2.7 KiB
Template Generation Request
TECHNOLOGY/FRAMEWORK:
Example: CrewAI multi-agent systems
Your technology: Pydantic AI agents
TEMPLATE PURPOSE:
What specific use case should this template be optimized for?
Your purpose: Building intelligent AI agents with tool integration, conversation handling, and structured data validation using Pydantic AI framework
CORE FEATURES:
What are the essential features this template should help developers implement?
Your core features:
- Agent creation with different model providers (OpenAI, Anthropic, Gemini)
- Tool integration patterns (web search, file operations, API calls)
- Conversation memory and context management
- Structured output validation with Pydantic models
- Error handling and retry mechanisms
- Testing patterns for AI agent behavior
EXAMPLES TO INCLUDE:
What working examples should be provided in the template?
Your examples:
- Basic chat agent with memory
- Tool-enabled agent (web search + calculator)
- Multi-step workflow agent
- Agent with custom Pydantic models for structured outputs
- Testing examples for agent responses and tool usage
DOCUMENTATION TO RESEARCH:
What specific documentation should be thoroughly researched and referenced?
Your documentation:
- https://ai.pydantic.dev/ - Official Pydantic AI documentation
- Model provider APIs (OpenAI, Anthropic) for integration patterns
- Tool integration best practices and examples
DEVELOPMENT PATTERNS:
What specific development patterns, project structures, or workflows should be researched and included?
Your development patterns:
- How to structure agent modules and tool definitions
- Configuration management for different model providers
- Environment setup for development vs production
- Logging and monitoring patterns for AI agents
SECURITY & BEST PRACTICES:
What security considerations and best practices are critical for this technology?
Your security considerations:
- API key management
- Input validation and sanitization for agent inputs
- Rate limiting and usage monitoring
- Prompt injection prevention
- Cost control and monitoring for model usage
COMMON GOTCHAS:
What are the typical pitfalls, edge cases, or complex issues developers face with this technology?
Your gotchas:
- Handling model provider rate limits and errors
- Managing conversation state across requests
- Tool execution error handling and retries
VALIDATION REQUIREMENTS:
What specific validation, testing, or quality checks should be included in the template?
Your validation requirements:
- Tool unit testing testing
- Agent unit testing