Stirling-PDF/engine/AGENTS.md

Stirling AI Engine Guide

This file is for AI agents working in engine/.

The engine is a Python reasoning service for Stirling. It plans and interprets work, but it does not own durable state, and it does not execute Stirling PDF operations directly. Keep the service narrow: typed contracts in, typed contracts out, with AI only where it adds reasoning value.

Commands

All engine commands can be run from the repository root using Task:

• task engine:check — run all checks (typecheck + lint + format-check + test)
• task engine:fix — auto-fix lint + formatting
• task engine:install — install Python dependencies via uv
• task engine:dev — start FastAPI with hot reload (localhost:5001)
• task engine:test — run pytest
• task engine:lint — run ruff linting
• task engine:typecheck — run pyright
• task engine:format — format code with ruff
• task engine:tool-models — generate tool_models.py from Java OpenAPI spec

Code Style

• Keep task engine:check passing.
• Use modern Python when it improves clarity.
• Prefer explicit names to cleverness.
• Avoid nested functions and nested classes unless the language construct requires them.
• Prefer composition to inheritance when combining concepts.
• Avoid speculative abstractions. Add a layer only when it removes real duplication or clarifies lifecycle.
• Add comments sparingly and only when they explain non-obvious intent.

Typing and Models

• Deserialize into Pydantic models as early as possible.
• Serialize from Pydantic models as late as possible.
• Do not pass raw dict[str, Any] or dict[str, object] across important boundaries when a typed model can exist instead.
• Avoid Any wherever possible.
• Avoid cast() wherever possible (reconsider the structure first).
• All shared models should subclass stirling.models.ApiModel so the service behaves consistently.
• Do not use string literals for any type annotations, including cast().

Configuration

• Keep application-owned configuration in stirling.config.
• Only add STIRLING_* environment variables that the engine itself truly owns.
• Do not mirror third-party provider environment variables unless the engine is actually interpreting them.
• Let pydantic-ai own provider authentication configuration when possible.

Architecture

Package Roles

• stirling.contracts: request/response models and shared typed workflow contracts. If a shape crosses a module or service boundary, it probably belongs here.
• stirling.models: shared model primitives and generated tool models.
• stirling.agents: reasoning modules for individual capabilities.
• stirling.api: HTTP layer, dependency access, and app startup wiring.
• stirling.services: shared runtime and non-AI infrastructure.
• stirling.config: application-owned settings.

Source Of Truth

• stirling.models.tool_models is the source of truth for operation IDs and parameter models.
• Do not duplicate operation lists if they can be derived from tool_models.OPERATIONS.
• Do not hand-maintain parallel parameter schemas when the generated tool models already define them.
• If a tool ID must match a parameter model, validate that relationship explicitly in code.

Boundaries

• Keep the API layer thin. Route modules should bind requests, resolve dependencies, and call agents or services. They should not contain business logic.
• Keep agents focused on one reasoning domain. They should not own FastAPI routing, persistence, or execution of Stirling operations.
• Build long-lived runtime objects centrally at startup when possible rather than reconstructing heavy AI objects per request.
• If an agent delegates to another agent, the delegated agent should remain the source of truth for its own domain output.

AI Usage

• The system must work with any AI, including self-hosted models. We require that the models support structured outputs, but should minimise model-specific code beyond that.
• Use AI for reasoning-heavy outputs, not deterministic glue.
• Do not ask the model to invent data that Python can derive safely.
• Do not fabricate fallback user-facing copy in code to hide incomplete model output.
• AI output schemas should be impossible to instantiate incorrectly.
  • Do not require the model to keep separate structures in sync. For example, instead of generating two lists which must be the same length, generate one list of a model containing the same data.
  • Prefer Python to derive deterministic follow-up structure from a valid AI result.
• Use NativeOutput(...) for structured model outputs.
• Use ToolOutput(...) when the model should select and call delegate functions.

Testing

• Test contracts directly.
• Test agents directly where behaviour matters.
• Test API routes as thin integration points.
• Prefer dependency overrides or startup-state seams to monkeypatching random globals.