glossary · What it keeps

Agent memory

definition

Agent memory is persistent, retrievable knowledge that carries across AI sessions: what past runs learned, what was decided and why, and where things live, recalled before acting rather than rediscovered.

in one line: What past runs learned, recalled before the next one acts.

in context

Where does agent memory fit?

Look first. Work. Keep what you learned. Every run asks before it acts and saves on the way out. The next run starts ahead.

recallask the brain
workright files
decideapproval
capturememory + reason
next runstarts ahead
two ways to say it

What is agent memory, in plain words?

Same idea at two depths: the plain version, then what the engine actually does.

Every new chat starts from zero. Memory keeps what your team decided, and where things live, so the next chat starts knowing it.

Teach it once. Every AI remembers.

for engineers

Memories (the what) carry importance from 1 to 10. Recall ranks by meaning, importance, recency and use. Routes (the where) map files and docs.

context_query returns routes, memories and related tasks in one call. Forgetting is soft and can be undone.

the longer answer

Why does agent memory matter?

It is distinct from two things it is often confused with. Context windows are working memory: everything in them evaporates when the session ends. And retrieval over documents (RAG) recalls what the organization wrote, but not what the agents themselves learned by doing the work.

Useful agent memory splits WHAT from WHERE. Knowledge (a decision, a correction, a learned constraint) ranks by relevance, importance, and recency. Location (which file, which module, which doc) is a map that spares the agent re-exploring territory it has already covered.

The capture side matters as much as recall: memory written automatically from everything becomes noise, and memory that depends on someone remembering to save it becomes empty. The durable pattern wires capture into the process itself, at the steps where decisions actually happen.

Memory also needs upkeep to stay useful. Knowledge goes stale: a decision is reversed, a file moves. A good memory system lets the next run correct an entry rather than add a contradicting one, and lets a forgotten entry come back when the forgetting was a mistake. Without that upkeep, the same question is answered again and again, and the answers drift apart.

in convops

How does agent memory work in ConvOps?

In ConvOps, memories carry a category and an importance from 1 to 10, and recall ranks them by meaning, importance, recency and use. Routes map where things live: files, modules, docs. One call returns the relevant routes, memories and related tasks together, so the agent looks first and acts second. A memory can be captured with the task step that produced it, recall spans every workspace you belong to, and forgetting is soft and can be undone. A route points the agent at the right file before it starts searching.

questions

What do people ask about agent memory?

What is the difference between agent memory and a context window?

A context window holds what the model sees in one session and is lost when the session ends. Agent memory persists across sessions: decisions, corrections and constraints that later runs recall. Memory feeds the context window the parts worth knowing.

Is agent memory the same as RAG?

No. Retrieval-augmented generation (RAG) retrieves from documents the organization wrote. Agent memory stores what agents learned by doing the work, such as a fix that worked or a rule a reviewer added. Many systems use both.

Can agent memory be shared across a team?

Yes, when it lives outside each person's client. In ConvOps memory belongs to the workspace, so every member's agent recalls it over MCP, and recall spans every workspace you belong to. Forgetting a memory is a soft delete that can be undone.