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- How to build your first ReAct agent in 20 minutes (step-by-step walkthrough)
How to build your first ReAct agent in 20 minutes (step-by-step walkthrough)
The complete beginner's guide to creating reasoning AI that thinks, acts, and self-corrects
Wyatt Brocato
June 07, 2025
In this newsletter, I'm going to show you exactly how to build your first ReAct agent from scratch—even if you've never touched AI development before.
Here's what you'll get: A working agent that can research topics, fact-check information, and generate comprehensive reports by combining multiple tools and reasoning through problems step-by-step.
By the end of this 20-minute tutorial, you'll have a practical understanding of how ReAct agents work and a functioning system you can expand for your own needs.
Unfortunately, most tutorials make this seem impossibly complex. They assume you already understand machine learning, have a computer science background, or want to spend weeks learning theory before building anything useful.
Here are the other reasons people struggle to get started with AI agents:
Too much theory, not enough practice - Most courses teach concepts without hands-on building
Overwhelming tool choices - Dozens of frameworks with no clear starting point
No real-world examples - Toy demos that don't solve actual problems
Complex setup requirements - Hours of configuration before writing a single line
Good news: I'm going to solve all these problems with a simple, practical approach that gets you building immediately.
Here's how, step by step:
Step 1: Set Up Your Development Environment
Why this matters: A clean setup prevents 90% of beginner frustration.
First, we'll use Hugging Face Agents because it requires minimal configuration and handles the complexity for you.
What you need:
Python 3.8+ installed
A Hugging Face account (free)
20 minutes of focused time
Quick setup:
pip install transformers[agents] That's it. No complex virtual environments or dependency hell.
Pro tip: If you get stuck on installation, use Google Colab instead. It has everything pre-installed and runs in your browser.
Step 2: Understand the ReAct Pattern
Why this matters: Understanding the pattern helps you design better agents.
Traditional AI: Input → Output (done)
ReAct agents: Observe → Reason → Act → Observe → Reason → Act (continues until goal achieved)
The magic happens in the reasoning step. Instead of just processing input, the agent thinks about:
What information do I have?
What information do I need?
Which tool should I use next?
Did my last action work?
How should I adjust my approach?
Real example: When I ask an agent to "research competitor pricing," it doesn't just search once. It reasons: "I found Company A's pricing, but this seems outdated. Let me search for more recent information and cross-reference with industry reports."
Step 3: Build Your First Agent
Why this works: Starting simple lets you see the concepts in action without getting overwhelmed.
Here's the complete code for a research agent:
from transformers.agents import HfAgent
# Initialize the agent
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
# Define the task
task = """
Research the topic of 'AI agent market trends' and provide:
1. Current market size
2. Key growth drivers
3. Top 3 companies in the space
4. Main challenges facing the industry
Use multiple sources and verify information where possible.
"""
# Run the agent
result = agent.run(task)
print(result) What happens when you run this:
Agent reads your task and breaks it down
Searches for relevant information using available tools
Cross-references findings from multiple sources
Organizes information into the requested format
Provides reasoning for its conclusions
Common beginner mistake: Trying to make the task too complex initially. Start with simple research tasks, then add complexity.
Step 4: Watch Your Agent Think
Why this is crucial: Visible reasoning builds trust and helps you improve the agent's performance.
Enable reasoning visibility:
agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder",
verbose=True)You'll see output like:
Reasoning: I need to research AI agent market trends. Let me start by searching for recent market reports...
Action: Searching for "AI agent market size 2024"
Observation: Found several sources mentioning $4.8B market size...
Reasoning: This information looks recent, but let me verify with additional sources...
Action: Searching for "AI agent industry growth drivers" This transparency is what makes ReAct agents special. You can see exactly how they approach problems and where they might need guidance.
Step 5: Your First Success
Right now, pick a research task you do manually and build an agent to handle it.
Examples:
Market research for your industry
Competitor analysis
Technology trend monitoring
Content fact-checking
Start with the basic template above, then customize the task description for your specific needs.
What you'll discover: Within 20 minutes, you'll have an AI that can think through problems, use tools strategically, and provide reasoning for its conclusions.
This isn't just automation—it's artificial reasoning that adapts to new situations.
Ready to Go Deeper?
This tutorial gets you started with the basics, but there's so much more you can do with ReAct agents. If you want to take your agent from basic research tool to production-ready system, I've created an advanced deep-dive tutorial that covers:
Adding custom tools and APIs
Memory and context management
Error recovery and self-correction
Multi-agent coordination
Performance optimization strategies
Get the advanced guide here: https://wyattbrocato.substack.com/
The advanced tutorial picks up exactly where this one leaves off and shows you how to build agents that can handle complex workflows, coordinate multiple tools, and continuously improve their performance.
But first, build your basic agent and watch it think. The reasoning process will change how you view AI forever.
Tell next week,
Wyatt
Connect With Me
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Forward this to a friend who's interested in AI but struggles to get good results. They'll thank you later.
Complete Production-Ready Code
Once you've mastered the basics above, here's the complete production-ready ReAct agent system that incorporates advanced techniques like memory management, error recovery, multi-agent coordination, and performance optimization:
from transformers.agents import HfAgent, tool
import requests
from bs4 import BeautifulSoup
import json
import time
from datetime import datetime
from collections import defaultdict
# Custom Tools
@tool
def advanced_web_scraper(url: str, extract_type: str = "text") -> str:
"""Advanced web scraping with content type detection"""
try:
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}
response = requests.get(url, headers=headers, timeout=10)
soup = BeautifulSoup(response.content, 'html.parser')
if extract_type == "text":
return soup.get_text()[:3000]
elif extract_type == "links":
return [a.get('href') for a in soup.find_all('a', href=True)][:20]
elif extract_type == "tables":
tables = soup.find_all('table')
return str(tables[0]) if tables else "No tables found"
except Exception as e:
return f"Error scraping {url}: {str(e)}"
@tool
def calculate_metrics(data: str, metric_type: str = "average") -> str:
"""Perform calculations on numerical data"""
try:
import re
numbers = [float(x) for x in re.findall(r'-?\d+\.?\d*', data)]
if metric_type == "average":
result = sum(numbers) / len(numbers) if numbers else 0
elif metric_type == "max":
result = max(numbers) if numbers else 0
elif metric_type == "min":
result = min(numbers) if numbers else 0
elif metric_type == "sum":
result = sum(numbers) if numbers else 0
return f"{metric_type.title()}: {result:.2f}"
except Exception as e:
return f"Calculation error: {str(e)}"
# Memory Management
class AgentMemory:
def __init__(self, memory_file="agent_memory.json"):
self.memory_file = memory_file
self.memory = self.load_memory()
def load_memory(self):
try:
with open(self.memory_file, 'r') as f:
return json.load(f)
except FileNotFoundError:
return {
"preferences": {},
"past_research": {},
"successful_patterns": {},
"failed_approaches": {}
}
def save_memory(self):
with open(self.memory_file, 'w') as f:
json.dump(self.memory, f, indent=2)
def remember_research(self, topic, findings):
self.memory["past_research"][topic] = {
"findings": findings,
"timestamp": datetime.now().isoformat()
}
self.save_memory()
def get_relevant_context(self, current_task):
relevant_items = []
for topic, data in self.memory["past_research"].items():
if any(keyword in current_task.lower() for keyword in topic.lower().split()):
relevant_items.append(f"Previous research on {topic}: {data['findings'][:200]}...")
return relevant_items
# Error Recovery
class ResilientAgent:
def __init__(self, base_agent):
self.agent = base_agent
self.error_patterns = {}
self.success_patterns = {}
def execute_with_recovery(self, task, max_retries=3):
for attempt in range(max_retries):
try:
adjusted_task = self.adjust_task_based_on_history(task)
result = self.agent.run(adjusted_task)
if self.validate_result(result):
self.record_success(task, adjusted_task, result)
return result
else:
task = self.refine_task_based_on_poor_result(task, result)
except Exception as e:
self.record_error(task, str(e))
task = self.adjust_for_error(task, e)
if attempt == max_retries - 1:
return f"Failed after {max_retries} attempts. Last error: {str(e)}"
def validate_result(self, result):
if len(result) < 100:
return False
if "error" in result.lower() or "failed" in result.lower():
return False
return True
def record_success(self, original_task, successful_task, result):
self.success_patterns[original_task] = successful_task
def record_error(self, task, error):
self.error_patterns[task] = error
def adjust_task_based_on_history(self, task):
for successful_original, successful_adjusted in self.success_patterns.items():
if successful_original in task:
return successful_adjusted
return task
def adjust_for_error(self, task, error):
return f"{task}\n\nNote: Previous similar task failed with '{error}'. Try alternative approach."
def refine_task_based_on_poor_result(self, task, result):
return f"{task}\n\nNote: Previous result was incomplete. Please provide more comprehensive analysis."
# Multi-Agent Orchestration
class AgentOrchestrator:
def __init__(self):
self.agents = {}
self.setup_specialized_agents()
def setup_specialized_agents(self):
base_model = "https://api-inference.huggingface.co/models/bigcode/starcoder"
# Research specialist
research_agent = HfAgent(base_model)
research_agent.toolbox.add_tool(advanced_web_scraper)
self.agents["research"] = research_agent
# Analysis specialist
analysis_agent = HfAgent(base_model)
analysis_agent.toolbox.add_tool(calculate_metrics)
self.agents["analysis"] = analysis_agent
# Validation specialist
validation_agent = HfAgent(base_model)
self.agents["validation"] = validation_agent
# Reporting specialist
reporting_agent = HfAgent(base_model)
self.agents["reporting"] = reporting_agent
def coordinate_complex_task(self, task):
results = {}
# Research phase
research_task = f"Research specialist: {task}. Gather comprehensive, current information from multiple sources."
results["research"] = self.agents["research"].run(research_task)
# Analysis phase
analysis_task = f"Analysis specialist: Analyze this research data and identify key patterns, insights, and trends: {results['research'][:1000]}..."
results["analysis"] = self.agents["analysis"].run(analysis_task)
# Validation phase
validation_task = f"Validation specialist: Fact-check and verify the accuracy of these findings: {results['analysis'][:1000]}..."
results["validation"] = self.agents["validation"].run(validation_task)
# Reporting phase
reporting_task = f"Reporting specialist: Create a comprehensive, actionable report from these validated findings: {results['validation'][:1000]}..."
results["final_report"] = self.agents["reporting"].run(reporting_task)
return results
# Performance Optimization
class PerformanceOptimizer:
def __init__(self):
self.metrics = defaultdict(list)
def track_performance(self, task_type, execution_time, success_rate, tools_used):
self.metrics[task_type].append({
"execution_time": execution_time,
"success_rate": success_rate,
"tools_used": tools_used,
"timestamp": time.time()
})
def get_performance_report(self):
report = {}
for task_type, data in self.metrics.items():
avg_time = sum(d["execution_time"] for d in data) / len(data)
avg_success = sum(d["success_rate"] for d in data) / len(data)
report[task_type] = {
"avg_execution_time": f"{avg_time:.2f} seconds",
"avg_success_rate": f"{avg_success:.2%}",
"total_executions": len(data)
}
return report
# Main Production Agent Class
class ProductionReActAgent:
def __init__(self):
# Initialize core components
self.base_agent = HfAgent("https://api-inference.huggingface.co/models/bigcode/starcoder")
self.memory = AgentMemory()
self.resilient_agent = ResilientAgent(self.base_agent)
self.orchestrator = AgentOrchestrator()
self.optimizer = PerformanceOptimizer()
# Setup tools
self.setup_tools()
def setup_tools(self):
"""Add all custom tools to the base agent"""
self.base_agent.toolbox.add_tool(advanced_web_scraper)
self.base_agent.toolbox.add_tool(calculate_metrics)
def execute_task(self, task, complexity="simple", remember=True):
"""Execute task with full production capabilities"""
start_time = time.time()
try:
# Add memory context
if remember:
context = self.memory.get_relevant_context(task)
if context:
task = f"{task}\n\nRelevant past context:\n" + "\n".join(context)
# Execute based on complexity
if complexity == "simple":
result = self.resilient_agent.execute_with_recovery(task)
elif complexity == "complex":
result = self.orchestrator.coordinate_complex_task(task)
else:
result = self.base_agent.run(task)
# Track performance
execution_time = time.time() - start_time
self.optimizer.track_performance(
task_type=complexity,
execution_time=execution_time,
success_rate=1.0,
tools_used=["web_scraper", "calculator"]
)
# Remember successful results
if remember and result:
self.memory.remember_research(task[:50], result[:500])
return result
except Exception as e:
execution_time = time.time() - start_time
self.optimizer.track_performance(
task_type=complexity,
execution_time=execution_time,
success_rate=0.0,
tools_used=[]
)
return f"Task failed: {str(e)}"
def get_performance_summary(self):
"""Get performance analytics"""
return self.optimizer.get_performance_report()
# Usage Example
if __name__ == "__main__":
# Initialize the production agent
agent = ProductionReActAgent()
# Simple task
simple_result = agent.execute_task(
"Research current AI market trends and provide key insights",
complexity="simple"
)
# Complex task with multi-agent coordination
complex_result = agent.execute_task(
"Analyze competitor pricing strategies in the AI tools market",
complexity="complex"
)
# Get performance analytics
performance = agent.get_performance_summary()
print("Simple Task Result:", simple_result[:200] + "...")
print("\nComplex Task Result:", complex_result["final_report"][:200] + "...")
print("\nPerformance Summary:", performance)This complete production system includes:
✅ Custom tool integration (web scraping, calculations)
✅ Memory management across sessions
✅ Error recovery and self-correction
✅ Multi-agent coordination for complex tasks
✅ Performance monitoring and optimization
✅ Production-ready architecture
To use this system:
Start with the basic tutorial above
Copy this complete code for advanced capabilities
Customize tools and prompts for your specific needs
Scale up to handle your production workloads
For even more advanced techniques and detailed explanations of each component, check out my deep-dive tutorial: https://wyattbrocato.substack.com/
The complete code above gives you everything you need to build enterprise-level ReAct agent systems that can handle real-world complexity.