REPOGEO REPORT · LITE

pig-dot-dev/muscle-mem

Default branch main · commit cee5db18 · scanned 6/3/2026, 6:02:59 AM

GitHub: 763 stars · 43 forks

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface pig-dot-dev/muscle-mem, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README H1 and opening paragraph to clarify AI agent focus

Why:

CURRENT

# Muscle Memory

`muscle-mem` is a behavior cache for AI agents.

COPY-PASTE FIX

# Muscle Memory: A Behavior Cache for AI Agents

`muscle-mem` is a Python SDK that records your AI 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.

hightopics#2
Add relevant topics to improve categorization
Why:
COPY-PASTE FIX
```
ai-agents, llm-agents, llm-optimization, caching, behavior-cache, python-sdk, tool-calling
```
mediumhomepage#3
Add a homepage URL to the repository About section
Why:
COPY-PASTE FIX
```
[Insert relevant URL here, e.g., a blog post, documentation, or project website]
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface pig-dot-dev/muscle-mem

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI GPT-3.5 Turbo

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI GPT-3.5 Turbo · recommended 1×
Anthropic Claude 3 Haiku · recommended 1×
Google Gemini 1.5 Flash · recommended 1×
Mistral 7B Instruct · recommended 1×
Fine-tuned GPT-3.5 Turbo · recommended 1×

CATEGORY QUERY
How can I reduce LLM token costs and latency for repetitive agent tasks?
you: not recommended
AI recommended (in order):
1. OpenAI GPT-3.5 Turbo
2. Anthropic Claude 3 Haiku
3. Google Gemini 1.5 Flash
4. Mistral 7B Instruct
5. Fine-tuned GPT-3.5 Turbo
6. OpenAI GPT-4o Mini
AI recommended 6 alternatives but never named pig-dot-dev/muscle-mem. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a Python library to cache and replay AI agent tool-calling sequences for efficiency.
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Tenacity (jd/tenacity)
4. functools.lru_cache
5. Cachetools (tkem/cachetools)
6. Redis-Py (redis/redis-py)
7. SQLAlchemy (sqlalchemy/sqlalchemy)
AI recommended 7 alternatives but never named pig-dot-dev/muscle-mem. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of pig-dot-dev/muscle-mem?
pass
AI did not name pig-dot-dev/muscle-mem — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts pig-dot-dev/muscle-mem in production, what risks or prerequisites should they evaluate first?
pass
AI named pig-dot-dev/muscle-mem explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo pig-dot-dev/muscle-mem solve, and who is the primary audience?
pass
AI named pig-dot-dev/muscle-mem explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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pig-dot-dev/muscle-mem — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite