CodeOrchestrator

Multi-agent orchestration for intelligent software engineering

CodeOrchestrator is a next-generation platform that uses AI-driven planning, multi-agent orchestration, judgment-based validation, and dynamic task workflows (DAGs) to tackle complex software engineering projects autonomously.

🚀 Key Features

• Planning Agent: Goal decomposition, dynamic DAG creation, and adaptive scheduling.
• Functional Agents: Specialized roles for analysis, architecture design, implementation, and QA.
• Judge Agents: LLM-as-a-Judge pattern for structured validation and quality assurance.
• Dynamic DAG Management: Task graph evolves as agents discover new requirements.
• Knowledge Base: Vector + relational DB for context retrieval and traceability.
• Metrics Layer: Full observability for accuracy, performance, and cost optimization.
• MCP Integration: Unified interface for tools (Git, Rust/Cargo, diagram generation, testing).

✅ Why CodeOrchestrator?

Unlike reactive prompt-based AI solutions, CodeOrchestrator combines:

• Planning + Execution → Achieve goals autonomously.
• Judgment + Governance → Maintain high-quality outputs.
• Metrics + Adaptation → Optimize for cost and performance.

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🏗 System Architecture

• Planning Layer: Defines goals, manages DAG, and adapts based on feedback.
• Execution Layer: Multi-agent framework with Judges ensuring quality.
• Knowledge Layer: Stores validated artifacts, diagrams, and metadata.
• Metrics Layer: Tracks accuracy, performance, and cost KPIs.

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📂 Project Structure

CodeOrchestrator/
├── README.md # Project overview and usage
├── pyproject.toml # Project configuration (PEP 517/518)
├── setup.cfg # Linting & code style configs
├── requirements.txt # Python dependencies
│
├── codeorchestrator/ # Main Python package
│ ├── init.py
│ │
│ ├── orchestrator/ # Core orchestration logic
│ │ ├── planning_agent.py # Goal decomposition & DAG manager
│ │ ├── task_scheduler.py # Task scheduling and state tracking
│ │ ├── dag_manager.py # Dynamic DAG creation & updates
│ │ └── init.py
│ │
│ ├── agents/ # Functional and Judge Agents
│ │ ├── analysis_agent.py # Analyzes existing codebases
│ │ ├── architecture_agent.py # Creates architecture diagrams/specs
│ │ ├── requirements_agent.py # Generates requirements docs
│ │ ├── rust_design_agent.py # Rust-specific design logic
│ │ ├── implementation_agent.py # Writes and refines code
│ │ ├── judge_agents/ # Judges for validation
│ │ │ ├── base_judge.py # Common judge logic
│ │ │ ├── analysis_judge.py
│ │ │ ├── architecture_judge.py
│ │ │ ├── requirements_judge.py
│ │ │ ├── rust_design_judge.py
│ │ │ ├── implementation_judge.py
│ │ │ └── test_judge.py
│ │ └── init.py
│ │
│ ├── schemas/ # Pydantic data models
│ │ ├── task.py # Task, DAG node schema
│ │ ├── evaluation.py # Judge evaluations
│ │ ├── planning.py # Planning state and updates
│ │ ├── metrics.py # Metrics and logging schema
│ │ └── init.py
│ │
│ ├── metrics/ # Logging and analytics
│ │ ├── metrics_collector.py # Collects & stores metrics
│ │ ├── dashboards/ # Optional visualization code
│ │ └── init.py
│ │
│ ├── tools/ # MCP integration tools
│ │ ├── git_tool.py # Git operations
│ │ ├── diagram_tool.py # PlantUML/Mermaid generator
│ │ ├── rust_tool.py # Cargo, Clippy, Rustfmt
│ │ ├── test_runner_tool.py # Executes Rust tests
│ │ └── init.py
│ │
│ ├── knowledge/ # Knowledge management
│ │ ├── vector_db.py # Semantic search
│ │ ├── relational_store.py # Structured state
│ │ └── init.py
│ │
│ └── main.py # CLI entry point
│
├── docs/ # Documentation
│ ├── diagrams/
│ │ ├── system-architecture.puml
│ │ ├── dynamic-dag-lifecycle.puml
│ │ └── metrics-pipeline.puml
│ ├── architecture-overview.md
│ ├── agent-design.md
│ └── metrics-design.md
│
└── tests/ # Test suite (Pytest)
├── test_agents.py
├── test_planner.py
├── test_dag.py
└── test_metrics.py

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✅ Installation

git clone https://github.com/your-username/CodeOrchestrator.git
cd CodeOrchestrator
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

✅ Usage

Run the orchestrator:

python -m codeorchestrator

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✅ Dependencies

• Python 3.10+
• pydantic for schema validation
• langchain or autogen for agent orchestration
• mcp for tool integration
• llama-index for knowledge base indexing

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✅ Roadmap

• Complete Pydantic schemas for all agent and planning components.
• Implement dynamic DAG update logic.
• Add MCP connectors for Git, PlantUML, Rust build tools.
• Implement metrics dashboard (Grafana / Streamlit).

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✅ Contributing

We welcome contributions! Please follow:

• PEP 8 for code style.
• PEP 257 for docstrings.
• Add tests for new features.

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✅ License

MIT License © 2025 Interwebshack Team