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pydantic-ai-model-integration

Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.

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Preis unbestätigt★ 80 GitHub-StarsVerzeichnis aktualisiert · 7. Sept. 2026agent-skill

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Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.

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PydanticAI Model Integration

Provider Model Strings

Format: provider:model-name

from pydantic_ai import Agent

# OpenAI
Agent('openai:gpt-4o')
Agent('openai:gpt-4o-mini')
Agent('openai:o1-preview')

# Anthropic
Agent('anthropic:claude-sonnet-4-5')
Agent('anthropic:claude-haiku-4-5')

# Google (API Key)
Agent('google-gla:gemini-2.0-flash')
Agent('google-gla:gemini-2.0-pro')

# Google (Vertex AI)
Agent('google-vertex:gemini-2.0-flash')

# Groq
Agent('groq:llama-3.3-70b-versatile')
Agent('groq:mixtral-8x7b-32768')

# Mistral
Agent('mistral:mistral-large-latest')

# Other providers
Agent('cohere:command-r-plus')
Agent('bedrock:anthropic.claude-3-sonnet')

Model Settings

from pydantic_ai import Agent
from pydantic_ai.settings import ModelSettings

agent = Agent(
    'openai:gpt-4o',
    model_settings=ModelSettings(
        temperature=0.7,
        max_tokens=1000,
        top_p=0.9,
        timeout=30.0,  # Request timeout
    )
)

# Override per-run
result = await agent.run(
    'Generate creative text',
    model_settings=ModelSettings(temperature=1.0)
)

Fallback Models

Chain models for resilience:

from pydantic_ai.models.fallback import FallbackModel

# Try models in order until one succeeds
fallback = FallbackModel(
    'openai:gpt-4o',
    'anthropic:claude-sonnet-4-5',
    'google-gla:gemini-2.0-flash'
)

agent = Agent(fallback)
result = await agent.run('Hello')

# Custom fallback conditions
from pydantic_ai.exceptions import ModelAPIError

def should_fallback(error: Exception) -> bool:
    """Only fallback on rate limits or server errors."""
    if isinstance(error, ModelAPIError):
        return error.status_code in (429, 500, 502, 503)
    return False

fallback = FallbackModel(
    'openai:gpt-4o',
    'anthropic:claude-sonnet-4-5',
    fallback_on=should_fallback
)

Streaming Responses

async def stream_response():
    async with agent.run_stream('Tell me a story') as response:
        # Stream text output
        async for chunk in response.stream_output():
            print(chunk, end='', flush=True)

    # Access final result after streaming
    print(f"\nTokens used: {response.usage().total_tokens}")

Streaming with Structured Output

from pydantic import BaseModel

class Story(BaseModel):
    title: str
    content: str
    moral: str

agent = Agent('openai:gpt-4o', output_type=Story)

async with agent.run_stream('Write a fable') as response:
    # For structured output, stream_output yields partial JSON
    async for partial in response.stream_output():
        print(partial)  # Partial Story object as parsed

    # Final validated result
    story = response.output

Dynamic Model Selection

import os

# Environment-based selection
model = os.getenv('PYDANTIC_AI_MODEL', 'openai:gpt-4o')
agent = Agent(model)

# Runtime model override
result = await agent.run(
    'Hello',
    model='anthropic:claude-sonnet-4-5'  # Override default
)

# Context manager override
with agent.override(model='google-gla:gemini-2.0-flash'):
    result = agent.run_sync('Hello')

Deferred Model Checking

Delay model validation for testing:

# Default: Validates model immediately (checks env vars)
agent = Agent('openai:gpt-4o')

# Deferred: Validates only on first run
agent = Agent('openai:gpt-4o', defer_model_check=True)

# Useful for testing with override
with agent.override(model=TestModel()):
    result = agent.run_sync('Test')  # No OpenAI key needed

Usage Tracking

result = await agent.run('Hello')

# Request usage (last request)
usage = result.usage()
print(f"Input tokens: {usage.input_tokens}")
print(f"Output tokens: {usage.output_tokens}")
print(f"Total tokens: {usage.total_tokens}")

# Full run usage (all requests in run)
run_usage = result.run_usage()
print(f"Total requests: {run_usage.requests}")

Usage Limits

from pydantic_ai.usage import UsageLimits

# Limit token usage
result = await agent.run(
    'Generate content',
    usage_limits=UsageLimits(
        total_tokens=1000,
        request_tokens=500,
        response_tokens=500,
    )
)

Provider-Specific Features

OpenAI

from pydantic_ai.models.openai import OpenAIModel

model = OpenAIModel(
    'gpt-4o',
    api_key='your-key',  # Or use OPENAI_API_KEY env var
    base_url='https://custom-endpoint.com'  # For Azure, proxies
)

Anthropic

from pydantic_ai.models.anthropic import AnthropicModel

model = AnthropicModel(
    'claude-sonnet-4-5',
    api_key='your-key'  # Or ANTHROPIC_API_KEY
)

Common Model Patterns

Use Case	Recommendation
General purpose	`openai:gpt-4o` or `anthropic:claude-sonnet-4-5`
Fast/cheap	`openai:gpt-4o-mini` or `anthropic:claude-haiku-4-5`
Long context	`anthropic:claude-sonnet-4-5` (200k) or `google-gla:gemini-2.0-flash`
Reasoning	`openai:o1-preview`
Cost-sensitive prod	`FallbackModel` with fast model first

Check gates before ship

Use these only where they prevent obvious misconfiguration; they do not replace integration tests.

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).
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).

Quelle ansehen ↗

Dateimetadaten

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.

Originaltext anzeigen

---
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.
---

# PydanticAI Model Integration

## Provider Model Strings

Format: `provider:model-name`

```python
from pydantic_ai import Agent

# OpenAI
Agent('openai:gpt-4o')
Agent('openai:gpt-4o-mini')
Agent('openai:o1-preview')

# Anthropic
Agent('anthropic:claude-sonnet-4-5')
Agent('anthropic:claude-haiku-4-5')

# Google (API Key)
Agent('google-gla:gemini-2.0-flash')
Agent('google-gla:gemini-2.0-pro')

# Google (Vertex AI)
Agent('google-vertex:gemini-2.0-flash')

# Groq
Agent('groq:llama-3.3-70b-versatile')
Agent('groq:mixtral-8x7b-32768')

# Mistral
Agent('mistral:mistral-large-latest')

# Other providers
Agent('cohere:command-r-plus')
Agent('bedrock:anthropic.claude-3-sonnet')
```

## Model Settings

```python
from pydantic_ai import Agent
from pydantic_ai.settings import ModelSettings

agent = Agent(
    'openai:gpt-4o',
    model_settings=ModelSettings(
        temperature=0.7,
        max_tokens=1000,
        top_p=0.9,
        timeout=30.0,  # Request timeout
    )
)

# Override per-run
result = await agent.run(
    'Generate creative text',
    model_settings=ModelSettings(temperature=1.0)
)
```

## Fallback Models

Chain models for resilience:

```python
from pydantic_ai.models.fallback import FallbackModel

# Try models in order until one succeeds
fallback = FallbackModel(
    'openai:gpt-4o',
    'anthropic:claude-sonnet-4-5',
    'google-gla:gemini-2.0-flash'
)

agent = Agent(fallback)
result = await agent.run('Hello')

# Custom fallback conditions
from pydantic_ai.exceptions import ModelAPIError

def should_fallback(error: Exception) -> bool:
    """Only fallback on rate limits or server errors."""
    if isinstance(error, ModelAPIError):
        return error.status_code in (429, 500, 502, 503)
    return False

fallback = FallbackModel(
    'openai:gpt-4o',
    'anthropic:claude-sonnet-4-5',
    fallback_on=should_fallback
)
```

## Streaming Responses

```python
async def stream_response():
    async with agent.run_stream('Tell me a story') as response:
        # Stream text output
        async for chunk in response.stream_output():
            print(chunk, end='', flush=True)

    # Access final result after streaming
    print(f"\nTokens used: {response.usage().total_tokens}")
```

### Streaming with Structured Output

```python
from pydantic import BaseModel

class Story(BaseModel):
    title: str
    content: str
    moral: str

agent = Agent('openai:gpt-4o', output_type=Story)

async with agent.run_stream('Write a fable') as response:
    # For structured output, stream_output yields partial JSON
    async for partial in response.stream_output():
        print(partial)  # Partial Story object as parsed

    # Final validated result
    story = response.output
```

## Dynamic Model Selection

```python
import os

# Environment-based selection
model = os.getenv('PYDANTIC_AI_MODEL', 'openai:gpt-4o')
agent = Agent(model)

# Runtime model override
result = await agent.run(
    'Hello',
    model='anthropic:claude-sonnet-4-5'  # Override default
)

# Context manager override
with agent.override(model='google-gla:gemini-2.0-flash'):
    result = agent.run_sync('Hello')
```

## Deferred Model Checking

Delay model validation for testing:

```python
# Default: Validates model immediately (checks env vars)
agent = Agent('openai:gpt-4o')

# Deferred: Validates only on first run
agent = Agent('openai:gpt-4o', defer_model_check=True)

# Useful for testing with override
with agent.override(model=TestModel()):
    result = agent.run_sync('Test')  # No OpenAI key needed
```

## Usage Tracking

```python
result = await agent.run('Hello')

# Request usage (last request)
usage = result.usage()
print(f"Input tokens: {usage.input_tokens}")
print(f"Output tokens: {usage.output_tokens}")
print(f"Total tokens: {usage.total_tokens}")

# Full run usage (all requests in run)
run_usage = result.run_usage()
print(f"Total requests: {run_usage.requests}")
```

## Usage Limits

```python
from pydantic_ai.usage import UsageLimits

# Limit token usage
result = await agent.run(
    'Generate content',
    usage_limits=UsageLimits(
        total_tokens=1000,
        request_tokens=500,
        response_tokens=500,
    )
)
```

## Provider-Specific Features

### OpenAI

```python
from pydantic_ai.models.openai import OpenAIModel

model = OpenAIModel(
    'gpt-4o',
    api_key='your-key',  # Or use OPENAI_API_KEY env var
    base_url='https://custom-endpoint.com'  # For Azure, proxies
)
```

### Anthropic

```python
from pydantic_ai.models.anthropic import AnthropicModel

model = AnthropicModel(
    'claude-sonnet-4-5',
    api_key='your-key'  # Or ANTHROPIC_API_KEY
)
```

## Common Model Patterns

| Use Case | Recommendation |
|----------|---------------|
| General purpose | `openai:gpt-4o` or `anthropic:claude-sonnet-4-5` |
| Fast/cheap | `openai:gpt-4o-mini` or `anthropic:claude-haiku-4-5` |
| Long context | `anthropic:claude-sonnet-4-5` (200k) or `google-gla:gemini-2.0-flash` |
| Reasoning | `openai:o1-preview` |
| Cost-sensitive prod | `FallbackModel` with fast model first |

## Check gates before ship

Use these only where they prevent obvious misconfiguration; they do not replace integration tests.

- **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).
- **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).

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Lizenz: Apache-2.0

Permission surface may require sandboxing
SKILL.md includes inline api_key='your-key' examples that could encourage hardcoded credentials; should consistently emphasize environment variables or secret managers.
No explicit limitations or prerequisite/setup section covering PydanticAI version compatibility and required provider packages/API keys.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 80 GitHub stars
Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution

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Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: existential-birds/beagle
Lizenz: Apache-2.0
Version: 1.0.0
Letzter GitHub-Push: 10. Aug. 2026
Verzeichnis aktualisiert: 7. Sept. 2026
Anleitungspfad: plugins/beagle-ai/skills/pydantic-ai-model-integration/SKILL.md @ d1a74899fbfe

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Permission surface may require sandboxing
SKILL.md includes inline api_key='your-key' examples that could encourage hardcoded credentials; should consistently emphasize environment variables or secret managers.
No explicit limitations or prerequisite/setup section covering PydanticAI version compatibility and required provider packages/API keys.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 80 GitHub stars
Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata
Permission surface: secrets or environment access, shell or command execution

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      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 80 GitHub stars",
      "Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata",
      "Permission surface: secrets or environment access, shell or command execution"
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    "metrics": {
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    "score": 72,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
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      "SKILL.md includes inline api_key='your-key' examples that could encourage hardcoded credentials; should consistently emphasize environment variables or secret managers.",
      "No explicit limitations or prerequisite/setup section covering PydanticAI version compatibility and required provider packages/API keys.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "GitHub adoption: 80 GitHub stars",
      "Stars/forks activity: 80 stars, 8 forks; issue activity unavailable in current metadata",
      "Permission surface: secrets or environment access, shell or command execution"
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    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
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    "blocked": true,
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  "quality": {
    "score": 62,
    "label": "Promising"
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  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Workflow automation",
    "maintenance": "2mo since push",
    "risk": "Needs review"
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  "alternative_skills": [
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      "slug": "orchestra-research-distributed-llm-pretraining-torchtitan",
      "name": "distributed-llm-pretraining-torchtitan",
      "url": "https://www.openagentskill.com/skills/orchestra-research-distributed-llm-pretraining-torchtitan",
      "stars": 13443,
      "install_command": "npx skills add Orchestra-Research/AI-Research-SKILLs --skill distributed-llm-pretraining-torchtitan",
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      "audit_score": 84
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      "audit_score": 77
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    "SKILL.md includes inline api_key='your-key' examples that could encourage hardcoded credentials; should consistently emphasize environment variables or secret managers.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Permission surface may require sandboxing",
    "No explicit limitations or prerequisite/setup section covering PydanticAI version compatibility and required provider packages/API keys.",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
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      "Audit: 72/100 Needs review",
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      "task": "Use pydantic-ai-model-integration in an agent workflow",
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      "error_type": null,
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      "time_to_useful_ms": 120000,
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    "audit": "https://www.openagentskill.com/skills/existential-birds-pydantic-ai-model-integration/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=existential-birds-pydantic-ai-model-integration&task=Use%20pydantic-ai-model-integration%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20pydantic-ai-model-integration%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
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    "install": "https://www.openagentskill.com/api/skills/existential-birds-pydantic-ai-model-integration/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/existential-birds-pydantic-ai-model-integration"
  }
}

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: existential-birds
Quelle: existential-birds/beagle
Indexiert von: OpenAgentSkill Community-Index

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