diff --git a/example_usage.py b/example_usage.py
new file mode 100644
index 0000000..7f46b7d
--- /dev/null
+++ b/example_usage.py
@@ -0,0 +1,65 @@
+"""
+Example usage of the Code Analyzer Agent.
+
+This script demonstrates how to use the Code Analyzer Agent to analyze
+GitHub repositories and get comprehensive code analysis.
+"""
+
+import asyncio
+import json
+from pitchbot.code_analyzer_agent import CodeAnalyzerAgent
+
+
+async def example_analysis():
+    """Example function showing how to use the Code Analyzer Agent."""
+    
+    # Initialize the agent (will use LLAMA_API_KEY from environment)
+    agent = CodeAnalyzerAgent()
+    
+    # Example GitHub repository URL
+    github_url = "https://github.com/your-username/your-repo"
+    
+    print(f"🔍 Analyzing repository: {github_url}")
+    print("This may take a few minutes as we analyze each code file...")
+    
+    try:
+        # Analyze the repository
+        result = await agent.analyze_github_repository(github_url)
+        
+        # Print results
+        print("\n" + "="*80)
+        print("📊 ANALYSIS RESULTS")
+        print("="*80)
+        
+        # Print as formatted JSON
+        print(json.dumps(result, indent=2, ensure_ascii=False))
+        
+        # Or access individual components
+        print(f"\n📋 Summary: {result.get('summary', 'N/A')}")
+        print(f"🛠️  Technologies: {', '.join(result.get('stacks', []))}")
+        print(f"💡 Problem Solved: {result.get('problem_solved', 'N/A')}")
+        print(f"⚠️  Pitfalls Found: {len(result.get('pitfalls', []))}")
+        print(f"🚀 Improvements Suggested: {len(result.get('improvements', []))}")
+        
+    except Exception as e:
+        print(f"❌ Error during analysis: {e}")
+
+
+def main():
+    """Main function to run the example."""
+    print("Code Analyzer Agent - Example Usage")
+    print("="*50)
+    
+    # Check if LLAMA API key is available
+    import os
+    if not os.getenv('LLAMA_API_KEY'):
+        print("⚠️  Warning: LLAMA_API_KEY environment variable not set.")
+        print("Make sure to set your LLAMA API key in the .env file.")
+        return
+    
+    # Run the example
+    asyncio.run(example_analysis())
+
+
+if __name__ == "__main__":
+    main() 
\ No newline at end of file
diff --git a/mock_validation_demo.py b/mock_validation_demo.py
new file mode 100644
index 0000000..55760f8
--- /dev/null
+++ b/mock_validation_demo.py
@@ -0,0 +1,290 @@
+"""
+Mock Validation Demo for Code Analyzer Agent
+
+This demo shows how the Code Analyzer Agent works by simulating
+the analysis of the yizucodes/memory repository with mock LLAMA responses.
+"""
+
+import asyncio
+import json
+import time
+import tempfile
+import shutil
+import os
+from typing import Dict, Any, List
+from pathlib import Path
+import subprocess
+
+from pitchbot.code_analyzer_agent.file_utils import CodeFileFilter
+
+
+class MockLlamaClient:
+    """Mock LLAMA client that returns predefined responses."""
+    
+    def __init__(self, api_key=None):
+        self.api_key = api_key
+        self.session = None
+    
+    async def analyze_code_file(self, file_path: str, content: str) -> str:
+        """Mock file analysis based on file type and content."""
+        file_name = Path(file_path).name.lower()
+        extension = Path(file_path).suffix.lower()
+        
+        # Simulate analysis based on file patterns
+        if 'init' in file_name:
+            return f"This is a package initialization file that sets up the module structure and exports. It contains essential imports and configurations for the {Path(file_path).parent.name} package."
+        
+        elif extension == '.py':
+            if 'api' in file_name:
+                return "This file implements API endpoints and request handling functionality. It contains REST API routes, request validation, and response formatting logic."
+            elif 'llama' in file_name or 'ai' in file_name:
+                return "This file contains AI integration code, specifically for LLAMA model interactions. It handles model initialization, prompt processing, and response generation."
+            elif 'video' in file_name or 'media' in file_name:
+                return "This file processes video and media content. It includes video transcription, frame extraction, and media format handling capabilities."
+            elif 'processor' in file_name:
+                return "This is a core processing module that handles data transformation and analysis. It contains algorithms for processing input data and generating insights."
+            else:
+                return f"This Python file contains core application logic with classes and functions for the main functionality. It appears to be well-structured with proper error handling."
+        
+        elif extension in ['.yaml', '.yml']:
+            return "This is a configuration file that defines settings, parameters, and deployment configurations for the application."
+        
+        elif extension == '.txt':
+            return "This text file contains dependencies, requirements, or documentation for the project setup and installation."
+        
+        else:
+            return f"This {extension} file contains supporting code or configuration for the application infrastructure."
+    
+    async def generate_response(self, prompt: str) -> str:
+        """Mock comprehensive analysis response."""
+        # Simulate processing time
+        await asyncio.sleep(0.5)
+        
+        # Return a realistic JSON analysis for the yizucodes/memory repository
+        mock_response = {
+            "summary": "This is a sophisticated Llama-powered personal memory system designed for processing Meta Ray-Ban glasses footage. The codebase implements a complete pipeline for video analysis, transcription, and intelligent querying. It uses WebAI Navigator for workflow management and provides both API and CLI interfaces for interaction. The system can process hours of video footage, extract meaningful conversations, and allow users to query their memories using natural language.",
+            
+            "stacks": [
+                "Python", "LLAMA", "Whisper", "OpenAI", "WebAI", "FastAPI", 
+                "Uvicorn", "OpenCV", "FFmpeg", "HTTPX", "AsyncIO", "Pydantic"
+            ],
+            
+            "problem_solved": "Solves the problem of memory recall from daily conversations and experiences captured via Meta Ray-Ban smart glasses. Users often struggle to remember specific details from meetings, conversations, or learning sessions. This system creates a searchable, AI-powered memory assistant that can answer questions about past interactions, extract key insights, and provide context-aware responses about personal experiences.",
+            
+            "pitfalls": [
+                "Manual video transfer requirement limits real-time processing capabilities",
+                "Heavy dependency on external APIs (LLAMA, Whisper) creates potential points of failure",
+                "Large video files may cause memory and processing bottlenecks",
+                "Limited error handling for corrupted or unsupported video formats",
+                "No user authentication or privacy controls for sensitive conversation data",
+                "Hardcoded file paths and configurations reduce deployment flexibility",
+                "Missing data encryption for stored transcripts and analysis results"
+            ],
+            
+            "improvements": [
+                "Implement direct Meta glasses API integration for seamless video upload",
+                "Add real-time processing capabilities with streaming video analysis",
+                "Implement robust error handling and recovery mechanisms for API failures",
+                "Add user authentication and role-based access controls for privacy",
+                "Implement data encryption for stored conversations and transcripts",
+                "Add configuration management system for easier deployment and scaling",
+                "Implement caching mechanisms to reduce API calls and improve performance",
+                "Add batch processing optimization for handling multiple videos efficiently",
+                "Implement conversation threading and context linking across multiple sessions"
+            ]
+        }
+        
+        return json.dumps(mock_response, indent=2)
+    
+    async def close(self):
+        """Mock session cleanup."""
+        pass
+
+
+class MockCodeAnalyzerAgent:
+    """Mock version of CodeAnalyzerAgent for demonstration."""
+    
+    def __init__(self):
+        self.llama_client = MockLlamaClient()
+        self.file_filter = CodeFileFilter()
+    
+    async def analyze_github_repository(self, github_url: str) -> Dict[str, Any]:
+        """Mock analysis of GitHub repository."""
+        print(f"🔍 Cloning repository: {github_url}")
+        
+        # Simulate repository cloning
+        repo_path = await self._mock_clone_repository(github_url)
+        
+        print("📁 Scanning for code files...")
+        # Get actual code files from the cloned repo
+        code_files = self.file_filter.get_code_files(repo_path)
+        print(f"Found {len(code_files)} code files to analyze")
+        
+        # Mock analyze each file
+        print("🧠 Analyzing files with LLAMA AI...")
+        file_contexts = []
+        for i, file_path in enumerate(code_files[:10]):  # Limit to first 10 files for demo
+            print(f"  📄 Analyzing {Path(file_path).name}... ({i+1}/{min(len(code_files), 10)})")
+            
+            try:
+                with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
+                    content = f.read()[:1000]  # Read first 1KB
+                
+                context = await self.llama_client.analyze_code_file(file_path, content)
+                file_contexts.append({
+                    "file_path": str(Path(file_path).relative_to(repo_path)),
+                    "context": context
+                })
+            except Exception as e:
+                print(f"    ⚠️ Skipped {file_path}: {e}")
+        
+        print("🔗 Merging contexts and generating final analysis...")
+        merged_context = self._merge_contexts(file_contexts)
+        
+        # Generate mock final analysis
+        final_analysis = await self._generate_final_analysis(merged_context)
+        
+        # Cleanup
+        shutil.rmtree(repo_path)
+        
+        return final_analysis
+    
+    async def _mock_clone_repository(self, github_url: str) -> str:
+        """Actually clone the repository for realistic file analysis."""
+        from urllib.parse import urlparse
+        
+        parsed_url = urlparse(github_url)
+        repo_name = parsed_url.path.strip('/').split('/')[-1]
+        if repo_name.endswith('.git'):
+            repo_name = repo_name[:-4]
+        
+        temp_dir = tempfile.mkdtemp()
+        repo_path = os.path.join(temp_dir, repo_name)
+        
+        try:
+            # Actually clone the repository
+            subprocess.run(['git', 'clone', '--depth', '1', github_url, repo_path], 
+                         check=True, capture_output=True)
+            print(f"✅ Successfully cloned to {repo_path}")
+        except subprocess.CalledProcessError as e:
+            print(f"❌ Failed to clone: {e.stderr.decode()}")
+            # Create mock structure if clone fails
+            os.makedirs(repo_path, exist_ok=True)
+            self._create_mock_files(repo_path)
+        
+        return repo_path
+    
+    def _create_mock_files(self, repo_path: str):
+        """Create mock files that represent the repository structure."""
+        mock_files = {
+            "llama__init__.py": "# LLAMA integration module\nimport llama_api",
+            "api__init__.py": "# API endpoints\nfrom fastapi import FastAPI",
+            "medialoader__init__.py": "# Media loading utilities\nimport cv2",
+            "requirements.txt": "llama-api-client\nfastapi\nuvicorn",
+            "README.md": "# Memory Assistant\nLlama-powered memory system",
+            "element_config.yaml": "# WebAI configuration\nversion: 1.0"
+        }
+        
+        for filename, content in mock_files.items():
+            with open(os.path.join(repo_path, filename), 'w') as f:
+                f.write(content)
+    
+    def _merge_contexts(self, file_contexts: List[Dict[str, str]]) -> str:
+        """Merge file contexts."""
+        merged = []
+        for fc in file_contexts:
+            merged.append(f"File: {fc['file_path']}")
+            merged.append(f"Analysis: {fc['context']}")
+            merged.append("-" * 40)
+        return "\n".join(merged)
+    
+    async def _generate_final_analysis(self, context: str) -> Dict[str, Any]:
+        """Generate mock final analysis."""
+        response = await self.llama_client.generate_response(context)
+        
+        try:
+            return json.loads(response)
+        except json.JSONDecodeError:
+            return {
+                "error": "Mock analysis completed successfully",
+                "summary": "Mock analysis of yizucodes/memory repository",
+                "stacks": ["Python", "LLAMA", "AI"],
+                "problem_solved": "Memory assistance for smart glasses",
+                "pitfalls": ["API dependencies", "Manual processes"],
+                "improvements": ["Real-time processing", "Better integration"]
+            }
+
+
+async def run_mock_validation():
+    """Run the mock validation demonstration."""
+    print("🎭 MOCK CODE ANALYZER AGENT DEMONSTRATION")
+    print("=" * 60)
+    print("This demo shows how the agent works using the yizucodes/memory repository")
+    print("with simulated LLAMA API responses.\n")
+    
+    # Initialize mock agent
+    agent = MockCodeAnalyzerAgent()
+    
+    # Run analysis
+    start_time = time.time()
+    result = await agent.analyze_github_repository("https://github.com/yizucodes/memory")
+    analysis_time = time.time() - start_time
+    
+    # Display results
+    print("\n" + "=" * 80)
+    print("📊 MOCK ANALYSIS RESULTS")
+    print("=" * 80)
+    print(f"🕒 Analysis Time: {analysis_time:.2f} seconds")
+    print(f"✅ Status: Success")
+    
+    if "error" in result:
+        print(f"⚠️ Note: {result['error']}")
+    
+    print(f"\n📋 Summary:")
+    print(f"  {result.get('summary', 'N/A')}")
+    
+    print(f"\n🛠️ Technology Stack ({len(result.get('stacks', []))} detected):")
+    for tech in result.get('stacks', []):
+        print(f"  • {tech}")
+    
+    print(f"\n💡 Problem Solved:")
+    print(f"  {result.get('problem_solved', 'N/A')}")
+    
+    print(f"\n⚠️ Identified Pitfalls ({len(result.get('pitfalls', []))}):")
+    for i, pitfall in enumerate(result.get('pitfalls', [])[:5], 1):
+        print(f"  {i}. {pitfall}")
+    
+    print(f"\n🚀 Recommended Improvements ({len(result.get('improvements', []))}):")
+    for i, improvement in enumerate(result.get('improvements', [])[:5], 1):
+        print(f"  {i}. {improvement}")
+    
+    # Save results
+    with open("mock_validation_results.json", "w") as f:
+        json.dump({
+            "status": "success",
+            "repository": "https://github.com/yizucodes/memory",
+            "analysis_time": f"{analysis_time:.2f} seconds",
+            "analysis_result": result,
+            "note": "This is a mock demonstration using simulated LLAMA responses"
+        }, f, indent=2)
+    
+    print(f"\n📁 Results saved to: mock_validation_results.json")
+    print("\n" + "=" * 80)
+    print("✅ MOCK VALIDATION COMPLETED SUCCESSFULLY!")
+    print("This demonstrates the full functionality of the Code Analyzer Agent.")
+    print("When a real LLAMA API is available, it will provide even more detailed analysis.")
+
+
+if __name__ == "__main__":
+    print("🧪 Mock Code Analyzer Agent Validation")
+    print("Demonstrating with yizucodes/memory repository...")
+    print("-" * 50)
+    
+    try:
+        asyncio.run(run_mock_validation())
+    except KeyboardInterrupt:
+        print("\n\n⚠️ Demo interrupted by user")
+    except Exception as e:
+        print(f"\n❌ Demo failed: {e}")
+        import traceback
+        traceback.print_exc() 
\ No newline at end of file
diff --git a/pitchbot/code_analyzer_agent/__init__.py b/pitchbot/code_analyzer_agent/__init__.py
new file mode 100644
index 0000000..c3fca82
--- /dev/null
+++ b/pitchbot/code_analyzer_agent/__init__.py
@@ -0,0 +1,20 @@
+"""
+Code Analyzer Agent Package
+
+This package provides tools and agents for analyzing code structure,
+quality, and generating insights about codebases.
+"""
+
+__version__ = "0.1.0"
+__author__ = "PitchBot"
+
+from .agent import CodeAnalyzerAgent
+from .file_utils import CodeFileFilter
+from .llama_client import LlamaClient
+
+# Package exports
+__all__ = [
+    'CodeAnalyzerAgent',
+    'CodeFileFilter', 
+    'LlamaClient'
+] 
\ No newline at end of file
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diff --git a/pitchbot/code_analyzer_agent/agent.py b/pitchbot/code_analyzer_agent/agent.py
new file mode 100644
index 0000000..b5db855
--- /dev/null
+++ b/pitchbot/code_analyzer_agent/agent.py
@@ -0,0 +1,323 @@
+"""
+Code Analyzer Agent
+
+Main agent for analyzing GitHub repositories and providing comprehensive code analysis.
+"""
+
+import os
+import json
+import tempfile
+import shutil
+from pathlib import Path
+from typing import Dict, List, Any, Optional
+import asyncio
+import aiohttp
+from urllib.parse import urlparse
+
+from .file_utils import CodeFileFilter
+from .llama_client import LlamaClient
+
+
+class CodeAnalyzerAgent:
+    """Main agent for analyzing code repositories."""
+    
+    def __init__(self, llama_api_key: Optional[str] = None, max_workers: int = 5):
+        """Initialize the code analyzer agent."""
+        self.llama_client = LlamaClient(llama_api_key)
+        self.file_filter = CodeFileFilter()
+        self.max_workers = max_workers
+        self.semaphore = asyncio.Semaphore(max_workers)
+        
+    async def analyze_github_repository(self, github_url: str) -> Dict[str, Any]:
+        """
+        Analyze a GitHub repository and return comprehensive analysis.
+        
+        Args:
+            github_url: GitHub repository URL
+            
+        Returns:
+            Dictionary containing analysis results
+        """
+        try:
+            # Clone repository to temporary directory
+            repo_path = await self._clone_repository(github_url)
+            
+            # Get all relevant code files
+            code_files = self.file_filter.get_code_files(repo_path)
+            
+            # Step 1: Analyze each file individually with simplified prompts
+            file_contexts = await self._analyze_files_step1(code_files)
+            
+            # Step 2: Generate exhaustive summary
+            exhaustive_summary = await self._generate_exhaustive_summary(file_contexts)
+            
+            # Step 3: Generate detailed analysis
+            detailed_analysis = await self._generate_detailed_analysis(file_contexts)
+            
+            # Clean up LLAMA client session
+            await self.llama_client.close()
+            
+            # Clean up temporary directory
+            shutil.rmtree(repo_path)
+            
+            # Combine results
+            result = detailed_analysis.copy()
+            result["exhaustive_summary"] = exhaustive_summary
+            
+            return result
+            
+        except Exception as e:
+            return {
+                "error": f"Failed to analyze repository: {str(e)}",
+                "summary": "",
+                "exhaustive_summary": "",
+                "stacks": [],
+                "problem_solved": "",
+                "pitfalls": [],
+                "improvements": []
+            }
+    
+    async def _clone_repository(self, github_url: str) -> str:
+        """Clone GitHub repository to temporary directory."""
+        # Parse GitHub URL to get repository name
+        parsed_url = urlparse(github_url)
+        repo_name = parsed_url.path.strip('/').split('/')[-1]
+        if repo_name.endswith('.git'):
+            repo_name = repo_name[:-4]
+        
+        # Create temporary directory
+        temp_dir = tempfile.mkdtemp()
+        repo_path = os.path.join(temp_dir, repo_name)
+        
+        # Clone repository
+        import subprocess
+        try:
+            subprocess.run(['git', 'clone', github_url, repo_path], 
+                         check=True, capture_output=True)
+        except subprocess.CalledProcessError as e:
+            raise Exception(f"Failed to clone repository: {e.stderr.decode()}")
+        
+        return repo_path
+    
+    async def _analyze_files_step1(self, code_files: List[str]) -> List[Dict[str, str]]:
+        """Step 1: Analyze each code file using simplified LLAMA API calls in parallel."""
+        print(f"🔄 Processing {len(code_files)} files with {self.max_workers} parallel workers...")
+        
+        # Create tasks for parallel processing
+        tasks = [self._analyze_single_file(file_path) for file_path in code_files]
+        
+        # Execute tasks in parallel with progress tracking
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        
+        # Filter out exceptions and None results
+        file_contexts = []
+        successful = 0
+        errors = 0
+        
+        for result in results:
+            if isinstance(result, Exception):
+                errors += 1
+                print(f"❌ Task failed: {result}")
+            elif result is not None:
+                file_contexts.append(result)
+                successful += 1
+            else:
+                errors += 1
+        
+        print(f"✅ Completed: {successful} files analyzed, {errors} errors")
+        return file_contexts
+
+    async def _analyze_single_file(self, file_path: str) -> Optional[Dict[str, str]]:
+        """Analyze a single file with semaphore-based concurrency control."""
+        async with self.semaphore:  # Limit concurrent API calls
+            try:
+                with open(file_path, 'r', encoding='utf-8') as f:
+                    content = f.read()
+                
+                # Skip very large files (> 10KB)
+                if len(content) > 10000:
+                    print(f"⏭️  Skipping large file: {file_path} ({len(content)} bytes)")
+                    return None
+                
+                print(f"🔍 Analyzing: {file_path}")
+                
+                # Use simplified prompt as specified
+                messages = [
+                    {"role": "system", "content": "You are a senior software architect."},
+                    {"role": "user", "content": f"Analyze this code:\n\n```{content}```"}
+                ]
+                
+                context = await self.llama_client.generate_response_with_messages(messages)
+                
+                print(f"✅ Completed: {file_path}")
+                
+                # Store with fileName, context, and code as specified
+                return {
+                    "fileName": file_path,
+                    "context": context,
+                    "code": content
+                }
+                
+            except Exception as e:
+                print(f"❌ Error analyzing file {file_path}: {e}")
+                return None
+    
+    async def _generate_exhaustive_summary(self, file_contexts: List[Dict[str, str]]) -> str:
+        """Step 2: Generate exhaustive summary of what problem the repo solves."""
+        # Prepare context with file information
+        context_data = []
+        for file_context in file_contexts:
+            context_data.append(f"File: {file_context['fileName']}")
+            context_data.append(f"Context: {file_context['context']}")
+            context_data.append(f"Code: {file_context['code']}")
+            context_data.append("-" * 50)
+        
+        context_string = "\n".join(context_data)
+        
+        # Generate exhaustive summary
+        messages = [
+            {"role": "system", "content": "You are a senior software architect."},
+            {"role": "user", "content": f"Analyze this repo and generative exhaustive summary:\n\n```{context_string}```"}
+        ]
+        
+        try:
+            summary = await self.llama_client.generate_response_with_messages(messages)
+            return summary
+        except Exception as e:
+            return f"Error generating exhaustive summary: {str(e)}"
+    
+    async def _generate_detailed_analysis(self, file_contexts: List[Dict[str, str]]) -> Dict[str, Any]:
+        """Step 3: Generate detailed analysis with specific structure."""
+        # Prepare context with file information
+        context_data = []
+        for file_context in file_contexts:
+            context_data.append(f"File: {file_context['fileName']}")
+            context_data.append(f"Context: {file_context['context']}")
+            context_data.append(f"Code: {file_context['code']}")
+            context_data.append("-" * 50)
+        
+        context_string = "\n".join(context_data)
+        
+        # Generate detailed analysis with specified prompt
+        messages = [
+            {"role": "system", "content": "You are a senior software architect."},
+            {"role": "user", "content": f"""Analyze this repo and generative exhaustive summary:
+
+```{context_string}```
+
+Please provide a detailed analysis covering:
+
+1. **Code Quality Assessment** :
+   - Code organization and structure
+   - Naming conventions and readability
+   - Error handling practices
+   - Code complexity and maintainability
+
+2. **Scalability Analysis**:
+   - Architecture patterns used
+   - Database design considerations
+   - Performance bottlenecks
+   - Resource management
+
+3. **Design Pitfalls** (List specific issues):
+   - Anti-patterns identified
+   - Tight coupling issues
+   - Missing abstractions
+   - Security vulnerabilities
+
+5. **Overall Recommendations**:
+   - Priority improvements
+   - Best practices to implement
+   - Long-term technical debt concerns
+
+Please return your analysis in the following JSON format:
+{{
+  "summary": "Brief overview of the codebase",
+  "stacks": ["list", "of", "technologies", "used"],
+  "problem_solved": "What problem this codebase solves",
+  "pitfalls": ["list", "of", "identified", "issues"],
+  "improvements": ["list", "of", "recommended", "improvements"]
+}}"""}
+        ]
+        
+        try:
+            response = await self.llama_client.generate_response_with_messages(messages)
+            
+            # Try to parse JSON response
+            try:
+                return json.loads(response)
+            except json.JSONDecodeError:
+                # If JSON parsing fails, create structured response
+                return {
+                    "summary": response[:500] + "..." if len(response) > 500 else response,
+                    "stacks": self._extract_technologies(response),
+                    "problem_solved": "Analysis available in summary",
+                    "pitfalls": self._extract_pitfalls(response),
+                    "improvements": self._extract_improvements(response)
+                }
+                
+        except Exception as e:
+            return {
+                "error": f"Failed to generate analysis: {str(e)}",
+                "summary": "",
+                "stacks": [],
+                "problem_solved": "",
+                "pitfalls": [],
+                "improvements": []
+            }
+    
+    def _extract_technologies(self, text: str) -> List[str]:
+        """Extract technologies from analysis text."""
+        # Simple keyword extraction for common technologies
+        technologies = []
+        tech_keywords = [
+            'python', 'javascript', 'typescript', 'react', 'vue', 'angular',
+            'node.js', 'express', 'fastapi', 'django', 'flask', 'spring',
+            'java', 'c++', 'c#', 'go', 'rust', 'php', 'ruby', 'rails',
+            'postgresql', 'mysql', 'mongodb', 'redis', 'docker', 'kubernetes'
+        ]
+        
+        text_lower = text.lower()
+        for tech in tech_keywords:
+            if tech in text_lower:
+                technologies.append(tech.title())
+        
+        return list(set(technologies))
+    
+    def _extract_pitfalls(self, text: str) -> List[str]:
+        """Extract pitfalls from analysis text."""
+        # Extract sentences containing pitfall indicators
+        pitfall_indicators = [
+            'issue', 'problem', 'pitfall', 'vulnerability', 'anti-pattern',
+            'tight coupling', 'missing', 'lacks', 'weakness'
+        ]
+        
+        sentences = text.split('.')
+        pitfalls = []
+        
+        for sentence in sentences:
+            sentence = sentence.strip()
+            if any(indicator in sentence.lower() for indicator in pitfall_indicators):
+                if len(sentence) > 10:  # Filter out very short sentences
+                    pitfalls.append(sentence)
+        
+        return pitfalls[:5]  # Return top 5 pitfalls
+    
+    def _extract_improvements(self, text: str) -> List[str]:
+        """Extract improvements from analysis text."""
+        # Extract sentences containing improvement indicators
+        improvement_indicators = [
+            'recommend', 'improve', 'enhancement', 'should', 'could',
+            'better', 'optimize', 'refactor', 'implement'
+        ]
+        
+        sentences = text.split('.')
+        improvements = []
+        
+        for sentence in sentences:
+            sentence = sentence.strip()
+            if any(indicator in sentence.lower() for indicator in improvement_indicators):
+                if len(sentence) > 10:  # Filter out very short sentences
+                    improvements.append(sentence)
+        
+        return improvements[:5]  # Return top 5 improvements 
\ No newline at end of file
diff --git a/pitchbot/code_analyzer_agent/cli.py b/pitchbot/code_analyzer_agent/cli.py
new file mode 100644
index 0000000..d4b9135
--- /dev/null
+++ b/pitchbot/code_analyzer_agent/cli.py
@@ -0,0 +1,129 @@
+"""
+Command-line interface for the Code Analyzer Agent.
+"""
+
+import asyncio
+import json
+import sys
+from typing import Optional
+import argparse
+
+from .agent import CodeAnalyzerAgent
+
+
+async def analyze_repository(github_url: str, api_key: Optional[str] = None) -> dict:
+    """
+    Analyze a GitHub repository using the Code Analyzer Agent.
+    
+    Args:
+        github_url: GitHub repository URL
+        api_key: Optional LLAMA API key
+        
+    Returns:
+        Analysis results as dictionary
+    """
+    try:
+        agent = CodeAnalyzerAgent(llama_api_key=api_key)
+        result = await agent.analyze_github_repository(github_url)
+        return result
+    except Exception as e:
+        return {
+            "error": f"Failed to analyze repository: {str(e)}",
+            "summary": "",
+            "stacks": [],
+            "problem_solved": "",
+            "pitfalls": [],
+            "improvements": []
+        }
+
+
+def main():
+    """Main CLI function."""
+    parser = argparse.ArgumentParser(
+        description="Analyze GitHub repositories using AI"
+    )
+    parser.add_argument(
+        "github_url",
+        help="GitHub repository URL to analyze"
+    )
+    parser.add_argument(
+        "--api-key",
+        help="LLAMA API key (optional if set in environment)"
+    )
+    parser.add_argument(
+        "--output",
+        choices=["json", "pretty"],
+        default="pretty",
+        help="Output format (default: pretty)"
+    )
+    
+    args = parser.parse_args()
+    
+    # Run the analysis
+    print(f"Analyzing repository: {args.github_url}")
+    print("This may take a few minutes...")
+    
+    try:
+        result = asyncio.run(analyze_repository(args.github_url, args.api_key))
+        
+        if args.output == "json":
+            print(json.dumps(result, indent=2))
+        else:
+            print_pretty_results(result)
+            
+    except KeyboardInterrupt:
+        print("\nAnalysis interrupted by user.")
+        sys.exit(1)
+    except Exception as e:
+        print(f"Error: {e}")
+        sys.exit(1)
+
+
+def print_pretty_results(result: dict):
+    """Print analysis results in a pretty format."""
+    print("\n" + "="*80)
+    print("🔍 CODE ANALYSIS RESULTS")
+    print("="*80)
+    
+    if "error" in result and result["error"]:
+        print(f"❌ Error: {result['error']}")
+        return
+    
+    # Summary
+    if result.get("summary"):
+        print("\n📋 SUMMARY")
+        print("-" * 40)
+        print(result["summary"])
+    
+    # Technology Stack
+    if result.get("stacks"):
+        print("\n🛠️  TECHNOLOGY STACK")
+        print("-" * 40)
+        for tech in result["stacks"]:
+            print(f"  • {tech}")
+    
+    # Problem Solved
+    if result.get("problem_solved"):
+        print("\n💡 PROBLEM IT SOLVES")
+        print("-" * 40)
+        print(result["problem_solved"])
+    
+    # Pitfalls
+    if result.get("pitfalls"):
+        print("\n⚠️  IDENTIFIED PITFALLS")
+        print("-" * 40)
+        for i, pitfall in enumerate(result["pitfalls"], 1):
+            print(f"  {i}. {pitfall}")
+    
+    # Improvements
+    if result.get("improvements"):
+        print("\n🚀 RECOMMENDED IMPROVEMENTS")
+        print("-" * 40)
+        for i, improvement in enumerate(result["improvements"], 1):
+            print(f"  {i}. {improvement}")
+    
+    print("\n" + "="*80)
+
+
+if __name__ == "__main__":
+    main() 
\ No newline at end of file
diff --git a/pitchbot/code_analyzer_agent/file_utils.py b/pitchbot/code_analyzer_agent/file_utils.py
new file mode 100644
index 0000000..323635a
--- /dev/null
+++ b/pitchbot/code_analyzer_agent/file_utils.py
@@ -0,0 +1,244 @@
+"""
+File utilities for filtering and processing code files.
+"""
+
+import os
+from pathlib import Path
+from typing import List, Set
+
+
+class CodeFileFilter:
+    """Utility class for filtering code files from repositories."""
+    
+    def __init__(self):
+        """Initialize the file filter with predefined patterns."""
+        # Code file extensions to include
+        self.code_extensions = {
+            '.py', '.js', '.ts', '.jsx', '.tsx', '.java', '.cpp', '.c',
+            '.h', '.hpp', '.cs', '.go', '.rs', '.php', '.rb', '.swift',
+            '.kt', '.scala', '.clj', '.hs', '.ml', '.fs', '.vb', '.pl',
+            '.sh', '.bash', '.zsh', '.fish', '.ps1', '.r', '.sql',
+            '.html', '.css', '.scss', '.less', '.vue', '.svelte'
+        }
+        
+        # Extensions to exclude (data, config, etc.)
+        self.exclude_extensions = {
+            '.md', '.txt', '.log', '.json', '.xml', '.yaml', '.yml',
+            '.csv', '.tsv', '.xlsx', '.xls', '.pdf', '.doc', '.docx',
+            '.png', '.jpg', '.jpeg', '.gif', '.svg', '.ico', '.bmp',
+            '.mp4', '.avi', '.mov', '.wmv', '.mp3', '.wav', '.ogg',
+            '.zip', '.tar', '.gz', '.rar', '.7z', '.jar', '.war',
+            '.pyc', '.pyo', '.class', '.o', '.so', '.dll', '.exe',
+            '.lock', '.toml', '.ini', '.cfg', '.conf', '.env'
+        }
+        
+        # Directory patterns to exclude
+        self.exclude_directories = {
+            '.git', '.svn', '.hg', '__pycache__', '.pytest_cache',
+            'node_modules', '.venv', 'venv', 'env', '.env',
+            'build', 'dist', 'target', 'bin', 'obj', '.idea',
+            '.vscode', '.vs', 'coverage', '.nyc_output', '.cache',
+            'logs', 'tmp', 'temp', '.tmp', '.sass-cache',
+            '.next', '.nuxt', 'out', 'public', 'static', 'assets'
+        }
+        
+        # File name patterns to exclude (test files)
+        self.exclude_patterns = {
+            'test_', '_test', '.test.', '.spec.',
+            'tests.', 'spec.', 'mock', 'fixture',
+            'conftest', 'setup.py', 'setup.cfg',
+            'requirements.txt', 'package.json', 'package-lock.json',
+            'yarn.lock', 'Dockerfile', 'docker-compose',
+            'makefile', 'Makefile', 'CMakeLists.txt',
+            'README', 'LICENSE', 'CHANGELOG', 'CONTRIBUTING'
+        }
+    
+    def get_code_files(self, root_path: str) -> List[str]:
+        """
+        Get all relevant code files from the repository.
+        
+        Args:
+            root_path: Root directory path to scan
+            
+        Returns:
+            List of file paths containing code files
+        """
+        code_files = []
+        root_path = Path(root_path)
+        
+        for file_path in self._walk_directory(root_path):
+            if self._is_code_file(file_path):
+                code_files.append(str(file_path))
+        
+        return code_files
+    
+    def _walk_directory(self, root_path: Path):
+        """Walk through directory structure, excluding unwanted directories."""
+        for item in root_path.iterdir():
+            if item.is_file():
+                yield item
+            elif item.is_dir() and not self._should_exclude_directory(item.name):
+                yield from self._walk_directory(item)
+    
+    def _should_exclude_directory(self, dir_name: str) -> bool:
+        """Check if directory should be excluded."""
+        dir_name_lower = dir_name.lower()
+        
+        # Check exact matches
+        if dir_name_lower in self.exclude_directories:
+            return True
+        
+        # Check patterns
+        exclude_patterns = [
+            'test', 'tests', '__test__', 'spec', 'specs',
+            'mock', 'mocks', 'fixture', 'fixtures',
+            'example', 'examples', 'demo', 'demos',
+            'doc', 'docs', 'documentation'
+        ]
+        
+        for pattern in exclude_patterns:
+            if pattern in dir_name_lower:
+                return True
+        
+        return False
+    
+    def _is_code_file(self, file_path: Path) -> bool:
+        """
+        Check if file is a code file that should be analyzed.
+        
+        Args:
+            file_path: Path to the file
+            
+        Returns:
+            True if file should be analyzed, False otherwise
+        """
+        # Get file extension
+        extension = file_path.suffix.lower()
+        
+        # Check if extension is excluded
+        if extension in self.exclude_extensions:
+            return False
+        
+        # Check if extension is a code extension
+        if extension not in self.code_extensions:
+            return False
+        
+        # Check file name patterns
+        file_name = file_path.name.lower()
+        
+        for pattern in self.exclude_patterns:
+            if pattern in file_name:
+                return False
+        
+        # Additional checks for test files
+        if self._is_test_file(file_path):
+            return False
+        
+        # Check file size (skip very large files > 100KB)
+        try:
+            if file_path.stat().st_size > 100 * 1024:
+                return False
+        except OSError:
+            return False
+        
+        return True
+    
+    def _is_test_file(self, file_path: Path) -> bool:
+        """Check if file is a test file based on various indicators."""
+        file_name = file_path.name.lower()
+        parent_dir = file_path.parent.name.lower()
+        
+        # Check file name patterns
+        test_indicators = [
+            'test_', '_test.', '.test.', '_test_',
+            'spec_', '_spec.', '.spec.', '_spec_',
+            'tests.', 'specs.',
+            'conftest.', 'test.py', 'tests.py'
+        ]
+        
+        for indicator in test_indicators:
+            if indicator in file_name:
+                return True
+        
+        # Check parent directory
+        test_dirs = ['test', 'tests', 'spec', 'specs', '__tests__']
+        if parent_dir in test_dirs:
+            return True
+        
+        return False
+    
+    def get_file_info(self, file_path: str) -> dict:
+        """
+        Get information about a code file.
+        
+        Args:
+            file_path: Path to the file
+            
+        Returns:
+            Dictionary with file information
+        """
+        path = Path(file_path)
+        
+        try:
+            stat = path.stat()
+            return {
+                'path': str(path),
+                'name': path.name,
+                'extension': path.suffix,
+                'size': stat.st_size,
+                'modified': stat.st_mtime,
+                'language': self._detect_language(path.suffix)
+            }
+        except OSError:
+            return {
+                'path': str(path),
+                'name': path.name,
+                'extension': path.suffix,
+                'size': 0,
+                'modified': 0,
+                'language': 'unknown'
+            }
+    
+    def _detect_language(self, extension: str) -> str:
+        """Detect programming language from file extension."""
+        language_mapping = {
+            '.py': 'Python',
+            '.js': 'JavaScript',
+            '.ts': 'TypeScript',
+            '.jsx': 'React JSX',
+            '.tsx': 'React TSX',
+            '.java': 'Java',
+            '.cpp': 'C++',
+            '.c': 'C',
+            '.h': 'C Header',
+            '.hpp': 'C++ Header',
+            '.cs': 'C#',
+            '.go': 'Go',
+            '.rs': 'Rust',
+            '.php': 'PHP',
+            '.rb': 'Ruby',
+            '.swift': 'Swift',
+            '.kt': 'Kotlin',
+            '.scala': 'Scala',
+            '.clj': 'Clojure',
+            '.hs': 'Haskell',
+            '.ml': 'OCaml',
+            '.fs': 'F#',
+            '.vb': 'Visual Basic',
+            '.pl': 'Perl',
+            '.sh': 'Shell',
+            '.bash': 'Bash',
+            '.zsh': 'Zsh',
+            '.fish': 'Fish',
+            '.ps1': 'PowerShell',
+            '.r': 'R',
+            '.sql': 'SQL',
+            '.html': 'HTML',
+            '.css': 'CSS',
+            '.scss': 'SCSS',
+            '.less': 'Less',
+            '.vue': 'Vue',
+            '.svelte': 'Svelte'
+        }
+        
+        return language_mapping.get(extension.lower(), 'Unknown') 
\ No newline at end of file
diff --git a/pitchbot/code_analyzer_agent/llama_client.py b/pitchbot/code_analyzer_agent/llama_client.py
new file mode 100644
index 0000000..f69c698
--- /dev/null
+++ b/pitchbot/code_analyzer_agent/llama_client.py
@@ -0,0 +1,238 @@
+"""
+LLAMA API Client for code analysis.
+"""
+
+import os
+import json
+import asyncio
+import aiohttp
+from typing import Optional, Dict, Any
+from pathlib import Path
+
+
+class LlamaClient:
+    """Client for interacting with LLAMA API."""
+    
+    def __init__(self, api_key: Optional[str] = None):
+        """
+        Initialize LLAMA client.
+        
+        Args:
+            api_key: LLAMA API key. If None, will try to get from environment.
+        """
+        self.api_key = api_key or os.getenv('LLAMA_API_KEY')
+        if not self.api_key:
+            raise ValueError("LLAMA API key is required. Set LLAMA_API_KEY environment variable or pass it directly.")
+        
+        self.base_url = "https://api.llama.com"
+        self.session = None
+    
+    async def __aenter__(self):
+        """Async context manager entry."""
+        self.session = aiohttp.ClientSession()
+        return self
+    
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit."""
+        if self.session:
+            await self.session.close()
+    
+    async def _get_session(self):
+        """Get or create aiohttp session."""
+        if not self.session:
+            # Create connector with SSL verification disabled for development
+            import ssl
+            ssl_context = ssl.create_default_context()
+            ssl_context.check_hostname = False
+            ssl_context.verify_mode = ssl.CERT_NONE
+            
+            connector = aiohttp.TCPConnector(ssl=ssl_context)
+            self.session = aiohttp.ClientSession(connector=connector)
+        return self.session
+    
+    async def analyze_code_file(self, file_path: str, content: str) -> str:
+        """
+        Analyze a single code file using LLAMA API.
+        
+        Args:
+            file_path: Path to the code file
+            content: Content of the code file
+            
+        Returns:
+            Analysis of the code file
+        """
+        # Create a prompt for analyzing individual code files
+        file_name = Path(file_path).name
+        language = self._detect_language(Path(file_path).suffix)
+        
+        # Create messages for file analysis
+        messages = [
+            {
+                "role": "system", 
+                "content": "You are a senior software architect and code reviewer. Analyze code files and provide concise, insightful feedback."
+            },
+            {
+                "role": "user", 
+                "content": f"""Analyze this {language} code file '{file_name}' and provide insights:
+
+```{language.lower()}
+{content}
+```
+
+Please provide a brief analysis focusing on:
+1. Purpose and functionality of this file
+2. Key components, classes, or functions
+3. Code quality observations
+4. Any potential issues or improvements
+5. Dependencies and relationships
+
+Keep the analysis concise but informative (2-3 sentences per point)."""
+            }
+        ]
+        
+        try:
+            response = await self.generate_response_with_messages(messages)
+            return response
+        except Exception as e:
+            return f"Error analyzing file {file_name}: {str(e)}"
+    
+    async def generate_response_with_messages(self, messages: list) -> str:
+        """
+        Generate response using LLAMA API with messages format.
+        
+        Args:
+            messages: List of message objects with role and content
+            
+        Returns:
+            Generated response from LLAMA
+        """
+        session = await self._get_session()
+        
+        headers = {
+            'Authorization': f'Bearer {self.api_key}',
+            'Content-Type': 'application/json'
+        }
+        
+        payload = {
+            'model': 'Llama-4-Maverick-17B-128E-Instruct-FP8',
+            'messages': messages,
+            'temperature': 0.3
+        }
+        
+        # Debug: Print request details
+        print(f"🔍 LLAMA API Request URL: {self.base_url}/v1/chat/completions")
+        print(f"🔍 LLAMA API Request Payload:")
+        print(json.dumps(payload, indent=2))
+        
+        try:
+            async with session.post(
+                f"{self.base_url}/v1/chat/completions",
+                headers=headers,
+                json=payload,
+                timeout=aiohttp.ClientTimeout(total=30)
+            ) as response:
+                
+                # Debug: Print response status and headers
+                print(f"🔍 LLAMA API Response Status: {response.status}")
+                print(f"🔍 LLAMA API Response Headers: {dict(response.headers)}")
+                
+                if response.status == 200:
+                    data = await response.json()
+                    
+                    # Debug: Print full response
+                    print(f"🔍 LLAMA API Full Response:")
+                    print(json.dumps(data, indent=2))
+                    
+                    # LLAMA API uses different response format than OpenAI
+                    if 'completion_message' in data:
+                        # LLAMA API format
+                        return data['completion_message']['content']['text']
+                    elif 'choices' in data:
+                        # OpenAI format (fallback)
+                        return data['choices'][0]['message']['content']
+                    else:
+                        raise Exception(f"Unexpected LLAMA API response format: {list(data.keys())}")
+                else:
+                    error_text = await response.text()
+                    print(f"🔍 LLAMA API Error Response: {error_text}")
+                    raise Exception(f"LLAMA API error {response.status}: {error_text}")
+                    
+        except asyncio.TimeoutError:
+            raise Exception("LLAMA API request timed out")
+        except aiohttp.ClientError as e:
+            raise Exception(f"LLAMA API client error: {str(e)}")
+        except KeyError as e:
+            raise Exception(f"Unexpected LLAMA API response format: {str(e)}")
+
+    async def generate_response(self, prompt: str) -> str:
+        """
+        Generate response using LLAMA API with simple prompt.
+        
+        Args:
+            prompt: The prompt to send to LLAMA
+            
+        Returns:
+            Generated response from LLAMA
+        """
+        messages = [
+            {
+                "role": "system",
+                "content": "You are a helpful assistant that provides detailed and accurate responses."
+            },
+            {
+                "role": "user",
+                "content": prompt
+            }
+        ]
+        
+        return await self.generate_response_with_messages(messages)
+    
+    def _detect_language(self, extension: str) -> str:
+        """Detect programming language from file extension."""
+        language_mapping = {
+            '.py': 'Python',
+            '.js': 'JavaScript',
+            '.ts': 'TypeScript',
+            '.jsx': 'React JSX',
+            '.tsx': 'React TSX',
+            '.java': 'Java',
+            '.cpp': 'C++',
+            '.c': 'C',
+            '.h': 'C Header',
+            '.hpp': 'C++ Header',
+            '.cs': 'C#',
+            '.go': 'Go',
+            '.rs': 'Rust',
+            '.php': 'PHP',
+            '.rb': 'Ruby',
+            '.swift': 'Swift',
+            '.kt': 'Kotlin',
+            '.scala': 'Scala',
+            '.clj': 'Clojure',
+            '.hs': 'Haskell',
+            '.ml': 'OCaml',
+            '.fs': 'F#',
+            '.vb': 'Visual Basic',
+            '.pl': 'Perl',
+            '.sh': 'Shell',
+            '.bash': 'Bash',
+            '.zsh': 'Zsh',
+            '.fish': 'Fish',
+            '.ps1': 'PowerShell',
+            '.r': 'R',
+            '.sql': 'SQL',
+            '.html': 'HTML',
+            '.css': 'CSS',
+            '.scss': 'SCSS',
+            '.less': 'Less',
+            '.vue': 'Vue',
+            '.svelte': 'Svelte'
+        }
+        
+        return language_mapping.get(extension.lower(), 'Unknown')
+    
+    async def close(self):
+        """Close the HTTP session."""
+        if self.session:
+            await self.session.close()
+            self.session = None 
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 3744166..1b8f24a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ dependencies = [
     "typing-extensions>=4.8.0",
     "httpx>=0.25.0",
     "aiofiles>=23.2.0",
+    "aiohttp>=3.8.0",
 ]
 
 [project.scripts]
diff --git a/validate_repo.py b/validate_repo.py
new file mode 100644
index 0000000..b57015d
--- /dev/null
+++ b/validate_repo.py
@@ -0,0 +1,311 @@
+"""
+Validation script for the Code Analyzer Agent.
+
+This script tests the Code Analyzer Agent with a real GitHub repository
+(yizucodes/memory) to validate its functionality and analysis capabilities.
+"""
+
+import asyncio
+import json
+import time
+import os
+from typing import Dict, Any
+from dotenv import load_dotenv
+from pitchbot.code_analyzer_agent import CodeAnalyzerAgent
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+class ValidationTest:
+    """Test suite for validating the Code Analyzer Agent."""
+    
+    def __init__(self):
+        """Initialize the validation test."""
+        self.test_repo = "https://github.com/yizucodes/memory"
+        self.agent = None
+        self.results = {}
+        
+    async def run_validation(self) -> Dict[str, Any]:
+        """Run the complete validation test suite."""
+        print("🧪 Code Analyzer Agent Validation Suite")
+        print("=" * 60)
+        
+        # Check prerequisites
+        if not self._check_prerequisites():
+            return {"error": "Prerequisites not met"}
+        
+        # Initialize agent
+        print("\n🤖 Initializing Code Analyzer Agent...")
+        try:
+            self.agent = CodeAnalyzerAgent()
+            print("✅ Agent initialized successfully")
+        except Exception as e:
+            print(f"❌ Failed to initialize agent: {e}")
+            return {"error": f"Agent initialization failed: {e}"}
+        
+        # Run analysis
+        print(f"\n🔍 Analyzing repository: {self.test_repo}")
+        print("This is the yizucodes/memory repository - a Llama-powered personal memory system")
+        print("for processing Meta Ray-Ban glasses footage.")
+        print("\nAnalysis may take 2-5 minutes depending on repository size...")
+        
+        start_time = time.time()
+        
+        try:
+            analysis_result = await self.agent.analyze_github_repository(self.test_repo)
+            analysis_time = time.time() - start_time
+            
+            # Validate results
+            validation_results = self._validate_analysis_results(analysis_result, analysis_time)
+            
+            return {
+                "status": "success",
+                "repository": self.test_repo,
+                "analysis_time": f"{analysis_time:.2f} seconds",
+                "analysis_result": analysis_result,
+                "validation": validation_results
+            }
+            
+        except Exception as e:
+            print(f"❌ Analysis failed: {e}")
+            return {
+                "status": "failed",
+                "repository": self.test_repo,
+                "error": str(e),
+                "analysis_time": f"{time.time() - start_time:.2f} seconds"
+            }
+    
+    def _check_prerequisites(self) -> bool:
+        """Check if all prerequisites are met."""
+        print("\n🔧 Checking prerequisites...")
+        
+        # Check LLAMA API key
+        llama_key = os.getenv('LLAMA_API_KEY')
+        if not llama_key:
+            print("❌ LLAMA_API_KEY not found in environment")
+            print("Please set your LLAMA API key in the .env file")
+            return False
+        else:
+            print("✅ LLAMA API key found")
+        
+        # Check git availability
+        try:
+            import subprocess
+            subprocess.run(['git', '--version'], capture_output=True, check=True)
+            print("✅ Git is available")
+        except (subprocess.CalledProcessError, FileNotFoundError):
+            print("❌ Git is not available or not in PATH")
+            return False
+        
+        # Check internet connectivity (basic)
+        try:
+            import urllib.request
+            urllib.request.urlopen('https://github.com', timeout=10)
+            print("✅ Internet connectivity confirmed")
+        except Exception as e:
+            print(f"⚠️  Internet connectivity issue: {e}")
+            print("Proceeding anyway - may be a temporary issue")
+            # Don't return False, just warn
+            pass
+        
+        return True
+    
+    def _validate_analysis_results(self, result: Dict[str, Any], analysis_time: float) -> Dict[str, Any]:
+        """Validate the analysis results."""
+        print(f"\n📊 Analysis completed in {analysis_time:.2f} seconds")
+        print("🔍 Validating results...")
+        
+        validation = {
+            "structure_valid": False,
+            "content_quality": "unknown",
+            "expected_technologies": [],
+            "detected_technologies": [],
+            "technology_accuracy": 0.0,
+            "summary_length": 0,
+            "pitfalls_count": 0,
+            "improvements_count": 0,
+            "overall_score": 0.0
+        }
+        
+        # Check if result has error
+        if "error" in result and result["error"]:
+            print(f"❌ Analysis returned error: {result['error']}")
+            validation["content_quality"] = "error"
+            return validation
+        
+        # Validate structure
+        required_fields = ["summary", "stacks", "problem_solved", "pitfalls", "improvements"]
+        if all(field in result for field in required_fields):
+            validation["structure_valid"] = True
+            print("✅ Result structure is valid")
+        else:
+            missing = [field for field in required_fields if field not in result]
+            print(f"❌ Missing fields: {missing}")
+            return validation
+        
+        # Validate content
+        validation["summary_length"] = len(result.get("summary", ""))
+        validation["pitfalls_count"] = len(result.get("pitfalls", []))
+        validation["improvements_count"] = len(result.get("improvements", []))
+        
+        # Expected technologies for yizucodes/memory repository
+        expected_techs = ["python", "llama", "whisper", "opencv", "ai", "ml"]
+        detected_techs = [tech.lower() for tech in result.get("stacks", [])]
+        validation["detected_technologies"] = result.get("stacks", [])
+        validation["expected_technologies"] = expected_techs
+        
+        # Calculate technology detection accuracy
+        matches = sum(1 for tech in expected_techs if any(tech in det.lower() for det in detected_techs))
+        validation["technology_accuracy"] = matches / len(expected_techs) if expected_techs else 0.0
+        
+        # Overall quality assessment
+        quality_score = 0
+        
+        # Summary quality (25 points)
+        if validation["summary_length"] > 100:
+            quality_score += 25
+        elif validation["summary_length"] > 50:
+            quality_score += 15
+        elif validation["summary_length"] > 0:
+            quality_score += 5
+        
+        # Technology detection (25 points)
+        quality_score += validation["technology_accuracy"] * 25
+        
+        # Pitfalls identification (25 points)
+        if validation["pitfalls_count"] > 3:
+            quality_score += 25
+        elif validation["pitfalls_count"] > 1:
+            quality_score += 15
+        elif validation["pitfalls_count"] > 0:
+            quality_score += 10
+        
+        # Improvements suggestions (25 points)
+        if validation["improvements_count"] > 3:
+            quality_score += 25
+        elif validation["improvements_count"] > 1:
+            quality_score += 15
+        elif validation["improvements_count"] > 0:
+            quality_score += 10
+        
+        validation["overall_score"] = quality_score
+        
+        # Determine content quality
+        if quality_score >= 80:
+            validation["content_quality"] = "excellent"
+        elif quality_score >= 60:
+            validation["content_quality"] = "good"
+        elif quality_score >= 40:
+            validation["content_quality"] = "fair"
+        else:
+            validation["content_quality"] = "poor"
+        
+        return validation
+    
+    def print_detailed_results(self, results: Dict[str, Any]):
+        """Print detailed validation results."""
+        print("\n" + "=" * 80)
+        print("📈 DETAILED VALIDATION RESULTS")
+        print("=" * 80)
+        
+        if results.get("status") == "failed":
+            print(f"❌ Validation Status: FAILED")
+            print(f"Error: {results.get('error', 'Unknown error')}")
+            return
+        
+        print(f"✅ Validation Status: SUCCESS")
+        print(f"🕒 Analysis Time: {results.get('analysis_time', 'Unknown')}")
+        
+        validation = results.get("validation", {})
+        print(f"\n📊 Overall Score: {validation.get('overall_score', 0):.1f}/100")
+        print(f"🏆 Content Quality: {validation.get('content_quality', 'unknown').upper()}")
+        
+        print(f"\n📋 Analysis Results:")
+        print(f"  • Summary Length: {validation.get('summary_length', 0)} characters")
+        print(f"  • Technologies Detected: {len(validation.get('detected_technologies', []))}")
+        print(f"  • Pitfalls Identified: {validation.get('pitfalls_count', 0)}")
+        print(f"  • Improvements Suggested: {validation.get('improvements_count', 0)}")
+        
+        print(f"\n🛠️  Technology Detection:")
+        detected = validation.get('detected_technologies', [])
+        if detected:
+            for tech in detected:
+                print(f"  • {tech}")
+        else:
+            print("  • No technologies detected")
+        
+        accuracy = validation.get('technology_accuracy', 0) * 100
+        print(f"  • Detection Accuracy: {accuracy:.1f}%")
+        
+        # Show actual analysis results
+        analysis = results.get("analysis_result", {})
+        if analysis and not analysis.get("error"):
+            print(f"\n💡 Repository Analysis:")
+            print(f"  • Problem Solved: {analysis.get('problem_solved', 'N/A')[:100]}...")
+            
+            if analysis.get("pitfalls"):
+                print(f"\n⚠️  Top Pitfalls:")
+                for i, pitfall in enumerate(analysis.get("pitfalls", [])[:3], 1):
+                    print(f"  {i}. {pitfall[:80]}...")
+            
+            if analysis.get("improvements"):
+                print(f"\n🚀 Top Improvements:")
+                for i, improvement in enumerate(analysis.get("improvements", [])[:3], 1):
+                    print(f"  {i}. {improvement[:80]}...")
+
+
+async def main():
+    """Main validation function."""
+    validator = ValidationTest()
+    
+    print("This script will validate the Code Analyzer Agent by analyzing:")
+    print("🎯 Repository: yizucodes/memory")
+    print("📝 Description: Llama-powered personal memory system for Meta Ray-Ban glasses")
+    print("🔧 Features: Video processing, AI transcription, multimodal analysis")
+    
+    # Run validation
+    results = await validator.run_validation()
+    
+    # Print results
+    validator.print_detailed_results(results)
+    
+    # Save results to file
+    with open("validation_results.json", "w") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    
+    print(f"\n📁 Results saved to: validation_results.json")
+    
+    return results
+
+
+if __name__ == "__main__":
+    print("🧪 Code Analyzer Agent Validation")
+    print("Testing with yizucodes/memory repository...")
+    print("-" * 50)
+    
+    # Check if running in virtual environment
+    if not os.getenv('VIRTUAL_ENV') and not os.path.exists('.venv'):
+        print("⚠️  Warning: Not running in virtual environment")
+        print("Consider activating the virtual environment first:")
+        print("source .venv/bin/activate")
+    
+    try:
+        results = asyncio.run(main())
+        
+        # Exit with appropriate code
+        if results.get("status") == "success":
+            quality = results.get("validation", {}).get("content_quality", "unknown")
+            if quality in ["excellent", "good"]:
+                exit(0)  # Success
+            else:
+                exit(1)  # Partial success
+        else:
+            exit(2)  # Failure
+            
+    except KeyboardInterrupt:
+        print("\n\n⚠️  Validation interrupted by user")
+        exit(130)
+    except Exception as e:
+        print(f"\n❌ Validation failed with exception: {e}")
+        exit(3) 
\ No newline at end of file
diff --git a/validation_results.json b/validation_results.json
new file mode 100644
index 0000000..7bae40f
--- /dev/null
+++ b/validation_results.json
@@ -0,0 +1,49 @@
+{
+  "status": "success",
+  "repository": "https://github.com/yizucodes/memory",
+  "analysis_time": "20.37 seconds",
+  "analysis_result": {
+    "summary": "### Code Analysis\n\n#### Code Quality Assessment\n\n1. **Code Organization and Structure**: The code is well-organized into clear sections, including imports, settings and outputs definitions, element definition, media loading functions, and the executor function. The structure is logical and easy to follow.\n\n2. **Naming Conventions and Readability**: The code adheres to Python's naming conventions (PEP 8). Variable and function names are descriptive, enhancing readability. However, the lack of doc...",
+    "stacks": [
+      "Go",
+      "Python"
+    ],
+    "problem_solved": "Analysis available in summary",
+    "pitfalls": [
+      "#### Design Pitfalls\n\n1",
+      "**Anti-patterns Identified**:\n   - The use of magic numbers (e",
+      "**Tight Coupling Issues**: The code is relatively loosely coupled, with separate functions for different tasks",
+      "**Missing Abstractions**: The code could benefit from additional abstractions, such as a more generic media loader interface that could be implemented differently for videos and images",
+      "**Security Vulnerabilities**: Potential security vulnerabilities include:\n   - Path resolution issues if `Path"
+    ],
+    "improvements": [
+      "However, it could benefit from more comprehensive error handling to cover a broader range of potential errors (e",
+      "However, some areas, such as the frame rate control and image loading, could be simplified or optimized for better maintainability",
+      "However, the `stay_alive` setting could potentially lead to resource leaks if not managed properly",
+      "**Missing Abstractions**: The code could benefit from additional abstractions, such as a more generic media loader interface that could be implemented differently for videos and images",
+      "- Lack of comprehensive error handling, which could lead to unexpected behavior or crashes"
+    ],
+    "exhaustive_summary": "**Comprehensive Analysis and Summary**\n\nThe provided Python code is a media loader element for an AI application, designed to load videos and images from files and directories, and output frames to be processed by AI models. The code is well-structured and follows good practices, but there are areas for improvement.\n\n### Code Structure and Organization\n\nThe code is divided into clear sections:\n\n1. **Imports**: The necessary libraries are imported, including OpenCV, NumPy, and the WebAI Element SDK.\n2. **Settings and Outputs**: The `Settings` and `Outputs` classes define the configuration and output interfaces for the element.\n3. **Element Definition**: The `element` object is created with the required metadata, settings, and outputs.\n4. **Media Loading Functions**: The `_load_video_file` and `_load_images_from_directory` functions implement the logic for loading media from files and directories.\n5. **Executor Function**: The `run` function is the main entry point for the element, responsible for loading media and outputting frames.\n\n### Code Quality and Best Practices\n\nThe code generally follows good practices:\n\n1. **Type Hints**: The code uses type hints to specify the types of variables, function parameters, and return values.\n2. **Error Handling**: The code raises informative error messages when encountering invalid input or unsupported media types.\n\nHowever, there are some areas for improvement:\n\n1. **Magic Numbers**: The code uses magic numbers (e.g., `0.5` in `_load_images_from_directory`) that could be replaced with named constants.\n2. **Code Duplication**: The `media_path` variable is assigned in two separate places; this could be simplified.\n3. **Potential Issues**: The `stay_alive` setting could cause the element to run indefinitely, potentially leading to resource leaks or other issues.\n\n### Performance and Optimization\n\nThe code has some performance considerations:\n\n1. **Frame Rate Control**: The `frame_rate` setting controls the output frame rate, but the implementation uses `time.sleep` to achieve this, which may not be precise or efficient.\n2. **Image Loading**: The `_load_images_from_directory` function uses `cv2.imread` to load images, which may not be the most efficient approach, especially for large images or directories.\n\n### Security and Robustness\n\nThe code appears to be generally secure and robust:\n\n1. **Input Validation**: The code checks the input media path and type, raising errors for invalid or unsupported inputs.\n2. **Resource Management**: The code releases the video capture object when finished, and the `stay_alive` setting is checked to prevent indefinite running.\n\nHowever, there are some potential issues:\n\n1. **Path Resolution**: The code uses `Path.resolve()` to resolve the media path, which may not handle all edge cases (e.g., symlinks, relative paths).\n2. **Error Handling**: While the code raises informative error messages, it may not handle all possible error scenarios (e.g., file I/O errors, OpenCV errors).\n\n### Recommendations\n\n1. **Simplify Media Path Assignment**: Combine the two `media_path` assignments into a single statement.\n2. **Replace Magic Numbers**: Define named constants for magic numbers (e.g., `IMAGE_LOAD_DELAY` instead of `0.5`).\n3. **Improve Frame Rate Control**: Consider using a more precise and efficient method for controlling the output frame rate (e.g., using a timer or scheduling library).\n4. **Optimize Image Loading**: Explore alternative image loading approaches (e.g., using a more efficient library or parallelizing image loading).\n5. **Enhance Error Handling**: Consider adding more comprehensive error handling to handle a wider range of error scenarios.\n\n### Example Refactoring\n\nHere's an example of how the media path assignment could be simplified:\n\n```python\nmedia_path = ctx.settings.video_file.value or ctx.settings.image_directory.value\nif not media_path:\n    raise ValueError(\"No media path provided. Quitting...\")\n```\n\nThis code uses the `or` operator to assign the first non-empty value to `media_path`, simplifying the logic and reducing duplication.\n\n### Code Improvements\n\nHere is an updated version of the code incorporating some of the recommended improvements:\n\n```python\nimport time\nfrom pathlib import Path\nfrom typing import Any, AsyncIterator\nfrom uuid import UUID\n\nimport cv2\nimport numpy as np\nfrom webai_element_sdk.comms.messages import ColorFormat, Frame\nfrom webai_element_sdk.element import Context, Element\nfrom webai_element_sdk.element.settings import (\n    BoolSetting,\n    ElementSettings,\n    NumberSetting,\n    TextSetting,\n)\nfrom webai_element_sdk.element.variables import ElementOutputs, Output\n\n# Define named constants for magic numbers\nIMAGE_LOAD_DELAY = 0.5\n\nclass Settings(ElementSettings):\n    video_file = TextSetting(\n        name=\"video_file\",\n        display_name=\"Video File\",\n        description=\"The path to the video file to be loaded.\",\n        default=\"\",\n        hints=[\"file_path\"],\n        required=False,\n    )\n    image_directory = TextSetting(\n        name=\"image_directory\",\n        display_name=\"Image Directory\",\n        description=\"The path to the image directory to be loaded.\",\n        default=\"\",\n        hints=[\"folder_path\"],\n        required=False,\n    )\n    frame_rate = NumberSetting[int](\n        name=\"frame_rate\",\n        display_name=\"Frame Rate\",\n        description=\"The amount of frames per second (FPS) that should be processed.\",\n        default=0,\n        hints=[\"advanced\"],\n    )\n    stay_alive = BoolSetting(\n        name=\"stay_alive\",\n        display_name=\"Stay Alive\",\n        description=\"Toggle to keep element running indefinitely after files complete.\",\n        default=False,\n        hints=[\"advanced\"],\n    )\n\n\nclass Outputs(ElementOutputs):\n    default = Output[Frame]()\n\n\nelement = Element(\n    id=UUID(\"1916c9ba-fca7-4ed3-b773-11f400def123\"),\n    name=\"media_loader\",\n    display_name=\"Media Loader\",\n    version=\"0.3.9\",\n    description=\"Imports videos and images into the application so that AI models can use them for inference\",\n    outputs=Outputs(),\n    settings=Settings(),\n)\n\n\ndef _load_video_file(video: cv2.VideoCapture, frame_rate: int):\n    counter: int = 0\n\n    while video.isOpened():\n        ret, frame = video.read()\n\n        if not ret:\n            print(\"End of file reached.\")\n            break\n\n        counter += 1\n\n        yield frame\n\n    video.release()\n\n\ndef _load_images_from_directory(filepath: Path):\n    for file in filepath.iterdir():\n        if file.is_file() and file.suffix.lower() in [\n            \".jpg\",\n            \".png\",\n            \".jpeg\",\n            \".npy\",\n            \".raw\",\n        ]:\n            time.sleep(IMAGE_LOAD_DELAY)\n            yield cv2.imread(str(file))\n\n\n@element.executor  # type: ignore\nasync def run(ctx: Context[None, Outputs, Settings]) -> AsyncIterator[Any]:\n    frame_rate: int = ctx.settings.frame_rate.value\n\n    # Simplify media path assignment\n    media_path = ctx.settings.video_file.value or ctx.settings.image_directory.value\n    if not media_path:\n        raise ValueError(\"No media path provided. Quitting...\")\n\n    media_path_obj = Path(media_path).resolve()\n\n    if media_path_obj.is_dir():\n        generator = _load_images_from_directory(media_path_obj)\n    elif media_path_obj.suffix.lower() in [\".mp4\", \".avi\", \".mov\"]:\n        video = cv2.VideoCapture(str(media_path_obj))\n\n        if frame_rate == 0:\n            frame_rate = int(video.get(cv2.CAP_PROP_FPS))\n\n        generator = _load_video_file(video, frame_rate)\n    else:\n        raise ValueError(f\"{media_path} is not a supported type or format\")\n\n    next_frame_time: float = time.perf_counter()\n\n    for img in generator:\n        if frame_rate != 0:\n            time_to_next_frame = next_frame_time - time.perf_counter()\n\n            if time_to_next_frame > 0:\n                time.sleep(time_to_next_frame)\n\n            next_frame_time += 1 / frame_rate\n\n        if img is None:  # type: ignore\n            continue\n\n        image_rgb = cv2.cvtColor(np.array(img), cv2.COLOR_BGR2RGB)\n\n        yield ctx.outputs.default(Frame(ndframe=np.asarray(image_rgb), rois=[], color_space=ColorFormat.BGR))  # type: ignore\n\n    while ctx.settings.stay_alive.value:\n        continue\n```\n\nThis updated code simplifies the media path assignment and replaces the magic number `0.5` with a named constant `IMAGE_LOAD_DELAY`. Further improvements can be made by addressing the other recommendations mentioned earlier."
+  },
+  "validation": {
+    "structure_valid": true,
+    "content_quality": "good",
+    "expected_technologies": [
+      "python",
+      "llama",
+      "whisper",
+      "opencv",
+      "ai",
+      "ml"
+    ],
+    "detected_technologies": [
+      "Go",
+      "Python"
+    ],
+    "technology_accuracy": 0.16666666666666666,
+    "summary_length": 503,
+    "pitfalls_count": 5,
+    "improvements_count": 5,
+    "overall_score": 79.16666666666666
+  }
+}
\ No newline at end of file