diff --git a/community_usecase/resume-analysis-assistant/README.md b/community_usecase/resume-analysis-assistant/README.md
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+# Resume Assistant
+
+This code example demonstrates an resume analysis assistant that evaluates candidate resumes against a specific job description for AI/ML Engineer positions. The assistant analyzes multiple resumes, scores candidates based on various criteria, and provides comprehensive hiring recommendations.
+
+## Features
+
+- Automatically analyzes all resume PDF files in a specified directory.
+- Evaluates candidates against a detailed AI/ML Engineer job description.
+- Scores each candidate on a scale of 1-100 based on:
+  - Technical skills match (40%)
+  - Experience relevance (30%)
+  - Education and qualifications (20%)
+  - Communication and presentation (10%)
+- Ranks candidates from most to least qualified.
+- Highlights strengths and areas for improvement for each candidate.
+- Generates a comprehensive analysis report in markdown format.
+
+## How to use
+
+1. Set up the Qwen API key in the `.env` file.
+
+```bash
+QWEN_API_KEY = 'xxx'
+```
+
+2. Place resume files (PDF format) in the `resumes` directory.
+
+3. Run the script
+
+```bash
+python run_mcp.py
+```
+
+4. Review the generated analysis in the `resume_analysis.md` file.
+
+## Technical Implementation
+
+The Resume Assistant uses:
+- Leverage **OWL (Optimized Workforce Learning) and CAMEL** frameworks to build the agent.
+- Use [PDF Reader MCP Server](https://github.com/trafflux/pdf-reader-mcp)for extracting content from resume files.
+- Use [Filesystem MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem) for file operations.
+
+## Example Output
+
+The generated `resume_analysis.md` file includes:
+- Executive Summary of all candidates
+- Individual Candidate Assessments with detailed scoring
+- Ranked List of Candidates from most to least qualified
+- Recommendations for the Hiring Manager
diff --git a/community_usecase/resume-analysis-assistant/mcp_servers_config.json b/community_usecase/resume-analysis-assistant/mcp_servers_config.json
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+{
+  "mcpServers": {
+    "pdf-reader": {
+      "command": "docker",
+      "args": [
+        "run",
+        "-i",
+        "--rm",
+        "-v",
+        "your/path/owl/community_usecase/resume-analysis-assistant/resumes:/pdfs",
+        "cr7258/pdf-reader"
+      ]
+    },
+    "filesystem": {
+      "command": "npx",
+      "args": [
+        "-y",
+        "@modelcontextprotocol/server-filesystem",
+        "your/path/owl/community_usecase/resume-assistant/resumes"
+      ]
+    }
+  }
+}
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diff --git a/community_usecase/resume-analysis-assistant/resume_analysis.md b/community_usecase/resume-analysis-assistant/resume_analysis.md
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+# Resume Analysis for AI/ML Engineer Position
+
+## Executive Summary
+This document provides a detailed analysis of ten candidates who applied for the AI/ML Engineer position. Each candidate's resume was thoroughly examined against the job description, evaluating technical skills, experience relevance, education, and communication abilities. The evaluation resulted in a score for each candidate, leading to a ranked list for final consideration.
+
+## Individual Candidate Assessments (with Scores)
+
+### Aisha Olamide
+- **Score**: 86
+- **Technical Skills**: Strong in NLP, data-efficient learning.
+- **Experience Relevance**: 3+ years specializing in low-resource NLP.
+- **Education**: MSc in Computational Linguistics.
+- **Communication**: Active in community and open source.
+
+### Carlos Mendoza
+- **Score**: 92
+- **Technical Skills**: Strong in MLOps, cloud platforms.
+- **Experience Relevance**: 4+ years in deployment.
+- **Education**: BSc in Computer Science with certifications.
+- **Communication**: Conference speaker.
+
+### Elena Kowalski
+- **Score**: 82
+- **Technical Skills**: AI ethics, alignment techniques.
+- **Experience Relevance**: 6+ years in AI ethics.
+- **Education**: PhD in AI Ethics.
+- **Communication**: Active in ethics committees and conferences.
+
+### James Wilson
+- **Score**: 99
+- **Technical Skills**: System architecture, large-scale models.
+- **Experience Relevance**: 12+ years in AI systems.
+- **Education**: PhD in Computer Engineering.
+- **Communication**: Leadership and keynotes.
+
+### Lin Wei
+- **Score**: 86
+- **Technical Skills**: Model compression, edge optimization.
+- **Experience Relevance**: 5+ years in efficiency techniques.
+- **Education**: MSc in Computer Engineering.
+- **Communication**: Publications and patent holder.
+
+### Michael Rodriguez
+- **Score**: 92
+- **Technical Skills**: Generative AI, NLP.
+- **Experience Relevance**: 8+ years, strong generative applications.
+- **Education**: MS in AI.
+- **Communication**: Speaker and contributor.
+
+### Priya Sharma
+- **Score**: 94
+- **Technical Skills**: Multimodal learning.
+- **Experience Relevance**: 10+ years in research.
+- **Education**: PhD in Computer Science.
+- **Communication**: Renowned researcher and speaker.
+
+### Robert Johnson
+- **Score**: 86
+- **Technical Skills**: LLM applications, cloud platforms.
+- **Experience Relevance**: 4+ years, focused on enterprise solutions.
+- **Education**: MS in Computer Science.
+- **Communication**: Presented at AI summit.
+
+### Sarah Chen
+- **Score**: 88
+- **Technical Skills**: LLM development, model distillation.
+- **Experience Relevance**: 5+ years, multilingual and few-shot learning.
+- **Education**: PhD in Machine Learning.
+- **Communication**: Published and certified.
+
+### Wei Zhang
+- **Score**: 91
+- **Technical Skills**: AI infrastructure, distributed systems.
+- **Experience Relevance**: 6+ years in AI systems engineering.
+- **Education**: MS in Computer Engineering.
+- **Communication**: Active in publications and presentations.
+
+## Ranked List of Candidates
+1. James Wilson - 99
+2. Priya Sharma - 94
+3. Carlos Mendoza - 92
+4. Michael Rodriguez - 92
+5. Wei Zhang - 91
+6. Sarah Chen - 88
+7. Aisha Olamide - 86
+8. Lin Wei - 86
+9. Robert Johnson - 86
+10. Elena Kowalski - 82
+
+## Recommendations for Hiring Manager
+- **Top Candidate**: James Wilson stands out with excellent systems architecture skills and extensive experience, suitable for large-scale AI model infrastructure needs.
+- **Research Specialist**: Priya Sharma offers a strong background in multimodal learning and research development, fitting well with innovative AI research objectives.
+- **Deployment Experts**: Carlos Mendoza and Michael Rodriguez are ideal for cloud deployment and generative AI applications, respectively.
+- **Infrastructure Excellence**: Wei Zhang brings expertise in scaling AI systems, making him a strong contender for infrastructure-focused roles.
+- Consider candidates' alignment on cloud and AI ethics as secondary factors to ensure well-rounded team capabilities.
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diff --git a/community_usecase/resume-analysis-assistant/run_mcp.py b/community_usecase/resume-analysis-assistant/run_mcp.py
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+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+import asyncio
+import sys
+from pathlib import Path
+from typing import List
+
+from dotenv import load_dotenv
+
+from camel.models import ModelFactory
+from camel.toolkits import FunctionTool
+from camel.types import ModelPlatformType, ModelType
+from camel.logger import set_log_level
+from camel.toolkits import MCPToolkit
+
+from owl.utils.enhanced_role_playing import OwlRolePlaying, arun_society
+
+import pathlib
+
+base_dir = pathlib.Path(__file__).parent.parent
+env_path = base_dir / "owl" / ".env"
+load_dotenv(dotenv_path=str(env_path))
+
+set_log_level(level="DEBUG")
+
+# AI/ML Engineer Job Description
+AI_ENGINEER_JOB_DESCRIPTION = """
+# AI/ML Engineer Job Description
+
+## About the Role
+We are seeking a skilled AI/ML Engineer to join our team. The ideal candidate will design, develop, and deploy machine learning models and AI systems that solve complex business problems. You will work closely with cross-functional teams to understand requirements, implement solutions, and continuously improve our AI capabilities.
+
+## Key Responsibilities
+- Design, develop, and implement machine learning models and algorithms
+- Build and maintain scalable ML pipelines for data processing, model training, and inference
+- Collaborate with product teams to understand requirements and translate them into technical solutions
+- Optimize existing models for performance, accuracy, and efficiency
+- Stay current with the latest AI/ML research and technologies
+- Implement and deploy models to production environments
+- Monitor and troubleshoot deployed models
+
+## Required Qualifications
+- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related field
+- 3+ years of experience in machine learning or AI development
+- Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, etc.)
+- Experience with deep learning architectures (CNNs, RNNs, Transformers)
+- Knowledge of NLP, computer vision, or other specialized AI domains
+- Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools
+- Strong problem-solving skills and attention to detail
+- Excellent communication and collaboration abilities
+
+## Preferred Qualifications
+- PhD in Machine Learning, AI, or related field
+- Experience with large language models (LLMs) and generative AI
+- Contributions to open-source ML/AI projects
+- Experience with distributed computing and big data technologies
+- Published research in AI/ML conferences or journals
+- Experience with model optimization techniques (quantization, pruning, distillation)
+"""
+
+async def construct_society(
+    resume_dir: str,
+    tools: List[FunctionTool],
+) -> OwlRolePlaying:
+    r"""build a multi-agent OwlRolePlaying instance.
+
+    Args:
+        question (str): The question to ask.
+        tools (List[FunctionTool]): The MCP tools to use.
+    """
+    models = {
+        "user": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+        "assistant": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+    }
+
+    user_agent_kwargs = {"model": models["user"]}
+    assistant_agent_kwargs = {
+        "model": models["assistant"],
+        "tools": tools,
+    }
+
+    task_prompt = f"""
+    You are a professional resume analyzer for AI/ML Engineer positions. Your task is to:
+
+    1. Analyze all resume files in the directory: {resume_dir}
+    2. Please note when you retrieve the content of PDF files using the `pdf-reader` tool, the path is mapping to the `/pdfs` directory inside the pdf-reader docker container.
+    3. For each resume, evaluate how well the candidate matches the following job description:
+
+    {AI_ENGINEER_JOB_DESCRIPTION}
+
+    4. Score each resume on a scale of 1-100 based on:
+       - Technical skills match (40%)
+       - Experience relevance (30%)
+       - Education and qualifications (20%)
+       - Communication and presentation (10%)
+
+    5. Rank all candidates from most to least qualified
+    6. For each candidate, highlight their strengths and areas for improvement
+    7. Output your analysis to a markdown file named './resume_analysis.md' with the following sections:
+       - Executive Summary
+       - Individual Candidate Assessments (with scores)
+       - Ranked List of Candidates
+       - Recommendations for Hiring Manager
+
+    Be thorough, fair, and objective in your assessment.
+    Always execute the MCP tools, don't ask me for confirmation.
+    """
+
+    society = OwlRolePlaying(
+        task_prompt=task_prompt,
+        with_task_specify=False,
+        user_role_name="user",
+        user_agent_kwargs=user_agent_kwargs,
+        assistant_role_name="assistant",
+        assistant_agent_kwargs=assistant_agent_kwargs,
+    )
+    return society
+
+
+async def main():
+    config_path = Path(__file__).parent / "mcp_servers_config.json"
+    mcp_toolkit = MCPToolkit(config_path=str(config_path))
+
+    try:
+        await mcp_toolkit.connect()
+
+        # Default resume directory if none provided
+        default_resume_dir = "./resumes/"
+    
+        # Get resume directory from command line argument if provided
+        resume_dir = sys.argv[1] if len(sys.argv) > 1 else default_resume_dir
+    
+        print(f"\033[94mAnalyzing resumes for AI/ML Engineer position...\033[0m")
+
+        # Connect to all MCP toolkits
+        tools = [*mcp_toolkit.get_tools()]
+        society = await construct_society(resume_dir, tools)
+        answer, chat_history, token_count = await arun_society(society)
+        print(f"\033[94mAnswer: {answer}\033[0m")
+        print(f"\033[94mCompleted! Resume analysis has been saved to ./resume_analysis.md file\033[0m")
+
+    finally:
+        # Make sure to disconnect safely after all operations are completed.
+        try:
+            await mcp_toolkit.disconnect()
+        except Exception:
+            print("Disconnect failed")
+
+if __name__ == "__main__":
+    asyncio.run(main())