REPOGEO REPORT · LITE

choosewhatulike/trainable-agents

Default branch main · commit c64d54af · scanned 5/30/2026, 3:32:51 PM

GitHub: 630 stars · 49 forks

AI VISIBILITY SCORE

22 /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

1 / 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 choosewhatulike/trainable-agents, 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 opening to emphasize its role as a framework for building character agents

Why:

CURRENT

This is the official repository of our EMNLP 2023 paper. Welcome! 🤩🤩🤩

We introduce **Character-LLMs** a trainable agent for role-playing that learns from actual experiences, characteristics, and emotions.

COPY-PASTE FIX

This repository provides the official code and datasets for **Character-LLM**, a framework for building and training AI agents specifically designed for realistic role-playing of historical or fictional figures. Unlike prompted agents, Character-LLMs are trainable agents that learn from actual experiences, characteristics, and emotions, enabling them to act as specific people like Beethoven or Queen Cleopatra with detailed character-related knowledge and personalities.

highhomepage#2
Add the arXiv paper link as the repository homepage
Why:
COPY-PASTE FIX
```
https://arxiv.org/abs/2310.10158
```

mediumtopics#3

Add more specific topics to improve categorization

Why:

CURRENT

agent, character, language-model, large-language-models, llm, natural-language-processing, roleplay, sft

COPY-PASTE FIX

agent, character, language-model, large-language-models, llm, natural-language-processing, roleplay, sft, character-simulation, trainable-agents, ai-agents, conversational-ai

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 choosewhatulike/trainable-agents

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI GPT-4

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI GPT-4 · recommended 1×
Anthropic Claude 3 · recommended 1×
Google Gemini · recommended 1×
Hugging Face Transformers · recommended 1×
Llama 2 · recommended 1×

CATEGORY QUERY
How can I develop AI agents capable of realistically role-playing specific historical or fictional figures?
you: not recommended
AI recommended (in order):
1. OpenAI GPT-4
2. Anthropic Claude 3
3. Google Gemini
4. Hugging Face Transformers
5. Llama 2
6. Mistral 7B
7. LangChain
8. LlamaIndex
9. Neo4j
10. Protégé
AI recommended 10 alternatives but never named choosewhatulike/trainable-agents. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks enable training large language models for detailed character simulation and role-play?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch Lightning (Lightning-AI/lightning)
3. DeepSpeed (microsoft/DeepSpeed)
4. JAX/Flax (google/flax)
5. TensorFlow/Keras (keras-team/keras)
6. OpenAI API
AI recommended 6 alternatives but never named choosewhatulike/trainable-agents. 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 choosewhatulike/trainable-agents?
pass
AI named choosewhatulike/trainable-agents explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts choosewhatulike/trainable-agents in production, what risks or prerequisites should they evaluate first?
pass
AI did not name choosewhatulike/trainable-agents — 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?
In one sentence, what problem does the repo choosewhatulike/trainable-agents solve, and who is the primary audience?
pass
AI did not name choosewhatulike/trainable-agents — 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?

Embed your GEO score

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Live preview

MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/choosewhatulike/trainable-agents"><img src="https://repogeo.com/badge/choosewhatulike/trainable-agents.svg" alt="RepoGEO" /></a>

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