would just caching llm responses work here?
replies(2):
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!