Better Context Retention with Agent Memory in PydanticAI

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Today we’re taking a detailed look at how to make your agents remember previous messages through short and long-term memory. In PydanticAI agent memory is supplied through the message_history parameter during agent runtime. This makes it quite simple to pass any list of formatted messages before executing the agent. Before we dig deeper though, let’s examine what agent memory is, why it is important and the types of agent memory.

💡 Examples:
1️⃣ Hello, World! - Portfolio Investment Agent - three sparring agents decide what I should invest in. Pretty cool!
2️⃣ Full Memory (Extended version) - Assistant agent explores the limits of full memory and a nifty tehnique on how to trim messages for more effective agents
3️⃣ Filtering Messages (Extended version) - Another research assistant example where we filter messages by type, removing noise and making agent responses 10x more effective
4️⃣ Memory Persistence (Extended version) - let's save this conversation and load it next time we chat! In this research assistant agent, we will be saving, retrieving and deleting memory to and from the disk with a simple Python pickle library
5️⃣ Multi-Agent Memory (Extended version) - Let our research, review and summary agents share a common message history, access messages, and add their own. Magic in action!
6️⃣ Marketing Advisor Agent (Extended version) - Should I run a President's Day Mattress Sale? Let the debate agents sort it out. Two agents: PRO and CON debate the merits of the idea, along with actionable insights, while a DeepSeek R1 agent makes the final decision!

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...
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Discord server:   / discord  
PydanticAI: https://ai.pydantic.dev

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