# Use Cases

Real-world applications and practical examples of MARSYS in action.

## 🎯 Application Categories

<div class="grid cards" markdown="1">

- :material-magnify:{ .lg .middle } **Research & Analysis**

    ---

    Multi-agent research systems

    - Academic research automation
    - Market intelligence gathering
    - Competitive analysis
    - Technical documentation

- :material-robot:{ .lg .middle } **Business Automation**

    ---

    Enterprise workflow automation

    - Customer service systems
    - Sales pipeline automation
    - HR screening processes
    - Document processing

- :material-code-braces:{ .lg .middle } **Development & DevOps**

    ---

    Software development assistance

    - Code review automation
    - Test generation
    - Documentation writing
    - CI/CD optimization

- :material-chart-line:{ .lg .middle } **Data & Analytics**

    ---

    Data processing pipelines

    - ETL workflows
    - Report generation
    - Anomaly detection
    - Predictive analytics

</div>

## 📊 Featured Use Cases

### 1. **AI Research Assistant**

Multi-agent system for comprehensive research and analysis.

```python
from marsys.coordination import Orchestra
from marsys.coordination.topology.patterns import PatternConfig

# Research team with specialized agents
topology = PatternConfig.hub_and_spoke(
    hub="ResearchCoordinator",
    spokes=["WebSearcher", "PaperAnalyzer", "FactChecker", "ReportWriter"],
    parallel_spokes=True
)

result = await Orchestra.run(
    task="Research latest advances in quantum computing",
    topology=topology
)
```

**Key Features:**
- Parallel information gathering
- Source validation
- Comprehensive report generation
- Citation management

[View Full Implementation →](examples/research-assistant.md)

---

### 2. **Customer Support Platform**

Intelligent multi-tier support system with escalation.

```python
topology = {
    "agents": ["User", "L1Support", "L2Support", "TicketManager"],
    "flows": [
        "User <-> L1Support",
        "L1Support -> L2Support",  # Escalation
        "L2Support -> TicketManager",
        "TicketManager -> User"
    ]
}

result = await Orchestra.run(
    task="Customer issue: Cannot login to account",
    topology=topology
)
```

**Key Features:**
- Automatic issue categorization
- Smart escalation routing
- Knowledge base integration
- Ticket tracking

[View Full Implementation →](examples/customer-support.md)

---

### 3. **Code Review Assistant**

Automated code review with multiple specialized reviewers.

```python
# Specialized code reviewers
topology = PatternConfig.pipeline(
    stages=[
        {"name": "syntax", "agents": ["SyntaxChecker"]},
        {"name": "security", "agents": ["SecurityAuditor"]},
        {"name": "performance", "agents": ["PerformanceAnalyzer"]},
        {"name": "style", "agents": ["StyleReviewer"]},
        {"name": "summary", "agents": ["ReviewSummarizer"]}
    ],
    parallel_within_stage=False
)

result = await Orchestra.run(
    task=f"Review this code:\n{code_content}",
    topology=topology
)
```

**Key Features:**
- Multi-aspect code analysis
- Security vulnerability detection
- Performance optimization suggestions
- Style guide compliance

[View Full Implementation →](examples/code-review.md)

---

### 4. **Financial Analysis System**

Real-time market analysis and reporting.

```python
from marsys.agents import Agent, AgentPool
from marsys.models import ModelConfig

# Create model config
config = ModelConfig(
    type="api",
    provider="openrouter",
    name="anthropic/claude-haiku-4.5",
    temperature=0.3
)

# Create pool for parallel analysis
analyst_pool = await AgentPool.create_async(
    agent_class=Agent,
    num_instances=5,
    model_config=config,
    goal="Analyze financial markets and trends",
    instruction="You are a financial analyst. Analyze market data and provide insights.",
    name="FinancialAnalyst"
)

topology = {
    "agents": ["MarketMonitor", "AnalystPool", "RiskAssessor", "ReportGenerator"],
    "flows": [
        "MarketMonitor -> AnalystPool",
        "AnalystPool -> RiskAssessor",
        "RiskAssessor -> ReportGenerator"
    ]
}
```

**Key Features:**
- Real-time market data processing
- Parallel sector analysis
- Risk assessment
- Automated report generation

[View Full Implementation →](examples/financial-analysis.md)

---

### 5. **Content Generation Pipeline**

Multi-stage content creation and optimization.

```python
topology = PatternConfig.pipeline(
    stages=[
        {"name": "research", "agents": ["TopicResearcher"]},
        {"name": "outline", "agents": ["OutlineCreator"]},
        {"name": "writing", "agents": ["ContentWriter", "TechnicalWriter"]},
        {"name": "editing", "agents": ["Editor", "FactChecker"]},
        {"name": "seo", "agents": ["SEOOptimizer"]},
        {"name": "publishing", "agents": ["Publisher"]}
    ],
    parallel_within_stage=True
)
```

**Key Features:**
- Research-backed content
- Multiple writing styles
- Fact verification
- SEO optimization

[View Full Implementation →](examples/content-pipeline.md)

## 🏢 Industry Applications

### Healthcare
- **Clinical Decision Support**: Multi-agent diagnosis assistance
- **Patient Triage**: Automated symptom assessment and routing
- **Medical Research**: Literature review and analysis
- **Drug Discovery**: Compound analysis and prediction

### Finance
- **Trading Systems**: Market analysis and execution
- **Risk Management**: Portfolio assessment and optimization
- **Fraud Detection**: Transaction monitoring and alerting
- **Compliance**: Regulatory report generation

### Education
- **Personalized Tutoring**: Adaptive learning systems
- **Curriculum Development**: Content generation and organization
- **Assessment Creation**: Test and quiz generation
- **Student Support**: 24/7 assistance and guidance

### Legal
- **Contract Analysis**: Review and risk assessment
- **Legal Research**: Case law and precedent search
- **Document Generation**: Automated drafting
- **Compliance Monitoring**: Regulatory tracking

### E-commerce
- **Product Recommendations**: Personalized shopping assistance
- **Inventory Management**: Demand prediction and ordering
- **Customer Service**: Order tracking and support
- **Review Analysis**: Sentiment and feedback processing

## 💡 Implementation Patterns

### Pattern 1: Research & Synthesis
```python
# Hub-and-spoke for coordinated research
topology = PatternConfig.hub_and_spoke(
    hub="Coordinator",
    spokes=["Researcher1", "Researcher2", "Synthesizer"],
    parallel_spokes=True
)
```

### Pattern 2: Quality Assurance
```python
# Pipeline for sequential validation
topology = PatternConfig.pipeline(
    stages=[
        {"name": "input", "agents": ["Validator"]},
        {"name": "process", "agents": ["Processor"]},
        {"name": "verify", "agents": ["Verifier"]},
        {"name": "output", "agents": ["Formatter"]}
    ]
)
```

### Pattern 3: Decision Making
```python
# Mesh for collaborative decisions
topology = PatternConfig.mesh(
    agents=["Analyst1", "Analyst2", "Analyst3", "DecisionMaker"],
    fully_connected=True
)
```

### Pattern 4: Escalation System
```python
# Hierarchical for tiered support
topology = PatternConfig.hierarchical(
    tree={
        "Manager": ["Supervisor1", "Supervisor2"],
        "Supervisor1": ["Agent1", "Agent2"],
        "Supervisor2": ["Agent3", "Agent4"]
    }
)
```

## 📈 Performance Metrics

### Research Assistant Performance
- **Speed**: 10x faster than manual research
- **Coverage**: Analyzes 100+ sources in parallel
- **Accuracy**: 95% fact verification rate
- **Cost**: 80% reduction vs human researchers

### Customer Support Metrics
- **Response Time**: < 2 seconds initial response
- **Resolution Rate**: 85% first-contact resolution
- **Satisfaction**: 4.8/5 average rating
- **Cost Savings**: 70% reduction in support costs

### Code Review Statistics
- **Review Speed**: 5 minutes per 1000 lines
- **Bug Detection**: 90% of common issues caught
- **False Positives**: < 5% rate
- **Developer Time Saved**: 2 hours per review

## 🚀 Getting Started

### Choose Your Use Case
1. Identify your business problem
2. Select appropriate pattern
3. Define agent specializations
4. Configure topology
5. Deploy and iterate

### Quick Start Templates
- [Research System Template](templates/research-system.md)
- [Support System Template](templates/support-system.md)
- [Analysis Pipeline Template](templates/analysis-pipeline.md)
- [Content System Template](templates/content-system.md)

### Best Practices
- Start simple, add complexity gradually
- Monitor agent performance metrics
- Implement proper error handling
- Use appropriate timeout configurations
- Enable state persistence for long tasks

## 📖 Resources

### Example Code
All examples available in [examples/real_world/](https://github.com/yourusername/marsys/tree/main/examples/real_world)

### Documentation
- [Architecture Overview](../concepts/overview.md)
- [Agent Development](../concepts/agents.md)
- [Topology Patterns](../concepts/advanced/topology.md)
- [API Reference](../api/overview.md)

### Community Examples
- [Community Showcase](https://github.com/yourusername/marsys-community)
- [Share Your Use Case](../contributing/index.md)

## 🎯 Next Steps

<div class="grid cards" markdown="1">

- :material-play-circle:{ .lg .middle } **[Quick Start](../getting-started/quick-start.md)**

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    Build your first system

- :material-school:{ .lg .middle } **[Tutorials](../tutorials/overview.md)**

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    Step-by-step guides

- :material-api:{ .lg .middle } **[API Reference](../api/overview.md)**

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    Complete documentation

- :material-help-circle:{ .lg .middle } **[Support](../support.md)**

    ---

    Get help and support

</div>

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!!! success "Ready to Build!"
    Choose a use case that matches your needs and start building. Each example includes complete code and deployment instructions.

!!! tip "Pro Tip"
    Start with a proven pattern and customize it for your specific needs. The examples provide excellent starting points for most applications.