6 AI Agent Frameworks
AI agents are becoming more and more common across businesses of all kinds. While the benefits of agentic AI are massive and well documented, their emergence nonetheless presents important challenges for developers.
In large part, this comes down to the fact that agents are highly complex systems. To alleviate this, a range of tools are available to speed up and simplify the process of AI agent development.
These vary in both scope and approach, aiming to empower a range of different user personas to create agentic systems.
Today, we’re exploring one important segment of this market by checking out some of the most popular AI agent frameworks available to us.
Specifically, we’ll be covering:
- What is an AI agent framework?
- What to look for in an AI agent framework
- 6 AI agent frameworks for 2025
Let’s start with the basics.
What is an AI agent framework?
AI agent frameworks are sets of reusable tools and elements to help us build, deploy, and manage agentic AI systems. Most often, these are code-based tools, providing a structured approach for developers to create agents of their own.
This generally includes defining the architecture and character of agents, including how they interact with tasks, tools, data, human users, and other agents.
The goal is to provide developers a more streamlined experience for configuring key elements of agentic systems, such as memory, tool-use, integrations, connection to models, oversight, and orchestration.
Frameworks often take the form of libraries for coding languages such as Python or TypeScript, although some also incorporate elements of visual development.
We can think of this like a blueprint for creating AI agents, determining how different elements should interact in order to create LLM-powered systems, capable of autonomous reasoning and actions.
For a different approach to creating agents, you might like our guide to low/no-code AI agent builders .
Benefits
With a better understanding of what AI agent frameworks are, we can move on to thinking in more detail about how these add value to development projects.
As we hinted at earlier, this largely relates to simplifying and expediting the process of building agents. At a high level, by offering reusable elements and tools, this reduced the amount we have to build from scratch.
In turn, this has a range of technical and business-level benefits, including:
- Simplified development - Reducing the complexity of agentic AI development projects.
- Efficiency - Frameworks enable us to roll agentic solutions out to a greater number of processes and workflows, enhancing operational efficiency.
- Scalability - Frameworks make it easier to scale and modify our agents as our needs evolve.
- Maintainability - With reusable elements, frameworks can make solutions easier to maintain compared to fully custom developments.
- Integration options - Agent frameworks are heavily focused on empowering developers to integrate LLMs with other tools and services.
- Customization - Frameworks offer a greater degree of flexibility and customization compared to some other approaches to building AI agents.
- Consistency and interoperability - Frameworks help to ensure we build consistent agents that can more easily interact with one another.
Of course, frameworks also help us to achieve the wider benefits of implementing AI agents. Namely, they make it easier to develop solutions that can act autonomously in pursuit of a goal, in much the way human users would.
You might also like our guide to AI agentic workflows .
Use cases
Building on what we’ve learned so far about the benefits of AI agent frameworks, we can now start to think about when they’re used. Specifically, we want to consider some of the core use cases, target personas, and situations where frameworks are an effective option.
We can approach this from a few different angles.
Firstly, we can consider the use cases for agents more broadly, including automating and replicating decisions, workflows, tasks, and processes that might otherwise require human colleagues to action.
For instance, internal services workflows such as ITSM and HR are popular use cases for AI agents.
The other question is when we would use an agent framework rather than another approach, such as a no-code builder or agentic AI capabilities within domain-specific SaaS tools.
Often, this comes down to the degree of flexibility, control, and scope for customization we require. Frameworks are a strong option in this regard, enabling us to implement agentic functionality within all kinds of hard-coded projects.
This includes new and existing code bases.
As such, agent frameworks are generally utilized by developers or even AI specialists, while other, more visually-focused development tools might be suited to other colleagues and user personas.
What to look for in an AI agent framework
Lastly, before we begin examining specific examples from across the market for agent frameworks, it’s worth considering the decision points we’re likely to encounter when comparing various options.
That is, while most frameworks offer functionality for achieving similar outcomes, they can nonetheless differ in terms of focus, architecture, and optimization for creating different kinds of solutions.
With that in mind, some of the factors we’ll want to pay attention to include:
- Core capabilities - including effectiveness at task decomposition, planning, decision-making, and more.
- Developer experiences - how we can interact with the provided elements and components, along with any testing or debugging tools.
- Integrations - to be effective, AI agents must be able to integrate with relevant systems, tools, and resources.
- Language support - which coding languages are supported by the framework?
- Memory - the tools and functions available to us for handling short-term and long-term memory.
- Multi-agent capabilities - coordination and orchestration for multi-agent systems.
- Oversight, visibility, and monitoring - for gaining insights into agent behavior and performance.
- Performance and scalability - how well an agent framework handles large volumes of tasks.
- Security features - measures to protect sensitive data or tools.
- Ease-of-use - how easily we can implement a solution with a given framework.
- Community and support - the extent of resources or support services that are available to help us implement solutions.
Of course, the specific balance of these factors that we’ll need to prioritize will vary from one project to the next. For instance, in an enterprise context, security will be key, but for small personal projects, we’ll likely be primarily concerned with ease-of-use.
In any case, we can keep these factors in mind as we move on to thinking about specific examples of AI agent frameworks.
6 AI agent frameworks for 2025
Now that we know some of the important variations between AI agent frameworks, we can begin to check out some of the most popular options available to us.
We’ve chosen a range of different frameworks to try and give a broad overview of the space.
Our picks are:
Let’s check each one out in turn.
1. LangGraph
LangGraph is one of the most prominent frameworks for building AI agents. Part of the wider LangChain ecosystem, it’s available as both a framework and a broader platform for creating AI agents, including visual development tools.
The LangGraph framework consists of a library of tools for creating what are known as graph-based agents. This enables us to build a range of state-based workflows, useful for a huge variety of applications.
A graph, in this context, consists of two types of components:
Nodes- including LLMs, tools, memory stores, and more.Edges- how we connect nodes, including sequentially or conditionally.
LangGraph also offers a range of capabilities that make it highly suited to production use cases, including for orchestration and memory handling. On top of this, the related LangGraph platform offers similar functionality, with visual experiences for creating and managing agents.
2. AutoGen
Microsoft’s AutoGen is a suite of tools, including frameworks, for building AI agents. More specifically, it’s made up of four elements, making it suitable for a variety of different types of developers and projects.
These are:
AutoGen Core- A framework for building multi-agent systems.AgentChat- A framework for prototyping single and multi-agent systems.AutoGen Studio- A web-based UI for prototyping agents without writing custom code.Extensions- For integrating AutoGen agents with external systems.
AutoGen is particularly effective for creating collaborative multi-agent systems, comprising several specialised agents, working in tandem to complete tasks alongside human colleagues.
It utilizes an event-driven architecture, which is suitable for creating scalable, long-running agentic systems.
3. Crew AI
Crew AI is another widely used AI agent framework that is popular with large enterprises. Part of this popularity comes down to blending traditional coding tools with visual experiences, offering both a framework and a wider multi-agent platform.
It’s built for teams that want to utilize AI agents to automate workflows. We can create a multi-agent automations from scratch using code or by utilizing a range of no-code tools and templates.
On top of this, Crew AI offers an impressive range of tools for managing agentic solutions in production, including deploying, tracking, and continuously improving our solutions.
This makes it a great fit for a range of different kinds of organizations, from large enterprises to smaller teams who want to implement multi-agent solutions within a variety of tasks and workflows.
4. Semantic Kernel
Semantic Kernel is another Microsoft tool. More specifically, it’s an open-source software development kit that enables developers to integrate AI services such as OpenAI and Hugging Face with more traditional programming languages.
We can think of this like middleware that allows us to build AI-powered applications that combine LLMs and other tools with traditional coding languages like Python, Java, or C#.
As such, it’s a strong choice for implementing AI functionality in a wide range of development projects.
As a Microsoft tool, Semantic Kernel is also well-positioned for production enterprise use cases, including offering orchestration and planning tools, alongside prompt templates, response handling, and more.
5. Atomic Agents
Atomic Agents is an open-source library designed to be a lightweight, modular, and maintainable framework for building agentic AI applications. This offers a developer-friendly experience for creating highly powerful solutions with AI.
Logic and control flows are written in Python, providing a flexible, familiar experience for building agents, utilizing best practices from traditional software development.
Atomic Agents’ agents are made up of several elements, including system prompts, input & output schemas, memory, and context providers. This structure provides a high degree of control, predictability, and extensibility, perfect for a range of different types of applications.
Many users also rate Atomic Agents highly for ease of use, providing simple components that can be leveraged and combined to create highly effective custom solutions.
6. OpenAI Agents SDK
Lastly, we have OpenAI’s offering. The OpenAI Agents SDK replaced Swarm, one of the most well-known AI agents frameworks. It offers a flexible experience for building multi-agent systems, using OpenAI models, as well as over 100 other models.
In particular, OpenAI’s SDK provides a flexible, comprehensive set of tools for creating agentic systems, including capabilities for managing handoffs, guardrails, and tracing. This positions it as a powerful solution for implementing agents in a wide range of scenarios.
It’s available as a framework for both Python and JavaScript/TypeScript, making OpenAI’s Agent SDK a good option for a variety of different teams and developers.
On the whole, the OpenAI Agents SDK offers a strong combination of capabilities and usability, owing in large part to a relatively small number of primitives, enabling production applications without an excessive learning curve.
Turn data into action with Budibase
Budibase is the open-source, low-code platform that empowers IT teams to turn data into action.
We offer connectivity for a range of RDBMSs, NoSQL tools, and APIs, alongside powerful AI functionality, autogenerated UIs, visual automations, optional self-hosting, free SSO, and much more.
Take a look at our features overview to learn more.