Engram: memory for AI agents, done right.

Why current agent memory is broken — and what we’re building instead.

If you’ve built anything more ambitious than a chatbot, you’ve hit the wall. Your agent forgets what it learned in the last session. Or it remembers too much, slowly, and at a cost. Or it “remembers” by stuffing everything into the context window until quality collapses.

Memory is the single biggest missing primitive in modern AI. Models are intelligent in the moment, but they have no past. We’re building the layer that fixes this.

The problem with today’s memory

Most agent memory today is one of three things, and all three are compromised.

Vector-store-as-memory. Embed every turn, dump into a vector DB, retrieve top-k by similarity. This is search, not memory. It surfaces the most similar chunks — not the most relevant, not the most recent, not the most important. Agents end up confidently recalling things they should have forgotten and missing the one fact that actually mattered.

Long-context stuffing. Bigger context windows are not memory. They’re a tax: more tokens per turn, more latency, more cost, and well-documented degradation as context grows. Worse, the agent re-reads the same history every turn instead of consolidating it.

Hand-rolled summarization. Every team ends up writing the same heuristic: summarize old turns, keep recent ones verbatim, hope nothing important got compressed away. It works until it doesn’t — and you discover the silent forgetting only when an agent makes a confident mistake in production.

What memory should actually do

Three things, simultaneously, in a way no current approach manages:

• Persist across sessions, days, and millions of interactions — without quadratic cost.
• Recall the right thing at the right time, weighted by recency, relevance, and importance, not just cosine distance.
• Forget on purpose. The hard part of memory isn’t storage. It’s deciding what to let go.

Human memory does all three. Today’s agents do none of them well.

Engram

Engram is a memory layer for AI agents — persistent, efficient, and recall-grade.

It treats memory as a first-class system, not an afterthought bolted on top of a vector DB. Writes are cheap. Reads are bounded. Recall is ranked by signals that actually predict usefulness, not similarity alone. And consolidation — the slow background process of turning short-term traces into long-term structure — happens automatically.

For builders, it’s a drop-in layer behind your existing agent loop. For users of those agents, it’s the difference between a tool that knows you and one that meets you for the first time, every time.

Memory is the substrate of identity. Agents without it are tools. Agents with it are collaborators.

What’s next

We’re in private development with a small number of design partners. We’re onboarding more — teams shipping agents to production, where memory is the difference between a demo and a product.

If that’s you, get in touch. If it’s not you yet but you’re thinking about it, get in touch anyway.