The future of software development is not about replacing developers, but amplifying their capabilities with intelligent AI collaboration.
Features β’ Quick Start β’ Architecture β’ Commands β’ Documentation
VALORA (Versatile Agent Logic for Orchestrated Response Architecture) is a next-generation TypeScript-based platform designed to orchestrate a sophisticated network of AI agents to automate the complete software development lifecycle. By moving beyond simple "code generation", VALORA manages the delicate interplay between requirements, architecture, and deployment. VALORA provides intelligent automation while maintaining human oversight.
Intelligent Orchestration: VALORA coordinates 11 specialised AI agents, from @lead technical oversight to @secops-engineer compliance, ensuring the right expert is assigned to every task.
Three-Tier Flexibility: The engine adapts to your resources, offering MCP Sampling, Guided Completion, or API Fallback modes.
Phased Governance: Every project follows a rigorous 8-phase lifecycle, moving from initialisation and planning through implementation to validation and PR creation.
Strategic Optimisation: To balance depth and speed, VALORA assigns specific LLMs (like GPT-5 for planning or Claude Haiku for validation) based on the task's complexity.
VALORA is not a replacement for the developer; it is the high-fidelity instrument through which the developer conducts a full symphony of AI agents.
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11 specialised AI agents with distinct expertise:
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Flexible execution modes for every use case:
*When available in Cursor Zero configuration required β works immediately with your Cursor subscription. |
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Complete workflow automation: Each phase has dedicated commands and agents optimised for the task. |
Strategic AI model assignment for cost efficiency:
31% strategic β’ 31% execution β’ 38% fast |
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Connect to 15 external MCP servers with user approval:
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Enterprise-grade security controls:
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- Docker
- ou
- Node.js 20+
- pnpm 10.x
# Navigate to the engine directory
cd .ai/.bin
# Install dependencies
pnpm install
# Build the project
pnpm build
# Install globally
pnpm link
# Verify installation
valora --version# Create an implementation plan
valora plan "Add user authentication with OAuth"The engine will:
- Select the appropriate agent (
@lead) - Gather codebase context
- Generate a detailed implementation plan
- Provide step-by-step guidance
No API keys? No problem. The engine works immediately using Guided Completion Mode:
valora plan "Add dark mode toggle"
# β Generates structured prompt for Cursor AI
# β Uses your Cursor subscription (free)For fully autonomous execution:
valora config setup --quick
# Or set environment variables
export ANTHROPIC_API_KEY=sk-ant-...
export OPENAI_API_KEY=sk-...βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β VALORA β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€
β β
β βββββββββββββββ ββββββββββββββββ βββββββββββββββ βββββββββββββββ β
β β CLI Layer β β Orchestrator β β Agent Layer β β LLM Layer β β
β β ββββ ββββ ββββ β β
β β β’ Commands β β β’ Pipeline β β β’ Registry β β β’ Anthropic β β
β β β’ Wizard β β β’ Executor β β β’ Selection β β β’ OpenAI β β
β β β’ Output β β β’ Context β β β’ Loading β β β’ Google β β
β βββββββββββββββ ββββββββββββββββ βββββββββββββββ βββββββββββββββ β
β β
β βββββββββββββββ ββββββββββββββββ βββββββββββββββ βββββββββββββββ β
β β Session β β Config β β MCP β β Services β β
β β β β β β β β β β
β β β’ State β β β’ Loader β β β’ Server β β β’ Logging β β
β β β’ Context β β β’ Schema β β β’ Tools β β β’ Cleanup β β
β β β’ History β β β’ Providers β β β’ Prompts β β β’ Utils β β
β βββββββββββββββ ββββββββββββββββ βββββββββββββββ βββββββββββββββ β
β β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
| Principle | Implementation |
|---|---|
| Modularity | Loosely coupled components with clear interfaces |
| Extensibility | Plugin architecture for agents, commands, providers |
| Testability | Comprehensive test suites (unit, integration, e2e) |
| Observability | Structured logging and session tracking |
| Resilience | Graceful fallbacks and error recovery |
| Command | Agent | Description |
|---|---|---|
refine-specs |
@product-manager | Collaboratively refine specifications |
create-prd |
@product-manager | Generate Product Requirements Document |
create-backlog |
@product-manager | Decompose PRD into tasks |
fetch-task |
@product-manager | Retrieve next priority task |
refine-task |
@product-manager | Clarify task requirements |
gather-knowledge |
@lead | Analyse codebase context |
plan |
@lead | Create implementation plan |
review-plan |
@lead | Validate plan quality |
implement |
Dynamic | Execute code changes |
assert |
@asserter | Validate implementation |
test |
@qa | Execute test suites |
review-code |
@lead | Code quality review |
review-functional |
@lead | Functional review |
commit |
@lead | Create conventional commits |
create-pr |
@lead | Generate pull request |
feedback |
@product-manager | Capture outcomes |
βββββββββββββββββββββββ βββββββββββββββββββββββ βββββββββββββββββββββββ
β Planning β β Implementation β β Delivery β
βββββββββββββββββββββββ€ βββββββββββββββββββββββ€ βββββββββββββββββββββββ€
β β’ refine-specs β β β’ implement β β β’ commit β
β β’ create-prd β β β’ assert β β β’ create-pr β
β β’ plan β β β’ test β β β’ feedback β
β β’ review-plan β β β’ review-code β β β
β β’ gather-knowledge β β β’ review-functional β β β
βββββββββββββββββββββββ βββββββββββββββββββββββ βββββββββββββββββββββββ
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Getting started, workflows, |
Architecture, codebase, |
System design |
documentation/
βββ README.md # Documentation entry point
βββ user-guide/ # For users
β βββ quick-start.md # 5-minute getting started
β βββ workflows.md # Common patterns
β βββ commands.md # Command reference
βββ developer-guide/ # For developers
β βββ setup.md # Development environment
β βββ codebase.md # Code structure
β βββ contributing.md # How to contribute
βββ architecture/ # For architects
β βββ system-architecture.md # C4 diagrams
β βββ components.md # Component design
β βββ data-flow.md # Data flow patterns
βββ adr/ # Decision records
βββ 001-multi-agent-architecture.md
βββ 002-three-tier-execution.md
βββ 003-session-based-state.md
βββ 004-pipeline-execution-model.md
βββ 005-llm-provider-abstraction.md
valora refine-specs "User authentication with OAuth"
valora create-prd
valora create-backlog
valora fetch-task && valora plan
valora implement
valora review-code && valora commit
valora create-prvalora plan "Fix: Login timeout issue"
valora implement
valora test --type=all
valora commit --scope=fixvalora review-code --focus=security
valora review-functional --check-a11y=true.ai/
βββ .bin/ # TypeScript implementation
β βββ dist/ # Built artefacts
β βββ src/ # Source code
β βββ tests/ # Test suites
βββ agents/ # Agent definitions (11 agents)
β βββ registry.json # Agent definitions
β βββ lead.md
β βββ product-manager.md
β βββ software-engineer-*.md
β βββ ...
βββ commands/ # Command specifications (16 commands)
β βββ registry.json # Command definitions
β βββ implement.md
β βββ plan.md
β βββ ...
βββ documentation/ # Comprehensive docs
βββ external-mcp.json # External MCP server registry
βββ logs/ # Execution logs
βββ prompts/ # Structured prompts by phase
β βββ 01_onboard/
β βββ 02_context/
β βββ 03_plan/
β βββ ...
βββ sessions/ # Persistent session state
βββ templates/ # Document templates
βββ config.json # Engine configuration
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| Innovation | Impact |
|---|---|
| Multi-Agent Orchestration | Specialised agents produce expert-level output |
| Three-Tier Execution | Flexibility from free to fully automated |
| Session Persistence | Context flows naturally between commands |
| Dynamic Agent Selection | Right expert for every task |
| Quality Gates | Multiple checkpoints prevent technical debt |
There are still many improvements to be made. Contributions and suggestions are welcome!
- Reducing prompt sizes for efficiency
- Memory management optimisation
- Distributing context window occupancy across agents
- Token usage tracking per agent/command
- Execution time metrics
- Cost analysis dashboards
- Buffer management improvements
- Animations and visual feedback
- Making the CLI fully autonomous
- Enhanced progress indicators
- Ability to add custom agents dynamically
- Override existing agent behaviours
- Plugin system for third-party commands
- Hot-reload for agent definitions
Have ideas or suggestions? Contributions are welcome!
| Category | Technologies |
|---|---|
| Runtime | Node.js 18+, TypeScript 5.x |
| Package Manager | pnpm 10.x |
| Build | tsc, tsc-alias |
| Testing | Vitest, Playwright |
| LLM SDKs | @anthropic-ai/sdk, openai, @google/generative-valora |
| CLI UI | Ink (React), Chalk, Commander |
| Validation | Zod |
| MCP | @modelcontextprotocol/sdk |
MIT Β© Damien TIVELET
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