Cadenza is a mem‑α–native memory layer that plugs into your existing LLM agents and gives them RL‑level adaptation — without retraining policies. Cheaper, more stable, deployable on real hardware.
No credit card. No commitment. Be first to run RL‑class agents without touching PPO, reward models, or multi‑million‑step training.
Most teams fall back to static prompts, hard‑coded rules, and naive vector stores — giving up on real learning.
Instead of learning a new policy network, Cadenza learns how your agent should write, organize, compress, and retrieve memory — and uses that to drive better behavior.
A mem‑α–native memory layer: a small RL‑trained controller that manages structured memory for your agents and replaces most of what you'd normally use RL policies for.
A compact RL-trained policy network that observes what just happened, sees a summary of current memory, and chooses memory actions: write, update, link, compress, retire. This is the only part trained with RL, and it runs on ARM/CPU — no giant GPUs.
Three-tier architecture: Core (short-term, high-resolution context), Episodic (full episodes with outcomes and rewards), Semantic (distilled summaries and patterns). Backed by a queryable database for full auditability.
Works with your existing agents. The agent calls Cadenza to read/write memory. Cadenza decides what to store and how. Downstream performance trains the controller — not your LLM weights.
“Cadenza replaces heavyweight policy RL with a lightweight, mem‑α–style memory controller — giving you RL‑grade specialization at a fraction of the cost and complexity.”
Receives requests, calls tools, talks to users, interacts with environments. Zero changes to your core logic.
After each step or episode: encodes the interaction, the controller chooses which memory ops to perform, writes and updates episodic + semantic memories.
On new tasks, Cadenza retrieves relevant episodes and summaries. Your agent conditions on rich, structured memory — not a dumb “last N turns” log.
Use your existing metrics — success rate, latency, revenue impact, user scores — as reward signals. Cadenza improves how it builds memory so agents get better over time.
Building multi-agent systems that need to actually learn from experience, not just retrieve docs.
Responsible for production agents; need learning and improvement without destabilizing core models.
Running agents on ARM or constrained hardware where full RL training loops are not realistic.
Want task-specialized copilots and operations agents that improve with usage but must stay safe and auditable.
Only the small memory controller is RL-trained. You avoid repeated end-to-end PPO/GRPO runs on huge models.
Train on logs and traces you already produce — conversations, episodes, feedback. No complex simulators needed.
Controller is small enough to run on ARM/CPU at low latency. Ideal for on-device agents, IoT, and low-power environments.
Learns what to keep from long histories and how to reuse it, so agents handle long tasks and rare events better.
Memory adapts to each environment/user/domain. Core model stays stable; memory is where specialization lives.
Memory entries live in a structured store. Inspect why the agent behaved a certain way, which episodes it recalled, and what it forgot.
We're building with a small group of design partners who have real production agents, clear metrics, and a desire to add learning without rewiring their whole stack.
Cadenza is not another “vector store with a nice UI.” It's built from first principles of mem‑α‑style RL for memory construction, memory‑augmented reinforcement learning, and real‑world constraints of ARM‑class hardware. If you've tried to bolt RL or long‑term memory onto your agents and it hurt, Cadenza is the piece you were missing.