Written by Rahul Kumar, Head of AI Engineering at Neurons Lab. Rahul is a 3x business founder, 2x author, AI scientist, deep learning practitioner, and independent researcher.
.
.
In today’s rapidly evolving AI landscape, orchestrating multiple AI agents to work together seamlessly is becoming increasingly important.
OpenAI’s recently released SWARM framework offers a simple yet powerful solution for creating an orchestration layer that delegates tasks between different agents.
In this post, we’ll explore how to build such a layer using SWARM, with a practical example from the telecom industry.
What is SWARM?
SWARM is an experimental framework designed to manage complex interactions between multiple AI agents. Its key features include:
1. Agent-Based Architecture: SWARM allows you to create specialized agents, each with its own set of instructions and available functions (tools).
2. Dynamic Interaction Loop: The framework manages a continuous loop of agent interactions, function calls, and potential handoffs between agents.
3. Stateless Design: SWARM is stateless between calls, offering transparency and fine-grained control over the orchestration process.
4. Direct Function Calling: Agents can directly call Python functions, enhancing their capabilities and integration with existing systems.
5. Context Management: SWARM provides context variables for maintaining state across agent interactions.
6. Flexible Handoffs: Agents can dynamically switch control to other specialized agents as needed.
7. Real-Time Interaction: The framework supports streaming responses for immediate feedback.
8. Model Agnostic: SWARM works with any OpenAI-compatible client, including models hosted on platforms like Hugging Face TGI or vLLM.
Why SWARM?
As an AI practitioner, I’ve come to value simplicity and rapid implementation. My philosophy is that if you can bring an abstract concept to life in just a few hours of focused deepwork, it’s worth exploring.
We often get caught up in complex frameworks and lengthy setups. But what if the most impactful ideas could be tested quickly, using nothing more than a simple colab notebook?
This approach challenges us to strip away unnecessary complexities. It’s about finding tools and methods that let us move swiftly from concept to prototype. By embracing this mindset, we open doors to innovation and make AI development more accessible.
Enough of the philosophy. Let’s do the real work.
Building a Telecom Orchestration Layer
Let’s walk through an example of how to build an orchestration layer for a telecom company using SWARM. The complete code is available here:
1. Define Your Agents
In our telecom orchestration layer, we created specialized agents to handle diverse tasks. Each agent is equipped with specific functions and knowledge, allowing them to efficiently address their designated areas of expertise.
- Triage Agent: Determines which agent is best suited to answer a user’s query.
- OLT Agent: Handles queries related to Optical Line Terminal (OLT) data.
- CRM Agent: Retrieves customer information from the CRM system.
- ACS Agent: Manages queries related to Auto Configuration Server (ACS) data.
- Solution Agent: Integrates with a knowledge graph to solve customer problems.
2. Implement Data Connectors (Tools)
For each agent, let’s define the tools they can use.
For example, here are the tools to load the data from different sources. These tools can be replaced into different kinds of tools such as text-to-SQL, API calls, or reading data from log files:
I have added an additional tool to interface with a knowledge graph that queries the Hugging Face space. This allows our agents to tap into a vast repository of structured information, enhancing their ability to process and understand complex information:
Learn how I build Knowledge Graphs – here is my webinar tutorial on using them to empower LLMs:
3. Defining Agent functions
Now let’s define the agent functions. These functions will be invoked using the agents we’ll define in a subsequent step.
They’ll be responsible for calling the previously defined tools in the background and executing the necessary actions like data retrieval or content generation:
4. Implement Agents
At the heart of SWARM’s orchestration layer are the specialized agents, each designed to handle specific tasks within the system.
In our telecom example, we defined several key agents:
- The Triage Agent which acts as the initial point of contact and directs queries to appropriate specialists;
- The OLT Agent responsible for handling Optical Line Terminal data;
- The CRM Agent which retrieves and manages customer information;
- The ACS Agent dealing with Auto Configuration Server data;
- The Solution Agent which integrates with a knowledge graph to solve complex customer issues.
By clearly defining each agent’s role and capabilities, we create a modular and efficient system that can handle a wide range of customer queries and technical tasks:
5. Define Handoff Logic
SWARM’s power lies in its ability to transfer control between agents seamlessly. We implemented this through simple transfer functions like transfer_to_olt() and transfer_to_crm().
These functions enable dynamic routing of queries, allowing agents to hand off tasks when they encounter questions outside their expertise. For example, the CRM agent can pass network status queries to the OLT agent.
This mechanism ensures that complex inquiries are addressed by the most appropriate specialists, resulting in comprehensive and accurate responses to user queries:
6. Let’s EXECUTE!!!
Using run_demo_loop() will execute an infinite while loop for the agent to run and do its amazing job:
Results
This implementation perfectly illustrates the orchestration layer’s capabilities. The system demonstrates its ability to:
- Identify and activate the most suitable agent for each query
- Determine and call appropriate functions or tools in the background
- Retrieve relevant information from various data sources
- Maintain context throughout the conversation
- Seamlessly assist the user with their query
For instance, when asked about customer data, the system activates the CRM agent, retrieving detailed information about the customer.
It then seamlessly transitions to providing ACS data when requested, maintaining the context of the previous interaction.
The system’s ability to understand context is further demonstrated when it correctly interprets “he” as referring to the previously mentioned customer, David.
The SWARM framework offers a powerful and flexible approach to building AI orchestration layers.
Conclusion
By breaking down complex tasks into specialized agents and managing their interactions, you can create sophisticated AI systems capable of handling diverse queries and scenarios.
As demonstrated in our telecom example, SWARM can be effectively applied to real-world use cases, providing a robust foundation for building intelligent, multi-agent systems.
The complete code is available here:
About Neurons Lab
Neurons Lab is an AI consultancy that provides end-to-end services – from identifying high-impact AI applications to integrating and scaling the technology. We empower companies to capitalize on AI’s capabilities.
As an AWS Advanced Partner, our global team comprises data scientists, subject matter experts, and cloud specialists supported by an extensive talent pool of 500 experts. This expertise allows us to solve the most complex AI challenges, mobilizing and delivering with outstanding speed, while supporting both urgent priorities and strategic long-term needs.
Ready to leverage AI for your business? Get in touch with the team here.
