Muscle Mem is an SDK that records your agent's tool-calling patterns as it solves tasks, and will deterministically replay those learned trajectories whenever the task is encountered again, falling back to agent mode if edge cases are detected. Like a JIT compiler, for behaviors.
At Pig, we built computer-use agents for automating legacy Windows applications (healthcare, lending, manufacturing, etc).
A recurring theme we ran into was that businesses already had RPA (pure-software scripts), and it worked for them in most cases. The pull to agents as an RPA alternative was not to have an infinitely flexible "AI Employees" as tech Twitter/X may want you to think, but simply because their RPA breaks under occasional edge-cases and agents can gracefully handle those cases.
Using a pure-agent approach proved to be highly wasteful. Window's accessibility APIs are poor, so you're generally stuck using pure-vision agents, which can run around $40/hr in token costs and take 5x longer than a human to perform a workflow. At this point, you're better off hiring a human.
The goal of Muscle-Mem is to get LLMs out of the hot path of repetitive automations, intelligently swapping between script-based execution for repeat cases, and agent-based automations for discovery and self-healing.
While inspired by computer-use environments, Muscle Mem is designed to generalize to any automation performing discrete tasks in dynamic environments. It took a great deal of thought to figure out an API that generalizes, which I cover more deeply in this blog: https://erikdunteman.com/blog/muscle-mem/
Check out the repo, consider giving it a star, or dive deeper into the above blog. I look forward to your feedback!
Instead of hundreds of thousands of us downloading the latest and greatest model that won't fundamentally update one bit until we're graced with the next one, I would think we should all be able to fine-tune the weights so that it can naturally memorize new additional info and preferences without using up context length.
Anyone else experimenting with letting LLMs generate their own or sub-agent system prompts?
In fact, systems should be LLM agnostic or use different models for different needs.
I don't believe building something into the model will ever be the solution though. It is interesting what Google is trying to do with model caching but at the end of the day I believe the strength of agents here will rely heavily upon modularity.