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Agentic Design Pattern: React

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    """
Pattern 001: ReAct (Reasoning + Acting)

Description:
    Agent alternates between reasoning about the task and taking actions.
    The ReAct pattern combines reasoning traces with task-specific actions,
    allowing the agent to reason about what to do and then take actions.

Components:
    - Thought: Agent reasons about current state
    - Action: Agent takes an action (e.g., tool use)
    - Observation: Agent observes the result
    - Loop continues until task is complete

Use Cases:
    - Question answering with external knowledge
    - Task completion requiring multiple steps
    - Interactive problem solving

LangChain Implementation:
    Uses LangChain's AgentExecutor with structured tools and ReAct prompt template.
"""

import os
from typing import List, Optional
from dotenv import load_dotenv

from langchain.agents import AgentExecutor, create_react_agent
from langchain.tools import Tool
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Load environment variables
load_dotenv()


# Define example tools
def calculator(expression: str) -> str:
    """Evaluates a mathematical expression."""
    try:
        # Safe evaluation for demo purposes
        result = eval(expression, {"__builtins__": {}}, {})
        return f"Result: {result}"
    except Exception as e:
        return f"Error: {str(e)}"


def search_tool(query: str) -> str:
    """Simulates a search tool that retrieves information."""
    # Simulated search results
    knowledge_base = {
        "capital of france": "Paris",
        "population of tokyo": "approximately 14 million",
        "height of mount everest": "8,848.86 meters (29,031.7 feet)",
        "speed of light": "299,792,458 meters per second",
        "boiling point of water": "100°C (212°F) at sea level",
    }
    
    query_lower = query.lower()
    for key, value in knowledge_base.items():
        if key in query_lower:
            return f"Search result: {value}"
    
    return f"Search result: Information about '{query}' not found in knowledge base."


def get_current_date() -> str:
    """Returns the current date."""
    from datetime import datetime
    return f"Current date: {datetime.now().strftime('%Y-%m-%d')}"


# Create tools list
tools = [
    Tool(
        name="Calculator",
        func=calculator,
        description="Useful for performing mathematical calculations. Input should be a valid mathematical expression."
    ),
    Tool(
        name="Search",
        func=search_tool,
        description="Useful for searching factual information. Input should be a search query."
    ),
    Tool(
        name="GetDate",
        func=get_current_date,
        description="Returns the current date. No input required."
    )
]


# ReAct prompt template
REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought: {agent_scratchpad}
"""


def create_react_agent_system():
    """Creates a ReAct agent using LangChain."""
    
    # Initialize LLM
    llm = ChatOpenAI(
        model="gpt-3.5-turbo",
        temperature=0,
        api_key=os.getenv("OPENAI_API_KEY")
    )
    
    # Create prompt template
    prompt = PromptTemplate.from_template(REACT_PROMPT)
    
    # Create agent
    agent = create_react_agent(
        llm=llm,
        tools=tools,
        prompt=prompt
    )
    
    # Create agent executor
    agent_executor = AgentExecutor(
        agent=agent,
        tools=tools,
        verbose=True,
        max_iterations=10,
        handle_parsing_errors=True
    )
    
    return agent_executor


def demonstrate_react_pattern():
    """Demonstrates the ReAct pattern with various examples."""
    
    print("=" * 80)
    print("PATTERN 001: ReAct (Reasoning + Acting)")
    print("=" * 80)
    print()
    
    # Create the ReAct agent
    agent_executor = create_react_agent_system()
    
    # Test cases demonstrating ReAct pattern
    test_questions = [
        "What is the capital of France?",
        "What is 25 * 4 + 10?",
        "What is the population of Tokyo and what is 100 divided by that number in millions?",
        "What's today's date?",
    ]
    
    for i, question in enumerate(test_questions, 1):
        print(f"\n{'=' * 80}")
        print(f"Example {i}: {question}")
        print('=' * 80)
        
        try:
            result = agent_executor.invoke({"input": question})
            print(f"\n✓ Final Answer: {result['output']}")
        except Exception as e:
            print(f"\n✗ Error: {str(e)}")
        
        print()
    
    # Summary
    print("\n" + "=" * 80)
    print("REACT PATTERN DEMONSTRATION COMPLETE")
    print("=" * 80)
    print()
    print("Key Features Demonstrated:")
    print("1. Thought-Action-Observation loop")
    print("2. Tool selection and usage")
    print("3. Multi-step reasoning")
    print("4. Dynamic decision making")
    print("5. Interpretable reasoning traces")
    print()
    print("LangChain Components Used:")
    print("- AgentExecutor: Manages agent execution loop")
    print("- create_react_agent: Creates ReAct-style agent")
    print("- Tool: Wraps functions as agent tools")
    print("- PromptTemplate: Structures the ReAct prompt format")
    print("- ChatOpenAI: LLM for reasoning and action selection")
    print()


if __name__ == "__main__":
    # Check for API key
    if not os.getenv("OPENAI_API_KEY"):
        print("Error: OPENAI_API_KEY not found in environment variables.")
        print("Please set it in your .env file or environment.")
        exit(1)
    
    demonstrate_react_pattern()

Agentic Design Pattern: React

Pattern 001: React – Line-by-Line Explanation

Overview

This document provides a detailed line-by-line explanation of the React pattern implementation.

Pattern Description

Pattern 001: ReAct (Reasoning + Acting)

Description: Agent alternates between reasoning about the task and taking actions. The ReAct pattern combines reasoning traces with task-specific actions, allowing the agent to reason about what to do and then take actions.

Components: – Thought: Agent reasons about current state – Action: Agent takes an action (e.g., tool use) – Observation: Agent observes the result – Loop continues until task is complete

Use Cases: – Question answering with external knowledge – Task completion requiring multiple steps – Interactive problem solving

LangChain Implementation: Uses LangChain’s AgentExecutor with structured tools and ReAct prompt template.

File Statistics

  • Total Lines: 209
  • Classes: 0
  • Functions: 5
  • Constants: 1

Module Documentation

"""
Pattern 001: ReAct (Reasoning + Acting)

Description:
    Agent alternates between reasoning about the task and taking actions.
    The ReAct pattern combines reasoning traces with task-specific actions,
    allowing the agent to reason about what to do and then take actions.

Components:
    - Thought: Agent reasons about current state
    - Action: Agent takes an action (e.g., tool use)
    - Observation: Agent observes the result
    - Loop continues until task is complete

Use Cases:
    - Question answering with external knowledge
    - Task completion requiring multiple steps
    - Interactive problem solving

LangChain Implementation:
    Uses LangChain's AgentExecutor with structured tools and ReAct prompt template.
"""

Purpose: This module implements the React pattern, providing a structured approach to agent design and implementation.

Import Statements

Standard Library Imports

import datetime.datetime
import langchain.agents.create_react_agent
import langchain_core.prompts.PromptTemplate
import os
import typing.List
import typing.Optional

Purpose: Core Python functionality: – datetime.datetime: Date and time handling – langchain.agents.create_react_agent: Regular expression operations – langchain_core.prompts.PromptTemplate: Regular expression operations – os: Environment variable access and system operations – typing.List: Type hints for better code documentation (List, Dict, Any, Optional) – typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)

LangChain Imports

from langchain.agents.AgentExecutor
from langchain.tools.Tool
from langchain_openai.ChatOpenAI

Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows – Agents: Autonomous decision-making entities – Tools: Functions that agents can invoke

Other Imports

from dotenv.load_dotenv

Purpose: Additional dependencies for specialized functionality.

Module Constants

REACT_PROMPT

REACT_PROMPT = """Answer the following questions as best you can. You have access to the following tools:

Line 94: Module-level constant definition.

Functions

calculator(expression)

Lines 38-45

Purpose:

Evaluates a mathematical expression.

Parameters: – expression: Expression

Returns: str

search_tool(query)

Lines 48-64

Purpose:

Simulates a search tool that retrieves information.

Parameters: – query: Input query or question

Returns: str

get_current_date()

Lines 67-70

Purpose:

Returns the current date.

Returns: str

create_react_agent_system()

Lines 116-145

Purpose:

Creates a ReAct agent using LangChain.

demonstrate_react_pattern()

Lines 148-198

Purpose:

Demonstrates the ReAct pattern with various examples.

Key Concepts Summary

Pattern Overview

The React pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.

Core Components

  1. Agent Class: Main implementation of the pattern logic
  2. Prompt Engineering: Carefully crafted prompts to guide LLM behavior
  3. Tool Integration: Optional tools for extended functionality
  4. Memory Management: State handling across interactions
  5. Output Parsing: Structured response processing

When to Use

✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability

❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)

Best Practices Demonstrated

1. Environment Configuration

  • ✅ Use .env files for API keys
  • ✅ Never hardcode credentials
  • ✅ Validate environment variables on startup

2. Error Handling

  • ✅ Graceful degradation on failures
  • ✅ Informative error messages
  • ✅ Logging for debugging

3. Prompt Engineering

  • ✅ Clear, specific instructions
  • ✅ Structured output formats
  • ✅ Few-shot examples when beneficial

4. Code Organization

  • ✅ Separation of concerns (class-based design)
  • ✅ Type hints for clarity
  • ✅ Docstrings for documentation

5. Performance Optimization

  • ✅ Appropriate temperature settings
  • ✅ Token budget management
  • ✅ Caching where applicable

LangChain Components Used

  • ChatOpenAI: OpenAI model interface
  • Chains: Sequential component composition
  • Agents: Autonomous decision-making
  • Tools: External function calling

Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development

Execution Flow

1. Initialize agent with configuration
   ↓
2. Load environment and dependencies
   ↓
3. Prepare prompt with input data
   ↓
4. Invoke LLM through LangChain
   ↓
5. Parse and validate response
   ↓
6. Return formatted result

Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation

Performance Considerations

Computational Cost

  • LLM Calls: Primary cost factor (based on tokens)
  • Latency: Depends on model and input size
  • Memory: Minimal for most patterns

Optimization Strategies

  1. Prompt Optimization: Minimize tokens while maintaining clarity
  2. Caching: Cache repeated queries/responses
  3. Model Selection: Use smaller models when appropriate
  4. Batch Processing: Group similar requests
  5. Streaming: For long responses, stream tokens

Cost Estimates (Approximate)

  • GPT-3.5-turbo: ~$0.002 per request
  • GPT-4: ~$0.03 per request
  • GPT-4-turbo: ~$0.01 per request

Actual costs vary based on input/output token counts

Common Pitfalls to Avoid

1. Prompt Design Issues

❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts

2. Error Handling

❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation

3. Token Management

❌ Exceeding model token limits
✅ Truncate inputs, chunk processing

4. Security

❌ Exposing API keys in code
✅ Use environment variables

5. Testing

❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs

6. Cost Management

❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring

Further Improvements

Production Enhancements

  1. Monitoring: Add logging, metrics, tracing
  2. Validation: Input/output schema validation
  3. Rate Limiting: Implement request throttling
  4. Caching: Redis or in-memory cache for responses
  5. Async Support: Non-blocking operations for scalability

Feature Extensions

  1. Multi-Model Support: Switch between providers
  2. Fallback Models: Graceful degradation strategy
  3. Custom Tools: Domain-specific function calling
  4. Advanced Memory: Vector stores for long-term memory
  5. Evaluation: Automated testing and benchmarking

Code Quality

  1. Testing: Unit tests, integration tests
  2. Documentation: API docs, usage examples
  3. Type Safety: Stricter type hints
  4. Configuration: Externalized config files

Related Patterns

  • Pattern 002
  • Other Reasoning Patterns (001-011)

Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…

Summary

The React pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:

✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns

🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability

📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/

File: 001_react.py
Total Lines: 209
Pattern Category: AI Agent Design Pattern #001

Generated from actual implementation code
For educational and reference purposes

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