LangGraphing 🦜🔗🤖

Created with Claude Code - A comprehensive Python project demonstrating LangChain and LangGraph integration with Claude Sonnet, featuring AI agents, workflows, structured output, and advanced tool calling capabilities.

🚀 Features

AI Agents & Workflows - Built with LangChain/LangGraph for intelligent agent development
Claude Sonnet Integration - Leveraging Anthropic's most capable model
Structured Output - Pydantic models for type-safe, structured responses
Tool Calling - Advanced examples of Claude calling external tools
Workflow Orchestration - Multi-step agent workflows and business logic
Production-Ready Patterns - Real-world examples for AI agent development

📋 What You'll Learn

LangChain Foundation - Core concepts for building AI applications
LangGraph Workflows - Creating complex agent workflows and state management
Claude Integration - Advanced patterns with Anthropic's Claude Sonnet
Structured Output - Type-safe responses using Pydantic models
Tool Orchestration - Building agents that can use external tools
Agent Architecture - Designing scalable AI agent systems
Production Patterns - Error handling, validation, and monitoring

🛠️ Setup

Prerequisites

Python 3.8+
Anthropic API key

Installation

Clone the repository:

git clone https://github.com/yourusername/langgraphing.git
cd langgraphing

Install dependencies:
```
pip install -r requirements.txt
```

Set up environment variables:

cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY

🎯 Usage

Basic Chat Example

python simple_invoke.py

Simple example showing basic Claude integration and foundation concepts.

Structured Output Example

python structured_search.py

Demonstrates how to get structured JSON responses using Pydantic models - perfect for building reliable AI agents.

Tool Calling & Workflows Example

python tool_calling.py

Advanced example showing Claude calling tools and providing structured analysis - the foundation for LangGraph workflows.

Prompt Chaining Trading Workflow

python prompt_chaining.py

Demonstrates two different approaches to multi-step prompt chaining for financial trading decisions:

Procedural: Class-based workflow with manual orchestration
LangGraph Graph API: StateGraph with node-based architecture

python prompt_chaining_functional.py

Demonstrates @entrypoint/@task functional approach using LangGraph decorators for workflow orchestration with proper execution context.

📁 Project Structure

langgraphing/
├── simple_invoke.py              # Basic Claude integration
├── structured_search.py          # Structured output with Pydantic
├── tool_calling.py              # Tool calling + agent workflows
├── prompt_chaining.py           # Procedural + Graph API trading workflows
├── prompt_chaining_functional.py # @entrypoint/@task functional approach
├── tools_models.py              # Tool definitions with schemas
├── search_models.py             # Pydantic models for structured data
├── requirements.txt             # Python dependencies
├── .env.example                # Environment variables template
├── CLAUDE.md                   # Claude Code guidance
└── README.md                   # This file

🔧 Available Tools

The project includes three example tools:

Calculator Tool

Purpose: Mathematical calculations
Input: Mathematical expressions
Output: Structured results with validation

Weather Tool

Purpose: Weather information and forecasts
Input: Location and number of days
Output: Current weather and forecast data

Database Tool

Purpose: Mock database queries
Input: Table name, filters, and limits
Output: Structured query results with metadata

📚 Key Concepts Demonstrated

1. Basic Integration

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    api_key=os.getenv("ANTHROPIC_API_KEY")
)
response = model.invoke("Hello, Claude!")

2. Structured Output

from pydantic import BaseModel

class MyResponse(BaseModel):
    answer: str
    confidence: float

structured_model = model.with_structured_output(MyResponse)
response = structured_model.invoke("What is 2+2?")

3. Tool Calling

from langchain_core.tools import tool

@tool
def my_tool(input: str) -> str:
    \"\"\"Description of what the tool does.\"\"\"
    return f"Tool result: {input}"

model_with_tools = model.bind_tools([my_tool])

🔄 Tool Calling Workflow Deep Dive

The tool_calling.py script demonstrates a powerful two-phase workflow pattern:

🏗️ Architecture Overview

User Task → Claude Analyzes → Tool Calls → Tool Results → Structured Analysis

Key Components:

Dual Model Setup: One for tool calling, one for structured output
Pydantic Schemas: Type-safe tool results and analysis
Intelligent Orchestration: Claude selects and sequences tools automatically

🎯 Two-Phase Workflow Pattern

Phase 1: Tool Execution

# Claude decides which tools to call
model_with_tools = model.bind_tools([calculator_tool, weather_tool, database_query_tool])
response = model_with_tools.invoke("Plan a company retreat with costs and weather")

# Tools are executed automatically
# - weather_tool.invoke({"location": "San Francisco", "days": 5})
# - calculator_tool.invoke({"expression": "25 * 150 * 3"})
# - database_query_tool.invoke({"table": "users", "filters": {"role": "admin"}})

Phase 2: Structured Analysis

# Structured analysis of all tool results
class ToolAnalysisResult(BaseModel):
    task_description: str
    tools_used: List[str]
    tool_results: List[Dict[str, Any]]
    analysis: str
    confidence: float
    recommendations: List[str]

structured_model = model.with_structured_output(ToolAnalysisResult)
final_analysis = structured_model.invoke(f"Analyze these tool results: {tool_results}")

💡 Key Learning Points

Intelligent Tool Selection: Claude automatically chooses relevant tools
Sequential Execution: Tools are called in logical order
Structured Reliability: All outputs follow Pydantic schemas
Business Logic Integration: Combines multiple tool results into actionable insights
Error Handling: Graceful handling of tool failures with fallback strategies
Confidence Scoring: Provides reliability metrics for business decisions

🎯 Real-World Example

Task: "Plan company retreat for 25 employees"
↓
Claude Analysis: Need weather, costs, and admin info
↓
Tools Called:
- Weather: San Francisco, 5 days → "Sunny, 22°C average"
- Calculator: 25 × $150 × 3 days → "$11,250 total"
- Database: Admin users → "3 admin users found"
↓
Structured Output:
{
  "analysis": "Weather favorable, budget reasonable for 25 employees",
  "confidence": 0.85,
  "recommendations": ["Book outdoor venues", "Budget $12K total"],
  "follow_up_questions": ["Dietary restrictions?", "Team building activities?"]
}

This pattern is foundational for LangGraph workflows where agents need to orchestrate multiple tools and provide reliable, structured outputs for business applications.

🔗 Prompt Chaining Deep Dive

The prompt_chaining.py script demonstrates advanced prompt chaining - a powerful pattern where complex tasks are decomposed into sequential LLM calls, with each step processing the structured output of the previous step.

🧠 What is Prompt Chaining?

Traditional Approach (Single Prompt):

"Analyze AAPL stock and make a trading decision considering market conditions, risk factors, and portfolio allocation"
→ Single massive prompt trying to do everything at once
→ Often inconsistent, hard to validate, difficult to debug

Prompt Chaining Approach:

Step 1: "Analyze AAPL market conditions" → MarketAnalysis
Step 2: "Assess risk given this market analysis" → RiskAssessment  
Step 3: "Make trading decision based on analysis + risk" → TradingDecision
→ Specialized prompts with structured handoffs
→ Consistent, validatable, modular workflow

🏗️ Architecture: Financial Trading Chain

Market Data Input → 🔍 Market Analysis → ⚠️ Risk Assessment → 💼 Trading Decision → 📊 Final Result
                     ↓ validate        ↓ validate         ↓ validate
                   Gate 1             Gate 2             Gate 3

🎯 The Three-Step Workflow

Step 1: Market Analysis 🔍

class MarketAnalysis(BaseModel):
    symbol: str = "AAPL"
    current_price: float = 150.25
    price_trend: str = "Upward momentum with strong volume"
    volume_analysis: str = "85M shares vs 65M average"
    technical_indicators: Dict[str, Any] = {
        "rsi_14": 68.5, "macd": 1.25, "sma_20": 148.75
    }
    market_sentiment: str = "Bullish with technology sector strength"
    confidence_score: float = 0.85

Specialized Prompt Focus:

Technical indicator analysis (RSI, MACD, Bollinger Bands)
Volume and momentum assessment
Market sentiment evaluation
Price trend identification

Step 2: Risk Assessment ⚠️

class RiskAssessment(BaseModel):
    symbol: str = "AAPL"
    risk_level: RiskLevel = "MEDIUM"
    volatility_score: float = 0.65
    market_conditions: str = "Stable with moderate volatility"
    position_size_recommendation: float = 0.08  # 8% of portfolio
    stop_loss_level: float = 145.00
    risk_factors: List[str] = [
        "Tech sector concentration risk",
        "Earnings volatility near resistance"
    ]
    confidence_score: float = 0.78

Risk-Specific Analysis:

Takes MarketAnalysis as input
Evaluates portfolio-specific risk factors
Calculates position sizing based on risk tolerance
Sets stop-loss levels using technical analysis

Step 3: Trading Decision 💼

class TradingDecision(BaseModel):
    symbol: str = "AAPL"
    action: TradingAction = "BUY"
    entry_price: float = 150.25
    target_price: float = 155.00
    stop_loss: float = 145.00
    position_size: float = 0.08
    reasoning: str = "Strong technical setup with controlled risk"
    expected_return: float = 0.032  # 3.2%
    time_horizon: str = "2-4 weeks"
    confidence_score: float = 0.82

Decision Synthesis:

Combines MarketAnalysis + RiskAssessment
Makes actionable BUY/SELL/HOLD decision
Sets specific entry, target, and stop prices
Provides detailed reasoning and return expectations

🛡️ Programmatic Validation Gates

Between each step, the workflow includes validation checkpoints:

class TradingPromptChain:
    def execute_trading_workflow(self, symbol, market_data, portfolio_info):
        # Step 1: Market Analysis
        market_analysis = self.step1_market_analysis(symbol, market_data)
        
        # 🚪 Gate 1: Confidence Threshold
        if market_analysis.confidence_score < 0.4:
            raise ValueError("Market analysis confidence below threshold")
        
        # Step 2: Risk Assessment  
        risk_assessment = self.step2_risk_assessment(symbol, market_analysis, portfolio_info)
        
        # 🚪 Gate 2: Risk Validation
        if risk_assessment.confidence_score < 0.4:
            raise ValueError("Risk assessment confidence below threshold")
        
        # Step 3: Trading Decision
        trading_decision = self.step3_trading_decision(symbol, market_analysis, risk_assessment)
        
        # 🚪 Gate 3: Decision Validation  
        if trading_decision.confidence_score < 0.4:
            raise ValueError("Trading decision confidence below threshold")
        
        # 🔍 Cross-Step Coherence Validation
        warnings = self.validate_workflow_coherence(
            market_analysis, risk_assessment, trading_decision
        )
        
        return final_result

🔍 Advanced Validation: Cross-Step Coherence

Beyond confidence scores, the workflow validates logical consistency:

def validate_workflow_coherence(self, market_analysis, risk_assessment, trading_decision):
    warnings = []
    
    # Risk-Action Alignment
    if risk_assessment.risk_level == "HIGH" and trading_decision.action == "BUY":
        if trading_decision.position_size > 0.05:  # 5%
            warnings.append("High risk trade with large position - reduce exposure")
    
    # Stop-Loss Consistency
    if abs(trading_decision.stop_loss - risk_assessment.stop_loss_level) > 0.1:
        warnings.append("Stop-loss levels inconsistent between steps")
    
    # Position Size Alignment
    if abs(trading_decision.position_size - risk_assessment.position_size_recommendation) > 0.05:
        warnings.append("Position size differs from risk recommendation")
    
    return warnings

💡 Key Advantages of Prompt Chaining

🎯 Specialized Focus: Each prompt optimized for specific domain knowledge
🔗 Structured Handoffs: Type-safe data flow between steps using Pydantic
🛡️ Quality Gates: Validation prevents bad data from propagating downstream
🔧 Modularity: Can modify/replace individual steps independently
📊 Confidence Tracking: Reliability metrics at each stage
🧪 Testability: Each step can be unit tested independently
🐛 Debuggability: Easy to identify which step failed and why

🎯 Real-World Example Output

{
  "symbol": "AAPL",
  "workflow_timestamp": "2024-01-15T14:30:00",
  "market_analysis": {
    "price_trend": "Bullish momentum above 20-day SMA",
    "technical_indicators": {"rsi_14": 68.5, "macd_signal": "bullish"},
    "confidence_score": 0.85
  },
  "risk_assessment": {
    "risk_level": "MEDIUM", 
    "position_size_recommendation": 0.08,
    "stop_loss_level": 145.00,
    "confidence_score": 0.78
  },
  "trading_decision": {
    "action": "BUY",
    "entry_price": 150.25,
    "target_price": 155.00,
    "reasoning": "Strong technical setup with controlled downside risk",
    "confidence_score": 0.82
  },
  "overall_confidence": 0.817,
  "warnings": []
}

🚀 When to Use Prompt Chaining

Perfect For:

✅ Complex multi-domain decisions (finance, legal, medical)
✅ Workflows requiring validation between steps
✅ Business processes with compliance requirements
✅ Tasks needing audit trails and explainability
✅ Scenarios where partial failure should be handled gracefully

Not Ideal For:

❌ Simple single-domain tasks
❌ Creative tasks requiring holistic thinking
❌ Real-time applications with latency constraints
❌ Tasks where intermediate steps don't make sense

🔧 Three Implementation Approaches

The script demonstrates the same trading workflow using three different patterns:

Procedural Approach 🏗️

class TradingPromptChain:
    def execute_trading_workflow(self, symbol, market_data, portfolio_info):
        # Manual step execution with validation gates
        market_analysis = self.step1_market_analysis(symbol, market_data)
        risk_assessment = self.step2_risk_assessment(symbol, market_analysis, portfolio_info)
        trading_decision = self.step3_trading_decision(symbol, market_analysis, risk_assessment)
        return self.aggregate_results(...)

LangGraph Graph API 🔗

workflow = StateGraph(TradingWorkflowState)
workflow.add_node("analyze_market", market_analysis_node)
workflow.add_node("assess_risk", risk_assessment_node)
workflow.add_node("make_decision", trading_decision_node)
workflow.add_edge("analyze_market", "assess_risk")
app = workflow.compile()
result = app.invoke(initial_state)

LangGraph @task Functional ⚡

@task
def analyze_market_task(symbol: str, market_data: Dict) -> MarketAnalysis:
    return market_analyzer.invoke(prompt)

@task  
def assess_risk_task(symbol: str, market_analysis: MarketAnalysis, portfolio_info: Dict) -> RiskAssessment:
    return risk_assessor.invoke(prompt)

# Functional composition
def execute_workflow(symbol, market_data, portfolio_info):
    market_analysis = analyze_market_task(symbol, market_data)
    risk_assessment = assess_risk_task(symbol, market_analysis, portfolio_info)
    return aggregate_final_result_task(symbol, market_analysis, risk_assessment, trading_decision)

Each approach demonstrates different paradigms for building enterprise AI workflows where reliability, auditability, and domain expertise matter more than raw speed.

📊 Visual Workflow Diagrams

When you run python prompt_chaining.py, it generates Mermaid diagrams showing the workflow structure for each approach:

Procedural Workflow

flowchart TD
    A[🚀 Start Trading Workflow] --> B[📊 Initialize TradingPromptChain Class]
    B --> C[🔍 Step 1: Market Analysis]
    C --> D{🚪 Gate 1: Confidence > 0.4?}
    D -->|❌ No| E[❌ Terminate: Low Confidence]
    D -->|✅ Yes| F[⚠️ Step 2: Risk Assessment]
    F --> G{🚪 Gate 2: Confidence > 0.4?}
    G -->|❌ No| H[❌ Terminate: Low Risk Confidence]
    G -->|✅ Yes| I[💼 Step 3: Trading Decision]
    I --> J{🚪 Gate 3: Confidence > 0.4?}
    J -->|❌ No| K[❌ Terminate: Low Decision Confidence]
    J -->|✅ Yes| L[🔍 Cross-Step Validation]
    L --> M[📊 Generate Final Result]
    M --> N[✅ Complete Workflow]
    
    style A fill:#e1f5fe
    style N fill:#e8f5e8
    style E fill:#ffebee
    style H fill:#ffebee
    style K fill:#ffebee

Graph API Workflow with Conditional Edges

flowchart TD
    A[🚀 Start StateGraph Workflow] --> B[📊 Initialize TradingWorkflowState]
    B --> C[🔍 analyze_market]
    C --> D[📝 Update State: market_analysis]
    D --> E{🚪 Check Errors in State}
    E -->|❌ Has Errors| F[❌ Workflow Failed]
    E -->|✅ No Errors| G[⚠️ assess_risk]
    G --> H[📝 Update State: risk_assessment]
    H --> I{🔄 CONDITIONAL EDGE: Risk Level?}
    I -->|🔴 HIGH/EXTREME| J[🚨 additional_risk_review]
    I -->|🟢 LOW/MEDIUM| K[💼 make_decision]
    J --> L[📝 Apply Risk Controls & Reduce Position]
    L --> K[💼 make_decision]
    K --> M[📝 Update State: trading_decision]
    M --> N{🚪 Check Errors in State}
    N -->|❌ Has Errors| O[❌ Workflow Failed]
    N -->|✅ No Errors| P[🔍 validate_results]
    P --> Q[📊 Generate Final Result in State]
    Q --> R[✅ Return Final State]
    
    subgraph "StateGraph Nodes"
    C
    G
    J
    K
    P
    end
    
    subgraph "⭐ Conditional Routing"
    I
    end
    
    style A fill:#e1f5fe
    style R fill:#e8f5e8
    style F fill:#ffebee
    style O fill:#ffebee
    style I fill:#fff3e0
    style J fill:#ffecb3

Functional @task Workflow

flowchart TD
    A[🚀 Start @task Functional Workflow] --> B[🔍 analyze_market_task]
    B --> C[📊 MarketAnalysis Result]
    C --> D{🚪 Confidence Gate}
    D -->|❌ No| E[❌ Raise ValueError]
    D -->|✅ Yes| F[⚠️ assess_risk_task]
    F --> G[📊 RiskAssessment Result]
    G --> H{🚪 Confidence Gate}
    H -->|❌ No| I[❌ Raise ValueError]
    H -->|✅ Yes| J[💼 make_trading_decision_task]
    J --> K[📊 TradingDecision Result]
    K --> L{🚪 Confidence Gate}
    L -->|❌ No| M[❌ Raise ValueError]
    L -->|✅ Yes| N[📊 aggregate_final_result_task]
    N --> O[🔍 Cross-Task Validation]
    O --> P[📊 TradingWorkflowResult]
    P --> Q[✅ Return Final Result]
    
    subgraph "@task Functions"
    B
    F
    J
    N
    end
    
    style A fill:#e1f5fe
    style Q fill:#e8f5e8
    style E fill:#ffebee
    style I fill:#ffebee
    style M fill:#ffebee

🌟 Advanced Agent Patterns

Tool Calling Patterns (`tool_calling.py`)

The tool calling script demonstrates foundational patterns for LangGraph workflows:

Multi-tool Orchestration - Coordinating multiple tools in agent workflows
Structured Analysis - Type-safe analysis of tool results
Business Logic Integration - Real-world workflow patterns
Error Handling & Validation - Production-ready error management
Confidence Scoring - Building reliable AI agents
Workflow State Management - Foundation for complex agent systems

Prompt Chaining Patterns (`prompt_chaining.py`)

The prompt chaining script demonstrates three distinct implementation approaches:

1️⃣ Procedural Approach (`demonstrate_trading_workflow_procedural`)

Class-Based Architecture - Object-oriented workflow management
Manual Orchestration - Direct control over step execution and validation
Custom Validation Gates - Programmatic checks between each step
Error Handling - Explicit exception management and workflow termination

2️⃣ LangGraph Graph API (`demonstrate_trading_workflow_graph`)

StateGraph Architecture - Typed state management with TradingWorkflowState
Node-Based Design - Each workflow step as a graph node
Conditional Edge Routing - Risk-based branching with add_conditional_edges()
State Persistence - Automatic state passing between nodes
Built-in Error Collection - Graph-level error aggregation
Advanced Validation - Cross-step coherence checks within the graph

3️⃣ LangGraph @task Functional API (`demonstrate_trading_workflow_functional`)

Pure Functional Design - Each step as a stateless @task function
Type-Safe Composition - Clear input/output types for data flow
Conditional Task Execution - Risk-based branching within functional workflow
Functional Orchestration - Task composition with validation gates
Performance Optimization - LangGraph can optimize task execution
High Testability - Each @task independently unit testable

🔄 Conditional Edge Routing

The Graph API implementation features sophisticated conditional edge routing that demonstrates enterprise-grade workflow control:

Risk-Based Branching Logic

def route_after_risk_assessment(state: TradingWorkflowState) -> str:
    risk_level = state["risk_assessment"].risk_level
    
    if risk_level in [RiskLevel.HIGH, RiskLevel.EXTREME]:
        return "additional_risk_review"  # Route to risk controls
    else:
        return "make_decision"  # Skip directly to trading decision

# Add conditional edge to workflow
workflow.add_conditional_edges(
    "assess_risk",
    route_after_risk_assessment,
    {
        "additional_risk_review": "additional_risk_review",
        "make_decision": "make_decision"
    }
)

Automated Risk Controls

HIGH Risk Trades: Position size reduced by 30% or capped at 5%
EXTREME Risk Trades: Position size capped at 2% + compliance approval required
LOW/MEDIUM Risk: Skip additional review, proceed directly to trading decision

Real-World Results

MEDIUM Risk: 8% position → Direct to decision (0 warnings)
HIGH Risk:   15% → 5% position reduction (1 warning)  
EXTREME Risk: 2% position cap + compliance (3 warnings)

This demonstrates how LangGraph conditional edges enable sophisticated business logic where workflow paths adapt dynamically based on data conditions - essential for enterprise AI applications.

⚡ Functional Conditional Logic

The @task Functional API also implements conditional logic, but using programmatic branching within the @entrypoint function:

@entrypoint(checkpointer=MemorySaver())
def trading_workflow_functional(inputs: Dict[str, Any]) -> TradingWorkflowResult:
    # Step 1: Market Analysis
    market_analysis = analyze_market_task(symbol, market_data).result()
    
    # Step 2: Risk Assessment
    risk_assessment = assess_risk_task(symbol, market_analysis, portfolio_info).result()
    
    # CONDITIONAL LOGIC: Additional risk review for HIGH/EXTREME risk
    if risk_assessment.risk_level in [RiskLevel.HIGH, RiskLevel.EXTREME]:
        print(f"Conditional Routing: {risk_assessment.risk_level} risk - triggering additional review")
        risk_assessment = additional_risk_review_task(symbol, risk_assessment).result()
    else:
        print(f"Conditional Routing: {risk_assessment.risk_level} risk - proceeding directly to decision")
    
    # Step 3: Trading Decision (using potentially updated risk assessment)
    trading_decision = make_trading_decision_task(symbol, market_analysis, risk_assessment).result()

Functional vs Graph API Conditional Logic

Approach	Implementation	Benefits	Use Cases
Graph API	`add_conditional_edges()` with routing functions	Visual workflow representation, built-in state management	Complex multi-path workflows, debugging, visualization
Functional API	Programmatic `if/else` within `@entrypoint`	Direct control, simple to understand, easy testing	Linear workflows with occasional branching, performance-critical paths

Both approaches achieve the same risk-based controls but with different paradigms for enterprise workflow design.

Enterprise Patterns Demonstrated:

Sequential Processing - Multi-step LLM workflows with structured handoffs
Dynamic Routing - Conditional edges based on business logic and data conditions
Domain Specialization - Each step optimized for specific expertise areas
Quality Gates - Validation checkpoints preventing error propagation
Cross-Step Validation - Logical consistency checks across workflow stages
Modular Architecture - Independent steps that can be modified/replaced
Audit Trails - Complete visibility into decision-making process

🔄 Pattern Comparison

Pattern	Best For	Strengths	Use Cases
Tool Calling	Multi-domain data gathering	Parallel execution, rich context	Research, analysis, data aggregation
Prompt Chaining (Procedural)	Custom workflow control	Direct control, explicit validation	Custom business logic, legacy integration
Prompt Chaining (Graph API)	Dynamic conditional workflows	Conditional routing, state management, error collection	Risk-based decisions, complex multi-step processes, debugging
Prompt Chaining (@task Functional)	Pure functional workflows	Performance, testability, reusability	Microservices, functional programming, scaling

📊 Implementation Approach Guide

Choose Procedural When:

✅ Need direct control over workflow execution
✅ Integrating with existing object-oriented systems
✅ Custom validation logic required
✅ Legacy system compatibility needed

Choose Graph API When:

✅ Complex state management required
✅ Need built-in error collection and debugging
✅ Workflow visualization important
✅ Dynamic routing or conditional logic needed
✅ Risk-based or data-driven workflow branching required
✅ Enterprise compliance workflows with automated controls

Choose @task Functional When:

✅ Performance and scalability critical
✅ High testability requirements
✅ Functional programming paradigm preferred
✅ Microservices architecture
✅ Task reusability across workflows

All patterns are foundational for LangGraph where agents need reliable, structured workflows for production applications.

🤖 Built with Claude Code

This project was created using Claude Code, demonstrating:

AI-Assisted Development - Rapid prototyping of AI agent systems
Best Practices - Production-ready patterns and architectures
Comprehensive Examples - Real-world scenarios and use cases
Modern AI Stack - LangChain, LangGraph, Claude Sonnet integration

🎯 Next Steps for LangGraph

This project provides the foundation for building more complex LangGraph workflows:

State Machines - Complex agent state management
Multi-Agent Systems - Coordinating multiple AI agents
Conditional Routing - Dynamic workflow branching
Memory & Context - Persistent agent memory systems
Human-in-the-Loop - Interactive agent workflows

🤝 Contributing

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

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Claude Code for AI-assisted development
LangChain for the amazing framework
LangGraph for agent workflow orchestration
Anthropic for Claude Sonnet
Pydantic for data validation

🔗 Useful Links

Ready to build AI agents? Start with these examples and scale to production LangGraph workflows! 🚀

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.env.example		.env.example
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
README.md		README.md
SESSION_SUMMARY.md		SESSION_SUMMARY.md
prompt_chaining.py		prompt_chaining.py
prompt_chaining_functional.py		prompt_chaining_functional.py
requirements.txt		requirements.txt
search_models.py		search_models.py
simple_invoke.py		simple_invoke.py
structured_search.py		structured_search.py
test_conditional_edge.py		test_conditional_edge.py
test_functional_conditional.py		test_functional_conditional.py
test_prompt_chaining_functional.py		test_prompt_chaining_functional.py
test_tool_calling.py		test_tool_calling.py
tool_calling.py		tool_calling.py
tools_models.py		tools_models.py

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