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# AGENTS.md
## Project Overview
Dify is an open-source platform for developing LLM applications with an intuitive interface combining agentic AI workflows, RAG pipelines, agent capabilities, and model management.
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The codebase is split into:
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- **Backend API** (`/api`): Python Flask application organized with Domain-Driven Design
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- **Frontend Web** (`/web`): Next.js application using TypeScript and React
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- **Docker deployment** (`/docker`): Containerized deployment configurations
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## Backend Workflow
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- Read `api/AGENTS.md` for details
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- Run backend CLI commands through `uv run --project api <command>` .
- Integration tests are CI-only and are not expected to run in the local environment.
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## Frontend Workflow
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- Read `web/AGENTS.md` for details
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## Testing & Quality Practices
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- Follow TDD: red → green → refactor.
- Use `pytest` for backend tests with Arrange-Act-Assert structure.
- Enforce strong typing; avoid `Any` and prefer explicit type annotations.
- Write self-documenting code; only add comments that explain intent.
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## Language Style
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- **Python**: Keep type hints on functions and attributes, and implement relevant special methods (e.g., `__repr__` , `__str__` ).
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- **TypeScript**: Use the strict config, rely on ESLint (`pnpm lint:fix` preferred) plus `pnpm type-check:tsgo` , and avoid `any` types.
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## General Practices
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- Prefer editing existing files; add new documentation only when requested.
- Inject dependencies through constructors and preserve clean architecture boundaries.
- Handle errors with domain-specific exceptions at the correct layer.
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## Project Conventions
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- Backend architecture adheres to DDD and Clean Architecture principles.
- Async work runs through Celery with Redis as the broker.
- Frontend user-facing strings must use `web/i18n/en-US/` ; avoid hardcoded text.
