Memco · Spark · Memory Layer
Smarter, together.
The memory layer that turns every agent interaction into compounding learning.
*Based on a 5-person team with high daily agent usage. See benchmarks.
One coding agent learns. Every coding agent benefits.
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The Memory Company enables agents to learn from each other, capturing workflows, surfacing best practices, and building collective intelligence that improves with every interaction. Free for individual developers on public APIs, with private subnets for teams and enterprises.
Compounding, not repeating
Every agent session makes the next one smarter. Knowledge accumulates automatically — no docs to write, nothing to maintain.
Memory is not storage
Active memory curates, forgets, and promotes what actually worked. Not a dump of traces — a learning system.
Any model gets better
Collective intelligence closes the gap between open-source and frontier models. The knowledge is in the network, not the weights.
Starts with you, scales to your team
Use it solo from day one. Add teammates and it compounds faster — with private subnets and permissioned sharing when you need them.
npm install -g @memco/sparkJoin the first developers building on shared memory
Why collective continual learning
The missing layer for AI agents
Every session makes the next one cheaper
Agents share what they discover as they work. Token costs drop from the second run and keep falling as collective intelligence grows.
- Learns from real agent execution, not curated docs
- 52% fewer tokens by Run 2 across 200+ evaluations
- Costs drop fast, then stay down permanently
Token cost per run
Knowledge keeps growing. Costs stay down.
Smaller models punch above their weight
Collective intelligence closes the gap between models. Your best model's discoveries make every other model better — so you can run routine work on a lighter model without losing quality.
- Pass rates jump 35 points on hard benchmarks
- Your best model's discoveries lift every model that follows
- Three separate FAIL→PASS cases in real evaluations
Pass rate — same model
DS-1000 benchmark, 1,000 problems
Knowledge that writes and maintains itself
Static context files hurt agent performance. Spark replaces them with active memory — learned from real work, validated by outcomes, and self-maintaining.
- Trust-scored by production outcomes, not publication date
- Stale knowledge degrades automatically
- Learned from real agent work — no one writes it
Pass rate
ETH Zurich AGENTS.md Study, 2026
Your code never leaves your machine
Spark generalizes on-device — extracting reusable patterns without source code, PII, or proprietary logic. Only abstracted insights reach the collective.
- All extraction happens on-device first
- Private subnets keep team knowledge internal
- You choose what gets shared
How knowledge flows
Integrations
Works with all major IDEs|
Seamlessly integrate with your favorite development tools.
Why shared memory
Intelligence that compounds
Humans didn't dominate Earth because we're the strongest. We dominated because we share what we learn. We're deploying millions of AI agents — and every one of them starts from scratch.
Learn about our missionEvery session starts from zero
Your agents solve the same problems your team solved last week. Every session burns tokens rediscovering knowledge that already exists.
Static docs don’t learn
AGENTS.md files are snapshots. RAG just retrieves. Fine-tuning is too slow and too blunt. None of them compound.
Memory that earns trust over time
Active memory curates, forgets, and promotes what actually worked — scored by real outcomes. Knowledge compounds instead of decaying.
What changes with memco
Same team, same agents, same tools — different outcomes when memory compounds instead of disappearing.
FAQ
Got questions?
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