Primeros pasos con agentes de IA: guía de inicio rápido para desarrolladores

If you have been hearing about AI Agents but have not built one yet, this guide is for you. We will go from zero to a working AI Agent in under 30 minutes — no prior agent-building experience required. We will use Python and the CrewAI framework, which has the gentlest learning curve of any agent framework we have tested.

Step 1: Set Up Your Environment

First, create a clean virtual environment so we do not pollute your system Python:

# Create a project directory
mkdir my-first-agent
cd my-first-agent

# Create and activate a virtual environment
python -m venv venv

# On macOS/Linux:
source venv/bin/activate
# On Windows:
# venv\Scripts\activate

# Install CrewAI
pip install crewai

Next, set your OpenAI API key as an environment variable. Never hardcode API keys in your source files:

# On macOS/Linux:
export OPENAI_API_KEY="sk-your-api-key-here"

# On Windows (PowerShell):
$env:OPENAI_API_KEY = "sk-your-api-key-here"

Step 2: Choose Your LLM

The LLM is the brain of your agent. For your first agent, we recommend GPT-4o-mini — it is fast, cheap ($0.15/1M input tokens), and capable enough for most tasks. Later, you can switch to more powerful models or even local models.

Here is a quick comparison of popular LLM choices for agents:

• GPT-4o-mini: Best balance of cost and capability. $0.15/1M input, $0.60/1M output. Recommended for beginners.
• GPT-4o: More capable but 17x more expensive. Use for complex reasoning tasks.
• Claude 3.5 Sonnet: Excellent at coding and analysis. $3/1M input, $15/1M output. Good alternative to GPT-4o.
• Local models (Llama 3, Qwen): Free but require a GPU. Good for privacy-sensitive use cases.

Step 3: Define Your Agent

Now let us build a simple research agent. This agent will take a topic, search the web, and write a summary report. Create a file called `agent.py`:

from crewai import Agent, Task, Crew, Process
from crewai.tools import tool

# Define a simple tool (in production, you would use real APIs)
@tool
def search_web(query: str) -> str:
    """Search the web for information on a given query."""
    # In production, integrate with a real search API
    # For this tutorial, we return a placeholder
    return f"Search results for: {query}"

# Create the researcher agent
researcher = Agent(
    role="Research Analyst",
    goal="Find comprehensive information about the given topic",
    backstory=(
        "You are an expert research analyst with 10 years of experience "
        "in synthesizing information from multiple sources into clear, "
        "actionable insights."
    ),
    tools=[search_web],
    verbose=True,
)

# Create the writer agent
writer = Agent(
    role="Technical Writer",
    goal="Write a clear, engaging summary of the research findings",
    backstory=(
        "You are a skilled technical writer who can distill complex "
        "information into easy-to-understand articles."
    ),
    verbose=True,
)

# Define tasks
research_task = Task(
    description="Research the topic: {topic}. Find key facts, trends, and insights.",
    expected_output="A detailed research brief with 5-10 key findings.",
    agent=researcher,
)

write_task = Task(
    description="Write a 500-word summary article based on the research findings.",
    expected_output="A well-structured article in Markdown format.",
    agent=writer,
)

# Create and run the crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    verbose=True,
)

# Run it!
result = crew.kickoff(inputs={"topic": "AI Agent frameworks in 2026"})
print(result)

Run the script:

python agent.py

You should see the agents working: the researcher searches for information, then the writer produces a summary article. Congratulations — you have built your first multi-agent system!

Step 4: Add Real Tools

The `search_web` function in our example is a placeholder. To make your agent actually useful, you need to connect real tools. Here are some easy-to-integrate options:

• Web search: Use the DuckDuckGo Search API (free, no API key needed) or Serper.dev (paid, higher quality results).
• Web scraping: Use the `requests` + `beautifulsoup4` libraries to extract content from web pages.
• File operations: Python's built-in `open()`, `os`, and `pathlib` modules for reading and writing files.
• Database access: Use `psycopg2` for PostgreSQL or `sqlite3` for SQLite.
• API calls: Use the `requests` library to call any REST API.

Here is how to add a real web search tool using DuckDuckGo:

from crewai.tools import tool
from duckduckgo_search import DDGS

@tool
def search_web(query: str) -> str:
    """Search the web for information on a given query."""
    with DDGS() as ddgs:
        results = list(ddgs.text(query, max_results=5))
    formatted = []
    for r in results:
        formatted.append(f"Title: {r['title']}\nURL: {r['href']}\nSnippet: {r['body']}\n")
    return "\n".join(formatted) if formatted else "No results found."

Step 5: Understanding the Agent Loop

When you run the crew, here is what happens under the hood:

1. The framework sends the task description and agent's system prompt to the LLM.
2. The LLM decides what to do — it might call the `search_web` tool with a specific query.
3. The framework executes the tool and sends the result back to the LLM.
4. The LLM processes the result and decides what to do next — search again, or start writing.
5. This loop continues until the LLM signals that the task is complete.
6. The output is passed to the next agent (the writer) as input.

Step 6: Deploying Your Agent

Once your agent works locally, you will want to deploy it. Here are three common deployment patterns:

Option A: REST API

Wrap your agent in a simple Flask or FastAPI server. Clients send a POST request with their task, and the server returns the agent's output. Best for web applications and integrations.

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class TaskRequest(BaseModel):
    topic: str

@app.post("/run")
async def run_agent(req: TaskRequest):
    result = crew.kickoff(inputs={"topic": req.topic})
    return {"result": str(result)}

Option B: Scheduled Job

Run your agent on a schedule using cron, systemd timers, or a cloud scheduler (AWS EventBridge, Google Cloud Scheduler). Best for periodic tasks like daily reports or monitoring.

Option C: CLI Tool

Package your agent as a command-line tool using `click` or `argparse`. Best for developer tools and internal utilities.

Common Pitfalls to Avoid

As you continue building agents, watch out for these common mistakes:

• Overcomplicating the system prompt: Keep it focused. A 500-word prompt is usually better than a 5000-word one. The LLM loses focus with overly long instructions.
• Adding too many tools: Each tool adds to the context window and increases the chance the LLM picks the wrong one. Start with 2-3 tools and add more only when needed.
• Not setting a max iteration limit: Without a limit, a stuck agent will loop forever, burning tokens. Always set `max_iter=10` (or similar) as a safety net.
• Ignoring error handling: Tools fail. APIs go down. If your agent does not handle errors gracefully, a single failed tool call can crash the entire run.

Next Steps

You now have a working AI Agent. Here is how to continue learning:

1. Browse the 265+ tools on OpenClawHub to see what other agents and frameworks exist.
2. Read our deep dive on AI Agent architecture to understand the ReAct loop and multi-agent patterns.
3. Experiment with different LLMs — try Claude, local models, or smaller models to see how they affect agent behavior.
4. Join the open-source community of your chosen framework. Contribute bug reports, feature requests, or code.

The AI Agent ecosystem is moving fast, but the fundamentals — tools, prompts, and the agent loop — remain constant. Master these, and you will be able to build with any framework that comes along.