​🧠 Class 9: Replay Long-Term Memory with /memory/replay for Multi-Agent Context

​Mastering the AI-Native Stack: Build Faster, Smarter with AI-Powered Tools

​In this final session, we unlock multi-turn memory and long-term context replay using the powerful /memory/replay endpoint. Learn how to help agents remember past conversations, sessions, decisions — and use that memory in real time.

​This is the key to building personalized, contextual, and emotionally aware agents.

​🧠 What You’ll Learn

​✅ How to store and retrieve session memory
✅ How to structure prompts that leverage replayed context
✅ How to filter and score memory for relevance
✅ How to inject replayed memory into your MCP workflows
✅ How to build agents that learn over time

​🧑‍💻 Live Coding

• ​Save agent interactions as memory entries

• ​Use /memory/replay to retrieve top matches

• ​Combine replay with vector + recency scoring

• ​Inject replayed memory into an LLM call or agent decision

​⚙️ Tools We’ll Use

• ​🔄 /memory/replay endpoint

• ​🧠 ZeroDB’s vector + metadata filter system

• ​🧰 LangChain / Autogen memory integrations

• ​🧬 Agent feedback + reinforcement flow

​👥 Who Should Attend

• ​Autonomous agent builders

• ​AI UX engineers creating personalized apps

• ​Anyone building contextual copilots or AI companions

• ​Developers implementing multi-session workflows

​🗓 When?

​Every Sunday — Final Session
Includes memory indexing labs, MCP memory injection demos, and user tracing walkthroughs.

​🎯 Why Join This Class?

​🧠 Build smarter agents that remember
📈 Increase user satisfaction and retention with contextual recall
🔍 Enable complex workflows that span multiple sessions
🔁 Power your own GPT-style memory backend

​✅ Class Take Aways

• ​Agent memory schema template

• ​Live MCP memory injection demo

• ​Vector + recency hybrid replay recipe

• ​Starter kit: Persistent agent memory for ZeroDB

​📌 Sign Up Now

​Spaces are limited — claim your ZeroDB vector storage workshop seat! 👉 Reserve Your Spot