Team Memory Network
Infrastructure for stateful AI agents.
Team Monet builds products that help agents remember, act, and be evaluated over time — starting with local-first memory, agentic automation, and behavioural benchmarks.
Agents need state.
Context windows are not memory. Monet keeps project knowledge alive across sessions.
Agents need safe workflows.
Agentic Automation turns intent into controlled execution with human approval and persistent context.
Agents need behavioural proof.
Behavioural Benchmark evaluates what agents actually do, not just what models answer.
Products
Remember. Act. Measure.
Three products, one thesis: long-running agents need durable state, safe action, and behavioural evaluation.
Monet
Living project state for coding agents. Local-first, MCP-native, and designed so every session starts smarter.
Open product page →Agentic Automation
Automation for agentic workflows: structured steps, approval gates, and state-aware execution.
Open product page →Behavioural Benchmark
Benchmark agents by behaviour across realistic tasks, workflows, tool use, and long-running sessions.
Open product page →Origin
Team Monet began as Team Memory Network.
The idea is simple: agents and teams should not lose what they already learned. Monet is the memory network; Agentic Automation uses state to act safely; Behavioural Benchmark measures whether the system actually improves.