REPOGEO REPORT · LITE

langchain-ai/openevals

Default branch main · commit 24f11d90 · scanned 6/19/2026, 1:02:11 PM

GitHub: 1,077 stars · 101 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)

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

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 langchain-ai/openevals, 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

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

["llm-evaluation", "llm-apps", "langchain", "evaluation-framework", "ai-testing", "generative-ai"]

highhomepage#2
Add a homepage URL to the repository
Why:
COPY-PASTE FIX
```
https://YOUR_PROJECT_HOMEPAGE_URL_HERE
```

mediumreadme#3

Strengthen the README's opening statement to clarify purpose and audience

Why:

CURRENT

# ⚖️ OpenEvals

Much like tests in traditional software, evals are an important part of bringing LLM applications to production. The goal of this package is to help provide a starting point for you to write evals for your LLM applications, from which you can write more custom evals specific to your application.

COPY-PASTE FIX

# ⚖️ OpenEvals: A Framework for LLM Application Evaluation

OpenEvals provides a collection of readymade evaluators and a structured framework to systematically assess the performance and reliability of your Large Language Model (LLM) applications. Designed for developers and researchers, it helps you bring LLM apps to production by offering a robust starting point for writing custom evaluations, much like tests in traditional software.

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 langchain-ai/openevals

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

langchain-ai/langchain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

langchain-ai/langchain · recommended 2×
Arize-AI/phoenix · recommended 2×
wandb/wandb · recommended 2×
Humanloop · recommended 2×
explodinggradients/ragas · recommended 2×

CATEGORY QUERY
How can I effectively evaluate the performance of my large language model applications?
you: not recommended
AI recommended (in order):
1. LangChain Evaluation Module (langchain-ai/langchain)
2. Phoenix (Arize-AI/phoenix)
3. W&B Prompts (wandb/wandb)
4. Humanloop
5. Ragas (explodinggradients/ragas)
6. DeepEval (confident-ai/deepeval)
7. Galileo Evaluate
AI recommended 7 alternatives but never named langchain-ai/openevals. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help automate evaluation of LLM responses and application quality?
you: not recommended
AI recommended (in order):
1. LangChain Evaluation (langchain-ai/langchain)
2. Arize AI (Phoenix) (Arize-AI/phoenix)
3. Weights & Biases (W&B Prompts) (wandb/wandb)
4. DeepEval (confident-ai/deepeval)
5. Galileo (Evaluate)
6. Ragas (explodinggradients/ragas)
7. Humanloop
AI recommended 7 alternatives but never named langchain-ai/openevals. 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 langchain-ai/openevals?
pass
AI did not name langchain-ai/openevals — 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 langchain-ai/openevals in production, what risks or prerequisites should they evaluate first?
pass
AI named langchain-ai/openevals 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 langchain-ai/openevals solve, and who is the primary audience?
pass
AI named langchain-ai/openevals explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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langchain-ai/openevals — 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