LlamaIndex Agent Memory: From Short-Term Storage to Intelligent Retention
The LlamaIndex team is leveling up memory for agents with their newest release:
Improved Long & Short-Term Memory for LlamaIndex Agents
Now, agents can remember more than just your last message.
In this event, we’ll unpack how the LlamaIndex memory component has evolved—from simple message history to advanced long-term memory systems powered by static content, extracted facts, and vector search.
We’ll walk through real-world use cases and explore how these memory blocks can transform agentic applications by allowing them to retain and use context across conversations.
📚 You’ll learn:
How to use the new LlamaIndex Memory component to persist chat history
How to implement long-term memory using static, fact-extraction, and vector blocks
How to extend memory with custom logic (yes, even a “Logan Mention Counter”)
When memory is essential—and when it’s not—for your app
🤓 Who should attend this event:
AI Engineers building agentic apps with conversational memory needs
Developers curious about building richer, more persistent LLM workflows
Anyone building tools where remembering user context can save time and clicks
Speakers:
Laura Funderburk, Developer Relations and Community LeadDeveloper Relations and Community Lead at AI Makerspace. Laura has a B.Sc. in Mathematics from Simon Fraser University. She has extensive experience as a data scientist and enjoys engaging the community through her role as a developer advocate. Laura received a Terry Fox gold medal from her Alma Mater in 2019.
Tuana Çelik, Sr. Developer Relations EngineerSr. Developer Relations Engineer at LlamaIndex. Tuana is a Developer Relations and AI Engineer at LlamaIndex where she focuses on their open source AI framework and community of builders. Most of her time is spent educating the engineering community on building with the latest LLMs and AI technology. Prior to this, she worked at Weaviate.
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