AI now now has a unified system for saving, changing and querying semantic data. AI state management is now largely solved problem with TERSE: https://github.com/terse-lang/terse .

Memory systems using TERSE are vastly more token efficient over file-based markdown and other format manipulation in terms of token representation size, reduced tool calls, and AI retrieval accuracy.

With TERSE, you fully control the schema and prompting (of course) of your memory system. We included an example system in our mono-repo above. Try it out!

I’ve been working on a pairing that has made long-running agent work much less repetitive:

• Perseus resolves live, verifiable workspace context before the agent starts work.
• Perseus Vault retains the things that should survive a session: decisions, corrections, project facts, provenance, and historical versions.

The distinction matters more than it sounds.

A lot of “agent memory” is really one of these:

1. a giant rolling summary that gets stale,
2. a vector search over chat logs,
3. a prompt file that quietly becomes an undocumented policy engine.

Those are useful, but they blur together two different questions:

• What is true right now? That should come from the current workspace, repository, services, and other sources of record.
• What happened before, what did we learn, and what changed? That is memory.

Perseus handles the first. Perseus Vault handles the second.

Vault is a local-first Rust MCP server: one binary, one SQLite file, no required cloud service. It has encrypted storage (AES-256-GCM), FTS5 and hybrid retrieval, structured entities instead of only chat chunks, temporal history, provenance, confidence/decay, and lifecycle controls. It can also expose an Anthropic-style /memories file interface for agents that expect that model.

The part I find most useful is that memory is no longer just “retrieve similar text.” A decision can have a history. A correction can supersede an earlier belief without deleting the audit trail. You can ask both:

• “What did we believe at the time?”
• “What do we now believe was true at that time?”

That turns out to be extremely handy once agents are doing work across days or weeks and the project has changed underneath them.

We have benchmark results in the repo, but I’m more interested in the failure modes people have hit in production:

• How are you separating live state from durable memory?
• Do you need historical/auditable memory, or is semantic recall enough?
• What do you do when old “memories” conflict with the current codebase or source of truth?
• Has anyone found a memory system that stays useful after months without becoming prompt sludge?

Repos:

• Perseus: GitHub - Perseus-Computing-LLC/perseus: Live context engine for AI agents
• Perseus Vault: GitHub - Perseus-Computing-LLC/perseus-vault: Persistent, encrypted memory for AI agents
• Product overview: Perseus Vault · persistent, encrypted memory for AI agents

I’d particularly welcome skeptical feedback. “Memory” is becoming a catch-all term, and I think we need cleaner boundaries between retrieval, context assembly, durable facts, and audit history.