RRepoGEO

REPOGEO REPORT · LITE

ag2ai/fastagency

Default branch main · commit f353682d · scanned 6/5/2026, 1:01:57 AM

GitHub: 540 stars · 64 forks

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 ag2ai/fastagency, 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 core differentiator in the README and Description

    Why:

    CURRENT
    Description: 'The fastest way to bring multi-agent workflows to production.'
README H1: '# FastAgency\n<b>The fastest way to bring multi-agent workflows to production.</b>'
    COPY-PASTE FIX
    Update the repository's 'Description' field to: 'A unified programming interface for deploying existing multi-agent workflows (e.g., AG2/AutoGen) to production, not another agent framework.' Also, ensure the very first lines of the README (after the H1) clearly state this distinction.
  • mediumtopics#2
    Expand repository topics to include deployment and production keywords

    Why:

    CURRENT
    autogen, llm, mesop, multiagent
    COPY-PASTE FIX
    autogen, llm, mesop, multiagent, deployment, production, agent-deployment, workflow-orchestration
  • lowcomparison#3
    Add a 'Comparison' or 'How is FastAgency different?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison' or 'How is FastAgency different?' that clarifies its role as a deployment layer for agent frameworks (like AG2/AutoGen) and how it interacts with or differs from general-purpose deployment platforms (like Ray, Kubernetes) or other agent frameworks (like LangChain).

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 ag2ai/fastagency
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. langchain-ai/langchain · recommended 2×
  3. kubernetes/kubernetes · recommended 1×
  4. Anyscale Platform · recommended 1×
  5. langchain-ai/langserve · recommended 1×
  • CATEGORY QUERY
    How to quickly deploy multi-agent AI systems into a production environment?
    you: not recommended
    AI recommended (in order):
    1. Ray (ray-project/ray)
    2. Ray AIR (ray-project/ray)
    3. Ray Serve (ray-project/ray)
    4. Kubernetes (kubernetes/kubernetes)
    5. Anyscale Platform
    6. LangChain (langchain-ai/langchain)
    7. LangServe (langchain-ai/langserve)
    8. AWS Fargate
    9. Google Cloud Run
    10. Azure Container Instances
    11. OpenAI Assistants API

    AI recommended 11 alternatives but never named ag2ai/fastagency. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks simplify building and deploying robust multi-agent LLM applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. AutoGen (microsoft/autogen)
    4. CrewAI (joaomdmoura/crewai)
    5. Haystack (deepset-ai/haystack)
    6. DSPy (stanfordnlp/dspy)
    7. Marvin (prefect-ai/marvin)

    AI recommended 7 alternatives but never named ag2ai/fastagency. 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 ag2ai/fastagency?
    pass
    AI named ag2ai/fastagency explicitly

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

  • If a team adopts ag2ai/fastagency in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ag2ai/fastagency 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 ag2ai/fastagency solve, and who is the primary audience?
    pass
    AI named ag2ai/fastagency explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite