REPOGEO REPORT · LITE

Arize-ai/phoenix

Default branch main · commit e64e067b · scanned 5/20/2026, 10:56:15 AM

GitHub: 9,750 stars · 882 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

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 Arize-ai/phoenix, 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

Strengthen README's initial positioning for LLM observability and evaluation

Why:

CURRENT

Phoenix is an open-source AI observability platform d

COPY-PASTE FIX

Phoenix is the open-source AI observability platform for evaluating, troubleshooting, and monitoring your LLM applications and AI agents. Gain deep insights into prompt engineering outputs, trace agent behavior, and ensure reliable AI performance in production.

mediumcomparison#2

Add a dedicated "Comparison with Alternatives" section to the README

Why:

COPY-PASTE FIX

## Phoenix vs. Alternatives

Phoenix's core differentiator is its **fully open-source nature** combined with its **unified platform for both LLM observability (tracing, logging) and evaluation capabilities**, offering an interactive UI for comprehensive analysis of LLM applications. While tools like LangSmith and W&B Prompts offer aspects of LLM monitoring, Phoenix provides a complete, open-source solution for evaluating, troubleshooting, and monitoring your LLM applications and AI agents from development to production.

mediumlicense#3

Clarify the project's license directly in the README

Why:

COPY-PASTE FIX

Phoenix is licensed under [Specify License Name(s) and terms, e.g., a custom license combining X and Y]. Please refer to the LICENSE file in the repository for full details.

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 Arize-ai/phoenix

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain Plus (now LangSmith)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

LangChain Plus (now LangSmith) · recommended 1×
Arize AI (Phoenix) · recommended 1×
Weights & Biases (W&B Prompts) · recommended 1×
OpenAI Evals · recommended 1×
MLflow · recommended 1×

CATEGORY QUERY
Need a way to monitor and evaluate the performance of my LLM applications effectively.
you: not recommended
AI recommended (in order):
1. LangChain Plus (now LangSmith)
2. Arize AI (Phoenix)
3. Weights & Biases (W&B Prompts)
4. OpenAI Evals
5. MLflow
6. Deepchecks
AI recommended 6 alternatives but never named Arize-ai/phoenix. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for tools to debug and observe AI agent behavior and prompt engineering outputs.
you: not recommended
AI recommended (in order):
1. LangSmith
2. W&B Prompts
3. OpenAI Playground
4. Azure OpenAI Studio
5. Helicone
6. Humanloop
7. DeepEval
8. Elasticsearch
9. Logstash
10. Kibana
11. Grafana Loki
12. Grafana
AI recommended 12 alternatives but never named Arize-ai/phoenix. 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 Arize-ai/phoenix?
pass
AI named Arize-ai/phoenix explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Arize-ai/phoenix in production, what risks or prerequisites should they evaluate first?
pass
AI named Arize-ai/phoenix 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 Arize-ai/phoenix solve, and who is the primary audience?
pass
AI named Arize-ai/phoenix explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of Arize-ai/phoenix. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/Arize-ai/phoenix.svg)](https://repogeo.com/en/r/Arize-ai/phoenix)

HTML

<a href="https://repogeo.com/en/r/Arize-ai/phoenix"><img src="https://repogeo.com/badge/Arize-ai/phoenix.svg" alt="RepoGEO" /></a>

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Arize-ai/phoenix — 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