RRepoGEO

REPOGEO REPORT · LITE

plurai-ai/intellagent

Default branch main · commit d0312269 · scanned 6/29/2026, 6:06:47 AM

GitHub: 1,240 stars · 155 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 plurai-ai/intellagent, 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 opening to clearly state its unique value for agent diagnosis and optimization

    Why:

    CURRENT
    Simulate interactions, analyze performance, and gain actionable insights for conversational agents. Test, evaluate, and optimize your agent to ensure reliable real-world deployment. IntellAgent is an advanced multi-agent framework that transforms the evaluation and optimization of conversational agents. By simulating thousands of realistic, challenging interactions, IntellAgent stress-tests agents to uncover hidden failure points. These insights enhance agent performance, reliability, and user experience.
    COPY-PASTE FIX
    IntellAgent is a specialized framework for **comprehensive diagnosis and optimization of AI agents**, particularly conversational ones. It uniquely provides a robust environment to **simulate realistic, challenging user interactions** and **automatically generate thousands of edge-case scenarios**, uncovering blind spots and enhancing agent performance and reliability before real-world deployment.
  • mediumtopics#2
    Add more specific topics to clarify the repo's focus on agent testing and optimization

    Why:

    CURRENT
    agent, evaluation, llmops, simulator, synthetic-data
    COPY-PASTE FIX
    agent, evaluation, llmops, simulator, synthetic-data, agent-testing, llm-agent-optimization, ai-agent-diagnosis, stress-testing
  • lowreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ## Comparison to Alternatives 

While tools like LangChain and LlamaIndex provide foundational frameworks for building LLM agents, IntellAgent specializes in the *diagnosis and optimization* of these agents. Unlike general testing utilities, IntellAgent offers a structured, multi-agent simulation environment to generate thousands of realistic, edge-case scenarios, specifically designed to stress-test and uncover hidden failure points in complex conversational AI systems.

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 plurai-ai/intellagent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. OpenAI Evals · recommended 1×
  4. unittest.mock · recommended 1×
  5. pytest-mock · recommended 1×
  • CATEGORY QUERY
    Tools for simulating user interactions to test LLM agent reliability?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI Evals
    4. unittest.mock
    5. pytest-mock
    6. Playwright
    7. Selenium

    AI recommended 7 alternatives but never named plurai-ai/intellagent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Frameworks for generating diverse edge-case scenarios to optimize AI agent performance?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Evals (openai/evals)
    2. Microsoft Counterfit (Azure/counterfit)
    3. DeepMind's Psychlab / AI Safety Gridworlds
    4. Ray RLib (ray-project/ray)
    5. Gymnasium (formerly OpenAI Gym) (Farama-Foundation/Gymnasium)
    6. American Fuzzy Lop (AFL++) (AFLplusplus/AFLplusplus)
    7. libFuzzer

    AI recommended 7 alternatives but never named plurai-ai/intellagent. 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 plurai-ai/intellagent?
    pass
    AI named plurai-ai/intellagent explicitly

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

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

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

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plurai-ai/intellagent — 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