RRepoGEO

REPOGEO REPORT · LITE

https-deeplearning-ai/agentic-ai-public

Default branch main · commit 0ee5559e · scanned 6/3/2026, 9:47:42 AM

GitHub: 558 stars · 317 forks

AI VISIBILITY SCORE
10 /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
0 / 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 https-deeplearning-ai/agentic-ai-public, 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 README H1 and opening sentence to clarify purpose

    Why:

    CURRENT
    # Reflective Research Agent (FastAPI + Postgres, single container)

A FastAPI web app that plans a research workflow, runs tool-using agents (Tavily, arXiv, Wikipedia), and stores task state/results in Postgres.
    COPY-PASTE FIX
    # Agentic AI Application Template: Reflective Research Agent

This repository provides a **ready-to-use template and example** for building web-based AI research agents. It demonstrates a multi-step agent workflow using FastAPI, integrating tools like Tavily, arXiv, and Wikipedia, with state persistence in Postgres. Designed as a practical reference for the Agentic Workflow course, it showcases how to deploy a reflective research agent service.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    agentic-ai, llm-agents, research-agent, fastapi, postgres, ai-workflow, deeplearning-ai, educational-example, tool-use, multi-agent-system
  • highlicense#3
    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 an appropriate open-source license (e.g., MIT, Apache-2.0, or a custom license if preferred, clearly stating its terms).

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 https-deeplearning-ai/agentic-ai-public
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. tiangolo/fastapi · recommended 1×
  4. facebook/react · recommended 1×
  5. vercel/next.js · recommended 1×
  • CATEGORY QUERY
    How to build a web-based AI research agent with tool integration and progress tracking?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. FastAPI (tiangolo/fastapi)
    4. React (facebook/react)
    5. Next.js (vercel/next.js)
    6. PostgreSQL
    7. MongoDB
    8. Celery (celery/celery)
    9. Redis Queue (rq/rq)
    10. Docker
    11. Kubernetes (kubernetes/kubernetes)

    AI recommended 11 alternatives but never named https-deeplearning-ai/agentic-ai-public. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to orchestrate multi-step AI agent workflows with state persistence and reporting.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Microsoft Semantic Kernel
    3. OpenAI Assistants API
    4. Apache Airflow
    5. Prefect
    6. Temporal
    7. AWS Step Functions

    AI recommended 7 alternatives but never named https-deeplearning-ai/agentic-ai-public. 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 https-deeplearning-ai/agentic-ai-public?
    pass
    AI did not name https-deeplearning-ai/agentic-ai-public — 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 https-deeplearning-ai/agentic-ai-public in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name https-deeplearning-ai/agentic-ai-public — 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?

  • In one sentence, what problem does the repo https-deeplearning-ai/agentic-ai-public solve, and who is the primary audience?
    pass
    AI did not name https-deeplearning-ai/agentic-ai-public — 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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https-deeplearning-ai/agentic-ai-public — 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