PydanticAI: Building Production-Grade AI Agents

From Fundamentals to Advanced Agent Workflows with PydanticAI

Introduction: Why PydanticAI?

Chapter 1: The New Frontier of AI Applications

1. The LLM Application Revolution
2. Why Traditional Frameworks Fall Short
3. The Type-Safety Advantage
4. PydanticAI in the Ecosystem
5. The Pydantic Connection: From Validation to Agents
6. Chapter Summary
7. Further Reading

Chapter 2: Foundations of PydanticAI

1. Getting Started: Installation and Setup
2. The Core Abstractions: Models, Messages and Agents
3. Your First Agent: A Complete Walkthrough
4. Adding Structure: Instructions and System Prompts
5. Configuration and Environment Variables
6. Understanding the Pydantic Connection
7. Capabilities: Composable Agent Behavior
8. HTTP Client Lifecycle and Resource Management
9. Concurrency and Rate Limiting
10. Fallback Models for Resilience
11. Chapter Summary
12. Further Reading

Chapter 3: Models, Prompts and Messages

1. Supported LLM Providers and Model Selection
2. The Message Protocol: System, User and Assistant Messages
3. Prompt Engineering Within PydanticAI
4. Temperature, Top-P and Other Hyperparameters
5. Streaming Responses and Real-Time Output
6. Usage Limits and Cost Control
7. Chapter Summary
8. Further Reading

Chapter 4: Structured Outputs and Type Safety

1. Pydantic Models as Response Schemas
2. The Structured Output Pipeline
3. Validation and Error Handling
4. Complex Nested Types and Enums
5. NativeOutput: Leveraging Provider-Side Structured Outputs
6. Fallback Strategies and Best Practices
7. Performance Characteristics
8. Chapter Summary
9. Further Reading

Chapter 5: Tool Calling and Agent Capabilities

1. What Are Tools and Why Agents Need Them
2. Defining Tools with Type Safety
3. Automatic Parameter Extraction
4. Error Handling in Tool Execution
5. Real-World Tool Design Patterns
6. Toolsets and MCP Integration
7. Tool Return Schemas
8. Chapter Summary
9. Further Reading

Chapter 6: Agents, Dependencies and Dependency Injection

1. The Agent Model: Lifecycle and State
2. Dependency Injection Fundamentals
3. Custom Dependency Providers
4. Shared Dependencies Across Agents
5. Architectural Patterns for Complex Applications
6. Dynamic Instructions and System Prompts
7. Template Strings for Agent Specs
8. Validation Context
9. Chapter Summary
10. Further Reading

Chapter 7: Workflows and Multi-Agent Systems

1. Sequential Agent Workflows
2. Parallel Execution Patterns
3. Multi-Agent Communication and Coordination
4. Orchestrator vs. Handoff Architectures
5. State Management Across Agents
6. Graph-Based Control Flow
7. Durable Execution: Surviving Crashes and Restarts
8. Deep Agents
9. Chapter Summary
10. Further Reading

Chapter 8: Testing, Debugging and Observability

1. Testing PydanticAI Applications
2. Mock Models and Deterministic Testing
3. Logging and Structured Observability
4. Tracing and Distributed Debugging
5. Common Pitfalls and How to Avoid Them
6. Chapter Summary
7. Further Reading

Chapter 9: Performance Optimization and Scaling

1. Understanding Token Economics
2. Caching Strategies for LLM Calls
3. Batching and Parallelism
4. Rate Limiting and Retry Logic
5. Cost Optimization Techniques
6. Scaling Patterns
7. Chapter Summary
8. Further Reading

Chapter 10: Security, Safety and Guardrails

1. Input Validation and Sanitization
2. Prompt Injection Defenses
3. Output Filtering and Content Safety
4. Access Control and Authentication
5. Compliance and Audit Trails
6. Guardrail Packages
7. Chapter Summary
8. Further Reading

Chapter 11: Deployment, Monitoring and Production Patterns

1. Production Deployment Architectures
2. Monitoring Dashboards and Metrics
3. Alerting and Incident Response
4. CI/CD Pipelines for AI Applications
5. A/B Testing and Canary Deployments
6. Web Chat UI and Interactive Applications
7. Production Checklist
8. Chapter Summary
9. Further Reading

Chapter 12: Real-World Case Studies and Future Directions

1. Case Study: Customer Support Automation
2. Case Study: Data Analysis and Reporting Agent
3. Case Study: Multi-Agent Research System
4. Emerging Patterns and Best Practices
5. The Future of Type-Safe AI Agents

Chapter 13: RAG and Knowledge Integration

1. What Is RAG and When Agents Need It
2. The RAG Pipeline: Four Stages
3. PydanticAI’s Embedder Interface
4. Chunking Strategies
5. Vector Stores and Indexing
6. Building a RAG Agent with PydanticAI
7. Advanced RAG Patterns
8. Common RAG Pitfalls
9. Chapter Summary
10. Further Reading

Chapter 14: Multimodal AI Agents

1. Beyond Text: The Rise of Multimodal Agents
2. PydanticAI’s Multimodal Input Types
3. Image Input: Vision Agents
4. Document Processing
5. Audio Input: Voice Agents
6. Image Generation
7. Combining Modalities
8. Model-Specific Considerations
9. Chapter Summary
10. Further Reading

Chapter 15: Agent Evaluation and Continuous Improvement

1. Why Evaluation Matters
2. Pydantic Evals: Core Concepts
3. Building a Test Dataset
4. Evaluating an Agent
5. Built-in Evaluators
6. A/B Testing Prompts and Models
7. Model Comparison
8. Structured Output Evaluation
9. Regression Testing
10. Continuous Evaluation in Production
11. Logfire Integration for Evaluation Reports
12. Chapter Summary
13. Further Reading

Chapter 16: Real-Time Voice Conversations with PydanticAI

1. Beyond Text: The Rise of Real-Time Agents
2. Real-Time Provider Ecosystem
3. Installation and Quick Start
4. Step-by-Step Walkthrough: Building a Production Voice Agent
5. Real-Time Deployment Patterns
6. Voice Agent Best Practices
7. Limitations and Considerations
8. Chapter Summary
9. Further Reading

Chapter 17: Agent Memory and Long-Term State

1. Why Agents Need Memory
2. Short-Term Memory: Conversation History
3. Medium-Term Memory: Persistent Session History
4. Long-Term Memory: Semantic Recall
5. Conversation Summarization
6. Memory Privacy and Security
7. Chapter Summary
8. Further Reading

Chapter 18: Advanced Multi-Agent Architecture Patterns

1. Beyond Simple Orchestration
2. The Router Pattern
3. The Supervisor Pattern
4. The Swarm Pattern
5. The Hierarchical Pattern
6. Pattern Selection Guide
7. Production Considerations
8. Chapter Summary
9. Further Reading

Conclusion

References