Multi-Agent Fleets

Run dozens or hundreds of agents with isolated per-bot memory namespaces. Each bot maintains its own grounded recall, self-model, and procedural skills — cross-contamination rate stays at 0% by construction, not by convention.

Real-World Scale

JaysonAI runs 126 bots on mem20 today. Isolated namespaces, concurrent access, zero cross-contamination.

What you get:

Long-Running Agents

Agents that work across days, weeks, or months need memory that persists beyond a single context window. mem20's ledger and deep-store give you that persistence — with full epistemic status tracking so the agent knows what's certain vs. speculative.

What you get:

Research & Simulation

Run counterfactuals, explore hypotheticals, and model scenarios — without polluting grounded recall. mem20's simulation partition is a sandbox you can write freely into, and promotion to grounded status requires real evidence.

What you get:

Customer-Facing AI

Chatbots and assistants that remember user preferences, past conversations, and stated needs — while keeping those memories grounded in real interactions, not hallucinated personas.

What you get:

Creative & 3D Workflows

With optional Blender and Unity modules, agents can persist project state, remember design decisions, and build on past creative work across sessions.

What you get:

Safety-Critical Agents

For any deployment where an agent acting on false memory is unacceptable. The contamination firewall, epistemic veto, and audit engine make mem20 the memory layer you can actually trust in production.

Non-Negotiable Safety

mem20 is designed so that a confabulated memory cannot influence agent behavior at the storage layer. This is not a feature you enable — it's the architecture.