Скачать или смотреть Build More Reliable Agents with Retries and Usage Limits in PydanticAI

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Today we’re making your agents more reliable and the AI costs more predictable through Retries and Usage Limits. Retries are parameters that can be passed to notify an agent how many times it should attempt to answer a user query. This prevents infinite loops and can add to more predictable apps. Similarly, usage limits can be set on the number of tokens or tool calls.

Retries in PydanticAI can be set at the agent level, for each tool call and for the result validator functions. We will work on examples for each category, so you will get plenty of practice.

🚀 Tutorial Examples:
1️⃣ Hello, World - AI knowledge summary agent. Summarizes the key points from the user-provided text. Ensures the summary is concise and captures the main ideas accurately
2️⃣ Agent Retries (Extended version) - Math agent tasked to calculate the square, cube and the n'th power of a number. Agent is provided a number and it returns a MathResponse (Pydantic model) type
3️⃣ Result Validator Retries (Extended version) - Math agent tasked to calculate the square, cube and the n'th power of a number. Agent is provided a number and it returns a MathResponse (Pydantic model) type. Using tools such as math_tool. When the user input is ambiguous, agent assumes the numbers and proceeds with calling the math tool to come up with an answer
4️⃣ Tool Retries (Extended version) - Math agent with results retry function. Extra validation of calculated results!
5️⃣ Usage Limits - Tokens (Extended version) - Haiku poetry example. Agent writes a short haiku matching user's prompt
6️⃣ Usage Limits - Requests (Extended version) - AI knowledge summary agent. Summarizes key points from the user-provided text

Masterclass Series:
▶️ Part 1:    • PydanticAI: The Best AI Agent Framework Ha...  
▶️ Part 2:    • From Chaos to Clarity: LLM Tracing with Lo...  
▶️ Part 3:    • 100% Reliable LLM Outputs with Structured ...  
▶️ Part 4:    • Dramatic Improvement! Design Better Agents...  
▶️ Part 5:    • Design Better AI Agents With Function Calling  
▶️ Part 6:    • Transform You Agents with Result Validator...  
▶️ Part 7:    • Improve Agent Scalability with Dependency ...  
▶️ Part 8:    • Build More Reliable Agents with Retries an...  
▶️ Part 9:    • Better Context Retention with Agent Memory...  
▶️ Part 10:    • Building Resilient Agents: Self-Reflection...  
▶️ Part 11:    • Better User Experience with Streaming Outp...  
▶️ Part 12:    • Hidden Model Settings That Will Transform ...  
▶️ Part 13: Multi-Model Agents in PydanticAI: Unlocking Next-Gen AI Capabilities
▶️ Part 14: Mastering RAG in PydanticAI: Better AI Agents with Real-Time Data
▶️ Part 15: Masterclass Final Project: AI Resume Writing with Multiple Agents

🎯 Whether you're building a chatbot, an AI agent, or any other LLM-powered system, this tutorial provides practical examples to elevate your application’s ability to get better outputs from AI.

What agents are you building? Join the conversation at   / discord  

🔗 Links & Resources:
Skool: https://www.skool.com/ai-software-dev...
Code the Revolution: Newsletter - https://aidev9.substack.com/
Discord server:   / discord  
PydanticAI: https://ai.pydantic.dev

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