RRepoGEO

REPOGEO REPORT · LITE

Chorus-AIDLC/Chorus

Default branch main · commit 6b7aae8f · scanned 6/16/2026, 7:37:06 PM

GitHub: 1,001 stars · 89 forks

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 Chorus-AIDLC/Chorus, 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
  • highabout#1
    Clarify 'About' description to counter miscategorization

    Why:

    CURRENT
    The Agent Harness for AI-Human Collaboration, inspired by the AI-DLC (AI-Driven Development Lifecycle)
    COPY-PASTE FIX
    An AI Agent Orchestration and Collaboration Harness for AI-Human Teams, inspired by the AI-Driven Development Lifecycle (AI-DLC). Not for IPC or code generation.
  • highreadme#2
    Reinforce core purpose and 'what it is not' in README's opening paragraph

    Why:

    CURRENT
    Chorus is an agent harness — the infrastructure that wraps around LLM agents to manage session lifecycle, task state, sub-agent orchestration, observability, and failure recovery. It lets multiple AI Agents (with fine-grained, configurable permissions) and humans collaborate through the full workflow from requirements to delivery.
    COPY-PASTE FIX
    Chorus is an **AI agent orchestration and collaboration harness** — the infrastructure that wraps around LLM agents to manage session lifecycle, task state, sub-agent orchestration, observability, and failure recovery. It lets multiple AI Agents (with fine-grained, configurable permissions) and humans collaborate through the full workflow from requirements to delivery, **distinct from IPC or code generation frameworks.**
  • mediumtopics#3
    Expand topics to improve category visibility for AI agent orchestration

    Why:

    CURRENT
    agent-harness, ai-agents, ai-dlc, claude-code, harness, kanban, multi-agent, task-management
    COPY-PASTE FIX
    agent-harness, ai-agents, ai-dlc, claude-code, harness, kanban, multi-agent, task-management, ai-agent-orchestration, human-in-the-loop-ai, ai-workflow-management, agent-collaboration, llm-agents, collaborative-ai

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 Chorus-AIDLC/Chorus
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. AutoGen · recommended 1×
  3. CrewAI · recommended 1×
  4. Open Interpreter · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    What frameworks exist for orchestrating multiple AI agents in a collaborative development workflow?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. AutoGen
    3. CrewAI
    4. Open Interpreter
    5. Haystack
    6. LlamaIndex

    AI recommended 6 alternatives but never named Chorus-AIDLC/Chorus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to manage AI agent-driven development lifecycles with human verification and task state?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangServe
    3. LangSmith
    4. OpenAI Assistants API
    5. Microsoft Semantic Kernel
    6. Azure AI Studio
    7. Apache Airflow
    8. Prefect
    9. Cortex
    10. Humanloop

    AI recommended 10 alternatives but never named Chorus-AIDLC/Chorus. 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 Chorus-AIDLC/Chorus?
    pass
    AI named Chorus-AIDLC/Chorus explicitly

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

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

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

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Chorus-AIDLC/Chorus — 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