Does Cognee offer an MCP server?

Yes, Cognee provides an MCP server in addition to its core library. The difference is that Cognee is a memory engine you ingest data into and operate, while Glen is a single MCP tool that provides org-shared memory with no pipeline to build.

Do I have to build a pipeline with Glen?

No. Glen captures observations from the conversation automatically and routes them into its memory store for you, whereas Cognee centers on an ECL pipeline you set up to ingest and structure your own data into a knowledge graph.

Glen vs Cognee

Glen and Cognee both give AI agents memory, but they differ in how the memory is built and who it serves. Cognee is an open-source memory engine that turns raw data into a knowledge graph through its ECL (Extract, Cognify, Load) pipeline, backed by a vector store and a graph database, and plugs into agent frameworks in a handful of lines of code — it is a developer library you ingest data into and query. Glen is shared memory for AI agents delivered as a single MCP tool, scoped to an organization so every agent on a team reads from and writes to the same store, with no pipeline to build. This comparison contrasts them honestly on scope, integration, MCP support, and the open-versus-managed tradeoff.

How they compare

Dimension	Glen	Cognee
Memory scope	Org-shared by default: every agent in the organization reads and writes one store, so a fact one teammate's agent captures is available to all.	A memory engine you instantiate over your own data; sharing across a whole team is an architecture you build on top of it.
How memory is built	Observations are captured from the conversation automatically as the agent works, then routed into shards behind the scenes.	An ECL pipeline ingests data from many sources and structures it into a knowledge graph with embeddings and typed relationships.
Integration model	MCP-native single glen tool any MCP client calls; no code or pipeline to write.	A developer library (a few lines of code) that plugs into frameworks like the Claude Agent SDK, LangGraph, and others; it also offers an MCP server.
Open vs managed	Managed, MCP-native shared memory you connect to and provision at the org level.	Open-source engine you run and operate, with the control and the operational ownership that implies.

Verdict

Cognee is a powerful fit when you want to build structured, graph-based memory over your own data: its ECL pipeline and knowledge graph give rich, queryable relationships, and being open source it puts you in full control. But if your goal is simply for a whole team's agents to share what they learn — without building and operating a pipeline — Glen is purpose-built for that. It delivers org-scoped memory as one MCP-native tool, captures observations from the conversation automatically, and pairs read and write in a single round trip, with new teammates inheriting the org's memory. Choose Cognee to engineer a knowledge graph you own; choose Glen when you want shared, low-setup memory for your organization's agents.

FAQ

Does Cognee offer an MCP server?: Yes, Cognee provides an MCP server in addition to its core library. The difference is that Cognee is a memory engine you ingest data into and operate, while Glen is a single MCP tool that provides org-shared memory with no pipeline to build.
Do I have to build a pipeline with Glen?: No. Glen captures observations from the conversation automatically and routes them into its memory store for you, whereas Cognee centers on an ECL pipeline you set up to ingest and structure your own data into a knowledge graph.