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║ ██████╗ ██████╗██╗ ║
║ ██╔══██╗██╔════╝██║ The World's First Programming Language ║
║ ██████╔╝██║ ██║ for AI Persona Management ║
║ ██╔═══╝ ██║ ██║ ║
║ ██║ ╚██████╗███████╗ Make AI behavior programmable, portable, ║
║ ╚═╝ ╚═════╝╚══════╝ and predictable. ║
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PCL (Persona Control Language) is a governance-first programming language for AI persona management and multi-agent orchestration. Unlike traditional application languages, PCL is designed for accountability, security, and compliance in AI systems.
PCL enables enterprises and developers to:
- Define personas with explicit capabilities, constraints, and risk classifications (ISO 42001)
- Govern AI behavior through auditable policies and access controls (ISO 27001)
- Orchestrate complex multi-agent workflows with human oversight
- Deploy consistently across Claude, GPT, Gemini, Azure, and open-source LLMs
- Audit every action with immutable logs aligned to compliance frameworks
- Comply with EU AI Act, GDPR, OWASP LLM Top 10, and Zero Trust principles
// Define a security analyst persona
pub persona SEC {
intent: "Identify and mitigate security vulnerabilities"
tone: vigilant
skills {
"OWASP Top 10"
"STRIDE threat modeling"
"Security code review"
}
constraints {
"Always assume breach"
maxResponseTime <= 5s
}
}
// Compose a security review team
pub team SecurityReview {
members: [SEC, AUDIT, ARCHI, CRITIC]
primary: SEC
merge: Debate
quorum: 3/4
}
// Define a code review workflow
pub workflow CodeReview {
steps: DEV -> (ARCHI || SEC) -> CRITIC -> merge(Consensus)
timeout: 60s
fallback: SIMPLIFY
}
# Clone the repository
git clone https://github.com/personamanagmentlayer/pcl.git
cd pcl
# Install dependencies
npm install
# Build PCL
npm run build
# Verify installation
node dist/cli/index.js --version📖 Complete Installation Guide →
- Quick Start Guide - Get started in 5 minutes
- Core Concepts - Personas, Teams, Workflows, and more
- Installation Guide - Complete setup instructions
- Features Overview - All features and capabilities
- Getting Started Tutorial - Practical working introduction
- VS Code Setup - IDE configuration
- Testing Guide - Test coverage and benchmarks
- API Reference - Parser, Semantic Analysis, Code Generation
- PCL Specification v1.0 - RFC-style formal specification
- Governance Framework - ISO 38500-aligned governance
- Security Model - ISO 27001/42001 security architecture
- Standards Overview - Complete standards alignment
- 5,720 total tests (96.3% pass rate)
- 50.66%+ code coverage (targeting 90%)
- 153 test files covering all major modules
- Comprehensive testing: LSP, Observability, MCP, Registry, Providers, CLI, Codegen, Parser, E2E
📊 Testing Status → | 🗺️ Coverage Roadmap →
Anthropic Claude • OpenAI GPT • Google Gemini • DeepSeek • Ollama • Azure OpenAI • AWS Bedrock • Mock
- ✅ Language Server Protocol (LSP) - Full IDE support with IntelliSense, diagnostics, navigation
- ✅ Skills Ecosystem - 100% compatible with agentskills.io and Claude Code
- ✅ Model Context Protocol (MCP) - Expose personas as standardized AI services
- ✅ Registry System - 4 backends (Memory, JSON, SQLite, PostgreSQL)
- ✅ Observability - Metrics, SLO tracking, tracing, telemetry, health checks
- ✅ Code Generation - Multi-target compilation (TypeScript, Python, JSON, YAML, Markdown)
Status: 🟡 APPROACHING PRODUCTION READY
Production Readiness Score: 78/100 (+33 from January 2026)
- ✅ Safe for: Development, prototyping, proof-of-concept, internal tools, beta testing
- 🟡 Approaching: Production, customer-facing applications (after final security audit)
- ❌ Not yet ready for: High-stakes regulated systems (needs 90% coverage)
📈 Production Readiness Report →
- ✅ Core compiler implementation
- ✅ 8 AI provider integrations
- ✅ Registry system with 4 backends
- ✅ 50%+ test coverage baseline
- ✅ Language Server Protocol (LSP)
- ✅ VS Code extension
- ✅ Skills ecosystem integration
- ✅ Model Context Protocol (MCP)
- 🔄 Advanced merge strategies
- 🔄 Event streaming & observability
- 🔄 90% test coverage
- 🔄 Production security audit
- 📅 Visual debugging tools
- 📅 Performance profiling
- 📅 Cloud deployment
- 📅 Marketplace
- 📖 Documentation - Complete guides and API reference
- 🐛 GitHub Issues - Bug reports and feature requests
- 💬 Discord - Community discussion
- 🐦 Twitter - Updates and announcements
We welcome contributions! See CONTRIBUTING.md for guidelines.
# Fork and clone the repository
git clone https://github.com/YOUR_USERNAME/pcl.git
cd pcl
# Install dependencies
npm install
# Run tests
npm test
# Build the project
npm run build📋 Contributing Guide → | 📜 Code of Conduct →
PCL uses dual licensing to support both software development and documentation sharing:
- Code (src/, tests/, scripts/): Apache 2.0 - Permissive software license with patent grant
- Documentation (docs/, SPEC/, GOVERNANCE/): CC BY 4.0 - Creative Commons for specs and guides
- Trademarks: IbIFACE - See Trademark Policy
This dual licensing approach follows industry best practices (Rust, Kubernetes, OpenAPI) and supports PCL's mission as a governance-first standard for enterprise AI.
For contribution licensing, see NOTICE.
PCL — Making AI behavior programmable, portable, and predictable.