Muscle-Mem

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muscle-mem is an AI Agents & Automation tool that provides a behavior cache for AI agents. It is a Python SDK that records and replays an agent's tool-calling patterns to reduce LLM usage for repetitive tasks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is muscle-mem?

muscle-mem is a Python SDK designed to act as a behavior cache for AI agents. It records an agent's tool-calling patterns as it solves tasks, and then deterministically replays these learned trajectories when the same task is encountered again. This approach aims to get Large Language Models (LLMs) out of the hotpath for repetitive tasks, significantly increasing speed, reducing variability, and eliminating token costs. The SDK allows for instrumenting tool functions and methods with decorators, and features a robust cache validation system using 'Checks' to ensure safe tool reuse. It also supports parameterization for dynamic arguments, making it adaptable to varying task inputs.

Best used for

Ideal for developers and data scientists who need to accelerate AI agent performance, reduce LLM token costs, and ensure consistent behavior for repetitive tasks. Especially valuable for scenarios where agents perform similar actions frequently and require reliable, cost-effective execution.

Common actions

optimize AI agent performance

reduce LLM costs

cache agent behaviors

automate repetitive tasks

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Capabilities

Key features

Behavior cache for AI agents
Python SDK
Records tool-calling patterns
Replays learned trajectories
Cache validation with Checks
Dynamic parameterization

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

How does muscle-mem reduce LLM calls?

muscle-mem acts as a behavior cache. Once an AI agent solves a task and its tool-calling patterns are recorded, muscle-mem can replay these learned trajectories for similar future tasks. This bypasses the need to invoke the LLM for every repetitive action, saving on token costs and increasing speed.

What is a 'Check' in muscle-mem and why is it important?

A 'Check' is a fundamental building block for cache validation in muscle-mem. It determines if it's safe to execute a cached action by comparing the current environment with the cached environment. This ensures that replayed behaviors are appropriate for the current context, preventing incorrect or outdated actions.

Can muscle-mem handle dynamic arguments in tool calls?

Yes, muscle-mem includes a top-level parameters system. This allows you to mark certain arguments as dynamic, meaning they can change per run while still leveraging cached trajectories. This is useful for scenarios like form filling where specific input values vary but the overall action pattern remains the same.

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