Why not just use each agent's built-in memory?

Built-in memory is usually private to one agent and one framework. Glen is shared across every agent in your organization and across frameworks, so a fact one agent learns is immediately available to all the others through one tool.

Does reading memory slow my agent down?

No. Glen retrieves relevant context and records new facts in a single round trip, so a memory call gives the agent the context it needs and captures what it learned without a separate retrieval and write step.

Shared memory for AI agents

An AI agent is only as good as what it remembers, and most agents remember almost nothing. The context window holds a single run, and the moment it closes, everything the agent learned, the facts it gathered, the decisions it made, the dead ends it hit, is gone. Every new run starts cold. Worse, when you run more than one agent, each is an island: what one agent discovers never reaches the others, so they relearn the same things and contradict each other. Glen, shared memory for AI agents, is the persistent, shared layer that fixes this, one MCP tool every agent reads from and writes to.

Connect Glen to any AI agent over MCP and it gains a single capability that retrieves relevant long-term context and records new facts in one round trip. At the start of a task the agent pulls what is already known so it does not begin from zero; as it works, it writes back the durable facts it produced. You do not build a vector store, an embedding pipeline, or a retrieval layer per agent, the memory is the tool, and reading and writing happen in the same call so the agent is never blocked waiting on a separate system.

What makes Glen different from per-agent memory is that the store is shared, not private. Memory is org-scoped, so every agent in the organization reads and writes the same knowledge: one agent learns a fact and every other agent has it next time, with no copying or syncing. That holds across frameworks too, because Glen is a standard MCP server, an agent in LangGraph, one in CrewAI, and a coding assistant all draw on the same store. The knowledge compounds with every run instead of resetting, so your agents get collectively smarter over time rather than each repeating the same cold start. Connect once over OAuth or an API key and let the memory grow.

FAQ

Why not just use each agent's built-in memory?: Built-in memory is usually private to one agent and one framework. Glen is shared across every agent in your organization and across frameworks, so a fact one agent learns is immediately available to all the others through one tool.
Does reading memory slow my agent down?: No. Glen retrieves relevant context and records new facts in a single round trip, so a memory call gives the agent the context it needs and captures what it learned without a separate retrieval and write step.