- ๐ Python Versions: 3.9, 3.10, 3.11, 3.12, 3.13
- ๐ฆ Package Size: Optimized for fast installation
- ๐ Performance: Built with modern async/await support
- ๐ง Dependencies: Minimal, modern stack
- ๐ Stability: Production-ready
# Latest stable version
pip install pyatlan
# Specific version
pip install pyatlan==7.1.3
# With uv (faster) - install uv first: curl -LsSf https://astral.sh/uv/install.sh | sh
uv add pyatlan# Clone the repository
git clone https://github.com/atlanhq/atlan-python.git
cd atlan-python
# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install with development dependencies
uv sync --group dev
# Run quality checks
uv run ./qa-checks
# Run tests
uv run pytest tests/unitThis project uses uv dependency groups for better dependency management:
- Core dependencies: Always installed (
uv sync) - Development dependencies: Testing, linting, formatting (
uv sync --group dev) - Documentation dependencies: Sphinx docs (
uv sync --group docs)
You can install multiple groups:
# Install both dev and docs dependencies
uv sync --group dev --group docs
# Install all dependencies
uv sync --all-groups# Latest main image
docker pull registry.atlan.com/public/pyatlan:main-latest
# Version + Python tag
docker pull registry.atlan.com/public/pyatlan:8.5.1-3.11
# Commit-specific image
docker pull registry.atlan.com/public/pyatlan:sha-1a064032# Interactive Python session
docker run -it --rm registry.atlan.com/public/pyatlan:main-latest
# Run a script
docker run -it --rm \
-v $(pwd):/app \
-e ATLAN_API_KEY=your_key \
-e ATLAN_BASE_URL=https://your-tenant.atlan.com \
registry.atlan.com/public/pyatlan:main-latest \
python your_script.pyuv export --all-extras --no-hashes > requirements.txt
snyk test --file=requirements.txt --severity-threshold=high --skip-unresolved
rm -f requirements.txt# Run all unit tests
uv run pytest tests/unit
# Run with coverage
uv run pytest tests/unit --cov=pyatlan --cov-report=html# Set up environment
cp .env.example .env
# Edit .env with your Atlan credentials
# Run integration tests
uv run pytest tests/integration# Run all QA checks (formatting, linting, type checking)
uv run ./qa-checks
# Individual checks
uv run ruff format . # Code formatting
uv run ruff check . # Linting
uv run mypy . # Type checkingWe welcome contributions! Here's how to get started:
# Fork and clone the repository
git clone https://github.com/your-username/atlan-python.git
cd atlan-python
# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Install development dependencies
uv sync --group dev
# Install pre-commit hooks
uv run pre-commit install# Create a feature branch
git checkout -b feature/amazing-feature
# Make your changes and test
uv run ./formatter
uv run ./qa-checks
uv run pytest tests/unit
# Commit with conventional commits
git commit -m "feat: add amazing feature"
# Push and create a pull request
git push origin feature/amazing-feature- โ Follow conventional commits
- โ Add tests for new features
- โ Update documentation as needed
- โ Ensure all QA checks pass
Generate asset models from your Atlan instance:
# Generate models automatically
uv run ./generator
# Use custom typedefs file
uv run ./generator ./my-typedefs.jsonThis will:
- ๐ฅ Retrieve typedefs from your Atlan instance
- ๐๏ธ Generate asset models, enums, and structures
- ๐จ Format code automatically
- โก Support incremental updates
The pyatlan_v9 package uses msgspec Struct-based models generated from Pkl type definitions in the atlanhq/models repo.
The recommended way to regenerate models is via the Claude Code skill:
# From the atlan-python repo root:
/generate-v9-models # Generate from models@master
/generate-v9-models <branch> # Generate from a specific models branch
/generate-v9-models test # Generate and run tests
/generate-v9-models <branch> testThe skill will:
- Clone/update
atlanhq/modelsat../models - Run the Pkl code generator with SDK mode (
pkl eval typedefs/*.pkl -m . -p sdk=true) - Selectively sync generated files to
pyatlan_v9/model/assets/(excluding hand-written types) - Apply post-sync patches (e.g.,
set[str]fields inasset.py) - Optionally run
tests_v9/unit/tests
Custom methods (creator(), updater(), policy helpers, etc.) live in pyatlan_v9/model/assets/_overlays/. These are Python files read by the Pkl renderer and injected into generated classes. Each overlay file uses import directives:
# IMPORT:โ external imports (not remapped)# INTERNAL_IMPORT:โ internal imports (remapped topyatlan_v9.*)# STDLIB_IMPORT:โ standard library imports
Some types are not yet fully generated and are maintained by hand:
- Infrastructure:
__init__.py,entity.py,referenceable.py - GTC types:
atlas_glossary.py,atlas_glossary_term.py,atlas_glossary_category.py - Others:
persona.py,purpose.py,badge.py,access_control.py,auth_policy.py, etc.
Understanding the codebase layout will help you navigate and contribute effectively:
atlan-python/
โโโ pyatlan/ # ๐ Main Python package
โ โโโ __init__.py # Package initialization
โ โโโ cache/ # ๐พ Caching mechanisms
โ โ โโโ atlan_tag_cache.py # Tag name โ GUID mapping
โ โ โโโ custom_metadata_cache.py # Custom metadata definitions
โ โ โโโ enum_cache.py # Enum value caching
โ โ โโโ aio/ # Async versions of caches
โ โโโ client/ # ๐ HTTP client implementations
โ โ โโโ atlan.py # Main synchronous client
โ โ โโโ asset.py # Asset operations (CRUD, search)
โ โ โโโ admin.py # Administrative operations
โ โ โโโ audit.py # Audit log operations
โ โ โโโ common/ # Shared client logic
โ โ โโโ aio/ # Async client implementations
โ โโโ model/ # ๐ Data models and assets
โ โ โโโ assets/ # Asset type definitions
โ โ โ โโโ core/ # Core asset types (Table, Database, etc.)
โ โ โ โโโ relations/ # Relationship models
โ โ โโโ fields/ # Search field definitions
โ โ โโโ open_lineage/ # OpenLineage specification models
โ โ โโโ packages/ # Package/workflow models
โ โ โโโ aio/ # Async model variants
โ โโโ generator/ # ๐๏ธ Code generation tools
โ โ โโโ templates/ # Jinja2 templates for generation
โ โ โโโ class_generator.py # Main generation logic
โ โโโ pkg/ # ๐ฆ Package creation utilities
โ โโโ events/ # ๐ Event handling (webhooks, lambdas)
โ โโโ samples/ # ๐ก Example code and scripts
โ โโโ test_utils/ # ๐งช Testing utilities
โโโ tests/ # ๐งช Test suite
โ โโโ unit/ # Unit tests (fast, no external deps)
โ โโโ integration/ # Integration tests (require Atlan instance)
โ โโโ data/ # Test fixtures and mock data
โโโ docs/ # ๐ Sphinx documentation
โ โโโ conf.py # Sphinx configuration
โ โโโ *.rst # Documentation source files
โโโ pyproject.toml # ๐ Project configuration (deps, tools)
โโโ uv.lock # ๐ Locked dependencies
โโโ qa-checks # โ
Quality assurance script
โโโ formatter # ๐จ Code formatting script
โโโ generator # ๐๏ธ Model generation script
- Synchronous: Direct HTTP operations using
httpx - Asynchronous: Async/await operations using
httpx.AsyncClient - Common: Shared business logic between sync/async clients
- Specialized: Domain-specific clients (admin, audit, lineage, etc.)
- Assets: 400+ asset types (tables, dashboards, pipelines, etc.)
- Core Models: Base classes, requests, responses
- Fields: Search and filtering field definitions
- OpenLineage: Data lineage specification compliance
- Tag Cache: Maps human-readable tag names to internal GUIDs
- Custom Metadata: Caches custom attribute definitions
- Connection Cache: Stores connector and connection metadata
- Async Variants: Full async implementations for all caches
- Templates: Jinja2 templates for assets, enums, documentation
- Generator: Retrieves typedefs and generates Python models
- Incremental: Only regenerates changed models for efficiency
- Unit Tests: Fast, isolated tests with mocks/fixtures
- Integration Tests: Real API calls (requires credentials)
- VCR Cassettes: Record/replay HTTP interactions for consistent testing
- Custom Packages: Framework for building Atlan-deployable packages
- Templates: Pre-built package structures and configurations
- Utilities: Helper functions for package development
- Models: Generated from your Atlan instance's typedefs
- Clients: Hand-crafted for optimal developer experience
- Tests: Mix of unit (fast iteration) and integration (real validation)
- Quality: Automated formatting, linting, and type checking
- Documentation: Auto-generated from docstrings and examples
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Portions of this SDK are based on original work from:
- Apache Atlas (Apache-2.0 license)
- Elasticsearch DSL (Apache-2.0 license)
Built with ๐ by Atlan
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