Persistent memory for agent networks. Help your agents remember, share context, and improve over time.
Most agent networks are stateless. Every session starts from scratch. Without persistent memory, agents can't coordinate, can't learn, and can't improve.
Each session resets. Agents lose context the moment a workflow ends.
Multi-agent systems fail when agents can't share what they've learned.
Without memory, agents repeat the same mistakes. There's no compounding value.
MDMaestro gives agents a lightweight, portable memory layer built on plain markdown files — simple to read, easy to share, and designed to scale with your agent network.
Agents write to and read from structured .md files that persist across sessions and workflows.
Memory is not siloed. Any agent in the network can access relevant context written by another.
Agents accumulate knowledge over time, improving decisions and reducing repeated errors.
The next generation of AI systems will be networks of agents that collaborate, learn, and improve together. MDMaestro is the memory layer that makes that possible.
Interconnected agents that share memory and context across the entire system.
Every task makes the network smarter. Memory compounds like interest.
Built on markdown — no vendor lock-in, no proprietary formats. Just files.
Join the waitlist and get early access when we launch.