Join us for an exploration of the next frontier in Retrieval-Augmented Generation (RAG): persistent agentic memory and dynamic context injection using MCP (Model Context Protocol) and Qdrant.

​We will be joined by n8n and Tensorlake.

​As LLM-powered agents become more capable, they need more than just one-off prompts — they need context that evolves, memory that persists, and infrastructure that scales. This event will show you how to use MCP to manage complex, multi-turn contexts, and how Qdrant enables high-performance vector memory at scale.

​Through live demos, lightning talks, and hands-on exploration, we’ll walk through how to:

• ​Store long-term memory using Qdrant
• ​Dynamically construct LLM context with MCP
• ​Design agents that remember, adapt, and collaborate across time

​Whether you're building copilots, assistants, or AI agents that go beyond single-turn interactions, this event will equip you with the building blocks of intelligent memory.

​What You’ll Learn:

• ​How the Model Context Protocol enhances agentic workflows
• ​Architecting scalable vector memory with Qdrant
• ​Best practices for context injection and memory retrieval
• ​Real-world demos of agentic systems powered by MCP and Qdrant

​Who Should Attend:
LLM engineers, AI product builders, agent framework devs, and anyone working on context-aware or memory-enabled AI.

​This isn’t just RAG — it’s memory with purpose.