RRepoGEO

REPOGEO REPORT · LITE

langchain-ai/langsmith-cookbook

Default branch main · commit 1cc7d013 · scanned 6/18/2026, 8:32:30 PM

GitHub: 1,032 stars · 185 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 langchain-ai/langsmith-cookbook, 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
  • highlicense#1
    Add a LICENSE file and declare the license in README

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository with the chosen license text (e.g., MIT, Apache-2.0). Add a line to the README, for example: 'This project is licensed under the [License Name] - see the LICENSE file for details.'
  • highabout#2
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Practical examples and patterns for debugging, evaluating, and improving LLM applications using LangSmith.

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/langsmith-cookbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangSmith
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangSmith · recommended 2×
  2. LangChain · recommended 1×
  3. Weights & Biases · recommended 1×
  4. W&B Prompts · recommended 1×
  5. OpenAI Evals · recommended 1×
  • CATEGORY QUERY
    How can I effectively debug and evaluate my large language model applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. Weights & Biases
    4. W&B Prompts
    5. OpenAI Evals
    6. Helicone
    7. Humanloop
    8. DeepEval
    9. ELK Stack
    10. Elasticsearch
    11. Logstash
    12. Kibana
    13. Grafana Loki

    AI recommended 13 alternatives but never named langchain-ai/langsmith-cookbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for tracing and continuously improving LLM application performance?
    you: not recommended
    AI recommended (in order):
    1. LangSmith
    2. Arize AI Phoenix (Arize-AI/phoenix)
    3. Weights & Biases W&B Prompts
    4. OpenReplay (openreplay/openreplay)
    5. Helicone (helicone-ai/helicone)
    6. Deepchecks LLM Evaluation (deepchecks/deepchecks)
    7. Grafana/Prometheus

    AI recommended 7 alternatives but never named langchain-ai/langsmith-cookbook. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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/langsmith-cookbook?
    pass
    AI named langchain-ai/langsmith-cookbook explicitly

    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/langsmith-cookbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named langchain-ai/langsmith-cookbook 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/langsmith-cookbook solve, and who is the primary audience?
    pass
    AI named langchain-ai/langsmith-cookbook explicitly

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

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langchain-ai/langsmith-cookbook — 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