Persistent Agent Memory Should Make the Next Task Easier

How we think about continuity, correction, and boundaries at MemTensor

Suppose a team discovers on Tuesday that integration tests fail unless the database is started first. On Friday, another agent picks up a related task. It needs the confirmed setup procedure, including when it applies. When the team changes that procedure, the stored instruction needs to change with it.

That is the experience we are building toward with MemOS: useful knowledge that carries over between tasks and stays open to correction.

A large context window is not a memory policy

A larger context window gives an agent more material to read. The application still needs to decide what to retain, who can reuse it, and how to handle updates.

In the testing example, the failure is an event. The confirmed setup sequence is a reusable procedure, with the relevant environment and conditions attached.

MemOS Cloud organizes memory by type so these records can serve different purposes. Event memories retain details such as when something happened; skills capture reusable task steps. Facts and preferences provide context about the user. You can control which categories are generated and which are included in a search.

Continuity is a four-step loop

Capture. Through Add Message, MemOS Cloud extracts memories from conversations, checks for duplicated or conflicting information, and updates existing records when needed. You can attach custom metadata, such as the source and environment, using info. For Tuesday’s test run, submit the discussion that confirms the setup sequence and identifies where it applies. The metadata also gives your application fields to filter on later.

Retrieve. Search Memory combines keyword and vector retrieval, ranks memories by relevance, and removes duplicate results. Filters narrow the results by tags, time, Agent, or custom metadata. Memory-type selection lets you include skills, while a result limit controls how many memories are returned. For Friday’s task, retrieve the relevant testing instructions and pass them to the model with the current request.

Correct. Suppose the team introduces a script that starts the database automatically. If you know which record needs changing, Update Memory lets you edit it by ID. With natural-language feedback, a user can describe the new procedure, and MemOS locates and updates the relevant memories. For changing facts and preferences, our time-awareness mechanism maintains historical versions and uses time cues in a query to distinguish current information from earlier states.

Stop using or delete. An outdated procedure can be archived through the update API. Delete Memory supports removing specific memories or clearing a user’s memories within the API key’s project. Include any copies kept in application caches or summaries in your deletion workflow.

Sharing needs an owner

Before sharing a procedure across agents, decide who maintains it and which users or projects should be able to use it.

MemOS Cloud’s isolation controls separate memory by project and user. Agent filters narrow a user’s records to a particular Agent; enabling independent Agent memory gives that Agent its own long-term memory across users and conversations. Map user_id values to authenticated users in your backend. A conversation_id influences retrieval context and does not enforce access control.

For shared setup guides, our knowledge bases turn uploaded documents into searchable memory that can be retrieved alongside user memory. A task can then combine the team’s documented procedure with context from earlier interactions.

In supported agent hosts, MemOS Local Plugin stores memory in a local SQLite database and connects capture and retrieval to the host’s task lifecycle. It records conversations, tool calls, and feedback after a task, then supplies relevant context before the next one. Recurring patterns can develop into reusable skills.

To check this in your application, use three sessions: establish the procedure, have an authorized agent apply it, then change it and ask again. Repeat the request from a different project and after deletion. Inspect the retrieved memories and the context passed to the model, alongside its answer, to spot stale instructions or scope errors.