RRepoGEO

REPOGEO REPORT · LITE

wandb/weave

Default branch master · commit 233d1c70 · scanned 5/20/2026, 12:36:27 AM

GitHub: 1,093 stars · 152 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
35 /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
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 wandb/weave, 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

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

OVERALL DIRECTION
  • highreadme#1
    Strengthen the README's opening sentence to highlight LLM focus and unique value

    Why:

    CURRENT
    Weave is a toolkit for developing Generative AI applications, built by Weights & Biases.
    COPY-PASTE FIX
    Weave is a toolkit by Weights & Biases for developing Generative AI applications, bringing rigor, best-practices, and composability to the inherently experimental process of building LLM software. It helps you log, debug, and evaluate language model inputs, outputs, and traces, and organize all information across your LLM workflow.
  • mediumreadme#2
    Add a 'Why Weave?' or 'Key Features' section emphasizing data lineage and tracing

    Why:

    COPY-PASTE FIX
    Consider adding a new section titled 'Why Weave?' or expanding on the 'You can use Weave to:' section to explicitly highlight Weave's unique strength in providing deep, granular, and interactive data lineage tracking and visualization for ML and LLM dataflows, contrasting it with general experiment tracking or orchestration tools.

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 wandb/weave
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain Plus (now LangSmith)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain Plus (now LangSmith) · recommended 1×
  2. Weights & Biases (W&B) Prompts · recommended 1×
  3. OpenReplay · recommended 1×
  4. Helicone · recommended 1×
  5. PostHog · recommended 1×
  • CATEGORY QUERY
    How can I effectively log and debug inputs/outputs for my LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain Plus (now LangSmith)
    2. Weights & Biases (W&B) Prompts
    3. OpenReplay
    4. Helicone
    5. PostHog
    6. Datadog
    7. Sentry

    AI recommended 7 alternatives but never named wandb/weave. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help with rigorous evaluation and organization of LLM development workflows?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. MLflow (mlflow/mlflow)
    3. LangChain (langchain-ai/langchain)
    4. Arize AI Phoenix (Arize-AI/phoenix)
    5. Humanloop
    6. DeepEval (confident-ai/deepeval)
    7. OpenAI Evals (openai/evals)

    AI recommended 7 alternatives but never named wandb/weave. 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 wandb/weave?
    pass
    AI named wandb/weave explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts wandb/weave in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wandb/weave 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 wandb/weave solve, and who is the primary audience?
    pass
    AI named wandb/weave 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 wandb/weave. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/wandb/weave.svg)](https://repogeo.com/en/r/wandb/weave)
HTML
<a href="https://repogeo.com/en/r/wandb/weave"><img src="https://repogeo.com/badge/wandb/weave.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

wandb/weave — 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