elastic memory for agents
Ricursor Elastic memory for AI.
Store corrections as constraints and share targeted context across connected agents. Explore preemptive guarding in the live simulator; savings depend on your agent integration.
- Context
- before execution
- Memory
- stored constraints
- MCP
- + context API
before / after
Hallucination loops vs. elastic memory
Illustrative simulation: compare repeated stale assumptions with a stored correction.
credit savings
Serve targeted context, not your whole repo
Estimate potential savings using your own token volume, price and assumed context reduction. This is not a measured result.
current spend / mo
$3,200
estimated spend / mo
$1,760
estimated savings / year
$17,280
autonomous intent
Architecture intent, enforced from day one
Illustrative scenarios show architectural constraints. Automatic repository indexing and enforcement are not connected yet.
intent.enforce() / simulation
✗ drift detected: components/Cart.tsx → infra/db.ts
◆ intent: UI never imports from /infra
✓ routed through domains/cart/service.ts
illustrative result — no agent wrote to disk.
multi-model accord
Many models. One memory.
Connected agents can read the same constraint ledger. Automatic model-output arbitration is not connected yet.
- 01
Shared constraint ledger
Store constraints once; connected agents request the same project context.
- 02
Drift arbitration
Planned: check competing outputs against the ledger before they land.
- 03
One accord
Reuse stored rules across integrations; each agent must request and apply its context.
Stop paying for the same mistake twice.
MCP and context API setup are available in Console → Connections.