Introducing Memco: the shared memory layer for AI agents
Every day, thousands of developers and their AI agents solve the same problems—then forget the solutions.
We're building Memco to fix that. Our shared memory layer lets AI agents and developer tools learn from each other's successes and failures, so your stack gets smarter with every attempt.
Spark, our first product, is live in early access. Join the waitlist.
The problem is everywhere
Watch any AI coding assistant for an hour. It'll suggest a fix that failed yesterday. It'll hallucinate an API that doesn't exist. It'll loop through the same broken approaches that a teammate already debugged last week.
This isn't a model problem—it's a memory problem. Every IDE, CLI, and CI pipeline creates valuable experience: what worked, what broke, and crucially, why. But that knowledge dies in isolation. Your Cursor session doesn't talk to your CI pipeline. Your teammate's successful webhook integration vanishes into Slack history. The next agent starts from zero.
The waste compounds. Teams burn cycles on solved problems. Support tickets repeat. Documentation stays stale while real solutions hide in closed PRs.
Enter Memco
We make memory a first-class primitive in the developer stack.
Here's how it works: Spark captures real developer experience as it happens—the intent, the attempts, the outcomes. Not just "this code works" but "this specific approach failed three times until we realized the API requires this undocumented header."
That experience becomes reusable memory. When you (or your agent, or your teammate) hit a similar problem, the right solution surfaces instantly—with context, rationale, and proof it actually worked.
The magic: memory compounds. Each fix makes the next one faster. Each integration makes the next one smoother. Your entire stack learns.
What Spark does differently
Progressive learning over time
Unlike RAG systems that just index code, Spark understands outcomes. It knows which fixes actually worked, in which contexts, and why. Our early benchmarks show pass rates climbing steadily as memory accumulates—smaller models start outperforming larger ones on familiar tasks.
Just-in-time, not just-in-case
Memory only matters when it's useful. Spark surfaces the right experience at the moment of need—inside your IDE when you're stuck, in CI when tests fail, in your agent when it's looping. No searching, no context switching.
Trust built in
Every memory comes with attribution and evidence. You control what's shared—keep it personal, share with your team, or contribute to the community. No black box suggestions, no mystery fixes.
Who needs this now
If you run a developer platform: Turn your recurring support tickets into self-improving documentation. Every integration failure becomes a future success.
If you're building agents: Stop watching them fail the same way twice. Give them memory that spans sessions, repos, and environments.
If you lead an engineering team: Make tribal knowledge explicit. Turn that one engineer who "knows how everything works" into shared institutional memory.
The path forward
We're not trying to replace your tools—we're making them remember. Spark plugs into existing workflows (IDEs, CLIs, CI) with minimal friction. The unit of value isn't another editor; it's the memory itself.
This is bigger than code generation. When tools share memory, the entire development experience transforms. Onboarding accelerates. Migrations stabilize. That weird API quirk that takes everyone three hours to discover? Solved once, remembered forever.
Join us
The waitlist for Spark is open. We're looking for teams ready to turn their hard-won experience into competitive advantage.
Over the coming weeks, we'll share:
- Deep dives on our memory architecture
- Raw benchmarks and methodology
- Stories from early adopters
The future of development isn't just smarter models—it's shared memory. Let's build it together.
— Scott & Valentin, co-founders of Memco
Memco is hiring. If you want to work on memory systems that make the entire developer ecosystem smarter, reach out.