RRepoGEO

REPOGEO REPORT · LITE

dapr/dapr-agents

Default branch main · commit 2156600a · scanned 6/13/2026, 8:41:38 AM

GitHub: 691 stars · 124 forks

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 dapr/dapr-agents, 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 README intro to clarify AI agent focus and distinguish from Dapr management

    Why:

    CURRENT
    Dapr Agents is a developer framework designed to build production-grade resilient AI agent systems that operate at scale. Built on top of the battle-tested Dapr project, it enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while leveraging built-in observability and stateful workflow execution to guarantee agentic workflows complete successfully, no matter how complex.
    COPY-PASTE FIX
    Dapr Agents is a developer framework designed to build production-grade resilient AI agent systems that operate at scale. Crucially, Dapr Agents is focused on empowering developers to create sophisticated AI agents, distinct from Dapr's core runtime which manages general microservices. Built on top of the battle-tested Dapr project, it enables software developers to create AI agents that reason, act, and collaborate using Large Language Models (LLMs), while leveraging built-in observability and stateful workflow execution to guarantee agentic workflows complete successfully, no matter how complex.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-agents, llm, large-language-models, dapr, agentic-ai, workflow-orchestration, distributed-systems, kubernetes, python
  • mediumreadme#3
    Add a sentence to README clarifying unique value for AI agents over generic workflow tools

    Why:

    COPY-PASTE FIX
    While leveraging robust distributed systems capabilities, Dapr Agents is purpose-built for the unique challenges of AI agent development, offering specialized primitives beyond general-purpose workflow or MLOps platforms. (Add this sentence after the introductory paragraph.)

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 dapr/dapr-agents
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. temporalio/temporal · recommended 1×
  3. uber/cadence · recommended 1×
  4. apache/airflow · recommended 1×
  5. akka/akka · recommended 1×
  • CATEGORY QUERY
    How to build resilient AI agents with stateful workflows and distributed execution?
    you: not recommended
    AI recommended (in order):
    1. Temporal (temporalio/temporal)
    2. Cadence (uber/cadence)
    3. Apache Airflow (apache/airflow)
    4. Ray (ray-project/ray)
    5. Akka (akka/akka)
    6. Azure Durable Functions
    7. AWS Step Functions
    8. Google Cloud Workflows

    AI recommended 8 alternatives but never named dapr/dapr-agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for deploying scalable LLM-based AI agents in Kubernetes with built-in observability?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow (kubeflow/kubeflow)
    2. Ray (ray-project/ray)
    3. KubeRay (ray-project/kuberay)
    4. OpenShift AI
    5. MLflow (mlflow/mlflow)
    6. Seldon Core (SeldonIO/seldon-core)
    7. Cortex (cortexlabs/cortex)
    8. BentoML (bentoml/bentoml)

    AI recommended 8 alternatives but never named dapr/dapr-agents. 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 dapr/dapr-agents?
    pass
    AI named dapr/dapr-agents explicitly

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

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

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

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