The Most Popular AI Agent Frameworks Worth Considering for Your Business Automation

The AI agent market is taking off as businesses strive to create intelligent systems that process complex tasks with little to no human oversight. But building such systems from scratch requires a lot of time, investment, and engineers with rare skills. Fortunately, AI agent frameworks make the development process faster and more cost-effective. 

Let’s review the top AI agent frameworks, their main features, and use cases to help you choose the best one for your business automation. We’ll also run through a checklist of the main aspects you need to consider when selecting your AI agent framework.

An AI agent framework is a set of tools and libraries that allow developers to quickly prototype, create, and deploy an AI agent. AI agent frameworks usually:

• ➤ Combine different AI models
• ➤ Provide ready-made prompt templates for training AI agents
• ➤ Include multi-agents with specified roles that collaborate and interact with each other
• ➤ Provide agents with APIs and tools to pull data from external data sources

Let’s say you want to develop a financial investment agent that will analyze stocks and market trends to show you potential investment returns. Although the final workflow will depend on the exact tool you choose, these are the most common steps of creating an AI agent using a framework:

1. Package installation. Installs the necessary packages for financial AI agent development with a simple command.
2. Configuring agents. Sets up the agents and configures the rules of the conversation.
3. Rules and instructions setup. Gives instructions for each agent by creating a prompt template that guides the assistant on how to communicate with the user (for example, a market analyst agent analyzes stock data, a researcher agent gathers information and makes conclusions, and a strategy agent creates suggestions and predictions).
4. Error-handling. Sets up instructions in case the agent is unable to respond to the user input.
5. Third-party tools fetch. Defines the tools and third-party APIs the assistant will use during the interaction.
6. Defining a workflow. Specifies the order in which agents cooperate with each other. For example, a research agent finds financial data and then passes it to the strategy agent.
7. Testing and deploying the agent. Finally, you have to test your agent, improve it based on performance metrics, and proceed to deployment.

Choosing the right framework is a challenging task unless you know what features to look for. 

Each AI agent framework is different. Here is what you should pay attention to before making your choice.

Foundation model support

The more foundation AI models the framework supports, the more opportunities you get. Most frameworks allow you to experiment with various models and pick the ones that suit your needs. 

For example, with the Amazon Bedrock model evaluation feature, you can use either automatic or human evaluation to select the best model for your use case. Also, there is an option to import your custom AI model through a single API.

Model customization options

Assess if the frameworks you are looking at offer the opportunity to fine-tune foundational AI models and customize them to your business use case. Some frameworks also have the option of continued pre-training. 

In contrast to fine-tuning, pre-training learns from the massive amount of general data from large datasets. This can include data from publicly available datasets, industry-specific data sources, or web scraping. Pre-training is mainly used if you’re working in a completely new domain or your use case involves private, company-specific, or sensitive data that cannot be fine-tuned on a public model.

Integration with your existing tools

Check if the framework integrates with the data sources, models, or vector stores you’re using. If there is no out-of-the-box integration, ask if it’s possible to build your custom component or integrate your tool via API.

Analytics dashboard and performance metrics

Building an AI agent is not about “set it and forget it.” The efficiency and accuracy of your agent depend on its regular training and improvement. Some frameworks (like CrewAI) provide performance metrics for each agent, alert you about errors and anomalies, and evaluate the agent’s behavior to make it better with each interaction.

On-premises deployment

If you have a data-sensitive project and want complete control over your agent, check for an option to deploy the framework within your own infrastructure. 

User interface

Start a trial or request a demo to test the interface of the AI agent builder. There are a bunch of tools with extensive functionality but clumsy interfaces. For beginners, choosing no-code visual frameworks with an intuitive drag-and-drop interface is better.

Data privacy

Carefully read the AI agent framework’s security compliance documents to make sure your data stays secure and you have full control over the data you use to fine-tune models. Ask about the security measures to keep your data private and not share it with model providers.

Community and support

Building AI agents isn’t easy, and having a strong community and support makes a big difference. A decent AI agent framework should provide detailed documentation and how-to guides on its website. Also, always check GitHub activity, forums, and Discord channels before choosing an AI framework to make sure there is an active community behind it.

Now, let’s proceed to our curated AI agent frameworks list, along with their features.

➤ AG2

Pricing: Open source, but costs may be charged for agent deployment

Deployment model: Local, cloud, and hybrid

Foundation models: Large LLM models such as GPT-3, GPT-4

AG2 (the framework AutoGen has evolved into) is an open-source framework for creating collaborative agents. It’s a no-code tool with a visual interface and autonomous code generation that lets you create agents that interact with each other.

Flexible agent construction and management

AG2 equips you with different types of agents, such as assistant agents, executor agents, and group chat managers. You can set instructions for each type, define their roles and behavior, and automatically initiate collaboration between them.

Conversation patterns

AG2 automatically manages conversations between agents. Built-in conversation patterns include two-agent conversations (when two agents talk to each other to solve a problem), group chats (when multiple agents interact in one conversation), and sequential chats (agents interact in a structured, turn-based manner, where each step depends on the previous one).

Human oversight integration

Decide in what cases a human has to step in and create human approval workflows if needed. With this oversight, AI can ask for clarification or wait for human approval before making a decision. 

➤ LangChain

Pricing: Free to use; however, it requires the purchase of LangSmith, which starts at $39 per seat, per month

Deployment model: Includes self-hosted deployment option in the Enterprise plan

Foundation models: OpenAI, Claude, Gemini, Llama, Mistral, Cohere

LangChain is a framework for designing agents powered by LLMs. It includes a set of building blocks and ready-to-use components, first-class streaming (the system processes and responds to information in real time as it arrives), and human-in-the-loop support.

Retrieval-augmented generation (RAG)

Retrieval-augmented generation is a technique where LangChain retrieves relevant information from external sources before generating a response. You can connect your company’s data to LLMs, and LangChain will process the data using special retrieval algorithms. 

Indexing API

There is no need to upload or format files manually, as the LangChain Indexing API synchronizes your data from any source into a numerical vector.

Support for creating and refining prompts

LangChain fine-tunes your commands on the go to make them more accurate and receive better output. Also, it provides prompt templates—you just have to fill in the blanks with your relevant information.

➤ CrewAI

Pricing: CrewAI provides a free, open-source Python framework that allows developers to create and manage AI agents. It also offers an enterprise version of its platform, but pricing details are not publicly listed.

Deployment model: Works in the cloud, self-hosted, or locally

Foundation models: OpenAI, Anthropic, Google, Azure, AWS Bedrock, Amazon SageMaker, Mistral, Nvidia NIM, Groq, IBM watsonx.ai, Ollama (Local LLMs), Fireworks AI, Perplexity AI, Hugging Face, SambaNova, Cerebras, OpenRouter

Crew AI is a multi-agent platform for building AI agent teams that solve complex tasks. It combines two types of frameworks: crews and flows. CrewAI Crews are more suitable for collaborative AI systems with autonomous problem-solving, while CrewAI Flows work better for predictable decision workflows where precise control is needed.

Intelligent decisions and natural interaction

Crew AI agents work together to make intelligent decisions depending on their role. They collaborate in a human-like manner and use a complex workflow that includes data research, analysis, and report generation.

A sophisticated memory system

Crew AI memory system combines short-term memory, long-term memory, entity memory, and contextual memory, helping agents maintain context, remember key entities, and learn from past interactions. 

Testing feature

With the CrewAI testing feature, you can run a crew on a specified number of tasks and get detailed agent performance metrics. 

➤ Microsoft Azure AI

Pricing: Pay-as-you-go, where you pay only for the resources you need

Deployment model: Cloud/on-premises

Foundation models: OpenAI, Phi, Meta, Mistral AI, Cohere, Hugging Face, Stability AI, Nixtla

Microsoft Azure AI is a framework for building, deploying, and scaling complex AI agents.

A wide selection of AI models

Depending on your business goals, you can create agents that use Azure OpenAI models or other models, like Llama 3, Mistral, and Cohere. 

Fully managed service

Azure’s fully managed service lets developers focus on creating AI agents without managing underlying compute and storage resources

Grounding with Bing search

Integration with Bing search allows agents to fetch real-time public web data and return relevant search results to the customer’s model.

➤ AWS Bedrock

Pricing: You are charged for model inference and customization; there’s a pay-and-go option and provisioned throughput mode, where you can purchase model units

Deployment model: Cloud-based

Foundation models: The most prominent providers are AI21 Labs, Amazon, Anthropic, Cohere, DeepSeek, Meta, Mistral AI, and Stability AI. Other providers include Arcee AI, BRIA AI, Camb.ai, EvolutionaryScale, PBC, Gretel, Hugging Face, IBM Data Studio and AI, John Snow Labs, Karakuri LM, LG AI, Liquid AI, NCSoft, NVIDIA, Preferred Networks, Stockmark, Upstage, and Widn.AI.

Amazon Bedrock is a serverless, fully managed framework that lets you create AI agents using various foundation models and train them with your data using fine-tuning and RAG.

Multi-agent collaboration

Amazon Bedrock agents support multi-agent collaboration, which allows them to solve complex tasks together under the supervision of the lead agent.

Memory retention

Agents remember their previous interactions and can recall the previous context if necessary. This results in more precise outputs, as well as a personalized user experience.

Code interpretation

If you work with data analysis, data visualization, and mathematical problem solving, you can set the assistant to run the code automatically. If the code fails, the assistant will modify it and run it again until it works.

➤ Open AI API

Deployment model: Cloud-based and API-driven

Foundation models: o1, o3-mini, GPT-4.5, GPT-4o, GPT-4o-mini

Open AI combines all the necessary models, tools, knowledge, memory, guardrails, and orchestration for building effective AI agents.

A variety of tools for agents to interact with the world

Open AI supports function calling to interface with your code and external services. Also, it includes web search for retrieving data from the web, file search for searching your documents semantically, and computer use.

Knowledge and memory

Open AI agents can store, retrieve, and process information beyond the data they’re trained on. You can also integrate your data using vector stores for semantic search and vector embeddings for search, clustering, and recommendations.

Guardrails

With proper guardrails, you can be sure that your agents do not violate safety rules and that their behavior stays consistent. 

Here are the main aspects you should consider before picking an AI agent framework.

Define your project requirements

What is the primary purpose of the agent, and what is the core problem you aim to solve? Create a list of your project requirements, including the following criteria:

• The main functions you need the agent to perform
• The types of data you want to process
• Third-party tools, databases, and integrations you need
• Your existing tech stack

Consider the level of expertise required

If you’re a beginner with no tech experience, it’s better to choose no-code platforms with a visual drag-and-drop interface. Additionally, check if the frameworks provide ready-to-use prompt templates you can customize according to your needs. 

If you want an AI agent that will solve your specific, often complex problems, it’s better to consult with an AI agent development company. They will help you choose an AI framework and set it up.

Consider scalability and customization

Ensure the framework can scale with increasing users, data, or complexity. Also, check if the framework allows you to modify or extend its functions (e.g., adding custom models or modifying workflows). Open-source frameworks like LangChain usually offer more flexibility for fine-tuning.

Evaluate performance and efficiency

Evaluate the framework’s speed, memory usage, and computational efficiency. If your use case requires real-time processing, consider more lightweight options. And when using large-scale AI models, ensure the framework supports optimization techniques like caching or distributed processing.

Going with the wrong framework can cost you a lot of resources. If you can’t choose an AI agent framework, our team can help you define the list of features your agent has to perform and suggest the best options for your use case. Contact DevCom and leave AI agent development to us, while you focus on your business.