For a small business in Russia choosing between n8n and Make, n8n wins when you need AI agents that remember customers, documents and past decisions - and the new n8n + Cognee integration is what makes that memory practical. Make is fine for linear if-this-then-that; n8n is the right tool when the workflow needs an LLM agent with state that survives across sessions.
In a fresh write-up on the n8n Blog, Cognee's Veljko Kovac explains why every LLM call starts from zero and how to fix it. Short-term memory holds the running session; long-term memory lives in an external store the agent writes back to - vector DB, graph, or a hybrid - and retrieves before each call. The combo turns one-shot chatbots into agents that recall user preferences, past tickets and outcomes.
What this changes for a small business
Concretely: a support bot stops asking the client their order number for the fifth time, a sales agent remembers that this customer wants EU figures in euros, a document pipeline knows which contract clauses already failed review last quarter. You can wire Cognee as a shared "company brain" node and query it from any workflow - a pattern that fits naturally on top of a tender analysis pipeline I built, where every new RFP benefits from lessons learned on the previous hundred. Cost is modest: a small Postgres or Qdrant instance plus embedding calls, usually under a few dollars a month at small-business volumes.
Why it matters: Memory is the difference between a demo agent and one that actually earns its keep in week two - plan the storage layer from day one, not after launch.
Source: n8n Blog