REPOGEO REPORT · LITE

AgentOps-AI/tokencost

Default branch main · commit e7f7c192 · scanned 6/27/2026, 6:47:18 AM

GitHub: 1,991 stars · 106 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 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 AgentOps-AI/tokencost, 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

Add a clear positioning statement to the README

Why:

CURRENT

Tokencost helps calculate the USD cost of using major Large Language Model (LLMs) APIs by calculating the estimated cost of prompts and completions.

COPY-PASTE FIX

Tokencost is a lightweight Python library for accurately calculating the USD cost of using major Large Language Model (LLMs) APIs by estimating the cost of prompts and completions. Unlike LLM APIs themselves or full-stack observability platforms, Tokencost focuses solely on providing precise, up-to-date token pricing and usage calculations.

mediumtopics#2

Add more specific topics for LLM cost management

Why:

CURRENT

analytics, claude, large-language-models, llm, observability, openai, price, price-tracker, token, tokenization

COPY-PASTE FIX

analytics, claude, cost-estimation, cost-management, developer-tools, large-language-models, llm, llm-api, openai, price, price-tracker, token, tokenization, usage-tracking

lowabout#3

Refine repository description

Why:

CURRENT

Easy token price estimates for 400+ LLMs. TokenOps.

COPY-PASTE FIX

A Python library for easy token price estimates across 400+ LLMs. TokenOps.

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 AgentOps-AI/tokencost

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI API

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI API · recommended 1×
Azure OpenAI Service · recommended 1×
Anthropic Claude API · recommended 1×
openai/tiktoken · recommended 1×
anthropics/anthropic-sdk-python · recommended 1×

CATEGORY QUERY
How can I accurately estimate the token cost of my LLM API calls?
you: not recommended
AI recommended (in order):
1. OpenAI API
2. Azure OpenAI Service
3. Anthropic Claude API
4. tiktoken (openai/tiktoken)
5. Anthropic Python SDK (anthropics/anthropic-sdk-python)
6. Google Gemini API
7. Google Generative AI SDK (google/generative-ai-python)
8. Hugging Face Transformers (huggingface/transformers)
9. LangChain (langchain-ai/langchain)
10. LiteLLM (BerriAI/litellm)
AI recommended 10 alternatives but never named AgentOps-AI/tokencost. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What's the best way to track and calculate LLM token usage prices in my application?
you: not recommended
AI recommended (in order):
1. LangChain
2. LangSmith
3. OpenAI Python Library
4. LiteLLM
5. Helicone
6. PromptLayer
7. tiktoken
AI recommended 7 alternatives but never named AgentOps-AI/tokencost. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 AgentOps-AI/tokencost?
pass
AI named AgentOps-AI/tokencost explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts AgentOps-AI/tokencost in production, what risks or prerequisites should they evaluate first?
pass
AI named AgentOps-AI/tokencost 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 AgentOps-AI/tokencost solve, and who is the primary audience?
pass
AI named AgentOps-AI/tokencost 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)

[![RepoGEO](https://repogeo.com/badge/AgentOps-AI/tokencost.svg)](https://repogeo.com/en/r/AgentOps-AI/tokencost)

HTML

<a href="https://repogeo.com/en/r/AgentOps-AI/tokencost"><img src="https://repogeo.com/badge/AgentOps-AI/tokencost.svg" alt="RepoGEO" /></a>

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AgentOps-AI/tokencost — 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