RRepoGEO

REPOGEO REPORT · LITE

wandb/weave

Default branch master · commit b7b7796f · scanned 7/1/2026, 10:46:24 AM

GitHub: 1,104 stars · 157 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

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
    generative-ai, llm, ai-applications, evaluation, tracing, observability, mlops, weights-biases, python, machine-learning
  • highreadme#2
    Strengthen the README's opening statement to emphasize Generative AI and LLM workflows

    Why:

    CURRENT
    Weave is a toolkit for developing Generative AI applications, built by Weights & Biases.
    COPY-PASTE FIX
    Weave by Weights & Biases is the definitive toolkit for developing, evaluating, and debugging Generative AI applications and LLM workflows.
  • mediumreadme#3
    Add a 'Why Weave?' section to the README to clarify differentiation

    Why:

    COPY-PASTE FIX
    ## Why Weave for Generative AI?

Weave is purpose-built for the unique challenges of Generative AI development, offering fine-grained lineage tracking, rigorous evaluation tools, and seamless integration with the Weights & Biases ecosystem. Unlike general-purpose ML tools, Weave provides specialized primitives for LLM tracing, prompt management, and human-in-the-loop evaluation, ensuring you can build, debug, and deploy reliable GenAI applications faster.

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. openai/evals · recommended 1×
  4. helicone/helicone · recommended 1×
  5. PostHog/posthog · recommended 1×
  • CATEGORY QUERY
    What tools help log and debug language model inputs, outputs, and execution traces?
    you: not recommended
    AI recommended (in order):
    1. LangChain Plus (now LangSmith)
    2. Weights & Biases (W&B) Prompts
    3. OpenAI Evals (openai/evals)
    4. Helicone (helicone/helicone)
    5. PostHog (PostHog/posthog)
    6. Datadog
    7. Sentry (getsentry/sentry)

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

    Show full AI answer
  • CATEGORY QUERY
    How can I perform rigorous evaluations and organize information for my generative AI applications?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. MLflow
    3. Arize AI
    4. LangChain
    5. Humanloop
    6. Label Studio
    7. Google Sheets / Microsoft Excel

    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?

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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