Search for a command to run...
AI Agent Series - Part 2
I am a Journalist-turned-Software Engineer. I love coding and the associated grind of learning every day. A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from. This is my contribution back to the community.
No comments yet. Be the first to comment.
Why Use Parent Document Retrieval? Traditional RAG methods can struggle with intricate questions due to their reliance on smaller text segments that may not encapsulate the broader themes or details of the original documents. When building a RAG-base...
📖 Introduction In modern AI applications, communication between clients and tools isn’t always as simple as calling an API. The Model Context Protocol (MCP) provides a standardized way for clients to exchange information, invoke tools, and maintain ...
If you’re curious about how to build an intelligent agent using Model Context Protocol (MCP), you’re in the right place. In this post, I’ll walk you through how to: Create an MCP Server using FastMCP Expose a tool that calculates BMI Build a Clien...
In our previous tutorial, we built a restaurant customer support chatbot using OpenAI's Agents SDK. In this follow-up, we’ll explore guardrails—a critical feature that enhances AI chatbot safety and reliability. What Are Guardrails in AI Agents? Guar...
Video https://www.youtube.com/watch?v=UPBMkFSJdBI The Problem at Hand Uber's data platform processes approximately 1.2 million interactive queries monthly, with 36% of these coming from the operations organization. This group—comprising engineers...
Video https://www.youtube.com/watch?v=FgRGwnpd2HY Google's recent white paper on "Agents" has created quite a buzz. The paper explores the concept of AI agents and delves into their architecture and potential. Let's break down what this white paper...
I am Zahiruddin Tavargere (Zahere). A social-learner, here to learn, share and grow with the tech community.
74 posts
I am Zahiruddin Tavargere (Zahere). A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from - this is my contribution back to the community.
In this blog post, we’ll delve into a new multi-agent framework called Swarm, developed by OpenAI, which has recently garnered significant attention within the AI community.
The goal is to explore how to use Swarm by building a simple agent that takes a user query, performs detailed research, and generates a structured blog post based on the findings.
This will help you understand the capabilities of Swarm, especially in orchestrating tasks among multiple agents.
Swarm is a multi-agent orchestration framework introduced by OpenAI. Although currently labeled as experimental and educational, it has quickly become a favorite among developers, earning praise as one of the best frameworks for building and coordinating multi-agent systems.
Swarm's design is centered around two fundamental components:
Agents: These are autonomous units that operate with a set of instructions and make decisions independently.
Each agent can specialize in a particular task, such as gathering data or writing content.
agent = Agent(
instructions="You are a helpful agent."
)
Handoffs: This mechanism allows one agent to transfer control of a task or conversation to another agent.
It enables seamless collaboration between agents, ensuring smooth execution of complex workflows.
sales_agent = Agent(name="Sales Agent")
def handoff_to_sales():
return sales_agent
agent = Agent(functions=[transfer_to_sales])
response = client.run(agent, [{"role":"user", "content":"Transfer me to sales."}])
print(response.agent.name)
By leveraging these components, Swarm helps developers create systems where tasks can be broken down into smaller, specialized sub-tasks, handled by different agents.
To illustrate the potential of Swarm, let’s build a multi-agent system that generates a blog post based on user input. The system comprises three agents:
Interface Agent: Interacts with the user, refines the query if needed, and passes the query to the Researcher Agent.
Researcher Agent: Conducts detailed research on the query and prepares a research report.
Blogger Agent: Uses the research report to create a structured blog post.
The workflow is simple: the user provides a theme or query, such as "Top 5 Technical Skills for 2025." The Interface Agent refines the query if necessary, then hands it over to the Researcher Agent, which gathers and analyzes relevant information. Finally, the Blogger Agent compiles the research into a well-written blog post.
Below is a high-level overview of the agent workflow:
User Input: The user provides a theme, e.g., "Why is R fast?".
Interface Agent: Takes the user’s input, asks clarifying questions if needed, and then passes the refined query to the Researcher Agent.
Researcher Agent: Gathers data from search engines, scrapes content from top results, and analyzes the information to produce a research report.
Blogger Agent: Converts the research report into a blog post and returns it to the Interface Agent.
Interface Agent: Delivers the completed blog post to the user.
This setup demonstrates how Swarm’s agent-based architecture simplifies the process of delegating tasks and orchestrating their execution.
Let’s dive into the code, which consists of the three agents and their respective functions.
Each agent is defined as a class with parameters like name, model, instructions, and functions. The functions include methods for interacting with APIs, databases, and other agents. A key method is the handoff method, which allows one agent to pass control to another:
| Field | Type | Description | Default |
| name | str | The name of the agent. | "Agent" |
| model | str | The model to be used by the agent. | "gpt-4o" |
| instructions | str or func() -> str | Instructions for the agent, can be a string or a callable returning a string. | "You are a helpful agent." |
| functions | List | A list of functions that the agent can call. | [] |
| tool_choice | str | The tool choice for the agent, if any. | None |
The Researcher Agent uses a package called GPT Researcher to gather and analyze content from various search engines.
This agent performs a deep dive into the topic provided by the user
import nest_asyncio # required for notebooks
nest_asyncio.apply()
from gpt_researcher import GPTResearcher
import asyncio
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
async def get_report(query: str) -> str:
report_type = "research_report"
researcher = GPTResearcher(query, report_type)
research_result = await researcher.conduct_research()
report = await researcher.write_report()
# Get additional information
research_context = researcher.get_research_context()
research_costs = researcher.get_costs()
research_images = researcher.get_research_images()
research_sources = researcher.get_research_sources()
return {'report':report}
def research_topic(query: str) -> str:
"""Generate research report"""
return asyncio.run(get_report(query))
def handoff_to_researcher():
"""Hand off the user query to the researcher agent."""
print("Handing off to Researcher Agent")
return researcher_agent
researcher_agent = Agent(
name="Researcher Agent",
model="gpt-4o-mini",
instructions="You are a researcher agent specialized in researching. If you are satisfied with the research, handoff the report to blogger",
functions=[research_topic, handoff_to_blogger],
)
The handoff method transfers control to the Blogger Agent once the research is complete.
The Blogger Agent creates a blog post based on the research report. It uses the language model to generate a well-structured article:
def generate_completion(role, task, content):
"""Generate a completion using OpenAI."""
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": f"You are a {role}. {task}"},
{"role": "user", "content": content}
]
)
return response.choices[0].message.content
def handoff_to_blogger():
"""Hand off the research report to the blogger agent."""
print("Handing off to Blogger Agent")
return blogger_agent
def generate_blog_content(research_data):
"""Generate technical blog content on research report using OpenAI."""
content = generate_completion(
"Technical Content Creator",
"Create compelling technical content for a blog based on the following research report.",
research_data
)
return {"content": content}
blogger_agent = Agent(
name="Blogger Agent",
model="gpt-4o-mini",
instructions="You are a top technical blogger agent specialized in creating compelling technical content for blogs based on research report. Be concise.",
functions=[generate_blog_content],
)
The Interface Agent communicates with the user, refines the query, and then initiates the workflow by handing control over to the Researcher Agent:
user_interface_agent = Agent(
name="User Interface Agent",
model="gpt-4o-mini",
instructions="You are a user interface agent that handles all interactions with the user. You need to always start with a theme or topic that the user wants to research. Ask clarification questions if needed. Be concise.",
functions=[handoff_to_researcher],
)
With the agents defined, you can now run the system:
Install the necessary packages:
! pip install git+https://github.com/openai/swarm.git openai gpt-researcher nest_asyncio
Initialize the agents and run the user input loop:
import os
from google.colab import userdata
from openai import OpenAI
from swarm import Agent
from swarm.repl import run_demo_loop
os.environ['OPENAI_API_KEY'] = userdata.get("OPENAI_API_KEY")
os.environ['TAVILY_API_KEY'] = userdata.get("TAVILY_KEY")
FAST_LLM="openai:gpt-4o-mini"
os.environ['FAST_LLM'] = FAST_LLM
os.environ['SMART_LLM'] = FAST_LLM
if __name__ == "__main__":
# Run the demo loop with the user interface agent
run_demo_loop(user_interface_agent, stream=True)
For the theme "Top 5 Technical Skills for 2025," the system will generate a structured blog post by conducting research and formatting the findings into readable content.
Here's an example of what the generated blog post might look like:
As we approach 2025, the pace of technological advancement presents both opportunities and challenges for software developers. The landscape is evolving with new innovations in artificial intelligence, cloud computing, and data analytics, among others. To remain competitive and relevant in the job market, developers must cultivate certain technical skills that are poised to dominate the industry. Here’s a detailed look at the top five technical skills every software developer should focus on in the coming years.
Artificial Intelligence (AI) and Machine Learning (ML) are no longer just buzzwords; they are core components of modern software applications. Industries from healthcare to finance are leveraging AI to automate processes, analyze data, and enhance customer experiences.
What to Learn:
Programming languages: Python is the go-to language for AI and ML, alongside familiarity with frameworks like TensorFlow and PyTorch.
Key areas: Focus on natural language processing (NLP) and computer vision, as they will continue to grow in demand.
Real-world application: Engage in projects that allow you to implement algorithms and create systems that learn and adapt based on data.
The migration to cloud computing is reshaping how businesses operate. Proficiency in cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform is becoming a mandatory skill for developers. Furthermore, an understanding of serverless architecture, which eliminates the need to manage servers, is a differentiator that can elevate your career.
What to Learn:
Cloud platforms: Gain hands-on experience with major cloud services and their offerings.
Serverless frameworks: Explore platforms like AWS Lambda to understand how to deploy applications more swiftly.
Collaboration: Familiarize yourself with DevOps practices to enhance collaboration in software development and operations.
With the surge in cyber threats, understanding cybersecurity is critical for developers. Knowledge of secure coding practices, data encryption, and vulnerability assessment is essential for protecting applications and sensitive user information.
What to Learn:
Security principles: Study secure coding standards and tools for threat modeling and incident response.
Sandbox testing: Experiment with penetration testing frameworks to gain practical knowledge.
Developing secure code: Integrate security considerations into your day-to-day coding practices and workflow.
Low-code and no-code platforms are transforming application development by making it accessible to non-developers. These platforms enable rapid application development, which can markedly accelerate a company’s digital transformation efforts.
What to Learn:
Tools and platforms: Explore tools like OutSystems, Mendix, or Bubble to understand how they work and their capabilities.
Integration skills: Learn how to integrate low-code solutions with traditional back-end systems.
Process optimization: Understand how these platforms can streamline workflows and provide rapid iterations.
In our data-centric world, the ability to analyze and interpret data is invaluable. Understanding data science principles can empower developers to turn insights into actionable strategies.
What to Learn:
Data tools and languages: Gain proficiency in SQL, R, and Python for data manipulation and visualization.
Statistical analysis: Understand key concepts of statistics and machine learning to derive meaningful insights from data.
Predictive analysis: Use machine learning algorithms to forecast trends and contribute to data-driven decision-making processes.
As technology continues to change at a rapid pace, developers must engage in continuous learning to stay up to date. The top five skills outlined above—AI and ML, cloud computing and serverless architecture, cybersecurity fundamentals, low-code/no-code development, and data science—are critical for any developer aiming to thrive in the industry by 2025.
Investing in these skills will not only enhance your employability but also equip you to contribute effectively to innovative projects that shape the technology landscape. Embrace this opportunity to upgrade your expertise and solidify your place in the future of software development.
Hadalgi, N. (2024). The Most In-Demand Programming Skills for 2025: Staying Ahead in a Rapidly Evolving Tech Landscape. LinkedIn.
Teal HQ. (2024). Top Skills for Software Developers in 2024 (+Most Underrated Skills). Teal HQ.
Educative. (2023). Top Software Developer Skills To Learn in 2024. Educative.
The Swarm framework simplifies the creation of complex multi-agent systems, allowing developers to break down intricate tasks into manageable components.
By combining specialized agents for tasks like research and content creation, developers can automate time-consuming processes, such as generating technical blog posts from user queries.
With this guide, you now have a foundational understanding of Swarm’s capabilities. The next step is to explore more advanced use cases, such as orchestrating agents for more complex workflows.
Stay tuned for future posts where we dive deeper into Swarm and its applications in AI development!