RRepoGEO

REPOGEO REPORT · LITE

FoundationAgents/awesome-foundation-agents

Default branch main · commit 692cfbba · scanned 5/9/2026, 7:57:37 AM

GitHub: 2,124 stars · 211 forks

AI VISIBILITY SCORE
22 /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
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 FoundationAgents/awesome-foundation-agents, 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
    Reposition the README's opening statement to clarify repo type

    Why:

    CURRENT
    We maintain a curated collection of papers exploring the path towards Foundation Agents, with a focus on formulating the core concepts and navigating the research landscape.
    COPY-PASTE FIX
    This is an awesome list and curated collection of essential research papers, open-source repositories, and insightful blogs exploring the path towards Foundation Agents. We focus on formulating core concepts and navigating the research landscape, distinct from agent development frameworks or tools.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    awesome-list, foundation-agents, ai-agents, large-language-models, llm-agents, artificial-intelligence, research-papers, machine-learning, agentic-ai
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/2504.01990

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 FoundationAgents/awesome-foundation-agents
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. TensorFlow · recommended 2×
  4. PyTorch · recommended 2×
  5. Kubernetes · recommended 2×
  • CATEGORY QUERY
    Where can I find a curated list of research on building intelligent autonomous agents?
    you: not recommended
    AI recommended (in order):
    1. arXiv
    2. Papers With Code
    3. DeepMind Publications
    4. OpenAI Research
    5. AAMAS (International Conference on Autonomous Agents and Multiagent Systems)
    6. IJCAI (International Joint Conference on Artificial Intelligence)
    7. AAAI (Association for the Advancement of Artificial Intelligence Conference)
    8. Google AI Blog
    9. Google Research Publications
    10. The Stanford Institute for Human-Centered Artificial Intelligence (HAI) Research

    AI recommended 10 alternatives but never named FoundationAgents/awesome-foundation-agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are key architectural components and challenges for developing advanced AI agent systems?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. SpeechRecognition
    3. Hugging Face Transformers
    4. Apache Kafka
    5. RabbitMQ
    6. LangChain
    7. LlamaIndex
    8. TensorFlow
    9. PyTorch
    10. Faiss
    11. Pinecone
    12. Weaviate
    13. Neo4j
    14. ArangoDB
    15. Prolog
    16. Datalog
    17. Redis
    18. PostgreSQL
    19. MongoDB
    20. Chroma
    21. Milvus
    22. Selenium
    23. Playwright
    24. Robotics Operating System (ROS)
    25. REST APIs
    26. gRPC
    27. OpenAI Gym
    28. Unity ML-Agents
    29. TensorFlow
    30. PyTorch
    31. Stable Baselines3
    32. Ray RLlib
    33. LangChain
    34. LlamaIndex
    35. Apache Airflow
    36. Prefect
    37. Kubernetes
    38. Docker Swarm
    39. Ray
    40. Dask
    41. ONNX Runtime
    42. TensorRT
    43. Kubernetes
    44. Prometheus
    45. Grafana
    46. SHAP
    47. LIME
    48. IBM AI Fairness 360
    49. OAuth2
    50. JWT

    AI recommended 50 alternatives but never named FoundationAgents/awesome-foundation-agents. 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 FoundationAgents/awesome-foundation-agents?
    pass
    AI did not name FoundationAgents/awesome-foundation-agents — 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 FoundationAgents/awesome-foundation-agents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named FoundationAgents/awesome-foundation-agents 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 FoundationAgents/awesome-foundation-agents solve, and who is the primary audience?
    pass
    AI did not name FoundationAgents/awesome-foundation-agents — 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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FoundationAgents/awesome-foundation-agents — 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