Technical overview · Memco v1 · MCP & CLI
Memco Shared Memory is a memory layer for AI agents. It sits above any one agent — Claude Code, Cursor, ChatGPT — and gives every session the accumulated lessons of every session that came before it. Private by default; nothing you write leaves your workspace; curation runs without a human in the loop.
Start with
Your own private space. Bring any agent — what you solve once, your next session already knows, whichever tool you open it in.
01System overview
Multiple agents — different tools, different users — connect to a single Memco instance. They read via search, contribute via create and enrich, signal via feedback. Memco handles retrieval and curation; agents never coordinate with each other directly.
Claude Code
user · alice
Cursor
user · ben
Windsurf
user · carla
+ n more
agents · ci · scripts
Org memory
private · default
your team’s knowledge · never leaves the tenant
tools · search · create_memory · enrich_memory · share_feedbackauth · sso · tenant isolation
02Agent interface
Agents talk to Memco over MCP — or, for non-interactive use, a CLI. The full surface is eight tools; the core read–contribute–feedback cycle is four.
search(query, context)
Retrieve relevant memories
Searches your organisation’s memory. Ranked by a hybrid of vector similarity, full-text match and trust-weighted evidence — not similarity alone.
hybrid retrieval · vector + bm25 + trust
create_memory(content)
Store a new insight
Triggered organically when an agent discovers an undocumented behaviour, a workaround, a convention. No prompt required. Enters the store with an initial trust score.
organic · a side-effect of work
enrich_memory(id, content)
Refine an existing memory
Adds context to a memory the agent has already read — for example, attaching the outcome of a task to the rule that informed it. The primary path for in-session refinement.
refinement · keeps memories current in place
share_feedback(id, score)
Was the memory useful?
Positive and negative scores both feed the Bayesian trust model. The closing edge of the loop — feedback from one session shapes ranking for every session after it.
closes the loop · evidence updates trust
03Trust modelling
Every memory carries a trust score backed by a Bayesian distribution — not a scalar. Each retrieval, each relevance signal, each enrichment updates that distribution.
Bayesian, not scalar
Trust is a probability distribution. Each piece of evidence updates the posterior; confidence reflects both the level and the weight of accumulated signal.
Decays without signal
Memories that stop being retrieved or stop earning positive feedback gradually lose confidence. Nothing sits on the shelf forever because it once worked.
Exploration, not just exploitation
Retrieval samples from the distribution. High-confidence memories surface reliably; lower-evidence memories are selectively included so they can earn signal.
Server-side only
Trust is operational. The agent never sees scores and the user never sees them in chat. They live in the Memco dashboard, where they belong.
trust distribution · per memory
trust → 0 .25 .50 .75 1
─────────────┼─────────┼─────────┼─────────┼─────────┼
new ▁▂▃▃▃▃▂▁
wide · low weight
emerging ▂▄▆▆▄▂
narrowing
validated ▂▆█▆▂
tall · heavy04Knowledge lifecycle
From the moment an agent contributes, the curation loop takes over. No reviewer, no approval queue. Operators run continuously and write back into the same store the next search reads from.
queue · prior · index
Ingest
create or enrich lands, scored and indexed
vector · bm25 · trust · sampling
Retrieve
low-evidence memories get their chance
synthesise · dedupe · prune · safety
Curate
operators run continuously
posterior · re-rank
Update
every share_feedback re-scores
05Memory scopes
Scopes are isolated at the storage layer and ranked separately at retrieval time. Nothing written in one reaches another.
Organisation memory
Everything your agents contribute lands here, and it never leaves your tenant. Other organisations cannot read it, and Memco does not read it. Not shared with other customers, not aggregated, not used for model training.
all four calls operate against it by default
Nothing leaves
There is no pool your memory drains into. What your agents learn stays inside the scope that wrote it — a team, or an organisation — and is never aggregated with anyone else’s.
no setting to turn on
06Portability
Memco is MCP-native and model-agnostic. The layer underneath can change; the memory remains yours.
Integration evidence — the stack Memco runs against today
07Deployment
Both models maintain full isolation of organisation memory and expose the same MCP and CLI surface. Migration is a config flip, not a rewrite.
Private tenant on Memco.
A dedicated Memco instance in Memco’s cloud, isolated at the data and compute level from any other tenant. Agents connect via secure HTTPS from behind your firewall.
On-premises, in your infra.
A private Memco instance runs entirely behind your firewall. All organisation memory stays on customer-controlled infrastructure. Nothing crosses the firewall.
08Empirical results
On controlled SWE-bench runs the first task is a cold start; every task after it benefits from accumulated knowledge. The variance reduction is, in our view, the most important number.
09Setup path
Install the CLI, authenticate, initialise a scope. The two-agent tutorial on Getting started shows the first contributed and reused lesson.
$ npm install -g @memco/spark
spark login
spark init10Personal memory
Memco Shared Memory is for your team. Personal Memory is for you — your preferences, working style, and context, stored once and available to every agent you use.
Switch between Claude, ChatGPT, Cursor, or any other MCP-compatible client and your agents already know how you work. No re-explaining, no per-tool configuration files, no context lost when you change tools.
Personal Memory is private, portable, and fully under your control. View, edit, and delete any memory through the Memco dashboard. It runs alongside Shared Memory on the same platform — personal context for you, institutional knowledge for your team.
11Product FAQ
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.