Use case · engineering teams

One agent learns.
Every engineer ships faster.

Memco turns fixes, failed paths, repo quirks, review comments and human corrections into governed shared memory for every coding agent on your team. Same tools, same repos, same models — with a team memory layer that compounds from the first rollout.

Start a team trial Install Spark

works with claude code · cursor · codex · copilot · windsurf · mcp

01The cold-start tax

Their learning still dies in the session.

A senior engineer corrects a bad assumption. An agent discovers a weird CI failure. A migration finally works after three failed approaches. Then the next agent starts cold and pays the same tax again.

Without shared memoryevery agent relearns the codebase

Engineer A · Claude Code · session 01: ↻ context reload · ↻ failed path · session ends
Engineer B · Cursor · session 02: ↻ context reload · ↻ same failed path · session ends
Engineer C · Codex · session 03: ↻ context reload · ↻ same review comment · session ends

context gets one agent through one task

With Memcoone run teaches the next

Engineer A’s run captures the lesson: fix · failed path · review preference
Memory is scoped, trusted, governed: capture → score → scope → decay
Engineers B and C start ahead: different person · session · tool

memory makes the team better on the next one

fig. 01 — three real runs, three tools, the same lesson

02The mechanism, concretely

Different person. Different tool. Reused lesson.

What actually travels between runs is a governed lesson — not a transcript, not source code.

run 412 · engineer a · cursortue 09:14repo · payments

task: fix CORS failures
  on /v2/webhooks
attempt 1: wildcard … rejected
attempt 2: per-route … passes
human: "gateway strips
  wildcards — per-route only"
→ lesson contributed

fix · cors-headersscope · teamtrust 0.86used 14×

Set Access-Control-Allow-Origin per-route. The gateway strips wildcard headers on every service behind /v2.

run 412 · cursorcorrection · humanteam scope

run 519 · engineer b · claude codethu 15:02same repo · new session

task: webhooks time out
  on /v2/exports
recall: fix · cors-headers
  (trust .86)
→ skips wildcard entirely
→ per-route, first try
task done · 0 repeated
  dead ends

fig. 02 — the activation event: a lesson contributed on one task, reused on a second

03Where it lands first

Four places shared memory changes the week.

Eight day-to-day engineering jobs, collapsed into the four that matter on a first rollout.

case 01onboarding · platform eng · internal apis

Onboarding & internal APIs

Problem. New engineers and new agents waste days on undocumented conventions, owners, setup quirks and private frameworks the model has never seen.

With Memco. Agents inherit repo-specific memory: setup commands, architecture notes, internal SDK quirks, owners, known bad paths.

case 02ci · build · qa

Recurring defects & CI

Problem. The same flaky tests, dependency issues and environment failures get debugged again and again — by different people, in different tools.

With Memco. Once a fix works, the next agent sees the warning before it retries the same dead end.

case 03platform · frameworks · long runs

Refactors & migrations

Problem. Framework migrations and large refactors are full of local exceptions and half-remembered decisions; long runs drift and repeat failed strategies.

With Memco. Migration scars, working patterns and forbidden approaches persist across long-running agent work.

case 04review · appsec · architecture

Review, security & architecture

Problem. Senior engineers keep writing the same review comments; agents produce plausible code that violates internal standards and data boundaries.

With Memco. Repeated corrections become durable guardrails agents follow before opening the next PR.

04Rollout

One repo. One team. Two weeks.

You don't need a six-month platform programme to test whether shared memory helps. Start with one recurring workflow — onboarding, CI failures, migrations or review comments — and measure repeated discovery, token use, review loops and task completion.

One repoquick wins from repeated fixes

One teamshared patterns · ci warnings

Orggates · audit · provenance

the same primitives, scoped tighter as you grow

spark — first queryrepo · team · org

$ npm install -g @memco/spark
spark login
spark init
spark --pretty query "what should I know
  before working in this repo?"

05The difference

Bigger context is not team memory.

Static files and retrieval move information. None of them decide what your team should trust next time.

Capability	Memco	AGENTS.md · repo rules	Vector DB · RAG	Vendor memory
Shared across people and sessions
Works across tools and modelscursor · claude code · codex · ci
Decides what to trustevidence-ranked, not similarity-ranked
Curated without a human queuededupe · synthesis · pruning
Stale knowledge decays				—
Survives the next vendor swap

YesPartial—Not published

Compiled July 2026 from public documentation. 'Partial' means the capability exists inside one product or requires manual upkeep.

06Coding evidence

Measured, not promised.

Numbers from measured settings, not guarantees for every team. Gains depend on agent usage, workflow repeatability and rollout scope.

arXiv preprint

−53%

Tokens per task with reused memory

−50%

Cost per task in measured runs

48%

Faster task completion in agent loops

98%

Recommendations judged useful

swe-bench variant · ds-1000 · public spark benchmarksarXiv:2511.08301 · PDF

ds-1000 · code-quality judge score1–5 scale · judged by a third-party llm

Judge score · shares of 5.04.23 · 4.89 · 4.78 · 4.83

qwen3-coder-30bqwen3 + sparkgpt-5-codexcodex + spark

open-weight models punch above their weight with shared memoryQwen3-Coder-30B moves from 4.23 to 4.89 with Spark — into the quality band of much larger commercial models in this setup. The point is not that every model beats every frontier model; it is that shared memory raises the floor.

field signalexternal research · not memco data

External research commentary

Stack Overflow 2025. High AI tool usage; trust and team-level impact lag. Most-cited friction: almost-right code, debugging overhead, weak collaboration gains.

DORA 2025. AI is an amplifier: the system around the tool decides whether it creates leverage or chaos. Team learning separates productive teams from the rest.

07Privacy & data flow

Code stays yours. Lessons travel.

Engineering memory is sensitive: architecture decisions, incidents, review standards, security lessons. Teams share the lesson without turning memory into a free-for-all.

Private memory spaces

Per-repo, per-team or per-org. Sharing across boundaries is opt-in and explicit.

Provenance

Every memory traces back to the run, agent, repo and human correction that produced it. Trust is auditable, not asserted.

No training on your code

Memco doesn’t train on your code, prompts or completions. What travels is the governed lesson.

Deploy inside the boundary

Managed tenant or self-hosted. Memory never leaves the boundary you set. Governance detail lives on Enterprise.

Governance, audit and deployment — Enterprise

08Before you roll out

The questions platform teams ask.

Stop letting agent learning disappear.

Your team is already paying for the discoveries. Memco makes sure the next agent can use them.

Start a team trial Try Memco free

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.

What data leaves our repos?

Where does memory live?

What does rollout actually involve?

Do we have to change tools?

Stop letting agent learning disappear.