RRepoGEO

REPOGEO REPORT · LITE

LiteLLM-Labs/litellm-agent-control-plane

Default branch main · commit 9a1bf3bd · scanned 6/17/2026, 2:37:56 AM

GitHub: 847 stars · 84 forks

AI VISIBILITY SCORE
27 /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
1 / 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 LiteLLM-Labs/litellm-agent-control-plane, 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

2 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 to clarify its role as an agent management layer

    Why:

    CURRENT
    # LiteLLM Agent Control Plane

1 place to call all your agents - OpenCode, Hermes, Claude Managed Agents, Cursor Agents API, DeepAgents.

LiteLLM Agent Control Plane sits on top of any runtime. Pick a runtime, create an agent, give your team one UI.
    COPY-PASTE FIX
    # LiteLLM Agent Control Plane: The Universal Management Layer for Your AI Agents

LiteLLM Agent Control Plane provides a single, unified platform to manage, deploy, schedule, and monitor *any* of your existing AI agents (OpenCode, Hermes, Claude Managed Agents, Cursor Agents API, DeepAgents, etc.), regardless of their underlying runtime. It's not an agent framework, but a control plane that sits *above* your agent runtimes, giving your team one UI for unified API access, session management, CRON schedules, and persistent memory across all agents.
  • mediumabout#2
    Refine the 'About' description to emphasize agent management and control plane

    Why:

    CURRENT
    1 place to call all your agents - OpenCode, Hermes, Claude Managed Agents, Cursor Agents API, DeepAgents.
    COPY-PASTE FIX
    A universal control plane to manage, deploy, schedule, and monitor all your AI agents (OpenCode, Hermes, Claude Managed Agents, Cursor Agents API, DeepAgents) across any runtime.

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 LiteLLM-Labs/litellm-agent-control-plane
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. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. microsoft/semantic-kernel · recommended 1×
  5. OpenAI Assistants API · recommended 1×
  • CATEGORY QUERY
    How can I manage and unify different AI agent APIs from various providers?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Microsoft Semantic Kernel (microsoft/semantic-kernel)
    4. OpenAI Assistants API
    5. Haystack (deepset-ai/haystack)
    6. NGINX
    7. Kong (Kong/kong)
    8. AWS API Gateway
    9. Zapier NLA
    10. Pipedream (PipedreamHQ/pipedream)
    11. Make

    AI recommended 11 alternatives but never named LiteLLM-Labs/litellm-agent-control-plane. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a platform to deploy, schedule, and maintain persistent state for multiple AI agents.
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. Kubeflow (kubeflow/kubeflow)
    3. AWS SageMaker
    4. Azure Machine Learning
    5. Google Cloud Vertex AI
    6. MLflow (mlflow/mlflow)
    7. Docker Swarm
    8. HashiCorp Nomad (hashicorp/nomad)
    9. Ray (ray-project/ray)
    10. Ray Serve (ray-project/ray)
    11. Hugging Face Inference Endpoints

    AI recommended 11 alternatives but never named LiteLLM-Labs/litellm-agent-control-plane. 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 LiteLLM-Labs/litellm-agent-control-plane?
    pass
    AI did not name LiteLLM-Labs/litellm-agent-control-plane — likely talking about a different project

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

  • If a team adopts LiteLLM-Labs/litellm-agent-control-plane in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LiteLLM-Labs/litellm-agent-control-plane 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 LiteLLM-Labs/litellm-agent-control-plane solve, and who is the primary audience?
    pass
    AI did not name LiteLLM-Labs/litellm-agent-control-plane — likely talking about a different project

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

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LiteLLM-Labs/litellm-agent-control-plane — 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