Xpersona

Crawler Summary

multi-agent-ielts-advisor answer-first brief

Agentic IELTS Learning System built with CrewAI. Uses multiple AI agents to analyze student IELTS performance, identify weaknesses, and generate personalized study plans based on current scores and target band goals. Agentic IELTS Learning System An AI-powered Learning Management System (LMS) demo built with CrewAI and Gemini. This project uses multiple AI agents to analyze IELTS student performance, identify weak skills, and generate personalized learning recommendations based on current scores and target band goals. --- Features * Analyze IELTS student data from CSV files * Evaluate Listening, Reading, Writing, and Speaking sco Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

multi-agent-ielts-advisor is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Agent DossierGITHUB REPOSSafety: 66/100

multi-agent-ielts-advisor

Agentic IELTS Learning System built with CrewAI. Uses multiple AI agents to analyze student IELTS performance, identify weaknesses, and generate personalized study plans based on current scores and target band goals. Agentic IELTS Learning System An AI-powered Learning Management System (LMS) demo built with CrewAI and Gemini. This project uses multiple AI agents to analyze IELTS student performance, identify weak skills, and generate personalized learning recommendations based on current scores and target band goals. --- Features * Analyze IELTS student data from CSV files * Evaluate Listening, Reading, Writing, and Speaking sco

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Buidinhtuyen24

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

View Source

Setup snapshot

  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Buidinhtuyen24

profilemedium
Observed Oct 9, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Oct 9, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

0

Examples

6

Snippets

0

Languages

python

Executable Examples

text

agentic-ielts-learning-system/
│
├── agents.py
├── tasks.py
├── main.py
├── students.csv
├── requirements.txt
├── .env
├── .env.example
├── .gitignore
│
└── output/
    └── reports.txt

csv

student_id,name,listening,reading,writing,speaking,target_band
1001,Nguyen Van A,5.5,5.0,4.5,5.0,6.5
1002,Tran Thi B,6.0,6.5,5.5,5.5,7.0
1003,Le Van C,4.5,4.0,4.5,5.0,6.0

bash

git clone https://github.com/yourusername/agentic-ielts-learning-system.git
cd agentic-ielts-learning-system

bash

python -m venv venv

bash

venv\Scripts\activate

bash

source venv/bin/activate

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Agentic IELTS Learning System built with CrewAI. Uses multiple AI agents to analyze student IELTS performance, identify weaknesses, and generate personalized study plans based on current scores and target band goals. Agentic IELTS Learning System An AI-powered Learning Management System (LMS) demo built with CrewAI and Gemini. This project uses multiple AI agents to analyze IELTS student performance, identify weak skills, and generate personalized learning recommendations based on current scores and target band goals. --- Features * Analyze IELTS student data from CSV files * Evaluate Listening, Reading, Writing, and Speaking sco

Full README

Agentic IELTS Learning System

An AI-powered Learning Management System (LMS) demo built with CrewAI and Gemini.

This project uses multiple AI agents to analyze IELTS student performance, identify weak skills, and generate personalized learning recommendations based on current scores and target band goals.

Features

  • Analyze IELTS student data from CSV files
  • Evaluate Listening, Reading, Writing, and Speaking scores
  • Compare current performance against target overall band
  • Identify strengths and weaknesses
  • Generate personalized study plans
  • Produce automated feedback reports
  • Demonstrate a Multi-Agent AI workflow using CrewAI

System Architecture

Agent 1 — Student Analyzer

Responsibilities:

  • Analyze student IELTS scores
  • Calculate current performance level
  • Identify weak and strong skills
  • Summarize learning needs

Agent 2 — Learning Advisor

Responsibilities:

  • Generate a personalized study plan
  • Recommend learning priorities
  • Suggest improvement strategies
  • Estimate progress path toward target band

Agent 3 — Feedback Agent

Responsibilities:

  • Review recommendations
  • Improve clarity and usefulness
  • Produce final student report

Project Structure

agentic-ielts-learning-system/
│
├── agents.py
├── tasks.py
├── main.py
├── students.csv
├── requirements.txt
├── .env
├── .env.example
├── .gitignore
│
└── output/
    └── reports.txt

Example Student Data

student_id,name,listening,reading,writing,speaking,target_band
1001,Nguyen Van A,5.5,5.0,4.5,5.0,6.5
1002,Tran Thi B,6.0,6.5,5.5,5.5,7.0
1003,Le Van C,4.5,4.0,4.5,5.0,6.0

Installation

Clone the repository:

git clone https://github.com/yourusername/agentic-ielts-learning-system.git
cd agentic-ielts-learning-system

Create a virtual environment:

python -m venv venv

Activate the environment:

Windows:

venv\Scripts\activate

Mac/Linux:

source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Environment Variables

Create a .env file:

GEMINI_API_KEY=your_api_key_here

Example template:

GEMINI_API_KEY=xxxxxxxxxxxxxxxxxxxx

Run the Project

python main.py

The system will:

  1. Load student data from students.csv
  2. Select a student
  3. Run the multi-agent workflow
  4. Generate a personalized IELTS report
  5. Save results to the output folder

Example Output

Student: Nguyen Van A

Current Scores:
Listening: 5.5
Reading: 5.0
Writing: 4.5
Speaking: 5.0

Target Overall Band: 6.5

Weakest Skill:
Writing

Recommended Learning Plan:
- Focus on Task 2 essay structure
- Practice grammar accuracy daily
- Write 3 essays per week
- Review high-band sample answers

Estimated Improvement Timeline:
3-4 months of consistent study

Technologies Used

  • Python
  • CrewAI
  • Google Gemini 2.5 Flash
  • Pandas
  • dotenv

Educational Purpose

This project was developed as a demonstration of Multi-Agent AI systems in an educational LMS scenario.

It showcases how AI agents can collaborate to analyze learner data and generate personalized recommendations automatically.

Future Improvements

  • Web interface with Streamlit
  • Student database integration
  • Progress tracking dashboard
  • Historical score analysis
  • CRM integration for student management
  • Email-based study recommendations

Author

Bui Dinh Tuyen

Data Science / AI Student

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

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Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T22:50:34.816Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Buidinhtuyen24",
    "href": "https://github.com/BuiDinhTuyen24/multi-agent-ielts-advisor",
    "sourceUrl": "https://github.com/BuiDinhTuyen24/multi-agent-ielts-advisor",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T19:13:38.183Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T19:13:38.183Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-buidinhtuyen24-multi-agent-ielts-advisor/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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