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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11REXA’s memory implementation, as described by its author Subhamoy Datta, saves information when a user explicitly asks the agent to remember it. The CLI validates the text and sends it to a backend, which authenticates the user, generates embeddings, and stores the data in PostgreSQL with pgvector. At the time Datta published his account on September 17, 2026, the save path was implemented but memory retrieval was not.
What “memory” means in REXA’s implementation
In Datta’s account, memory begins with a direct request such as “Remember that I prefer PostgreSQL for my backend projects.” The agent does not silently save every preference or capture every conversation. Instead, the CLI can invoke a save_memory tool in response to an explicit request.
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This is a save pipeline, not complete long-term recall: storing information does not mean the agent can later find or use it. Datta stated that retrieval had not yet been implemented when he published the article.
How a memory moves from the CLI to storage
1. The CLI validates the requested text
Before sending a memory, the CLI trims the text, rejects an empty value, and enforces an 8,192-character limit. Datta describes this as an implementation limit; it is not presented as an external standard.
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2. The CLI sends an authenticated request
The CLI sends a POST request to the REXA backend. Datta’s example endpoint is https://rexa-server.onrender.com/api/cli/memory. The request includes the memory text and a bearer token, but does not include a user ID.
3. The backend determines ownership
The backend checks the bearer token and associates the memory with the identity authenticated by that token. Datta’s stated rationale is to prevent a client from selecting another user’s identity by supplying a user ID in the request body.
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4. The backend processes and stores the memory
Datta assigns processing to the backend, including chunking, batching, and generating embeddings. His article does not identify the embedding model, chunk size, batch size, or embedding performance, so those implementation details cannot be inferred from the account.
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The storage stack he names is PostgreSQL, pgvector, and Prisma. The illustrative record contains a user identifier, text, embedding, and creation time, though Datta notes that the exact schema may evolve. The CLI therefore handles interaction and the save-tool call; it does not connect directly to PostgreSQL.
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5. The backend returns a save confirmation
The example success response contains success: true and the message Data saved in memory. That confirms the described save operation, not that the saved item can already be recalled.
Why embeddings do not establish that recall works
pgvector provides a way to store and work with vector representations, but the presence of embeddings in storage is not proof of a completed retrieval feature. A working recall path also needs to query stored memories, determine which results are relevant, and make those results available to the agent. Datta’s article describes similarity or relevance-based retrieval as a later stage and reports no completed recall path, retrieval evaluation, or results.
Datta makes the status explicit: “The important distinction is that REXA does not currently retrieve these memories yet.” This describes the project at the article’s publication; it should not be read as confirmation of its status on a later date.
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What the design separates—and what remains unknown
- User control: saving follows an explicit request rather than automatic conversation-wide capture.
- Identity: the backend derives ownership from the authenticated bearer token instead of trusting a user ID supplied by the client.
- Responsibilities: the CLI handles agent interaction and input validation; the backend authenticates and processes requests; PostgreSQL and pgvector hold the described text and vector representation.
- Unspecified implementation details: the article does not name the embedding model or provide chunking parameters, performance data, costs, or accuracy results.
- Capability boundary at publication: Datta describes saving memories, not retrieving them for later conversations.
These are claims from Datta’s first-person implementation account, not independent code review or production validation. The publication date is September 17, 2026; the article does not establish whether the project’s capabilities changed afterward.
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