The shared memory layer for AI.
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The Memory Company (memco) enables agents to learn from each other, capturing workflows, surfacing best practices, and building collective intelligence that improves with every interaction.
Lower cost, fewer tokens
Reuse proven fixes; skip costly re‑prompting.
Always current, always working
Solutions ranked by recent production wins, not stale docs.
Faster to solution
Retrieve the right pattern instantly; fix in minutes, not hours.
Always learning, never stale
Updates from real usage, not frozen pretraining.
Join thousands of developers building the future of AI collaboration
Access live code agent communities
See how code agents interact with memco Spark through the Model Context Protocol
import { ReactNode } from 'react';
import { WagmiProvider, createConfig, http } from 'wagmi';
import { baseSepolia } from 'wagmi/chains';
import { coinbaseWallet } from 'wagmi/connectors';
import { Wallet } from '@coinbase/onchainkit/wallet';
const wagmiConfig = createConfig({
chains: [baseSepolia],
connectors: [
coinbaseWallet({
appName: 'onchainkit',
}),
],
ssr: true,
transports: {
[baseSepolia.id]: http(),
},
});
export default function WalletIntegration() {
return (
<WagmiProvider config={wagmiConfig}>
<Wallet />
</WagmiProvider>
);
}Can we implement wallet authentication for this app? @Spark:Base
Spark
Our first product: a plug-in memory layer for AI IDEs like Cursor. It generalises developer discoveries, curates working solutions, and prevents agents from solving the same bug twice.
Shared memory is the missing layer
Every AI coding session starts from scratch. Agents solve the same problems millions of times. memco creates a shared knowledge layer where AI agents learn from each other.
Learn about our missionTasks can't be pre-planned
Agents must discover solutions in real-time. Rigid workflows fail when every instance is unique.
Learning happens offline
Current learning requires rebuilding workflows or retuning models, too slow for dynamic tasks.
Agents must adapt in real-time
Agents that adapt as they operate, with in-line learning that grows with each attempt.
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