RRepoGEO

REPOGEO REPORT · LITE

FareedKhan-dev/production-grade-agentic-system

Default branch master · commit 20def050 · scanned 6/2/2026, 2:01:49 AM

GitHub: 813 stars · 182 forks

AI VISIBILITY SCORE
33 /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
2 / 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 FareedKhan-dev/production-grade-agentic-system, 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 sentence to clarify it's a reference architecture

    Why:

    CURRENT
    Modern agentic AI systems, whether running in development, staging, or production, are built as a set of well-defined architectural layers rather than a single service.
    COPY-PASTE FIX
    This repository presents a **production-grade reference architecture and blueprint** for modern agentic AI systems, built as a set of well-defined architectural layers rather than a single service.
  • mediumtopics#2
    Expand repository topics to include architectural and system design terms

    Why:

    CURRENT
    agentic-ai, langchain, langgraph, production
    COPY-PASTE FIX
    agentic-ai, langchain, langgraph, production, system-architecture, ai-system-design, reference-implementation, best-practices
  • lowreadme#3
    Add a 'What this is NOT' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## What this is NOT (and what it IS)
    This repository provides a **reference architecture and implementation blueprint** for building robust, production-ready agentic AI systems. It is **not a new AI agent framework** like LangChain, AutoGen, or CrewAI. Instead, it demonstrates how to integrate and orchestrate such frameworks (e.g., using LangChain/LangGraph) within a comprehensive, layered system designed for reliability, scalability, and observability in production environments.

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 FareedKhan-dev/production-grade-agentic-system
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. Haystack · recommended 2×
  3. LlamaIndex · recommended 1×
  4. OpenAI Assistants API · recommended 1×
  5. Ray · recommended 1×
  • CATEGORY QUERY
    How to architect a reliable and scalable agentic AI system for production environments?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Assistants API
    4. Haystack
    5. Ray
    6. Kubernetes
    7. PostgreSQL with pgvector

    AI recommended 7 alternatives but never named FareedKhan-dev/production-grade-agentic-system. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help manage memory, orchestration, and fault handling in multi-agent AI systems?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. Haystack
    4. CrewAI
    5. SPADE
    6. Mesa
    7. JADE

    AI recommended 7 alternatives but never named FareedKhan-dev/production-grade-agentic-system. 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 FareedKhan-dev/production-grade-agentic-system?
    pass
    AI named FareedKhan-dev/production-grade-agentic-system explicitly

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

  • If a team adopts FareedKhan-dev/production-grade-agentic-system in production, what risks or prerequisites should they evaluate first?
    pass
    AI named FareedKhan-dev/production-grade-agentic-system 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 FareedKhan-dev/production-grade-agentic-system solve, and who is the primary audience?
    pass
    AI did not name FareedKhan-dev/production-grade-agentic-system — 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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