In Part 2b of The Context Layer, I take what we built in Part 2A—temporal memory with Graphiti—and make it shippable. The localhost Neo4j prototype is fine for demos, but you can’t run that architecture across real tools and real users without losing context. So in this video I go live with three upgrades: I connect memory to your actual tools through MCP so Claude Desktop and Cursor (and VS Code) share the same brain, I swap the backend to a graph database that’s ~10x faster for agentic workloads, and I walk through the production patterns that took me weeks to learn the hard way. Graphiti now has an official MCP server, and that changes the architecture fundamentally. Before MCP, every tool needed custom integration and memory didn’t carry over—context died the moment you switched environments. With one Graphiti MCP server, multiple clients connect (Studio child-process for Claude Desktop, SSE/HTTP for Cursor/VS Code) and they all read/write to the same temporal knowledge graph. Then I bench-test FalkorDB (built on Redis, written in C for the operations Graphiti needs) and show why sub-140ms P99 matters for real-time agents. Finally, I cover production correctness: valid time vs transaction time for auditability, and Graphiti v0.27 “Sagas” to prevent corrupted graphs when multi-step LLM extraction fails mid-ingestion. I also share pragmatic optimizations: use add_bulk for backfills, and use smaller models for extraction to cut cost dramatically while keeping bigger models for complex queries.