RRepoGEO

REPOGEO REPORT · LITE

modelscope/sirchmunk

Default branch main · commit 5b427f94 · scanned 6/28/2026, 7:12:03 AM

GitHub: 1,159 stars · 129 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
35 /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
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 modelscope/sirchmunk, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, rag, agentic-ai, knowledge-base, real-time, data-processing, ai-agents, information-retrieval, self-evolving-ai
  • highreadme#2
    Reposition the README H1 to explicitly state its LLM/RAG domain

    Why:

    CURRENT
    # Sirchmunk: Raw data to self-evolving intelligence, real-time.
    COPY-PASTE FIX
    # Sirchmunk: An agentic RAG framework for self-evolving LLM knowledge bases, real-time.
  • mediumreadme#3
    Add a sentence clarifying Sirchmunk's identity within ModelScope

    Why:

    COPY-PASTE FIX
    Sirchmunk is an open-source project developed under the ModelScope organization, providing an agentic RAG framework for self-evolving LLM knowledge bases.

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 modelscope/sirchmunk
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. Apache Kafka · recommended 1×
  3. Kafka Streams · recommended 1×
  4. ksqlDB · recommended 1×
  5. Apache Flink · recommended 1×
  • CATEGORY QUERY
    How to build a dynamic, self-evolving knowledge base for real-time data processing?
    you: not recommended
    AI recommended (in order):
    1. Apache Kafka
    2. Kafka Streams
    3. ksqlDB
    4. Apache Flink
    5. Apache Cassandra
    6. ScyllaDB
    7. Elasticsearch
    8. OpenSearch
    9. Neo4j
    10. TigerGraph
    11. Confluent Platform
    12. Materialize
    13. Redis
    14. ArangoDB
    15. Pub/Sub
    16. Dataflow
    17. BigQuery
    18. Cloud Natural Language API
    19. Kinesis
    20. Amazon Kinesis Data Analytics
    21. DynamoDB
    22. Amazon Neptune
    23. Apache Pulsar
    24. Apache Spark Structured Streaming
    25. Delta Lake
    26. Dgraph
    27. FaunaDB
    28. Hasura

    AI recommended 28 alternatives but never named modelscope/sirchmunk. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for agentic search and knowledge clustering without traditional vector indexes.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Neo4j
    3. GraphDB
    4. Prolog
    5. Datalog
    6. OpenCog Hyperon
    7. RDFox
    8. Stardog
    9. Gensim
    10. NetworkX

    AI recommended 10 alternatives but never named modelscope/sirchmunk. 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 modelscope/sirchmunk?
    pass
    AI named modelscope/sirchmunk explicitly

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

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

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

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modelscope/sirchmunk — 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