RRepoGEO

REPOGEO REPORT · LITE

OpenBB-finance/experimental-openbb-platform-agent

Default branch main · commit 1cfee33d · scanned 5/19/2026, 8:47:09 AM

GitHub: 1,336 stars · 169 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
17 /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
1 / 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 OpenBB-finance/experimental-openbb-platform-agent, 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
    Reposition the README H1 and opening paragraph to specify it's a framework

    Why:

    CURRENT
    # OpenBB LLM Agents
    Work-in-progress.
    
    This is a project that leverages LLMs and OpenBB Platform to create financial
    analyst agents that can autonomously perform financial research, and answer
    questions using up-to-date data. This is possible as a result of agents
    utilizing function calling to interact with the OpenBB Platform.
    COPY-PASTE FIX
    # OpenBB LLM Agents: A Framework for Financial AI
    Work-in-progress.
    
    This project provides a framework for building financial analyst agents that leverage LLMs and the OpenBB Platform to autonomously perform financial research and answer questions using up-to-date data. This is possible as a result of agents utilizing function calling to interact with the OpenBB Platform.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a suitable open-source license (e.g., MIT, Apache-2.0).

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 OpenBB-finance/experimental-openbb-platform-agent
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. QuantConnect/Lean · recommended 1×
  4. quantopian/zipline · recommended 1×
  5. mementum/backtrader · recommended 1×
  • CATEGORY QUERY
    Looking for a framework to develop AI agents for financial market analysis.
    you: not recommended
    AI recommended (in order):
    1. QuantConnect (Lean) (QuantConnect/Lean)
    2. Zipline (quantopian/zipline)
    3. Backtrader (mementum/backtrader)
    4. Alpaca Trade API
    5. TensorFlow (tensorflow/tensorflow)
    6. PyTorch (pytorch/pytorch)
    7. Quantopian

    AI recommended 7 alternatives but never named OpenBB-finance/experimental-openbb-platform-agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build intelligent systems that perform automated stock analysis and research?
    you: not recommended
    AI recommended (in order):
    1. Python (python/cpython)
    2. Pandas (pandas-dev/pandas)
    3. NumPy (numpy/numpy)
    4. Scikit-learn (scikit-learn/scikit-learn)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. PyTorch (pytorch/pytorch)
    8. Alpha Vantage
    9. Quandl (Nasdaq Data Link)
    10. IEX Cloud
    11. NLTK (Natural Language Toolkit) (nltk/nltk)
    12. spaCy (explosion/spaCy)
    13. Streamlit (streamlit/streamlit)
    14. Dash (Plotly Dash) (plotly/dash)
    15. Docker (docker/docker)

    AI recommended 15 alternatives but never named OpenBB-finance/experimental-openbb-platform-agent. 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 OpenBB-finance/experimental-openbb-platform-agent?
    pass
    AI did not name OpenBB-finance/experimental-openbb-platform-agent — 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 OpenBB-finance/experimental-openbb-platform-agent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenBB-finance/experimental-openbb-platform-agent 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 OpenBB-finance/experimental-openbb-platform-agent solve, and who is the primary audience?
    pass
    AI did not name OpenBB-finance/experimental-openbb-platform-agent — 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?

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OpenBB-finance/experimental-openbb-platform-agent — 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