How does Glen fit a stateless serverless deployment?

Glen stores memory centrally and is reached over MCP, so even though each Vercel function invocation is stateless, your agent can read and write durable, shared context on every call.

Do I need my own vector store with Glen?

No. Glen handles relevance and persistence behind one MCP tool, so you do not provision a vector database or embedding pipeline just to give AI SDK agents long-term memory.

Shared memory for the Vercel AI SDK

The Vercel AI SDK makes it fast to ship AI features, streaming responses, tool calls, and agentic loops, into a Next.js or Node app, and it supports MCP so you can register servers as tools your model can invoke. But it is a toolkit for building, not a memory system: each request is stateless, and any knowledge your agent gathers is gone the moment the response streams unless you build storage for it. Glen, shared memory for AI agents, supplies that missing layer as a single MCP tool your AI SDK agents read from and write to, backed by one organization-wide store.

Register Glen as an MCP server with the Vercel AI SDK and your model gains one tool that retrieves relevant long-term context and records new facts in a single round trip. In a chat route or an agentic loop, the agent can pull what your organization already knows about a user, an account, or a task before it generates, and write durable observations back afterward, without you provisioning a vector database, an embeddings pipeline, and a custom persistence path. Memory stops being infrastructure you own and becomes a tool you call.

Because the AI SDK is so often used to build the user-facing surface, multiple routes, multiple features, sometimes multiple apps, the value of org-scoped shared memory is that they all draw on one store. A fact learned in your support assistant is available to your onboarding agent; a detail captured in a serverless function persists across stateless invocations. And because Glen is a standard MCP server, that same memory is readable from Claude Code, Cursor, or any other MCP client your team uses, so the product agents you ship and the dev agents your team runs share one source of truth. Connect once over OAuth or an API key and your AI SDK features gain memory that compounds rather than resetting every request.

FAQ

How does Glen fit a stateless serverless deployment?: Glen stores memory centrally and is reached over MCP, so even though each Vercel function invocation is stateless, your agent can read and write durable, shared context on every call.
Do I need my own vector store with Glen?: No. Glen handles relevance and persistence behind one MCP tool, so you do not provision a vector database or embedding pipeline just to give AI SDK agents long-term memory.