Upload README.md

README.md

@@ -1,6 +1,17 @@
-# 🚀 Multi-Agent Reasoning System for Job Change & ICP Detection
+---
+title: Multi-Agent Reasoning System for Job Change & ICP Detection
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: "4.44.0"
+app_file: supervisor_agent.py
+pinned: false
+---
+
+# Multi-Agent Reasoning System for Job Change & ICP Detection
 
-## 📋 Problem Statement
+## Problem Statement
 
 You are tasked with designing and implementing a **true multi-agent reasoning solution** (no fixed workflow) that, given partial or complete professional profile data, can autonomously determine whether a given person has changed jobs, identify their current company, assess if they fit a specific Ideal Customer Profile (ICP), and validate their business email.
 
@@ -8,7 +19,7 @@ The system should be capable of **dynamic orchestration**, where independent rea
 
 **The challenge is not to hardcode rules or build a static pipeline, but to create agents that can intelligently reason about the data, decide what other agents or tools to invoke, and resolve ambiguous or incomplete inputs.**
 
-## 🎯 Functional Requirements
+## Functional Requirements
 
 ### Core Capabilities
 - **Autonomous Reasoning**: The system should be capable of making intelligent decisions on what data to fetch, how to interpret it, and when to request help from other agents.
@@ -22,41 +33,41 @@ The system should be capable of **dynamic orchestration**, where independent rea
 - **Business Email Validation**: Identify the most probable business email if one exists.
 - **Fault Tolerance**: Handle incomplete, noisy, or conflicting inputs.
 
-## 🏗️ Solution Architecture
+## Solution Architecture
 
 ### Multi-Agent System Design
 Our solution implements a **LangGraph Supervisor** that orchestrates multiple specialized agents, each with autonomous reasoning capabilities:
 
-#### 🤖 **Supervisor Agent**
+#### **Supervisor Agent**
 - **Role**: Central orchestrator that decides which agents to invoke and when
 - **Capability**: Dynamic workflow management based on data analysis needs
 - **Intelligence**: Routes requests to appropriate agents based on current information state
 
-#### 🔍 **Profile Researcher Agent**
+#### **Profile Researcher Agent**
 - **Role**: Primary data gatherer using real-time web search
 - **Tools**: Tavily search integration for current professional information
 - **Autonomy**: Decides what search queries to run based on available data
 - **Output**: Current company, title, and professional status
 
-#### 💼 **Job Change Analyst Agent**
+#### **Job Change Analyst Agent**
 - **Role**: Determines employment transitions and company relationships
 - **Intelligence**: Analyzes company relationships, mergers, acquisitions, rebranding
 - **Reasoning**: Considers multiple factors beyond simple company name matching
 - **Output**: Job change status with detailed reasoning
 
-#### 🎯 **ICP Assessor Agent**
+#### **ICP Assessor Agent**
 - **Role**: Evaluates fit against Ideal Customer Profile criteria
 - **Flexibility**: Adapts to different ICP definitions dynamically
 - **Analysis**: Considers role seniority, engineering focus, and leadership level
 - **Output**: ICP match status with confidence level
 
-#### 📧 **Email Finder Agent**
+#### **Email Finder Agent**
 - **Role**: Discovers and validates business email addresses
 - **Intelligence**: Uses LLM to generate probable emails based on company research
 - **Fallback**: Creates realistic email patterns when exact matches aren't found
 - **Output**: Most probable business email with confidence metrics
 
-### 🔄 Dynamic Orchestration
+### Dynamic Orchestration
 The system doesn't follow a fixed pipeline. Instead:
 
 1. **Initial Assessment**: Supervisor analyzes input data completeness
@@ -65,7 +76,7 @@ The system doesn't follow a fixed pipeline. Instead:
 4. **Conflict Resolution**: Multiple agents collaborate to resolve discrepancies
 5. **Final Synthesis**: Supervisor combines all findings into coherent output
 
-## 🛠️ Technical Implementation
+## Technical Implementation
 
 ### Technology Stack
 - **LangGraph**: Multi-agent orchestration and workflow management
@@ -81,7 +92,7 @@ The system doesn't follow a fixed pipeline. Instead:
 - **Error Handling**: Graceful fallbacks and fault tolerance
 - **Extensible Architecture**: Easy to add new agents and capabilities
 
-## 📊 Example Use Cases
+## Example Use Cases
 
 ### Example 1: True Job Change
 **Input:**
@@ -148,7 +159,7 @@ The system doesn't follow a fixed pipeline. Instead:
 3. **ICP Assessor**: Confirms CTO role meets ICP criteria
 4. **Email Finder**: Updates email to reflect new company domain
 
-## 🎯 Evaluation Criteria
+##  s   Evaluation Criteria
 
 ### 1. **Reasoning Quality**
 - How well the system dynamically decides what to do next and why
@@ -170,7 +181,7 @@ The system doesn't follow a fixed pipeline. Instead:
 - Ability to extend the system to other reasoning tasks without re-architecting
 - **Our Solution**: Modular agent design with easy addition of new capabilities
 
-## 🚀 Getting Started
+## Getting Started
 
 ### Prerequisites
 - Python 3.10+
@@ -220,7 +231,7 @@ result = analyze_profile({
 print(result.model_dump())
 ```
 
-## 🔍 System Capabilities
+## System Capabilities
 
 ### Autonomous Decision Making
 - **Data Prioritization**: Agents decide what information to gather first
@@ -237,7 +248,7 @@ print(result.model_dump())
 - **Error Recovery**: Continues analysis even when individual agents fail
 - **Confidence Metrics**: Provides reliability indicators for all outputs
 
-## 🚀 Future Enhancements
+## Future Enhancements
 
 ### Planned Capabilities
 - **Industry-Specific ICPs**: Specialized criteria for different sectors
@@ -250,7 +261,7 @@ print(result.model_dump())
 - **Custom Tools**: Integration with additional data sources
 - **Workflow Customization**: Configurable agent interaction patterns
 
-## 📈 Performance Metrics
+## Performance Metrics
 
 ### Current Capabilities
 - **Response Time**: 30-60 seconds for complete analysis
@@ -264,7 +275,7 @@ print(result.model_dump())
 - **Reasoning Traces**: Detailed explanation of agent decisions
 - **Fallback Indicators**: When alternative methods were used
 
-## 🤝 Contributing
+## Contributing
 
 This system demonstrates advanced multi-agent reasoning capabilities. Contributions are welcome for:
 
@@ -273,7 +284,7 @@ This system demonstrates advanced multi-agent reasoning capabilities. Contributi
 - **Performance Optimization**: Faster analysis and better accuracy
 - **Documentation**: Improved usage examples and tutorials
 
-## 📄 License
+## License
 
 This project is open source and available under the MIT License.