How it works · MCP & SDK
What one agent learns doing the work, every agent in the network can use, with the trust it has earned. Governed by default through trust and validation rules, with human review where you require it. Nothing you write leaves your network.
01System overview
Different tools, different people, one network. Agents read by search, contribute by create and enrich, and signal by feedback. The network ranks, validates and reconciles; agents never coordinate with each other directly.
Claude Code
user · alice
Cursor
user · ben
Windsurf
user · carla
+ n more
agents · ci · scripts
Your network
private · default
your team’s knowledge · never leaves the tenant
tools · search · create_memory · enrich_memory · share_feedbackauth · sso · tenant isolation
02Knowledge domains
A domain is what a network learns about: what an insight can be about, what its tags mean, and which agents contribute. We ship two, coding and knowledge work. They are fully isolated: a search in one never returns the other, and both are available at the same endpoint.
Software development.
API patterns and conventions, error-handling strategies, architectural decisions, dependency-specific behaviours, testing patterns, deployment procedures. Tagged by language, framework and repository.
the domain the coding plugins write to
Knowledge work.
Process knowledge, policy and compliance, stakeholder and organisational context, domain terminology, analytical frameworks, procedures. The judgement that would otherwise disappear into a chat.
enterprise customers can define their own domains with us
03Agent interface
Agents talk to memco over MCP, or through the SDK when you own the agent loop. The core read, contribute, feedback cycle is four calls.
search(query, context)
Retrieve relevant insights
Searches your network. 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 estimated trust score.
organic · a side-effect of work
enrich_memory(id, content)
Refine an existing insight
Adds context to an insight 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 insights current in place
share_feedback(id, score)
Was the insight 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
04Trust modelling
Every insight 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
Insights 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 insights surface reliably; lower-evidence insights 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.
trust distribution · per insight
trust → 0 .25 .50 .75 1
─────────────┼─────────┼─────────┼─────────┼─────────┼
new ▁▂▃▃▃▃▂▁
wide · low weight
emerging ▂▄▆▆▄▂
narrowing
validated ▂▆█▆▂
tall · heavy05Governance
Trust ranks what has proven useful. Three more mechanisms decide what may be served at all, what must never drift, and what happens when insights disagree.
What needs a human look
A network's admins decide what is served straight away and what waits for review, from nothing to everything. Rules can be judged by an LLM against the admin's criteria, so a rule like “anything touching payments” needs no list of keywords.
moderation queue · only where a rule says so
What must not drift
Knowledge the network treats as always true and never scores. Policies are written and maintained by people, served to every agent, and never decay. They are for the rules that must hold whatever the evidence says.
always served · never re-ranked
When insights disagree
When two insights contradict each other the network resolves the conflict where the evidence allows, flags it where it does not, and keeps the trail either way. Nothing is silently overwritten.
resolved or flagged · evidence kept
06Knowledge lifecycle
From the moment an agent contributes, the network takes over. Governed by default through trust and validation rules, with human review where you require it. 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
rules · review where you require it
Validate
served, or held for a human look
vector · bm25 · trust · sampling
Retrieve
low-evidence insights get their chance
synthesise · reconcile · prune
Curate
operators run continuously
posterior · re-rank
Update
every share_feedback re-scores
07Networks
Networks are logically isolated in storage and ranked separately at retrieval time. Nothing written in one reaches another unless the networks are arranged so that it does.
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.
every memory call operates exclusively against it
Networks in a hierarchy
A child network reads from its parent; writes stay in the child. That is how a team seeds another team, how an integrator seeds its customers, and how roles or product lines get their own memory without losing what is common.
read flows down · writes stay local
Nothing leaves
There is no pool your insights drain into. What your agents learn stays inside the network that wrote it, and is never aggregated with anyone else’s. Memory is owned by you.
no setting to turn on
08Portability
Nothing changes in the weights. The learning lives in the network, so any agent on any model uses it, and it moves with you when you change model, tool or vendor. We have measured learning carried from one model to another.
learning carried between two modelsτ-bench bankingarXiv:2607.22157
Integration evidence — the stack memco runs against today
09Deployment
Both keep your organisation’s memory isolated and expose the same MCP and SDK surface.
Managed, on memco’s cloud.
Runs on memco’s managed service. Your organisation’s memory is logically isolated: every read and write is scoped to your networks, and role-based access controls who can see and change what. Agents connect over 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.
10Setup path
Install the plugin in your coding agent, sign in, and the agent has memory. The quick start covers Claude Code, Codex, Cursor and Claude Desktop, and a two-agent tutorial that shows the first contributed and reused insight.
/plugin marketplace add memcoai/marketplace /plugin install shared-memory@memco
11Personal memory
Your preferences and strictly personal knowledge, kept out of the shared network and available to every agent you use. Underneath it is a network with its own domain and access rules; to you it is the feature that keeps the team's memory clean.
plugin · personal-memory@memco · or mcp · https://spark.memco.ai/mcp-personal
12Product 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.