RRepoGEO

REPOGEO REPORT · LITE

context-labs/HALO

Default branch main · commit 0d0e42d6 · scanned 5/8/2026, 6:33:00 AM

GitHub: 534 stars · 48 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)

3 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 context-labs/HALO, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ["ai-agents", "agent-optimization", "reinforcement-learning", "llm-agents", "agent-framework", "self-improving-agents", "opentelemetry"]
  • highreadme#3
    Add a concise, explicit positioning statement to the README's opening

    Why:

    CURRENT
    <h4 align="center">✨ RLM-based Automatic Agent Optimization Loop ✨</h4>
    COPY-PASTE FIX
    <h4 align="center">✨ RLM-based Automatic Agent Optimization Loop ✨</h4>

HALO is a Python framework for building and optimizing recursively self-improving AI agent systems using Reinforcement Learning Models (RLMs).

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 context-labs/HALO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ray Tune
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray Tune · recommended 1×
  2. Optuna · recommended 1×
  3. RLlib · recommended 1×
  4. Weights & Biases Sweeps · recommended 1×
  5. Google Cloud AI Platform Vizier · recommended 1×
  • CATEGORY QUERY
    How can I automatically optimize my AI agent's performance and make it self-improving?
    you: not recommended
    AI recommended (in order):
    1. Ray Tune
    2. Optuna
    3. RLlib
    4. Weights & Biases Sweeps
    5. Google Cloud AI Platform Vizier
    6. Azure Machine Learning Hyperdrive
    7. AWS SageMaker Automatic Model Tuning
    8. AutoKeras
    9. AutoGluon
    10. DEAP

    AI recommended 10 alternatives but never named context-labs/HALO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Python library for building hierarchical and recursively self-improving AI agent systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. CrewAI
    5. Haystack
    6. OpenAI Assistants API

    AI recommended 6 alternatives but never named context-labs/HALO. 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 context-labs/HALO?
    pass
    AI named context-labs/HALO explicitly

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

  • If a team adopts context-labs/HALO in production, what risks or prerequisites should they evaluate first?
    pass
    AI named context-labs/HALO 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 context-labs/HALO solve, and who is the primary audience?
    pass
    AI named context-labs/HALO explicitly

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

Embed your GEO score

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MARKDOWN (README)
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context-labs/HALO — 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