evermind vs innernet

Read evermind's headline carefully: "Give Your AI Agent Self-evolving Memory." The possessive is the whole comparison. The memory belongs to the agent. You appear inside it as an entity the agent has learned to handle well.

innernet inverts the sentence. The memory belongs to the person, and the agent is the thing that gets read access.

Both designs are defensible. They produce entirely different products, and the difference shows up the moment you stop using one particular agent.

EverOS — memory infrastructure that turns stateless models into agents that hold context across days, sessions and platforms. Three pieces do the work: mRAG handles multimodal retrieval across PDFs, images, spreadsheets, decks, emails and URLs through one API; agent trajectories are recorded as Cases and distilled into reusable Skills; and a Memory Bank gives a transparent view of user, group and agent memory. Temporal tracking separates current facts from outdated ones. Available as a managed cloud service or self-hosted under Apache 2.0.

The self-evolution loop is the interesting part and it's aimed squarely at making agents better at tasks over time — they "improve over time instead of starting from scratch."

A personal memory layer that lives outside every tool. npx innernet connects Claude Code, Cursor, Codex, Windsurf, Gemini CLI, Claude Desktop, VS Code, Cline, Zed and Continue to the same memory. netti — one per person, never the same twice — files what accumulates into Context Maps: dimensions that read as living documents, nodes for recurring people and decisions, and a version history you can branch and roll back.

What evolves is not an agent's skill set. It's the picture of you.

evermind distils what worked — a trajectory becomes a reusable procedure. That's an operational asset and it compounds beautifully for repetitive agent work. innernet distils what's true about you and when it stopped being true — a decision, its reasoning, its sources, and the later decision that overturned it. One produces a better worker. The other produces a legible person.

Memory attached to an agent has the lifespan of that agent. Frameworks get replaced, models get deprecated, the team switches harnesses in a sprint. A memory whose subject is you has no such expiry, because you don't get deprecated. This is why innernet connects by URL and refuses to be a chat client, an IDE or a model of its own.

evermind separates current facts from outdated ones — sensible, and necessary for correctness. innernet keeps both readable, because "outdated" and "changed my mind" look identical to a system and mean opposite things to a person. The version you moved away from is the evidence that you moved.

• ·Multimodal ingestion through one API. Spreadsheets, decks, images and scanned documents are a real gap for us.
• ·Self-hosting under Apache 2.0. A serious answer for anyone who won't put memory in someone else's database.
• ·The Cases → Skills loop. For agents doing the same class of task repeatedly, distilling procedure is the right abstraction and we don't do it.
• ·Built for multi-agent systems. Group and agent memory scopes are a thoughtful design for a problem we've deliberately not taken on.

• ·The memory survives the agent. It's keyed to you and connected by URL, so switching tools costs nothing.
• ·Something reads for insight, not just for reuse. netti writes conclusions across sessions that no single session contained.
• ·Per-fact disclosure, enforced at the read surface. Private and self-only facts never leave your Self Map, even with an AI connected.
• ·A Self Map above every project. The cross-project layer that says something about the person, not just about the work.

Use evermind if you're building an agent or a multi-agent system and you want it to get measurably better at its job over time.

Use innernet if you're a person who uses several AI tools and you want all of them to understand your work — and to keep understanding it after you've swapped half of them out.

"Their agent memory is self-evolving. Isn't that strictly more advanced?"

It's more advanced at a different thing. Self-evolution there means an agent refines its own procedures. It doesn't mean anything has formed a view of you — and an agent that has mastered your codebase's conventions still doesn't know why you rejected the architecture you rejected, or that you changed your mind about it in June. That's the memory we're building, and it can't be distilled from trajectories because it was never in a trajectory.

Accurate as of 3 September 2026, based on evermind's public site. Their benchmark figures are theirs and we haven't reproduced them. If we've got something wrong, tell us and we'll correct it.