Muscle-Mem Behavior Caching Concepts in Roast #30

obie · 2025-05-14T20:43:00Z

Overview

Explore integrating concepts from muscle-mem into Roast to optimize repetitive AI-driven workflows through behavior caching.

The muscle-mem project introduces a behavior cache for AI agents that:

Records tool-calling patterns as agents solve tasks
Deterministically replays learned trajectories when similar tasks are encountered
Falls back to the full agent only when needed for edge cases
Gets LLMs out of the hotpath for repetitive tasks, improving speed and reducing costs

We could enhance Roast's efficiency by implementing similar caching mechanisms:

Workflow Pattern Recording: Record the sequence of steps, tool calls, and decision paths taken when executing workflows
Environment Validation Checks: Implement pre/post checks to determine when cached behaviors can be safely applied
Auto-replay for Common Patterns: Enable automatic replay of cached patterns for similar tasks

Cache Storage:
- Use the.roast/cache/ directory structure for storing workflow execution patterns
- Store inputs, environment conditions, and resulting tool calls
Cache Validation System:
- Implement a Check system similar to muscle-mem that captures environment state
- Define comparison logic to determine when cached patterns can be safely reused
Execution Engine Enhancement:
- Modify the workflow executor to first check if a cached pattern exists
- Add fallback mechanisms when validation checks fail

Repeated AI Tool Invocations:
- Skip redundant calls to expensive AI models for identical or similar inputs
- Cache file search, grep, and other tool results to reduce overhead
Multi-step Workflow Optimization:
- Cache partial workflows for common sub-tasks
- Allow fast-path execution when conditions match

Performance: Significantly faster execution for repetitive tasks
Cost Reduction: Minimize API calls to AI models for tasks that have clear patterns
Consistency: More deterministic behavior for similar inputs
Development Efficiency: Complementary to our existing session replay feature

How do we balance caching with flexibility when workflows evolve?
What level of granularity should we use for caching? How configurable is it? (Step-level vs tool-call level)
How might this interact with our existing session replay and function caching feature?
What validation checks would be most appropriate for our typical workflows?

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obie added the enhancement New feature or request label May 14, 2025

obie changed the title ~~Muscle Memory~~ Muscle-Mem Behavior Caching Concepts in Roast May 20, 2025