Architecture Deep Dive

Architecture Deep Dive 🏗️

Canary follows a strictly layered, modular pipeline. Each component has a single responsibility, ensuring the system is extensible and vendor-agnostic.

The Intelligence Pipeline

1. Test Classifier (agent/core/classifier.py)

The entry point of intelligence. It uses rule-based intent detection to analyze the user's prompt. It categorizes the request into test types like e2e_ui, api, performance, or unit.

2. Framework Recommender (agent/core/recommender.py)

The decision engine. It consults the Framework Registry (agent/frameworks/registry.json) to select the best tool for the detected test type. It considers strengths, maturity, and ecosystem alignment.

3. Orchestrator (agent/core/orchestrator.py)

The system kernel. It coordinates the entire flow:

• Receives input.
• Invokes Classifier & Recommender.
• Builds the LLM generation prompt.
• Calls the LLM Client.
• Writes the generated code to the filesystem.
• (Optional) Invokes the Executor for the feedback loop.

4. LLM Client & Factory (agent/llm/)

The abstraction layer.

• Factory: Dynamically selects the provider (OpenAI, Gemini, Mock).
• Providers: Implements specific vendor logic, ensuring the rest of the system never "speaks" directly to an API.

5. Test Executor (agent/core/executor.py)

The mechanical hand. It runs the generated code in a secure subprocess, capturing exit codes and standard error for the self-healing loop.

Data Flow Diagram

User Prompt → CLI → Orchestrator → Classifier → Recommender → LLM → Executor → Error Feedback → LLM (Fix) → Final Test Output

Security & Sanitization

Canary includes a _sanitize_extension layer to prevent path traversal and ensure that all generated files follow a strict whitelist of allowed extensions (.ts, .py, .js, etc.).