memco turns what worked, what failed and what changed into memory you own, carrying across models, tools and teams. Each agent starts with what you already know, within boundaries you control.
Start with
Your own private space. Bring any agent — what you solve once, your next session already knows, whichever tool you open it in.
Trusted by engineers at
Agents learned from corrections without retraining and achieved 2.6× the task success of static retrieval. Read the research ↗
Works with your agent stack — model-agnostic, IDE-agnostic, harness-agnostic
Independently audited by CompAI
The memory loop
A run produces a trace — a fix, a dead end, a human correction. Review promotes the useful part to a scoped, owned lesson.
The next agent recalls it before it starts spending tokens. In our benchmark, agents learned 70% more from each other's memory than from their own experience.
cursor · opus
Agent run
fix · dead end
Trace
scoped · owned
Reviewed lesson
org scope
Shared memory
01Starting cold
A new run rediscovers repo quirks, failed paths and human corrections. The agent may look smart, but the company gets no smarter.
Every session pays the cold-start tax again — in tokens and in senior time.
09:14 read AGENTS.md 09:14 fetch internal docs · 6 calls 09:17 staging auth 401 · retry 09:21 ask #platform · waiting 09:36 fix found · rotates nightly 09:36 session ends · lesson lost
Why it matters
A context window is a tenancy: it resets when the session ends and you pay for it again tomorrow. A reviewed lesson is an asset your organisation owns, scopes and retires on its own terms.
Expires when the session ends. You pay rent again tomorrow — in tokens and in senior time.
Yours to review, edit and retire. It survives tool changes, vendor changes and re-orgs.
Cheaper runs at steady stateFewer repeated failuresLess senior re-teachingMemory that outlives the tool
02Compounding
When a similar task shows up, the agent gets the prior fix, warning or decision before it burns tokens rediscovering it.
Waste goes down as trusted memory gets richer. The shape is the point, not a promised rate.
Where Memco sits
Most memory products are a store plus a retriever. The hard part in a company is not remembering — it is deciding what deserves to be remembered, who may reuse it, and when it stops being true.
| Capability | Memco | Mem0 | Supermemory | Letta | Cognee |
|---|---|---|---|---|---|
| Model-agnostic | |||||
| Self-host or VPC | |||||
| Shared memory across agentsone org, many harnesses | |||||
| Review before reusehuman approval gate | — | — | — | — | |
| Provenance on every lessonrun · reviewer · scope | — | ||||
| Retirement as a first-class action | — | — | — | — | |
| Scope boundariesteam / org / public commons | — | ||||
| Published benchmarks | — | — | — |
Compiled from public documentation in July 2026 and not yet verified with each vendor. Cells marked “not published” mean we could not find a public answer — not that the capability is missing. Correct us and we will update the table.
Where it applies
Memco started where the pain is loudest: engineering teams running agents against the same repositories every day.
The mechanism is not language-specific. Any team whose agents rediscover the same facts can capture the lesson once and own it instead.
Control · 04
Teams decide what becomes memory, who can reuse it and where it can run. Public commons and private company memory are separate objects, never a blended pool.
12 repos
Team runs
scope: team
Reviewed lesson
rbac
Private memory
Research & journal
Questions
Lessons, not repositories. A lesson is a short, scoped statement — a fix, a constraint, a decision — with the run it came from, the person who approved it and the scope it applies to.
Start with the memory gap
Free to start · no credit card · running in two minutes
the loop
benchmarks · product · research
A short dispatch on shared memory for AI agents — the numbers behind the product, what we're shipping, and the research we're reading. No filler.