{"slug":"existential-birds-pydantic-ai-model-integration","name":"pydantic-ai-model-integration","description":"Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.","long_description":"---\nname: pydantic-ai-model-integration\ndescription: Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.\n---\n\n# PydanticAI Model Integration\n\n## Provider Model Strings\n\nFormat: `provider:model-name`\n\n```python\nfrom pydantic_ai import Agent\n\n# OpenAI\nAgent('openai:gpt-4o')\nAgent('openai:gpt-4o-mini')\nAgent('openai:o1-preview')\n\n# Anthropic\nAgent('anthropic:claude-sonnet-4-5')\nAgent('anthropic:claude-haiku-4-5')\n\n# Google (API Key)\nAgent('google-gla:gemini-2.0-flash')\nAgent('google-gla:gemini-2.0-pro')\n\n# Google (Vertex AI)\nAgent('google-vertex:gemini-2.0-flash')\n\n# Groq\nAgent('groq:llama-3.3-70b-versatile')\nAgent('groq:mixtral-8x7b-32768')\n\n# Mistral\nAgent('mistral:mistral-large-latest')\n\n# Other providers\nAgent('cohere:command-r-plus')\nAgent('bedrock:anthropic.claude-3-sonnet')\n```\n\n## Model Settings\n\n```python\nfrom pydantic_ai import Agent\nfrom pydantic_ai.settings import ModelSettings\n\nagent = Agent(\n    'openai:gpt-4o',\n    model_settings=ModelSettings(\n        temperature=0.7,\n        max_tokens=1000,\n        top_p=0.9,\n        timeout=30.0,  # Request timeout\n    )\n)\n\n# Override per-run\nresult = await agent.run(\n    'Generate creative text',\n    model_settings=ModelSettings(temperature=1.0)\n)\n```\n\n## Fallback Models\n\nChain models for resilience:\n\n```python\nfrom pydantic_ai.models.fallback import FallbackModel\n\n# Try models in order until one succeeds\nfallback = FallbackModel(\n    'openai:gpt-4o',\n    'anthropic:claude-sonnet-4-5',\n    'google-gla:gemini-2.0-flash'\n)\n\nagent = Agent(fallback)\nresult = await agent.run('Hello')\n\n# Custom fallback conditions\nfrom pydantic_ai.exceptions import ModelAPIError\n\ndef should_fallback(error: Exception) -> bool:\n    \"\"\"Only fallback on rate limits or server errors.\"\"\"\n    if isinstance(error, ModelAPIError):\n        return error.status_code in (429, 500, 502, 503)\n    return False\n\nfallback = FallbackModel(\n    'openai:gpt-4o',\n    'anthropic:claude-sonnet-4-5',\n    fallback_on=should_fallback\n)\n```\n\n## Streaming Responses\n\n```python\nasync def stream_response():\n    async with agent.run_stream('Tell me a story') as response:\n        # Stream text output\n        async for chunk in response.stream_output():\n            print(chunk, end='', flush=True)\n\n    # Access final result after streaming\n    print(f\"\\nTokens used: {response.usage().total_tokens}\")\n```\n\n### Streaming with Structured Output\n\n```python\nfrom pydantic import BaseModel\n\nclass Story(BaseModel):\n    title: str\n    content: str\n    moral: str\n\nagent = Agent('openai:gpt-4o', output_type=Story)\n\nasync with agent.run_stream('Write a fable') as response:\n    # For structured output, stream_output yields partial JSON\n    async for partial in response.stream_output():\n        print(partial)  # Partial Story object as parsed\n\n    # Final validated result\n    story = response.output\n```\n\n## Dynamic Model Selection\n\n```python\nimport os\n\n# Environment-based selection\nmodel = os.getenv('PYDANTIC_AI_MODEL', 'openai:gpt-4o')\nagent = Agent(model)\n\n# Runtime model override\nresult = await agent.run(\n    'Hello',\n    model='anthropic:claude-sonnet-4-5'  # Override default\n)\n\n# Context manager override\nwith agent.override(model='google-gla:gemini-2.0-flash'):\n    result = agent.run_sync('Hello')\n```\n\n## Deferred Model Checking\n\nDelay model validation for testing:\n\n```python\n# Default: Validates model immediately (checks env vars)\nagent = Agent('openai:gpt-4o')\n\n# Deferred: Validates only on first run\nagent = Agent('openai:gpt-4o', defer_model_check=True)\n\n# Useful for testing with override\nwith agent.override(model=TestModel()):\n    result = agent.run_sync('Test')  # No OpenAI key needed\n```\n\n## Usage Tracking\n\n```python\nresult = await agent.run('Hello')\n\n# Request usage (last request)\nusage = result.usage()\nprint(f\"Input tokens: {usage.input_tokens}\")\nprint(f\"Output tokens: {usage.output_tokens}\")\nprint(f\"Total tokens: {usage.total_tokens}\")\n\n# Full run usage (all requests in run)\nrun_usage = result.run_usage()\nprint(f\"Total requests: {run_usage.requests}\")\n```\n\n## Usage Limits\n\n```python\nfrom pydantic_ai.usage import UsageLimits\n\n# Limit token usage\nresult = await agent.run(\n    'Generate content',\n    usage_limits=UsageLimits(\n        total_tokens=1000,\n        request_tokens=500,\n        response_tokens=500,\n    )\n)\n```\n\n## Provider-Specific Features\n\n### OpenAI\n\n```python\nfrom pydantic_ai.models.openai import OpenAIModel\n\nmodel = OpenAIModel(\n    'gpt-4o',\n    api_key='your-key',  # Or use OPENAI_API_KEY env var\n    base_url='https://custom-endpoint.com'  # For Azure, proxies\n)\n```\n\n### Anthropic\n\n```python\nfrom pydantic_ai.models.anthropic import AnthropicModel\n\nmodel = AnthropicModel(\n    'claude-sonnet-4-5',\n    api_key='your-key'  # Or ANTHROPIC_API_KEY\n)\n```\n\n## Common Model Patterns\n\n| Use Case | Recommendation |\n|----------|---------------|\n| General purpose | `openai:gpt-4o` or `anthropic:claude-sonnet-4-5` |\n| Fast/cheap | `openai:gpt-4o-mini` or `anthropic:claude-haiku-4-5` |\n| Long context | `anthropic:claude-sonnet-4-5` (200k) or `google-gla:gemini-2.0-flash` |\n| Reasoning | `openai:o1-preview` |\n| Cost-sensitive prod | `FallbackModel` with fast model first |\n\n## Check gates before ship\n\nUse these only where they prevent obvious misconfiguration; they do not replace integration tests.\n\n- **Fallback chain order:** **Pass:** The first model passed to `FallbackModel(...)` is the intended primary; each subsequent model is a deliberate fallback (not reversed by mistake).\n- **Secrets:** **Pass:** Production and shared scripts load API keys from environment variables or a platform secret store; no real keys committed (placeholders only in examples).\n","tagline":"Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. 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