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Basic Information

Muscle-mem is a Python SDK that provides a behavior cache for AI agents by recording their tool-calling patterns and deterministically replaying previously learned action trajectories when the same task and environment are encountered. The engine wraps an existing agent implementation rather than replacing it, deciding on each invocation whether to replay a cached trajectory or forward the request to the agent on cache misses. The project aims to remove repeated LLM calls for routine tasks to increase execution speed, reduce variability, and cut token costs. It is intended for developers who want to add safe, validated tool reuse to agents, and it includes examples, a demo, and community channels for feedback. The README emphasizes cache validation as the critical design concern and documents installation, API usage, and a working example of integrating muscle-mem with an agent.

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App Details

Features
The repository exposes an Engine that wraps any callable agent and manages a cache of recorded trajectories with optional tagging to create buckets. Tools are instrumented via decorators: @engine.function for functions and @engine.method for object methods, enabling recording of tool calls and replaying them later. Cache safety is enforced with Check objects that provide capture and compare callbacks for environment feature extraction and validation; checks can run as prechecks or postchecks. A top-level params system marks dynamic arguments so later runs can substitute runtime values. The engine supports context injection for method replays via set_context and includes finalize to validate dependencies. The README includes examples, an example computer-use agent, and guidance on deterministic replay and fallbacks to agent mode.
Use Cases
Muscle-mem helps developers reduce reliance on LLM calls for repetitive agent workflows by caching and replaying validated action sequences, which speeds execution, reduces variability, and lowers token costs for repeatable tasks. It lets teams keep existing agent implementations while adding safe automation through configurable Checks that ensure an action is only replayed when the environment matches cached conditions. The params feature enables per-run customization of stored trajectories so dynamic inputs can be substituted without re-running the LLM. Context injection allows replaying method calls that depend on runtime objects. Overall, it is useful for turning predictable agent behavior into deterministic scripts while retaining agent fallback for edge cases and preserving developer control over safety and validation.

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