Is this overkill for a small team?

No, small teams benefit most. With only a few engineers, each agent learning the codebase in isolation is pure waste; sharing that memory across the team is high leverage for little setup.

What happens when someone leaves?

The memory belongs to the organization, not the individual, so what a departing teammate's agent learned stays available to everyone who remains.

Shared memory for startups

Startups move fast and write things down rarely. Context lives in people's heads, in Slack threads that scroll away, and in the working memory of whoever shipped the feature last week. As you adopt AI coding agents, that fragility shows up in a new form: every engineer's agent rediscovers the same facts, and nothing one agent learns helps the next. Glen, shared memory for AI agents, is built to fix exactly this for small, fast-moving teams: one organization-wide memory that every agent reads from and writes to, delivered as a single MCP tool.

For a startup, the appeal of Glen is leverage. You probably have a handful of engineers, each running an AI agent in Cursor, Claude Code, or VS Code, and each of those agents is currently learning your codebase in isolation. With Glen connected, the first time any agent figures out a non-obvious truth about your system, how billing is wired, which environment variable is load-bearing, why a service must not be called a certain way, that fact becomes part of a shared store the whole team's agents can draw on. The knowledge compounds instead of evaporating.

That matters most at the moments startups feel pain: onboarding and turnover. A new engineer's agent inherits the accumulated memory of everyone who came before, so ramp-up stops meaning the agent relearns everything from scratch, and when someone leaves, what their agent knew does not leave with them. Setup fits a startup's tolerance for overhead: connect the MCP server once, and every teammate inherits the org's memory automatically with no per-user store to seed. Because reads and writes happen in one round trip, there is nothing to maintain, the memory grows as a byproduct of normal work. For a small team trying to punch above its weight with AI agents, shared memory is one of the highest-leverage things you can add.

FAQ

Is this overkill for a small team?: No, small teams benefit most. With only a few engineers, each agent learning the codebase in isolation is pure waste; sharing that memory across the team is high leverage for little setup.
What happens when someone leaves?: The memory belongs to the organization, not the individual, so what a departing teammate's agent learned stays available to everyone who remains.