RRepoGEO

REPOGEO REPORT · LITE

MiniMax-AI/Mini-Agent

Default branch main · commit d76a4f63 · scanned 5/23/2026, 11:38:21 PM

GitHub: 2,652 stars · 393 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 MiniMax-AI/Mini-Agent, 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 paragraph to emphasize its role as a production-grade framework

    Why:

    CURRENT
    Mini Agent is a minimal yet professional demo project that showcases the best practices for building agents with the MiniMax M2.5 model. Leveraging an Anthropic-compatible API, it fully supports interleaved thinking to unlock M2's powerful reasoning capabilities for long, complex tasks.
    COPY-PASTE FIX
    Mini Agent is a production-grade framework and library for building robust AI agents, showcasing best practices with the MiniMax M2.5 model. Leveraging an Anthropic-compatible API, it fully supports interleaved thinking to unlock powerful reasoning capabilities for long, complex tasks, and is designed for simplicity, extensibility, and rapid prototyping.
  • mediumtopics#2
    Expand GitHub topics to include more descriptive terms for AI agent frameworks

    Why:

    CURRENT
    agent, llm, minimax
    COPY-PASTE FIX
    agent, llm, minimax, ai-agents, agent-framework, context-management, tool-use, memory, orchestration, production-ready
  • lowreadme#3
    Add a 'Comparison' or 'Why Mini Agent?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Mini Agent? / Comparison to other frameworks

Mini Agent differentiates itself by emphasizing a lightweight, modular, and less opinionated framework for building AI agents. It prioritizes simplicity, extensibility, and rapid prototyping, making it easier to adapt and use for specific needs compared to more comprehensive, opinionated alternatives.

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 MiniMax-AI/Mini-Agent
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 2×
  2. run-llama/llama_index · recommended 2×
  3. microsoft/semantic-kernel · recommended 2×
  4. deepset-ai/haystack · recommended 2×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to build a production-ready AI agent with persistent memory for long, complex tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. Pinecone
    3. Weaviate (weaviate/weaviate)
    4. Chroma (chroma-core/chroma)
    5. LlamaIndex (run-llama/llama_index)
    6. Milvus (milvus-io/milvus)
    7. Qdrant (qdrant/qdrant)
    8. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    9. Azure AI Search
    10. Haystack (deepset-ai/haystack)
    11. Elasticsearch (elastic/elasticsearch)
    12. OpenSearch (opensearch-project/OpenSearch)
    13. PostgreSQL
    14. pgvector (pgvector/pgvector)
    15. FastAPI (tiangolo/fastapi)
    16. Flask (pallets/flask)
    17. OpenAI API
    18. Anthropic API

    AI recommended 18 alternatives but never named MiniMax-AI/Mini-Agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to develop AI agents with integrated tools and intelligent context management.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. AutoGPT (Significant-Gravitas/AutoGPT)
    5. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    6. CrewAI (joaomdmoura/crewai)

    AI recommended 6 alternatives but never named MiniMax-AI/Mini-Agent. 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 MiniMax-AI/Mini-Agent?
    pass
    AI named MiniMax-AI/Mini-Agent explicitly

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

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

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

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MiniMax-AI/Mini-Agent — 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