In 2024, the global market for AI agents reached about $5.4 billion, and it’s expected to grow to nearly $47 billion by 2030. To support this shift, many open-source frameworks have emerged to help developers build, manage, and deploy AI agents faster. These frameworks provide key building blocks like memory handling, tool orchestration, task planning, and multi-agent coordination.
If you're making a chatbot, a writing assistant, or a custom workflow, the right framework can save you time and make things easier. In this article, we'll talk about some of the best AI agent frameworks and help you choose the one that fits your project.
Below is the comparison table of AI agent frameworks mentioned in this article. Let’s take an overview of these frameworks first!
Frameworks | Type | Best For | Language | Open Source |
LangChain | LLM Toolkit | Building modular LLM pipelines | Python, JavaScript | Yes |
Microsoft AutoGen | Multi-agent Orchestration | Cooperative multi-agent tasks with humans | Python | Yes |
LangGraph | Graph Framework for LangChain | Multi-step workflows with complex logic | Python | Yes |
CrewAI | Agent Orchestration | Team-role workflows | Python | Yes |
Semantic Kernel | Cognitive Agent SDK | Building LLM agents with plugins | C#, Python | Yes |
NeMo Microservices | Enterprise AI Agents | Private, GPU-accelerated agent apps | Python | Partially |
LangChain
LangChain makes it easy for developers to develop apps that work with large language models. You don't have to write code from start because this framework takes care of memory, using tools, and thinking through multiple steps. It is free to use, has a lot of users, and works with both JavaScript and Python. LangChain is a great place to start if you want to make something that isn't just chatting.
Key Features
- Handles multi-step reasoning and decision flows in one pipeline
- Supports tools, memory, agents, and retrieval in a unified setup
- Built for chaining together LLM tasks in a controlled, modular way
- Ideal for building AI apps that need structured step-by-step logic
Cons:
LangChain can be too complicated for new programmers or simple projects. If you're new to it, it's better to start with simple use cases and follow the ready-made examples, rather than trying to build a full agent workflow from scratch.
Microsoft AutoGen
Microsoft AutoGen is a framework that lets multiple AI agents work together on the same task. Each agent can have a different role, like planner, coder, or reviewer, and they can talk to each other or even include a human in the process. It’s written in Python, open source, and gives developers a way to build and test team-based agent setups where tasks are handled step by step through communication.
Key Features
- Created to help different agents with different roles work together
- Supports human involvement alongside AI agents
- Agents can exchange messages and results during each step
- Helpful for jobs that require working together, like writing, planning, and evaluating
Cons:
Managing multiple agents in one system can get complex over time. It’s easy to lose track of what each agent is doing during long conversations. For better control, start with two or three agents, test their functions, then add agents as the system stabilizes.
LangGraph
LangGraph is a tool built on LangChain that lets you create AI workflows using graphs. Instead of running steps in a straight line, you can add conditions, loops, or branches to control what happens next. This gives you more flexibility when building agents that need to make choices, repeat actions, or take different paths. It’s open source, works in Python, and is a good choice if you’re already using LangChain and want more control over your agent’s flow.
Key Features
- Builds LLM workflows using graph structures, not just chains
- Supports branching, loops, retries, and condition-based flows
- Ideal for agents that need to revisit steps or choose between paths
- Adds advanced flow control to LangChain-based apps
Cons:
LangGraph is powerful, but it may be too much for simple projects that don’t need complex logic. To avoid getting stuck in setup details, it helps to start with a small graph and test each part step by step before adding more paths or decisions.
CrewAI
CrewAI is a lightweight Python framework designed for building multi-agent systems where each agent has a defined role and goal. Agents collaborate by sharing messages and working together to complete tasks. It is ideal for structured teamwork like content creation, brainstorming, or research.
Key Features
- Focuses on lightweight multi-agent teamwork with clear roles
- Agents communicate through message-passing to complete tasks
- Works well with LangChain for memory and tool access
- A good choice for structured collaboration, like content writing or research
Cons:
CrewAI manages memory and tool use with LangChain, which can be a problem if you need to make a lot of changes or use complicated workflows. CrewAI is best for light, organized multi-agent jobs when LangChain's built-in capabilities are enough. This makes it easier to set up and keep up with.
Semantic Kernel
The open-source Microsoft Semantic Kernel enables you to develop AI agents using large language models and normal code. You can add plugins, use tools from outside the program, and manage memory while still being in charge of how the agent functions. It works well with Python and C# languages. A lot of developers use it to add AI functions to real software applications, such as sending emails, making reports, or searching through content.
Key Features
- Uses a plugin-style system for adding reusable AI skills
- Combines LLMs with traditional code functions in a clean way
- Supports C# and Python for enterprise-ready integration
- Great for structured apps that need tight control over behavior
Cons:
In the beginning, setting up plugins or plans with Semantic Kernel can be difficult. To simplify, start with official examples and focus on one simple use case before adding functionality. This way, you may understand how the pieces go together without feeling too stressed.
NeMo Microservices
NeMo Microservices is a platform from NVIDIA that allows teams to make AI agents using their own data and open models. It works well with massive tasks and is fast because it operates on NVIDIA GPUs. You can use more than one AI tool in one system, such as chat, speech, or text reading. NeMo is suitable for organizations that wish to build chatbots and smart assistants without APIs and maintain confidential data.
Key Features
- Built for private, high-performance AI agent setups
- Supports speech, vision, and language models together
- Runs on-premise deployment using NVIDIA GPUs for full data control
- Ideal for companies needing secure, multi-modal agents
Cons:
However, NeMo Microservices requires considerable infrastructure and deployment skills. It's not the best choice for small groups or quick prototypes. Starting with a simple setup on a single GPU and testing each service before adding more is the best way to avoid problems early on. This makes it easier to keep track of and helps find problems before they get worse.
>> Read more:
- Top 9 Best Deep Learning Frameworks for Developers
- Top 9 Best Chatbot Development Frameworks
- Top 9 Machine Learning Platforms for Developers
- Top 12 Best Free AI Chatbots for Businesses
- Top 5 Best Generative AI Tools
Conclusion
Choosing between AI agent frameworks depends on your goals, your tech stack, and how much control you need over the agent’s actions. Some are made for quick chatbot building, while others are better for handling complex workflows or connecting multiple agents. The good news is that most of these frameworks are open source or easy to try. Start small, test what fits your needs, and build from there. As these tools grow, they’ll open up even more ways to build smarter, more capable AI agents.
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