12 AI agent frameworks for businesses to consider in 2025

AI agents are quickly gaining popularity for their ability to connect with different systems in a company, automate processes, and accomplish specific tasks.

An AI agent framework provides a structured platform to deploy and manage these AI agents in an enterprise. It offers a cohesive environment with LLMs, pre-built components and APIs that ease the creation and scaling of customized AI agents.

In this article, we’ll explore the top AI agent frameworks and their pros and cons, helping you determine which might fit your organization's needs.

1. AutoGen

Developed by Microsoft Research, AutoGen is a powerful framework for multi-agent AI systems. The framework enables complex conversations between multiple AI agents, making it particularly valuable for enterprises tackling complex problems.

AutoGen's architecture supports synchronous and asynchronous agent interactions, allowing for flexible deployment scenarios. Its adoption by major tech companies, including Google, Meta, and MongoDB, demonstrates its enterprise readiness.

Pros:

• Creates sophisticated agent networks with built-in support for various interaction patterns, conversation management, and agent specialization. This allows enterprises to build complex systems where multiple AI agents can work together seamlessly
• Offers extensive customization options and integrates smoothly with other frameworks and tools. Teams can adapt AutoGen to their specific needs while maintaining compatibility with existing systems
• Provides advanced capabilities like parallel processing, error handling, and conversation management that are crucial for production deployments. These features make it particularly suitable for large-scale enterprise applications.

Cons:

• Requires significant technical expertise and understanding of multi-agent architectures. New teams might find the initial setup and configuration challenging

Best for: Large enterprises needing multi-agent systems, especially for complex tasks requiring multiple AI agents to work together.

2. LangChain

LangChain takes a component-based approach to AI development, providing building blocks that developers can combine to create sophisticated applications.

The framework connects different AI capabilities, from document processing to reasoning chains. Its architecture focuses on reusability and modularity, making it particularly valuable for teams building complex AI applications.

Pros:

• Offers an extensive collection of pre-built components for various AI tasks, significantly reducing development time. Teams can leverage these components to quickly build sophisticated applications
• Supports a wide range of LLMs, databases, and tools, allowing organizations to build solutions that fit their specific technology stack

Cons:

• The abstraction layers can introduce performance overhead that might impact application speed in production environments

Best for: Teams that need a well-documented framework with many pre-built components.

3. CrewAI

CrewAI represents a fresh approach to multi-agent systems, focusing on accessibility and ease of use. The framework simplifies creating and managing AI agent teams through an intuitive task allocation system.

It stands out for its straightforward implementation of role-based agents that can collaborate on complex tasks. The framework is beneficial for managing sequential task workflows where different specialized agents need to contribute their expertise.

Pros:

• Provides a clear and straightforward way to define agent roles and assign tasks, making it easier to organize complex workflows. This reduces the learning curve for teams new to multi-agent systems

Cons:

• As a newer framework, it has fewer third-party integrations and community resources compared to more established alternatives

Best for: Small to medium businesses looking for a straightforward way to implement AI agents.

4. Semantic Kernel

Semantic Kernel, developed by Microsoft, allows developers to create AI-powered applications that effectively understand and process natural language instructions. Semantic Kernel's architecture centers around "skills" - reusable components that can be combined to create complex AI behaviors.

Pros:

• Offers seamless integration with Azure services and other Microsoft tools, making it ideal for organizations in the Microsoft ecosystem. This reduces integration complexity
• Includes memory handling capabilities that help manage context and state across conversations and tasks effectively
• Provides out-of-the-box security features, including authentication, authorization, and data protection mechanisms

Cons:

• While cross-platform, it works best within the Microsoft ecosystem, which often limits flexibility for some organizations
• Requires understanding of specific architectural concepts unique to Semantic Kernel

Best for: Organizations heavily invested in Microsoft technologies.

5. LlamaIndex

LlamaIndex specializes in data connection and retrieval for AI applications, focusing on making enterprise data accessible to LLMs. The framework excels at creating efficient indexes of corporate knowledge and enabling natural language queries against structured and unstructured data.

LlamaIndex's architecture is designed to handle large-scale data operations while maintaining performance and accuracy.

Pros:

• Provides advanced capabilities for processing and indexing various data types, making enterprise information easily accessible to AI systems
• Supports multiple data sources and formats, allowing organizations to unify their knowledge bases effectively

Cons:

• Complex indexing operations can require significant computational resources, especially for large datasets
• Initial configuration and optimization can be challenging, particularly for organizations with diverse data sources

Best for: Data-intensive applications requiring robust information retrieval.

6. Langflow

Langflow brings a visual approach to building AI agent systems, making it accessible to teams with varying technical expertise. The framework provides a drag-and-drop interface for creating AI workflows while maintaining the power of underlying LangChain components.

Langflow bridges the gap between no-code and traditional development approaches, enabling rapid prototyping and development of AI applications.

Pros:

• Offers an intuitive interface for building AI workflows, reducing the technical barrier to entry
• Enables quick testing and iteration of AI agent configurations through its visual interface
• Ensures better communication between technical and non-technical team members through visual representation of workflows

Cons:

• Some advanced features may require direct coding outside the visual interface

Best for: Teams wanting to visualize and build agent workflows without heavy coding.

7. AgentGPT

AgentGPT focuses on creating autonomous AI agents to perform complex tasks with minimal human intervention. The framework focuses on practical task automation and provides tools for building agents to understand context, make decisions, and execute actions independently. With modern deployment practices in mind, AgentGPT makes deploying AI agents in production environments straightforward.

The framework particularly shines in scenarios requiring autonomous decision-making and task execution. Its architecture supports simple automation tasks and complex workflows requiring multiple decision points.

Pros:

• Provides tools for creating self-directed AI agents that can operate independently
• Offers streamlined deployment options that make it easier to move from development to production
• Allows detailed specification of agent behaviors and decision-making parameters

Cons:

• It may require additional development for enterprise-grade security and compliance features

Best for: Businesses looking to automate repetitive tasks with AI agents.

8. ChatDev

ChatDev specializes in building conversational AI applications, providing a comprehensive framework for developing sophisticated chat-based interfaces. The framework excels at handling natural language interactions and managing conversation flows.

It includes specialized tools for building customer service applications, virtual assistants, and other conversational interfaces.

Pros:

• Offers sophisticated tools for handling complex dialogue flows and maintaining context
• Supports various messaging platforms and communication channels
• Includes built-in tools for tracking conversation quality and agent performance

Cons:

• Not the best choice for non-conversational AI applications

Best for: Customer service and support automation scenarios.

9. MetaGPT

MetaGPT takes an innovative approach to multi-agent systems by providing a framework for creating hierarchical agent networks. The framework enables the development of complex AI systems where agents can collaborate, compete, or operate independently based on defined rules and objectives.

Pros:

• Creates complex agent networks with clear organizational structures
• Supports various interaction models between agents
• Designed to handle large-scale multi-agent deployments effectively

Cons:

• Complex agent networks can demand substantial computational resources
• Understanding the hierarchical agent model takes time

Best for: Teams building sophisticated multi-agent applications.

10. SuperAGI

SuperAGI provides a comprehensive platform for developing and managing AI agents, focusing on practical business applications. The framework includes agent development, deployment, and monitoring tools in production environments.

SuperAGI is focused on end-to-end agent lifecycle management, from development through deployment to maintenance. SuperAGI's architecture supports simple and complex agent implementations, making it suitable for various enterprise needs.

Pros:

• Offers all necessary tools for the full agent development lifecycle
• Includes comprehensive monitoring and management capabilities

Cons:

• Some features are tied to the SuperAGI ecosystem only

Best for: Teams needing a complete agent development solution.

11. TaskWeaver

TaskWeaver focuses on workflow automation through AI agents, providing specialized tools for creating and managing automated business processes. TaskWeaver excels at converting complex business workflows into automated sequences handled by AI agents.

Backed by Microsoft, The framework is well-suited for organizations looking to automate routine business tasks while maintaining control and visibility.

Pros:

• Excellent tools for converting business processes into automated workflows
• Robust support for connecting with existing business systems

Cons:

• It may be limiting for non-workflow automation scenarios
• It can require significant effort to integrate with legacy systems

Best for: Enterprise workflow automation projects

12. React Agent

React Agent specializes in integrating AI capabilities into web applications, particularly those built with React. The framework provides tools for creating AI-powered user interfaces and interactive web experiences. It emphasizes seamless integration of AI capabilities into frontend applications while maintaining performance and user experience.

React Agent's architecture focuses on component-based AI integration, making it natural for React developers to adopt.

Pros:

• A natural fit for React-based applications
• Designed for efficient frontend AI operations
• Familiar programming model for React developers

Cons:

• Primarily focused on frontend applications
• Requires React as the underlying framework

Best for: Web application teams building AI-powered interfaces

Why do enterprises need specialized frameworks for developing AI agents?

While open-source AI agent frameworks offer great flexibility, enterprises often face unique challenges that require specialized solutions:

1. Managing enterprise-level operations at scale

Scaling an enterprise presents unique challenges that many open-source frameworks struggle to handle effectively. It involves:

• Systems must handle thousands of simultaneous requests without degradation in performance. This often means managing 100+ requests per second per instance
• Large organizations typically need to run hundreds of agent instances simultaneously. Each instance must maintain its context while sharing relevant information across the system
• Enterprises generate massive amounts of data - often terabytes daily. For example, a typical enterprise IT support system handles 20,000+ tickets monthly, each requiring context from historical data, user information, and system states
• Need for intelligent load balancing and resource allocation across different geographic regions and data centers

2. Meeting enterprise compliance and protection standards

Regulatory frameworks and industry standards heavily influence enterprise security requirements. The IBM Cost of a Data Breach Report 2024 shows that security breaches can cost organizations an average of $4.88 million.

Enterprise-grade frameworks must provide the following:

• Authentic systems that support multiple authentic methods like SSO, MFA, OAuth
• Role-based access control (RBAC) with granular permissions
• Adherence to regulations like GDPR, CCPA, HIPAA
• Data encryption both at rest and in transit
• Regular security audits and certifications (SOC 2, ISO 27001)

3. Connecting with enterprise applications

Enterprise integration requirements are complex and multifaceted. According to the Okta Businesses at Work report, companies use 93 apps. On average, large companies use 231 apps per company, a solid 11% increase from last year.

This creates significant integration challenges, including:

• Integration with legacy systems (some 20+ years old)
• Support for multiple databases, data formats, and protocols (REST, SOAP, GraphQL)
• Workflow complexities like multi-step approval processes, SLA management across different process stages, exception handling, and escalation paths
• Training and documentation requirements
• Audit trails for cross-department interactions

While powerful for prototyping and small-scale deployments, generic frameworks often lack the robust security controls and audit capabilities required in enterprise environments.

This reality led Atomicwork to develop its proprietary enterprise framework, drawing insights from real-world deployment scenarios. Our framework takes a unique approach by implementing a central service agent orchestrating specialized department-specific agents.

For instance, when an employee submits a request, the service agent intelligently delegates to the appropriate specialized agent - IT, HR, or Finance - while maintaining consistent security protocols and audit trails.

Atomicwork’s agentic framework distinguishes itself through granular control over agent interactions. Unlike open-source alternatives that allow unrestricted agent communication, it enables precise control over agents' interactions, ensuring data privacy and maintaining clear organizational boundaries.

The framework's architecture reflects how enterprises work - with defined processes, clear handoffs, and comprehensive audit trails. This isn't just about scaling AI capabilities; it's about integrating AI agents into existing enterprise workflows while maintaining security, compliance, and operational efficiency.

Whether you go open-source or opt for a specialized solution like Atomicwork's framework, the key is finding one that hits all the right notes for your organization's unique composition.