ONTO vs Mem0: Choosing a Memory Layer for AI Agents

Mem0 has built one of the more interesting dedicated memory layers for LLMs in the past year. If you’re shopping for “agent memory” as a standalone primitive, it’s a serious option.

ONTO takes a different shape: an agent SDK where typed memory (UPO + SMO) is one of the runtime primitives, alongside Plan Mode, Policy Guard, and the rest. You can’t fully separate them.

This post compares them as memory layers and is honest about when each is the right choice.

What Mem0 ships

• A standalone memory layer with REST, Python, and TS SDKs.
• Automatic fact extraction from conversations.
• Categorization and dedup.
• Vector + graph hybrid storage.
• Hosted cloud option and self-hosted option.

Mem0 is essentially “add memory to your existing agent” as a service. You bring your own agent loop and call Mem0 for writes and reads.

What ONTO ships

• UPO + SMO typed memory with provenance, decay, confidence, and consent scopes.
• Four-level extraction routing (session / profile / domain / tool).
• Conflict detection and resolution via Plan Mode tasks.
• Memory is one component of a broader agent runtime that also includes Plan Mode, Policy Guard, async writes, and cost meter.

ONTO is “the whole agent SDK.” Memory is built in but not separable from the rest.

When Mem0 makes more sense

• You already have an agent framework you like. If you’re committed to LangChain or a custom loop and just need a memory store, Mem0 slots in cleanly. ONTO would mean migrating the whole agent runtime.
• Memory is your only structural concern. No regulated industry pressure, no multi-tenant SaaS billing requirements, no Plan Mode requirement.
• You want a hosted memory service. Mem0 has a managed cloud; ONTO is self-hosted (your Postgres, your Sled).

When ONTO makes more sense

• You’re starting fresh. Building an agent SDK is a big bite. ONTO gives you the whole runtime with memory included, not bolted on.
• You need consent scopes on tool calls, not just memory writes. Mem0 handles memory access control, but the consent question for tools sits at the agent layer.
• You need Plan Mode. Mem0 doesn’t gate execution; ONTO does.
• You need multi-tenant cost rollup. ONTO’s cost meter is built around tenants; Mem0 bills you for memory but you do the multi-tenant attribution yourself.
• You need on-prem with open-weight defaults. Both can run on-prem; ONTO’s defaults are open-weight, Mem0 typically pairs with OpenAI/Claude.

The architectural difference

The fundamental difference is boundaries.

Mem0’s boundary is “memory layer below the agent.” Your agent code holds the loop; Mem0 holds the facts. This is a clean separation. It also means the agent layer makes its own choices about Plan Mode, consent scopes, and audit — Mem0 doesn’t have an opinion.

ONTO’s boundary is “the runtime owns the agent loop, the memory, the consent enforcement, and the audit trail.” This is a larger commitment but produces stronger guarantees. The same consent scope that gates a memory write gates a tool call. The audit log threads through all of it.

Neither boundary is wrong. They’re suited to different teams.

Performance and scale

Both can scale to millions of users / assertions when run on Postgres. Mem0’s hybrid vector + graph store is good for the “find related memories” query; ONTO’s typed-by-default approach is good for the “what is true about X” query. As covered in the typed memory vs embeddings piece, these are different shapes of question.

For the typical agent workload, you want both: ONTO-style typed lookups for facts, vector retrieval for content. ONTO ships both internally; with Mem0 you’d pair it with a document index like LlamaIndex.

The interop question

Can you use Mem0 inside an ONTO agent? Yes — wrap Mem0’s API as ONTO tools. The consent scope mapping is straightforward. This makes sense if you’ve already standardized on Mem0 in your org and want to add ONTO’s runtime guarantees on top.

Can you use ONTO’s memory inside a Mem0-style architecture? Less clean today, because ONTO’s memory APIs assume the broader runtime. We’re working on better separability for a 1.0.

A simple decision flow

Just need a memory layer to drop into your existing agent? → Mem0.
Need consent scopes on tools, not just memory?              → ONTO.
Need Plan Mode?                                             → ONTO.
Want a hosted service to reduce ops burden?                 → Mem0 cloud.
Need on-prem with open-weight defaults?                     → ONTO.
Building from scratch and want fewer moving parts?          → ONTO.

The bigger point

The right question isn’t “ONTO or Mem0.” It’s “what’s the right boundary for memory in my system?”

If you draw the line at “memory is a service my agent calls,” Mem0 is well-suited. If you draw the line at “memory is one primitive in a runtime that also handles consent, Plan Mode, and audit,” ONTO is well-suited. Both lines are defensible.

Both teams are making the agent ecosystem better. Pick the one whose line matches yours.

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The features page covers ONTO’s typed memory model in detail. The thesis post on why agent memory is broken explains the underlying argument for typed memory in any agent system.

Frequently asked questions

Can I use ONTO's memory without using the rest of ONTO?

Today, partially. The memory APIs (UPO writes, queries) are accessible without the full agent loop, but they're tightly tied to the runtime that enforces consent scopes. We expect to split this out further pre-1.0; track the issue on GitHub.

Does Mem0 have plan mode or consent scopes?

Mem0 is a memory layer, not an agent runtime. It doesn't ship plan mode or per-call consent scopes — those are concerns of the layer above the memory store.

Which has better memory quality?

Both extract typed facts from conversations with reasonable quality. ONTO's four-level extraction is more explicit about scope (session vs profile vs domain vs tool); Mem0 leans on automatic categorization. For most apps the difference is small.