> ## Documentation Index > Fetch the complete documentation index at: https://docs.run-agent.ai/llms.txt > Use this file to discover all available pages before exploring further. # Lead Scoring Agent > Build an AI-powered lead scoring system with automated email generation **Prerequisites**: Completed the [Deploy Your First Agent](/tutorials/deploy-your-first-agent) tutorial ## What You'll Build In this tutorial, you'll create a lead scoring agent that: * Scores candidates based on job descriptions using AI * Ranks leads automatically with intelligent analysis * Generates personalized emails for top candidates * Provides a complete SaaS solution with frontend and backend * Uses CrewAI flows for multi-agent orchestration ## The Lead Scoring Challenge Recruiting and lead qualification is time-consuming: * **Manual screening**: Reviewing hundreds of resumes manually * **Inconsistent scoring**: Different recruiters score differently * **Email personalization**: Writing personalized emails takes hours * **Scalability**: Hard to scale manual processes RunAgent solves this by automating the entire lead scoring workflow with AI agents that can analyze, score, and communicate with candidates. ## Architecture Overview The lead scoring agent uses a multi-layered architecture: ``` ┌─────────────────┐ │ React Frontend │ │ (Port 5173) │ └────────┬────────┘ │ HTTP REST API ↓ ┌─────────────────┐ │ Flask Backend │ │ (Port 8000) │ └────────┬────────┘ │ RunAgent Python SDK ↓ ┌─────────────────┐ │ RunAgent Agent │ │ (CrewAI Flow) │ └─────────────────┘ ``` ## Step 1: Understanding the Agent Structure The lead scoring agent uses CrewAI flows to orchestrate multiple AI agents: **Key Components:** * **LeadDataCollectionCrew**: Collects and validates lead data * **LeadAnalysisCrew**: Analyzes candidate profiles * **LeadScoringCrew**: Scores candidates based on job requirements * **EmailGenerationCrew**: Creates personalized emails **Entrypoints:** * `lead_score_flow`: Main flow that orchestrates the entire process * `score_candidate`: Score a single candidate ## Step 2: Agent Configuration The agent is configured using `runagent.config.json`: ```json theme={null} { "agent_name": "lead-scoring", "description": "AI-powered lead scoring with email generation", "framework": "crewai", "agent_architecture": { "entrypoints": [ { "file": "main.py", "module": "lead_score_flow", "tag": "lead_score_flow" }, { "file": "main.py", "module": "score_candidate", "tag": "score_candidate" } ] } } ``` ## Step 3: Core Agent Logic (Gist) The agent processes candidates through multiple stages: ```python theme={null} # Simplified flow structure def lead_score_flow( candidates: List[Dict], job_description: str, top_n: int = 3, generate_emails: bool = True, additional_instructions: str = "" ) -> Dict[str, Any]: """ Main flow that: 1. Collects and validates candidate data 2. Analyzes each candidate's profile 3. Scores candidates based on job requirements 4. Ranks candidates and selects top N 5. Generates personalized emails """ # Data collection validated_candidates = collect_lead_data(candidates) # Analysis and scoring scored_candidates = score_leads( validated_candidates, job_description, additional_instructions ) # Ranking top_candidates = rank_candidates(scored_candidates, top_n) # Email generation if generate_emails: emails = generate_emails(top_candidates, job_description) return { "scored_candidates": scored_candidates, "top_candidates": top_candidates, "emails": emails } return { "scored_candidates": scored_candidates, "top_candidates": top_candidates } ``` ## Step 4: Backend Integration The Flask backend provides REST API endpoints: ```python theme={null} # Backend API structure @app.route('/api/score-leads', methods=['POST']) def score_leads(): """ Endpoint that: - Receives candidates and job description - Calls RunAgent agent - Returns scored results """ client = RunAgentClient( agent_id=agent_id, entrypoint_tag="lead_score_flow", local=False ) result = client.run( candidates=candidates, job_description=job_description, top_n=top_n, generate_emails=generate_emails ) return jsonify(result) ``` ## Step 5: Frontend Integration The React frontend provides a user-friendly interface: **Key Features:** * CSV upload for candidate data * Job description input * Real-time scoring results * Email preview and download * Top candidates visualization ## Step 6: Deployment ### Local Deployment ```bash theme={null} # 1. Deploy the RunAgent agent cd examples/lead-agent/lead-score-flow runagent serve . # 2. Start the backend cd ../backend python app.py # 3. Start the frontend cd ../frontend npm run dev ``` ### Production Deployment The agent can be deployed to RunAgent Cloud: ```bash theme={null} runagent deploy . ``` ## What You've Accomplished You've built a complete lead scoring SaaS solution: Automated candidate scoring using multi-agent AI workflows Automatic ranking of candidates based on job fit Personalized email generation for top candidates Complete SaaS application with frontend and backend ## Key Features ### Multi-Agent Orchestration * Uses CrewAI flows to coordinate multiple specialized agents * Each agent handles a specific aspect of the scoring process * Parallel processing for efficient candidate evaluation ### Intelligent Scoring * Analyzes candidate profiles against job requirements * Considers skills, experience, and cultural fit * Provides detailed scoring breakdowns ### Email Personalization * Generates context-aware emails for each candidate * Incorporates specific candidate details * Maintains professional tone and structure ## Example Usage ```python theme={null} from runagent import RunAgentClient client = RunAgentClient( agent_id="your_agent_id", entrypoint_tag="lead_score_flow", local=False ) result = client.run( candidates=[ { "id": 1, "name": "John Doe", "email": "john@example.com", "bio": "Experienced React developer", "skills": "React, Node.js, TypeScript" } ], job_description="Looking for a senior React developer...", top_n=3, generate_emails=True ) ``` ## Next Steps Customize scoring criteria and weights Add features like candidate tracking and analytics Deploy to production with proper scaling Explore the complete example code ## Repository View the complete example code and documentation: **Repository**: [https://github.com/runagent-dev/runagent/tree/main/examples/lead-agent](https://github.com/runagent-dev/runagent/tree/main/examples/lead-agent) The repository includes: * Complete agent implementation with CrewAI flows * Flask backend API * React frontend application * Deployment guides and documentation * Example CSV files and test data **🎉 Great job!** You've learned how to build a production-ready lead scoring system using RunAgent and CrewAI. 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