Django Ninja

{
  "service_type": "platform",
  "base_url": "https://django-ninja.dev",
  "auth_method": "none",
  "auth_config": {},
  "endpoints": [],
  "pricing_model": {
    "type": "free",
    "details": {
      "note": "Open-source framework distributed under the MIT license. No paid tiers, no hosted service; users self-host their own Django applications."
    }
  },
  "rate_limits": {},
  "capabilities": [
    "Web framework / library for building REST APIs on top of Django",
    "Async-ready request handling (ASGI/async views)",
    "Automatic OpenAPI (Swagger UI + ReDoc) schema generation",
    "Type-hint based request/response validation via Pydantic",
    "Path, query, header, cookie, and body parameter parsing",
    "Dependency injection system for reusable logic",
    "Authentication and permission classes (API key, session, token, custom)",
    "Throttling support",
    "Router-based modular API organization",
    "Serialization and de-serialization of Django ORM models",
    "File upload handling",
    "Pagination helpers",
    "Testing utilities (TestClient)",
    "No public REST API of its own; it is a tool developers install via pip"
  ],
  "raw_analysis": "Django Ninja is an open-source Python library/framework, not a hosted SaaS platform. It is designed to help developers build HTTP APIs on top of the Django web framework with minimal boilerplate, leveraging Python type hints and Pydantic for validation and serialization. Its primary value propositions are speed of development, async support, and automatic OpenAPI documentation generation.\n\nIntegrations: It integrates tightly with the Django ecosystem (Django ORM, Django auth, Django settings, middleware, testing framework) and with Pydantic for schemas and FastAPI-style decorators for routing. It exposes Swagger UI/ReDoc endpoints for generated docs but offers no external SaaS API, webhook service, or managed cloud component.\n\nMaturity: Well-established and actively maintained in the Python community, often described as a Django-idiomatic alternative to FastAPI for teams already invested in Django. Distributed on PyPI under the MIT license.\n\nPricing: Entirely free and open-source. There is no commercial hosting, subscription, or usage-based billing tied to the project itself. Any costs would come from the infrastructure the developer chooses to deploy their Django app on.\n\nAuth model for the project itself: Not applicable — there is no service endpoint to authenticate against. Authentication features (API keys, tokens, etc.) exist only as building blocks for APIs that developers create using the framework.\n\nFrom a platform-integration standpoint, there is nothing to connect to: no public REST API, no CLI service, no OAuth provider. A Prowl-style integration would not apply unless the goal were static analysis of the library's documentation or source, in which case data would come from the project's GitHub repository, PyPI package, or documentation site rather than a live API."
}

2/3 tests passed

{
  "overall": 72,
  "dimensions": {
    "token_efficiency": 9.0,
    "first_try_success": 8.0,
    "response_parseability": 9.0,
    "error_clarity": 7.0,
    "doc_quality": 8.0,
    "auth_simplicity": 10.0,
    "latency": 9.5,
    "consistency": 8.0
  },
  "pricing_normalized": {
    "model": "free",
    "type": "open_source",
    "license": "MIT",
    "note": "No paid tiers, no hosted service; self-hosted framework installed via pip"
  },
  "issues": [
    "No llms.txt file (404) — reduces agent-native discoverability",
    "No security headers on website responses",
    "No public REST API of its own — it is a pip-installed library, not a service",
    "robots.txt includes content-signal restrictions that may limit agent content gathering",
    "Onboarding requires Python/Django knowledge; not a plug-and-play service"
  ],
  "recommendations": [
    "Publish an llms.txt to improve agent discoverability",
    "Add security headers to website responses",
    "Surface comprehensive OpenAPI/Swagger docs at predictable URLs for agents",
    "Provide quickstart snippets optimized for LLM consumption",
    "Clarify in docs any rate limits or constraints for programmatic access to documentation"
  ]
}