RRepoGEO

REPOGEO REPORT · LITE

Agent-RL/ReCall

Default branch main · commit aaf16b31 · scanned 6/29/2026, 1:58:45 PM

GitHub: 1,400 stars · 87 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Agent-RL/ReCall, 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
  • highreadme#1
    Add a concise 'What ReCall Is (and Isn't)' statement to the README

    Why:

    COPY-PASTE FIX
    Add a short, direct statement near the top of the README, perhaps right after the initial intro paragraph, like: "ReCall focuses specifically on training LLMs for advanced tool use and reasoning via RL, distinct from general continual learning or memory systems in RL."
  • mediumabout#2
    Refine the repository description for maximum clarity and keyword density

    Why:

    CURRENT
    ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning & ReCall: Learning to Reason with Tool Call for LLMs via Reinforcement Learning
    COPY-PASTE FIX
    ReCall: A novel framework for training LLMs to reason with and call arbitrary tools via Reinforcement Learning, without supervised tool-use data. Successor to ReSearch (for search tools).
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## 💡 Comparison with Alternatives
    Unlike general LLM orchestration frameworks (e.g., LangChain, LlamaIndex) or rule-based autonomous agents (e.g., AutoGPT, BabyAGI), ReCall specifically focuses on *training* LLMs to *learn* tool use and reasoning from scratch using reinforcement learning, without requiring extensive supervised datasets. Our approach is distinct from methods like Toolformer which rely on self-supervised data generation for tool augmentation.

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 Agent-RL/ReCall
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AutoGPT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AutoGPT · recommended 2×
  2. LangChain · recommended 2×
  3. LlamaIndex · recommended 2×
  4. Toolformer · recommended 1×
  5. BabyAGI · recommended 1×
  • CATEGORY QUERY
    How can I train an LLM to effectively use external tools without supervised datasets?
    you: not recommended
    AI recommended (in order):
    1. Toolformer
    2. AutoGPT
    3. BabyAGI
    4. GPT-4
    5. Claude 3 Opus
    6. LangChain
    7. LlamaIndex
    8. Hugging Face's TRL

    AI recommended 8 alternatives but never named Agent-RL/ReCall. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable LLMs to reason and combine multiple tools agentically using RL?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Stable Baselines3
    3. Ray RLlib
    4. LlamaIndex
    5. Hugging Face Transformers Agents
    6. transformers library
    7. AutoGPT
    8. RL4LLM
    9. Gymnasium

    AI recommended 9 alternatives but never named Agent-RL/ReCall. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 Agent-RL/ReCall?
    pass
    AI named Agent-RL/ReCall explicitly

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

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

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

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Agent-RL/ReCall — 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