RRepoGEO

REPOGEO REPORT · LITE

apache/burr

Default branch main · commit 6ec8ffcb · scanned 5/22/2026, 9:27:07 AM

GitHub: 2,012 stars · 134 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 apache/burr, 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
    Strengthen README opening to explicitly position as an AI agent framework

    Why:

    CURRENT
    Apache Burr (incubating) makes it easy to develop applications that make decisions (chatbots, agents, simulations, etc...) from simple python building blocks.
    COPY-PASTE FIX
    Apache Burr (incubating) is a Python framework for building robust, stateful AI applications and agents (chatbots, simulations, etc...). Unlike general workflow tools, Burr offers an explicit, graph-based approach to defining application workflows and state transitions, coupled with integrated real-time monitoring and tracing.
  • mediumtopics#2
    Add more specific AI agent and LLM orchestration topics

    Why:

    CURRENT
    ai, burr, chatbot-framework, dags, generative-ai, graphs, hacktoberfest, llmops, llms, mlops, persistent-data-structure, state-machine, state-management, visibility
    COPY-PASTE FIX
    ai, burr, chatbot-framework, dags, generative-ai, graphs, hacktoberfest, llmops, llms, mlops, persistent-data-structure, state-machine, state-management, visibility, ai-agents, llm-orchestration
  • lowreadme#3
    Add a 'Why Burr?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Apache Burr?
While many tools exist for building AI applications, Apache Burr stands out with its explicit, graph-based approach to defining application workflows and state transitions. This provides unparalleled visibility and control over complex decision-making logic, making it ideal for robust, production-ready AI agents and chatbots. Unlike general-purpose workflow engines, Burr is purpose-built for the unique challenges of stateful AI applications, offering integrated real-time monitoring, tracing, and pluggable persistence.

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 apache/burr
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. langchain-ai/langserve · recommended 1×
  3. LangSmith · recommended 1×
  4. microsoft/semantic-kernel · recommended 1×
  5. Azure AI Studio · recommended 1×
  • CATEGORY QUERY
    How to build conversational AI agents with persistent state and real-time tracing?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LangServe (langchain-ai/langserve)
    3. LangSmith
    4. Semantic Kernel (microsoft/semantic-kernel)
    5. Azure AI Studio
    6. Azure Monitor
    7. Azure Cosmos DB
    8. Azure SQL Database
    9. Rasa (RasaHQ/rasa)
    10. Rasa X
    11. PostgreSQL
    12. MongoDB
    13. Redis (redis/redis)
    14. Haystack (deepset-ai/haystack)
    15. Deepset Cloud
    16. OpenAI API
    17. Anthropic API
    18. Flask (pallets/flask)
    19. Django (django/django)
    20. Express.js (expressjs/express)
    21. OpenTelemetry
    22. Datadog
    23. New Relic
    24. Honeycomb
    25. Botpress (botpress/botpress)
    26. Voiceflow

    AI recommended 26 alternatives but never named apache/burr. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for managing stateful workflows and complex decision-making logic in Python applications?
    you: not recommended
    AI recommended (in order):
    1. Temporal
    2. Cadence
    3. Apache Airflow
    4. Prefect
    5. AWS Step Functions
    6. Bonobo

    AI recommended 6 alternatives but never named apache/burr. 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 apache/burr?
    pass
    AI named apache/burr explicitly

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

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