datamodel-code-generator¶

🚀 Generate Python data models from schema definitions in seconds.

✨ What it does¶

Pick any one of the supported inputs and pick the Python model style you want as output. --input-model path/to/file.py:ClassName can even retarget an existing Pydantic, dataclass, or TypedDict class defined in another Python file to a different output type.

• 📄 Converts OpenAPI 3, AsyncAPI, JSON Schema, Apache Avro, XML Schema, Protocol Buffers/gRPC, GraphQL, MCP tool schemas, and raw data (JSON/YAML/CSV) into Python models
• 🐍 Generates from existing Python types (Pydantic, dataclass, TypedDict) via --input-model
• 🎯 Generates Pydantic v2, Pydantic v2 dataclass, dataclasses, TypedDict, or msgspec output
• 🔗 Handles complex schemas: $ref, allOf, oneOf, anyOf, enums, and nested types
• ✅ Produces type-safe, validated code ready for your IDE and type checker

🧪 Try It In Your Browser¶

Generate models in your browser without installing anything.

Open Playground

🚀 Start Here¶

Install the CLI and generate your first model from Getting Started.

📥 Choose Your Input¶

✅ Conformance Signals¶

CI exercises datamodel-code-generator against pinned external corpora for XML Schema, JSON Schema, AsyncAPI, Apache Avro, and Protocol Buffers. See the Conformance Dashboard for the generated summary of runner scripts, tox environments, CI jobs, expected corpus counts, and upstream sources.

📤 Choose Your Output¶

# 🆕 Pydantic v2 (recommended for new projects)
datamodel-codegen --output-model-type pydantic_v2.BaseModel ...

# 🏗️ Python dataclasses
datamodel-codegen --output-model-type dataclasses.dataclass ...

# 📝 TypedDict (for type hints without validation)
datamodel-codegen --output-model-type typing.TypedDict ...

# ⚡ msgspec (high-performance serialization)
datamodel-codegen --output-model-type msgspec.Struct ...

See Supported Data Types for the full list.

🍳 Common Recipes¶

CLI option quick starts¶

Use these starting points when combining options; each option links to the generated CLI reference for details and examples.

• Generate a local schema file: Pin the input type and destination when the source extension is ambiguous or generated output needs a stable path. Options: --input, --input-file-type, --output.
• Target Pydantic v2 on modern Python: Set the output model family and Python/Pydantic compatibility targets together. Options: --output-model-type, --target-python-version, --target-pydantic-version.
• Use modern Python annotations: Target a recent Python version and prefer built-in collection and union syntax in generated types. Options: --target-python-version, --use-union-operator, --use-standard-collections.
• Normalize incoming field names: Convert source names to Python identifiers while preserving explicit alias data for runtime IO. Options: --snake-case-field, --original-field-name-delimiter, --aliases.
• Generate operation-focused models: Limit OpenAPI output to operation shapes and name models from operation IDs and status codes. Options: --openapi-scopes, --use-operation-id-as-name, --use-status-code-in-response-name.
• Resolve remote references deliberately: Enable remote $ref loading and configure request metadata, timeouts, or local ref roots. Options: --allow-remote-refs, --http-headers, --http-timeout, --http-local-ref-path.

See the CLI Reference for the full option list and category-specific recipes.

🤖 Get CLI Help from LLMs¶

Generate a prompt to ask LLMs about CLI options:

datamodel-codegen --generate-prompt "Best options for Pydantic v2?" | claude -p

See LLM Integration for more examples.

🌐 Generate from URL¶

pip install 'datamodel-code-generator[http]'
datamodel-codegen --url https://example.com/api/openapi.yaml --output model.py

⚙️ Use with pyproject.toml¶

[tool.datamodel-codegen]
input = "schema.yaml"
output = "src/models.py"
output-model-type = "pydantic_v2.BaseModel"

Then simply run:

datamodel-codegen

See pyproject.toml Configuration for more options.

🔄 CI/CD Integration¶

Validate generated models in your CI pipeline:

# Replace vX.Y.Z with a released action version.
- uses: koxudaxi/datamodel-code-generator@vX.Y.Z
  with:
    input: schemas/api.yaml
    output: src/models/api.py

See CI/CD Integration for more options.

📚 Next Steps¶

• 🖥️ CLI Reference - All command-line options with examples
• 🧰 Presets - Recommended immutable option bundles
• ⚙️ pyproject.toml Configuration - Configure via pyproject.toml
• 🚀 One-liner Usage - uvx, pipx, clipboard integration
• 🔄 CI/CD Integration - GitHub Actions and CI validation
• ✅ Conformance Dashboard - External corpus and CI coverage signals
• 📈 Performance Benchmarks - Release benchmark tables and interactive charts
• 🎨 Custom Templates - Customize generated code with Jinja2
• 🖌️ Code Formatting - Configure black, isort, and ruff
• ❓ FAQ - Common questions and troubleshooting

💖 Sponsors¶

Astral

OpenAI

🏢 Used by¶

These projects use datamodel-code-generator. See the linked examples for real-world usage.

• openai/codex - Python SDK dev dependency
• browser-use/browser-use - Eval dependency
• modelcontextprotocol/python-sdk - Generate MCP protocol models from vendored JSON Schemas
• vllm-project/vllm - Test dependency for model tests
• modular/modular - Generate MAX Serve KServe schemas from OpenAPI with datamodel-codegen
• apache/airflow - Generate OpenAPI datamodels for airflow-ctl and task-sdk via pyproject codegen config
• stanfordnlp/dspy - Generate Pydantic models from JSON Schema for reliability tests
• PostHog/posthog - Generate models via npm run
• airbytehq/airbyte - Generate Python, Java/Kotlin, and Typescript protocol models
• apache/iceberg - Generate Python code
• open-metadata/OpenMetadata - datamodel_generation.py
• topoteretes/cognee - Runtime generation of graph data models from JSON Schema
• e2b-dev/E2B - Generate MCP server TypedDict models via Makefile
• firebase/genkit - Generate core typing models from JSON Schema
• DataDog/integrations-core - Config models
• open-telemetry/opentelemetry-python - Generate SDK configuration dataclasses from JSON Schema

See all dependents →