{"enrichment":{"faq":[{"a":"PydanticAI agent creation starts with importing the Agent class and defining your LLM model. Specify a system prompt or instructions, choose your provider (OpenAI, Anthropic, Google, etc.), and optionally inject dependencies for external services. Use type hints on your agent methods to ensure Pydantic validates all inputs and outputs automatically.","q":"How do I create pydantic ai agents?"},{"a":"PydanticAI agent instructions and system prompts both guide model behavior, but instructions are typically agent-level directives you set during initialization, while system prompts are lower-level model parameters. Instructions shape the agent's overall task context, whereas system prompts fine-tune how the underlying LLM processes requests.","q":"What is the difference between pydantic agent instructions vs system prompt?"},{"a":"PydanticAI agents are type-safe by default through Pydantic model validation. Define your agent with typed parameters and return types, use Pydantic models for structured outputs, and leverage dependency injection to pass validated services. The framework enforces type contracts at runtime, catching mismatches before they reach your LLM.","q":"How do I set up type-safe AI agents in Python?"},{"a":"Yes. PydanticAI supports building chat systems by combining agents with Pydantic model validation for all message exchanges. Define your chat schema as Pydantic models, configure your agent with appropriate instructions, and use async execution for responsive interactions. Streaming responses are also supported for real-time chat experiences.","q":"Can I build chat systems with pydantic validation?"},{"a":"PydanticAI agents support both async and sync execution modes. Use `await agent.run_async()` for asynchronous execution or `agent.run()` for synchronous calls. For streaming, configure your agent with streaming enabled and iterate over response chunks as they arrive from the LLM provider.","q":"How do I implement async execution and streaming for PydanticAI agents?"},{"a":"PydanticAI structured outputs enforce schema validation on agent responses using Pydantic models. Define a model class with your desired fields and types, pass it as the result_type to your agent, and PydanticAI ensures the LLM returns data matching that schema. This guarantees type safety and enables reliable downstream processing.","q":"What are pydantic ai structured outputs and how do I use them?"}],"shadow_tags":["agent-framework","llm-integration","type-safety","structured-generation","dependency-injection","async-patterns","validation-retry","multi-provider","chat-systems"],"summary_rewrite":"Set up PydanticAI agents with full type safety, Pydantic model validation, and configurable dependencies. Choose from OpenAI, Anthropic, Google, and other LLM providers, then define structured output schemas and inject external services. Execute synchronously, asynchronously, or via streaming."},"files":[{"bytes":4529,"path":"plugins/beagle-ai/skills/pydantic-ai-agent-creation/SKILL.md","sha256":"0e48ba0ae2c6630da55255e983845a15aa824c465d578a398e275e3e20787911","url":"https://skillfed.io/files/existential-birds/beagle/pydantic-ai-agent-creation/37dfb1d3/SKILL.md"}],"id":"existential-birds/beagle/pydantic-ai-agent-creation","links":{"html":"https://skillfed.io/existential-birds/beagle/pydantic-ai-agent-creation","md":"https://skillfed.io/existential-birds/beagle/pydantic-ai-agent-creation.md","repo":"https://github.com/existential-birds/beagle"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":10,"language":"TypeScript","last_updated":"2026-07-21","license":"Apache-2.0","name":"pydantic-ai-agent-creation","publisher":"existential-birds","stars":74},"relations":{"similar":[{"id":"DougTrajano/pydantic-ai-skills/pydanticai-docs"},{"id":"existential-birds/beagle/pydantic-ai-testing"},{"id":"existential-birds/beagle/pydantic-ai-common-pitfalls"},{"id":"existential-birds/beagle/pydantic-ai-dependency-injection"},{"id":"itechmeat/llm-code/pydantic-ai"},{"id":"cline/skills/building-pydantic-ai-agents"},{"id":"pydantic/skills/building-pydantic-ai-agents"},{"id":"existential-birds/beagle/pydantic-ai-model-integration"},{"id":"existential-birds/beagle/pydantic-ai-tool-system"},{"id":"fernandofuc/nextjs-c-s/agent-builder-pydantic-ai"}]},"slug":{"owner":"existential-birds","repo":"beagle","skill":"pydantic-ai-agent-creation"},"version":"37dfb1d3"}