How do fallback models work in PydanticAI?

Fallback models allow you to define a list of models that the agent will try in sequence. If the primary model fails due to rate limits or server errors, it automatically switches to the next available model.

Which LLM providers are supported by this PydanticAI skill?

The skill supports a wide range of providers including OpenAI, Anthropic, Google (GLA & Vertex), Groq, Mistral, Cohere, and AWS Bedrock.

How can I monitor and limit token usage?

You can use the UsageLimits class to set caps on total, request, or response tokens, and utilize the usage() method on results to track actual consumption.

Can I stream structured output with this skill?

Yes, the skill provides patterns for using run_stream, which allows you to stream partial JSON results that are validated against Pydantic models in real-time.

PydanticAI Model Integration

Name: PydanticAI Model Integration
Author: existential-birds

byexistential-birds

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Data Science & ML

Integrates and manages diverse LLM providers in PydanticAI with support for fallback models, streaming, and usage tracking.

About

This skill simplifies the configuration and orchestration of Large Language Models within the PydanticAI ecosystem. It enables developers to switch between providers like OpenAI, Anthropic, and Gemini seamlessly, implement robust error handling via fallback chains, and optimize performance through streaming and granular model settings. It is particularly useful for building production-grade AI agents that require high availability, cost management, and structured output parsing.

Key Features

Multi-provider configuration for OpenAI, Anthropic, Gemini, Groq, and Mistral
Real-time streaming for both raw text and structured Pydantic model outputs
Granular model settings control including temperature, token limits, and timeouts
Resilience through automated fallback model chaining and custom error handling
Integrated usage tracking and token limits for cost and performance monitoring
14 GitHub stars

Use Cases

Building resilient AI agents that switch models automatically during provider outages
Standardizing LLM integration across different cloud providers within a single Python codebase
Implementing real-time streaming interfaces for complex structured data applications

About

Key Features

Multi-provider configuration for OpenAI, Anthropic, Gemini, Groq, and Mistral
Real-time streaming for both raw text and structured Pydantic model outputs
Granular model settings control including temperature, token limits, and timeouts
Resilience through automated fallback model chaining and custom error handling
Integrated usage tracking and token limits for cost and performance monitoring
14 GitHub stars

Use Cases

Building resilient AI agents that switch models automatically during provider outages
Standardizing LLM integration across different cloud providers within a single Python codebase
Implementing real-time streaming interfaces for complex structured data applications