ΔQ metrics
Genchi Genbutsu (現地現物): Direct AST traversal, no heuristics
Jidoka (自働化): Automated quality gates with fail-fast semantics
Zero SATD Policy: Compile-time enforcement of zero technical debt

Service Architecture

// Unified service layer with dependency injection
pub trait Service: Send + Sync {
    type Input: Serialize + DeserializeOwned;
    type Output: Serialize + DeserializeOwned;
    
    async fn process(&self, input: Self::Input) -> Result<Self::Output, Self::Error>;
}

// All protocols use unified request/response
#[derive(Serialize, Deserialize)]
pub struct UnifiedRequest {
    pub operation: Operation,
    pub params: Value,
    pub context: RequestContext,
}

Performance Characteristics

Startup: 4ms hot, 127ms cold (mmap'd grammar cache)
Analysis: 487K LOC/s single-thread, 3.9M LOC/s multi-core
Memory: 47MB base + 312KB per KLOC
SIMD: 43% vectorized paths, 2.7x AVX2 speedup

🛠️ Development

Requirements

Rust 1.80.0+
Git (for repository analysis)

Build from Source

git clone https://github.com/paiml/paiml-mcp-agent-toolkit
cd paiml-mcp-agent-toolkit

# Setup Toyota Way quality enforcement
make setup-quality

# Build and test
make build
make validate

# Run examples
make examples

Library Usage

[dependencies]
pmat = "2.88.0"

use pmat::services::code_analysis::CodeAnalysisService;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let service = CodeAnalysisService::new();
    
    // Generate AI-optimized context
    let context = service.generate_context(".", None).await?;
    
    // Analyze complexity with Toyota Way standards
    let complexity = service.analyze_complexity(".", Some(10)).await?;
    
    Ok(())
}

Feature Flag Options

PMAT uses Rust's feature flag system for customizing language support:

# Default configuration (all features enabled)
pmat = "2.158.0"

# Custom configuration with specific language support
pmat = { version = "2.158.0", default-features = false, features = ["polyglot-java", "polyglot-typescript"] }

# Available language-specific features:
# - polyglot-java
# - polyglot-kotlin
# - polyglot-scala
# - polyglot-typescript
# - polyglot-javascript
# - polyglot-csharp
# - polyglot-ruby

See Polyglot AST Feature Flags for detailed documentation.


## 🔍 Language Support

- **Rust**: Full cargo integration with syn AST
- **TypeScript/JavaScript**: SWC-based parsing
- **Python**: RustPython AST analysis  
- **C/C++**: Tree-sitter with goto tracking
- **Ruchy**: v1.5.0 support with advanced analysis
  - Full AST parsing with 35+ token types
  - Halstead metrics (volume, difficulty, effort, time, bugs)
  - Dead code detection (unused functions/variables)  
  - Type inference for literals and binary operations
  - Actor message flow analysis with deadlock detection
  - Enhanced pattern matching complexity scoring
  - Import/export dependency tracking
- **Kotlin**: Tree-sitter based analysis
- **30+ Languages**: Via tree-sitter grammar support

### Cross-Language Analysis

PMAT provides a unified cross-language AST framework that enables analysis across language boundaries:

- **Polyglot AST**: Common node representation across all languages
- **Cross-Language Dependencies**: Track relationships between nodes in different languages
- **Language Mappers**: Translate language-specific ASTs to unified nodes
- **Configurable Support**: Build with only the language support you need ([Feature Flags](../docs/polyglot-ast-feature-flags.md))

## 🤖 MCP Integration

PMAT provides 18 MCP tools via unified pmcp SDK server:

```bash
# Start MCP server (auto-detects transport)
pmat mcp

# Test with Claude Code
cargo run --example mcp_server_pmcp
cargo run --example test_pmcp_server

Available Tools

analyze_tdg - NEW! Technical Debt Grading with 6-metric scoring
analyze_tdg_compare - NEW! Compare TDG scores between files/projects
analyze_complexity - Complexity metrics
analyze_satd - Technical debt detection
analyze_dead_code - Unused code analysis
quality_gate - Comprehensive quality validation
refactor_start - Begin refactoring workflow
pdmt_deterministic_todos - Generate quality todos
github_create_issue - Create GitHub issues
NEW: AI recommendation tools for intelligent repository analysis
And 9 more...

🤖 Claude Code Agent Mode (NEW v2.10.0)

Transform PMAT into a persistent background quality agent that continuously monitors your codebase:

Quick Start with Claude Code

# Start agent as MCP server for Claude Code
pmat agent mcp-server

# Configure in Claude Code settings.json:
{
  "mcpServers": {
    "pmat": {
      "command": "pmat",
      "args": ["agent", "mcp-server"],
      "env": {}
    }
  }
}

Background Daemon Mode

# Start monitoring a project
pmat agent start --project-path /path/to/project

# Check monitoring status
pmat agent status

# Stop monitoring
pmat agent stop

Key Features

Real-time Monitoring: File system watching with instant quality feedback
Persistent State: Maintains metrics across restarts with auto-save
Toyota Way Compliance: Enforces ≤20 complexity with zero SATD tolerance
Production Ready: Systemd service with health checks and auto-restart
MCP Native: Seamless Claude Code integration via stdio transport

Available Agent Tools

start_quality_monitoring - Begin monitoring a project
stop_quality_monitoring - Stop monitoring
get_quality_status - Current quality metrics
run_quality_gates - Execute quality checks
analyze_complexity - Complexity analysis
health_check - Agent health status

See Claude Code Agent Guide for detailed setup and deployment instructions.

🌐 Web Demo API Endpoints (NEW)

# AI-powered repository recommendations
GET /api/recommendations

# Multi-language project intelligence
GET /api/polyglot

# Repository showcase gallery
GET /api/showcase

# Core analysis APIs
GET /api/summary
GET /api/metrics
GET /api/hotspots
GET /api/dag

📊 Quality Standards

PMAT enforces extreme quality standards:

Complexity: ≤20 cyclomatic, ≤15 cognitive
Technical Debt: 0 SATD comments allowed
Test Coverage: >80% with property-based testing
Code Quality: 0 lint warnings, 0 dead code
Documentation: Synchronized with every commit

Quality Gates

# Run comprehensive quality analysis
pmat quality-gate --strict

# CI/CD integration
pmat analyze complexity --fail-on-violation
pmat analyze satd --fail-on-violation
pmat quality-gate --strict --fail-on-violation

🚀 Contributing

PMAT follows Toyota Way development principles:

Setup quality enforcement: make setup-quality
Start development: make dev
Make changes with documentation updates
Quality-enforced commit: make commit
Sprint verification: make sprint-close

All contributions must meet:

Zero SATD comments
Complexity ≤20 per function
Full test coverage
Documentation updates

Contribution guidelines coming soon.

📋 License

Licensed under the MIT License.

Built with ❤️ by Pragmatic AI Labs

pmat 2.213.1