Personal Resume Agent

A personalized AI agent that reads your resume and provides intelligent responses about your professional background. Supports both local MCP (Model Context Protocol) integration with Claude Desktop and deployment to the NANDA (Network of Autonomous Decentralized Agents) for Agent-to-Agent (A2A) communication.

Features

Core Capabilities

Resume Processing: Automatically reads and processes resume files (PDF, DOCX, TXT, MD)
RAG System: Uses ChromaDB and sentence transformers for intelligent content retrieval
MCP Server: Exposes functionality through standardized MCP protocol for Claude Desktop
Skill Matching: Analyzes how well your skills match job requirements
Natural Language Interface: Ask questions about your experience, skills, education, etc.

NANDA Network Integration

A2A Communication: Connect with 484+ agents in the NANDA network
Agent-to-Agent Messaging: Your resume agent can communicate with other specialized agents
AWS Deployment: Production-ready deployment on EC2 with supervisor
Interactive Dashboards: CLI tools for sending tasks, viewing messages, and chatting with agents
Network Discovery: Registered in NANDA registry for agent discoverability

Quick Start

Local Development (MCP Server)

Install Dependencies
pip install -r requirements.txt
Add Your Resume
# Place your resume files in the data/ directory cp your-resume.pdf data/
Test the Agent
cd src python personal_resume_agent.py
Run as MCP Server
cd src python mcp_resume_server.py

NANDA Network Deployment (A2A)

Deploy to AWS EC2
# Configure deployment script with your details ./deploy_aws_nest.sh
Upload Resume Files
./upload_resume.sh
Verify Deployment
# Check agent status ssh ubuntu@<your-ec2-ip> "sudo supervisorctl status resume-agent"
Use Interactive Dashboards
# Send tasks to your agent python3 send_task.py # View incoming messages python3 view_messages.py --monitor # Chat with other agents in NANDA network python3 agent_chat.py

Project Structure

personal-resume-agent/ ├── src/ # Core source code │ ├── resume_rag.py # RAG system (ChromaDB + embeddings) │ ├── personal_resume_agent.py # Main agent logic │ └── mcp_resume_server.py # MCP server for Claude Desktop │ ├── NANDA Integration │ ├── nest_resume_agent.py # NANDA-compatible agent wrapper │ ├── deploy_aws_nest.sh # AWS EC2 deployment script │ └── upload_resume.sh # Resume file upload script │ ├── Interactive Dashboards │ ├── send_task.py # Send tasks to resume agent │ ├── view_messages.py # View incoming A2A messages │ └── agent_chat.py # Chat with other NANDA agents │ ├── Documentation │ ├── README.md # This file │ ├── DASHBOARD_GUIDE.md # Dashboard usage guide │ ├── A2A_CHAT_GUIDE.md # Agent-to-Agent chat guide │ ├── A2A_GUIDE.md # A2A protocol documentation │ └── MESSAGE_DASHBOARD.md # Message viewing guide │ ├── data/ # Resume files (gitignored) ├── tests/ # Test files └── requirements.txt # Python dependencies

Usage Examples

Direct Agent Usage

from personal_resume_agent import PersonalResumeAgent agent = PersonalResumeAgent() await agent.initialize() # Ask questions about your resume result = await agent.process_query("What programming languages do I know?") print(result['response']) # Analyze skill match for a job match = await agent.get_skill_match("Python, React, AWS, Docker") print(f"Match: {match['match_percentage']}%")

MCP Server Tools

The MCP server exposes these tools:

query_resume: Ask questions about resume content
get_agent_info: Get agent capabilities and status
analyze_skill_match: Compare skills with job requirements
get_resume_summary: Get overview of resume knowledge base

Interactive Dashboards

1. Send Task Dashboard (`send_task.py`)

Interactive CLI for sending queries to your resume agent:

# Interactive mode python3 send_task.py # Direct query python3 send_task.py "What are my technical skills?"

Features:

Color-coded interface
Session statistics tracking
Conversation history (last 10)
Loading animations
Special commands: /help, /history, /stats, /clear

2. Message Viewer (`view_messages.py`)

Monitor incoming A2A messages from other agents:

# View recent messages python3 view_messages.py # Real-time monitoring mode python3 view_messages.py --monitor # Show statistics python3 view_messages.py --stats # Filter by sender python3 view_messages.py --filter tech-expert

Features:

Real-time message monitoring
Sender statistics with bar charts
Message filtering
Auto-refresh capability

3. A2A Network Chat (`agent_chat.py`)

Connect with 484+ agents in NANDA network:

python3 agent_chat.py

Commands:

/list - Show all available agents
/search <keyword> - Find agents by expertise
/info <agent-id> - View agent details
/chat <agent-id> - Start chatting with an agent
/history - View conversation history
/help - Show all commands

Example session:

📝 > /list 📝 > /chat tech-expert 💬 [tech-expert] > What are the key technical skills? 💬 [tech-expert] > back 📝 > /history

NANDA Network Deployment

Prerequisites

AWS account with EC2 access
SSH key pair for EC2 instances
NANDA framework installed locally

Step-by-Step Deployment

1. Configure Deployment Script

Edit deploy_aws_nest.sh with your details:

KEY_NAME="your-key-name" KEY_FILE="/path/to/your-key.pem" SECURITY_GROUP_ID="sg-xxxxxxxxx"

2. Deploy to AWS EC2

chmod +x deploy_aws_nest.sh ./deploy_aws_nest.sh

This script will:

Launch t3.small EC2 instance (20GB disk, CPU-optimized)
Install Python 3.11 and dependencies
Install PyTorch CPU-only version (saves ~2GB space)
Clone repository from GitHub
Configure supervisor for process management
Register agent in NANDA registry

3. Upload Resume Files

chmod +x upload_resume.sh ./upload_resume.sh

4. Verify Deployment

# Check agent status ssh -i your-key.pem ubuntu@<ec2-ip> "sudo supervisorctl status resume-agent" # View logs ssh -i your-key.pem ubuntu@<ec2-ip> "sudo tail -f /var/log/resume-agent.log" # Test agent endpoint curl http://<ec2-ip>:6050/a2a \ -H 'Content-Type: application/json' \ -d '{"content": {"text": "What are my skills?", "type": "text"}, "role": "user", "conversation_id": "test-001"}'

NANDA Network Information

Once deployed, your agent will be:

Registered in NANDA registry (http://registry.chat39.com:6900)
Discoverable by 484+ other agents in the network
Accessible via A2A protocol endpoint
Visible on NANDA dashboard (https://index.projectnanda.org)

Architecture: NANDA Deployment

┌─────────────────────────────────────────────────────────────┐ │ NANDA Network │ │ (484+ Connected Agents) │ └──────────────────────┬──────────────────────────────────────┘ │ │ A2A Protocol │ ┌──────────────────────▼──────────────────────┐ │ AWS EC2 (t3.small) │ │ ┌────────────────────────────┐ │ │ │ Supervisor Process Mgr │ │ │ └──────────┬─────────────────┘ │ │ │ │ │ ┌──────────▼─────────────────┐ │ │ │ NANDA Adapter │ │ │ │ (nest_resume_agent.py) │ │ │ └──────────┬─────────────────┘ │ │ │ │ │ ┌──────────▼─────────────────┐ │ │ │ Resume Agent + RAG │ │ │ │ (ChromaDB + Embeddings) │ │ │ └────────────────────────────┘ │ │ │ │ Port: 6050/a2a │ │ Registry: registry.chat39.com:6900 │ └─────────────────────────────────────────────┘

Environment Variables

Set these in your deployment:

AGENT_ID=resume-agent # Your agent identifier PORT=6050 # HTTP server port REGISTRY_URL=http://registry.chat39.com:6900 # NANDA registry PUBLIC_URL=http://<ec2-ip>:6050 # Your agent's public URL DATA_DIR=/home/resume/resume-agent/data # Resume files directory

Configuration

Claude Desktop Integration

Add to your Claude Desktop config (claude_desktop_config.json):

{ "mcpServers": { "personal-resume": { "command": "python", "args": ["/path/to/personal-resume-agent/src/mcp_resume_server.py"], "cwd": "/path/to/personal-resume-agent" } } }

Supported File Formats

PDF: Extracted using PyPDF2
DOCX: Processed with python-docx
TXT/MD: Plain text files

Requirements

Python 3.8+
ChromaDB for vector storage
Sentence Transformers for embeddings
PyPDF2 for PDF processing
python-docx for Word documents

Privacy & Security

🔒 Important Privacy Notes:

All resume data is processed locally on your machine
No personal information is sent to external services
Vector database is stored locally in data/resume_vectordb/
The data/ directory is excluded from version control
Never commit personal resume files to public repositories

Architecture

Local MCP Mode

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Resume Files │───▶│ RAG System │───▶│ MCP Server │ │ (PDF/DOCX) │ │ (ChromaDB + │ │ (Claude Tool) │ │ │ │ Transformers) │ │ │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ ▼ ┌─────────────────┐ │ Personal Resume │ │ Agent │ │ (Query Engine) │ └─────────────────┘ │ ▼ ┌─────────────────┐ │ Claude Desktop │ └─────────────────┘

NANDA Network Mode

┌────────────────────────────────────────────────────────────┐ │ NANDA Network (484+ Agents) │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │tech- │ │python- │ │financial-│ │ai- │ │ │ │expert │ │expert │ │advisor │ │researcher│ │ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ │ │ │ │ └───────┼─────────────┼──────────────┼─────────────┼─────────┘ │ │ │ │ └─────────────┴──────────────┴─────────────┘ │ A2A Protocol ▼ ┌────────────────────────┐ │ NANDA Adapter │ │ (nest_resume_agent.py) │ └────────────┬───────────┘ │ ┌────────────▼───────────┐ │ Resume Agent + RAG │ │ (ChromaDB + LLM) │ └────────────┬───────────┘ │ ┌────────────▼───────────┐ │ Resume Data Store │ │ (Vector Database) │ └────────────────────────┘ Accessible via: - A2A Endpoint: http://<ec2-ip>:6050/a2a - Registry: http://registry.chat39.com:6900 - Dashboard: https://index.projectnanda.org

Documentation

Comprehensive guides are available:

DASHBOARD_GUIDE.md - Interactive dashboard usage
- Send Task Dashboard
- Message Viewer
- Command reference and examples
A2A_CHAT_GUIDE.md - Agent-to-Agent communication
- Connecting with 484+ agents
- Chat commands and workflows
- Use cases and examples
A2A_GUIDE.md - A2A protocol details
- Message format
- Protocol specification
- Integration patterns
MESSAGE_DASHBOARD.md - Message monitoring
- Viewing incoming messages
- Log analysis
- Real-time monitoring

Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

License

MIT License - See LICENSE file for details.