In our latest blog, we discuss how to effectively build multi-agent AI systems by comparing eight different agent frameworks. Each framework has its strengths, but they are crucial for managing multiple specialized AI agents that collaborate to tackle complex tasks. We found that while pure Python offers flexibility, agentic frameworks enhance team alignment and streamline workflows. Our testing highlighted that Smolagents stood out for its ease of use, while LlamaIndex is recommended for production-ready applications. We encourage AI developers to review our findings as a guide for choosing the right framework based on their project needs. For assistance in developing agentic AI systems, our consulting services are available.
In the world of artificial intelligence, building effective multi-agent systems is crucial for tackling complex tasks. In a recent blog on orchestrating multi-agent AI systems, we dug into how specialized AI agents can work together under the guidance of an orchestrating agent. But before that orchestration can happen, the first step is building those agents. So, how do we effectively create these AI agents?
Using Pure Python provides great flexibility and control. However, leveraging agentic frameworks can yield distinct advantages, especially for larger projects. These frameworks promote best practices and streamline workflows by aligning development teams around common design structures. This helps to reduce complexity and improves decision-making as we develop our agents.
To identify the best agentic frameworks available today, we evaluated eight notable options, some of which have only recently been released:
- Autogen
- CrewAI
- Langflow
- LangGraph
- LlamaIndex
- n8n
- PydanticAI
- Smolagents
The evaluation focused on the developers’ experience and the effectiveness of each framework in a financial multi-agent assistant workflow, which involved five specialized agents like the Orchestrator Agent and the Database Agent, each with its unique capabilities.
We identified three main criteria for our evaluation:
- Developer Experience: This measures how user-friendly the framework is.
- Features: This looks at how well the framework allows customization and orchestration of agents.
- Maturity: Here, we assess how well-tested a framework is for use in production.
Based on our analysis, we recommend using PydanticAI, Smolagents, or LlamaIndex for building AI agents. LlamaIndex stands out as a battle-tested option ideal for production use, while PydanticAI and Smolagents show promise for the future.
Ultimately, the "best" framework will depend on your project needs, team skills, and specific use cases. We encourage AI practitioners to take our findings as a starting point for their evaluations. If you need assistance in developing agentic AI systems, our team is here to help with our Data & AI consulting services.
Tags: Multi-Agent AI, AI Frameworks, Developer Experience, Agentic Frameworks, Artificial Intelligence Solutions
What are AI agent frameworks?
AI agent frameworks are software platforms that help developers create intelligent agents. These agents can perform tasks, respond to questions, and learn from experiences. They make it easier to build AI systems for various applications.
Why did we test 8 AI agent frameworks?
We tested 8 AI agent frameworks to see how well they perform in different scenarios. Our goal was to find out which frameworks are user-friendly, efficient, and versatile. This helps developers choose the right tools for their projects.
What did we learn from the testing?
From our testing, we learned that some frameworks are better for specific tasks, while others are more general-purpose. We also found that ease of use and community support are important factors for developers. Performance can vary significantly based on the framework chosen.
How can I choose the best AI agent framework for my needs?
To choose the best framework, consider these factors:
– What tasks you want the agent to perform.
– The level of support and documentation available.
– The programming languages you’re comfortable with.
– Your budget, since some frameworks may have costs associated with them.
Can these frameworks work with existing systems?
Yes, many AI agent frameworks are designed to integrate easily with existing systems. They often come with APIs and tools that help developers connect them to other software. This flexibility makes it easier to incorporate AI capabilities into current projects.