12 AI Agent Frameworks for Enterprises in 2025

Building intelligent AI systems from scratch can be costly and time-consuming. AI agent frameworks offer a solution, providing enterprises with modular building blocks and standardized approaches that accelerate development and lower complexity.

Depending on their design, frameworks may specialize in structured tasks, such as invoice processing and automated customer service, or more intricate workflows, like diagnosing complex technical issues or facilitating dynamic decision-making.

In this article, we’ll examine some of the most popular AI agent frameworks, outline their unique strengths, and discuss key factors enterprises should consider when selecting the right framework for their needs.

What are AI agent frameworks?

AI agent frameworks are pre-built modules that can be used to build intelligent AI systems without starting from scratch. They support the development of agents, ranging from simple single-agent applications to complex multi-agent environments.

AI frameworks assume the role of operating systems in the AI technology ecosystem and provide developers with a development interface and an efficient execution platform. 

With frameworks in place, enterprises can access more advanced AI agent capabilities without starting development from scratch while also picking and choosing the elements to suit their needs. 

Factors to consider when choosing an AI agent framework

Each framework offers unique capabilities, complexities, advantages, and considerations, depending on the use case and proposed application. 

Complexity

The challenges an AI agent needs to meet will be unique, ranging from a simple conversational chatbot to collaborative systems leveraging inter-agent communication. The proper framework needs to be appropriate and adaptable. 

Data privacy and security

Many powerful AI agents need to be capable of complex reasoning and task execution, and this requires access to multiple external and internal data sources, such as APIs. However, 2024 saw a 76% spike in data theft and a 75% increase in cloud intrusions. Ensuring compliance with regulatory requirements is key not only for security but also for the buy-in of the agent once developed.

Ease of integration

When selecting an AI agent framework, consider how easily it integrates with your existing infrastructure. The framework should support connections to various data sources, external software tools, multiple language models, and internal enterprise systems. This capability reduces deployment complexity, saves development time, and ensures the AI agent works effectively within your organization’s current technology landscape.

Performance and scalability

In 1994, while at MIT, cognitive scientist Steven Pinker helped popularize Moravec’s paradox through his book “The Language Instinct.” Drawing insights from the preceding decades of AI development, he explained the counterintuitive finding that abilities natural to humans, such as recognizing faces or navigating the world, were computationally very challenging for AI. In contrast, tasks requiring high-level reasoning or complex calculation were often easier to automate.

This fundamental challenge, highlighting the heavy computational resources required for tasks involving perception and interaction, remains crucial when considering modern AI systems. 

Achieving the right level of performance needed for advanced enterprise challenges is, therefore, of core importance. Enterprises may require a framework suitable for working with complex interactive agents and multi-agent systems – precisely the kinds of systems tackling tasks on the ‘harder’ side of Moravec’s paradox – or a simpler framework where integration takes precedence, potentially focusing on tasks computationally ‘easier’ for AI.”

Open-source vs. proprietary

Open-source frameworks refer to software with publicly accessible source code that users can freely modify, use, and redistribute. These tools offer clear benefits such as greater transparency, the ability to customize code easily, widespread accessibility, and lower costs.

Despite these advantages, open-source solutions can pose specific challenges, particularly in enterprise contexts. For instance, they often have fewer built-in security measures and lack certain compliance certifications commonly found in proprietary software. Companies adopting open-source tools must take extra steps to manage security risks and compliance. Additionally, open-source AI models, similar to other AI technologies, may be affected by biases or inaccuracies stemming from their training datasets.

Proprietary frameworks, on the other hand, are privately owned software products that require licenses or purchase agreements. They usually undergo extensive testing, are backed by dedicated support, and often integrate more easily into existing enterprise systems. Such frameworks typically feature advanced security measures, audit support, and certifications suitable for use in regulated or sensitive industries.

However, proprietary solutions have trade-offs, including limited insight into their internal workings, possible reliance on a single vendor, and less control over data privacy. Organizations using these frameworks need to carefully consider how their data is handled and protected by the provider, assessing potential risks around data security and vendor dependency.

Top AI agent frameworks for enterprises in 2025 

As the technology behind AI agents evolves, so do the underlying AI agent frameworks. We’ve gathered a range of AI agent frameworks ranked based on enterprise applicability, security, and integration capabilities.

1. LangChain 

• Best for: Enterprise AI workflow automation.
• Strengths: Strong integration with LLMs for modular development.
• Weaknesses: Limited built-in debugging tools, complexity, or beginners.
• Applications: Chatbots, automated document processing, AI-driven decision-making.

LangChain is a versatile framework for developing and deploying AI applications powered by Large Language Models (LLMs). 

With a comprehensive suite of tools for text generation, translation, summarization, and question-answering, intelligent systems for content creation, automated translations, and knowledge retrieval can be built. It can also interact with other AI models to create sophisticated AI workflows, for example, for use in sales and marketing automation for lead scoring, email personalization, and campaign analysis. 

However, while LangChain excels in automating complex processes and aids users with prompt engineering and modular development, it’s often noted as complex for beginners, requiring a significant time investment to learn the nuances. 

2. LangGraph

• Best for: Enterprises needing automated human-in-the-loop AI.
• Strengths: Offers transparency at each step, enabling dynamic decision-making and complex multi-agent workflows.
• Weaknesses: Not ideal for simple, single-agent applications.
• Applications: Customer support automation, legal document processing, risk analysis.

LangGraph (compatible with LangChain and LangSmith) is a library that enterprises can use to create agent and multi-agent workflows. It can visually and structurally map out every step, decision, and interaction, making it ideal for building multi-step processes. 

LangGraph excels in automated decision-making and human-in-the-loop (a collaborative approach that combines human intelligence with machine learning). 

With customer support agents that pause for human approval or for enterprises who need to ensure manual intervention at critical points, it’s a great tool for use in customer support or risk analysis.

However, LanGraph can come with a development learning curve if you aren’t familiar with graph-based logic and multi-agent coordination. 

3. CrewAI

• Best for: Enterprises wanting structured AI teams working cohesively.
• Strengths: Supports sequential or hierarchical execution of multiple agents.
• Weaknesses: Agents follow set tasks rigidly, lacking adaptability to unexpected challenges.
• Applications: Market analysis, research automation.

CrewAI structures AI agents as a team of specialized workers, each assigned a role, goal, and task. This makes a collaborative workflow ideal for building multi-agent systems and role-based AI agents. 

For defined goals and tools and teams familiar with task orchestration and AI agent workflows, it’s a great framework that can integrate with a variety of external tools to gather data, analyze trends, and generate reports or be used to manage projects.

It can be used for tasks like content creation, with research and writing articles done by specialized agents, or project management, breaking down complex projects into smaller tasks, assigning tasks to team members, and tracking progress, CrewAI excels in structured workflows but lacks adaptability for tasks requiring autonomous decision-making or real-time role reassignment.

4. Microsoft Semantic Kernel

• Best for: Large enterprises wanting AI agents embedded within Microsoft tools.
• Strengths: Seamless integration with Microsoft ecosystem tools and systems.
• Weaknesses: Some elements are experimental, leading to stability concerns and limited documentation.
• Applications: Chat completion agents, AI assistants.

Semantic Kernel is Microsoft’s open-source framework for integrating AI models (like LLMs) into applications. It offers process frameworks to organize AI agents into steps that define their tasks, making it ideal for IT support AI-powered applications in task automation, such as email sorting, meeting summaries, and workflow management.

The benefits of using the Semantic Kernel include the ability to incorporate gradual AI adoption in legacy systems without completely overhauling the code base. 

An organization that uses Microsoft systems extensively will likely benefit from the ability to integrate tools such as Dynamics 365 and Outlook. However, despite the framework’s open-source nature, organizations using non-Microsoft tools may face additional integration challenges. 

5. AutoGen

• Best for: Enterprises looking to build multi-agent AI. 
• Strengths: Multi-agent system development for use in real-time environments. 
• Weaknesses: Struggles with high-load, enterprise-scale AI applications that require advanced reasoning and real-time adaptability.
• Applications: Conversational and task-completing AI applications, data analysis and visualization, advanced conversational AI systems.

Autogen, made by Microsoft,  is an open-source, low-code framework used to create multi-agent AI applications where specialized agents work together as a dynamic agent collaboration, meaning they communicate, delegate tasks, and adapt to workflows as one. 

It’s ideal for tasks requiring multiple AI agents, such as customer support, data analysis, or IT support. 

While optimized for Microsoft tools, it’s open source, The simple UI and ability to use the ‘low-code’ studioGen appeals to developers looking for a standardized, modular framework for creating intelligent agents. It’s modular and extensible, allowing users to customize systems with pluggable components. However, it offers less flexibility in designing custom logic, agent autonomy, and intricate workflow than other frameworks. 

6. LlamaIndex

• Best for: Enterprises wanting simplified systems for parsing documents, searching indexes and knowledge retrieval.
• Strengths: Converts structured and unstructured data into LLM-powered workflows.
• Weaknesses: Struggles with real-time data updates, requiring frequent index refreshes.
• Applications: Corporate knowledge libraries, HR and employee assistance portals, turning unstructured data into an LLM-powered workflow. 

LlamaIndex is an open-source framework that allows users to connect various data sources like PDFs, databases and APIs to large language models (LLMs). 

It’s ideal for situations where an AI agent needs to understand natural language requests and be able to draw information from a library or index, like a knowledge base, such as chatbots, knowledge assistants, or question-answering systems. 

However, the setup can be slow, with developers needing to undertake data organization tasks, such as adding filters and analyzing any logs to check agent behavior before the system is truly optimized. 

7. Smolagents

• Best for: Enterprises needing simple tools with easy deployment.
• Strengths: Pre-trained model hub with thousands of ready-to-use models for NLP tasks.
• Weaknesses: Costs vary significantly based on computational requirements and model complexity.
• Applications: Basic text classification, advanced generative AI applications.

Smolagents offers pre-trained templates to help deploy robust agents based on Large Language Models with just a few lines of code. 

Smolagents is an offshoot of Hugging Face and integrates seamlessly with many large language models and APIs, as well as the wider Hugging Face ecosystem. It allows developers to create agents for tasks like data retrieval, summarization, and code execution. This makes it ideal for creating intelligent research assistants and automated problem-solving tools for tasks like email sorting, summarization, and report generation. 

It’s particularly useful for creating intelligent research assistants, automated problem-solving tools, and retrieving data, but it may need significant fine-tuning to meet enterprise-level needs. 

8. Phidata 

• Best for: Enterprises looking for fast-to-deploy simple frameworks.
• Strengths: An agent can search its knowledge base for knowledge to achieve a task instead of inserting context into a prompt. 
• Weaknesses: Lacks advanced use cases such as multi-agent cohesion.
• Applications: Chat agents, image search, web crawls, data analysis.

Phidata is a framework that is multimodal (able to support text, images, audio, and video.) It’s designed for simplicity, with the ability to create a web search agent with just a few strips of code. 

Phidata was one of the first framework providers to offer Agentic RAG (where an Agent can search its knowledge base for knowledge to achieve a task instead of needing context in a prompt.) 

It is best suited to simpler tasks such as fast document searches, web crawls, or data analysis, such as creating chat agents, an image search tool, or an assistant for internal HR. 

While easy to use, it’s more suited to simpler tasks, such as generating reports, than some of the capabilities of larger multi-agent frameworks. 

9. OpenAI Swarm

• Best for: Enterprises exploring early-stage multi-agent AI development.
• Strengths: Lightweight and simple approach to agent-based workflows.
• Weaknesses: Lacks advanced features for complex workflows at this stage.
• Applications: Learning agent-based workflows, and prototyping simple multi-agent interactions.

OpenAI’s Swarm is a simple framework that focuses on basic handoffs (when an AI assistant transfers to a human), making it a good entry point for those looking to understand multi-agent orchestration. 

Unlike full-scale AI agent frameworks, Swarm is a good entry point for those looking to understand multi-agent orchestration without the overhead of heavier frameworks.

It can work very well for handoffs, such as when a customer interacts with a general support agent but then needs to be handed off to an agent with specialized assistance.

However, Swarm is purely experimental and not actively supported by OpenAI, meaning it lacks the stability and long-term reliability of other frameworks like LangGraph or CrewAI. Additionally, it appears to be highly limited in functionality, primarily serving as an educational tool rather than a scalable enterprise solution.

For those interested in exploring simple agent collaboration, Swarm provides a lightweight, minimal option. However, more robust frameworks would be necessary for production-ready applications or complex multi-agent environments.

10. Botpress

• Best for: Enterprises wanting quickly tunable and deployable conversational AI.
• Strengths: Pre-built templates with automated decision-making and real-time responses.
• Weaknesses: Rigid responses with limited contextual understanding.
• Applications: Customer service agents, support chatbots.

Botpress runs AI-generated or custom scripts that can connect with CRMs, databases, APIs, and ticketing systems and follow predefined conversational flows and automation logic. 

It relies on rigid, predefined paths, which can be effective for simple queries or highly structured workflows. This is especially true for enterprises looking to get started with AI-assisted customer support, such as automating customer service, answering FAQs, and offering live chat. 

While effective for simple queries or highly structured workflows, unanticipated questions or scenarios can be a challenge.

This framework may appeal to enterprises keen to start offering customer support, but scalability may be an issue. 

11. Langflow

• Best for: Visual workflow building and quick LLM-based application development.
• Strengths: A visual flow builder with drag-and-drop functionality. 
• Weaknesses: Sits between low-code and no-code, with too much complexity for true beginners.
• Applications: Intelligent chatbots, customer service support, document analysis systems.

Langflow is a visual flow builder with drag-and-drop functionality, allowing non-technical users to build Generative AI applications using LLMs. Langflow can use any API, model, or database, which means it’s flexible. It can also fetch answers with RAG (retrieval-augmented generation), 

Using Langflow, it would be possible to create agents for customer support tasks who can check ticket details in real-time and even utilize a voice agent to call customers. 

LangFlow is particularly useful for those who want to experiment with AI agents, test prompts, and automate tasks visually. It can even orchestrate multi-agent applications. However, it may still be too complex for complete beginners while lacking the deep flexibility required for advanced AI engineering.

For those looking to rapidly prototype AI solutions, LangFlow provides a structured, visual approach that helps streamline development while leveraging the power of LLMs and LangChain.

12. ChatDev

• Best for: Enterprises seeking conversational AI applications.
• Strengths: Handles complex dialogue flows while maintaining context.
• Weaknesses: Limited exclusively to conversational AI use cases.
• Applications: AI chatbots, support chatbots, virtual assistants.

ChatDev is a chat-powered framework that allows AI agents with specialized roles (like CTO, designer, and programmer) to collaboratively develop software applications through a chat-based interface. 

ChatDev ensures seamless collaboration through natural language communication. Using ChatDev, simple software can be built, and highly customizable conversational-based AI agents, such as support agents and chatbots, can be made from large language models (LLMs).

How AI agent frameworks are transforming enterprises

AI agent frameworks are revolutionizing enterprises by automating workflows, enhancing decision-making, and enabling seamless multi-agent collaboration for complex tasks.

Automating high-volume workflows

Gartner predicts that through 2026, 20% of organizations will use AI to automate management tasks, making it a critical investment for business survival. Handling large-scale enterprise operations with AI is now a reality for enterprises of all sizes.  By automating repetitive tasks, reducing errors, and enabling real-time decision-making, they can ensure efficient operations across industries.

For example, in HR, AI automates candidate screening and onboarding, allowing recruiters to focus on strategy. In warehouses, AI tracks shipments and manages inventory, improving fulfillment speed. In finance, it processes transactions and detects fraud with precision, while in customer service, AI-powered chatbots handle inquiries and escalate complex cases, enhancing efficiency.

Enhancing security and compliance

AI agents are transforming IT support, cybersecurity, and system monitoring by handling tasks that require speed, accuracy, and real-time responsiveness. AI-driven systems analyze network traffic, detect anomalies, and identify potential threats before they escalate. Using machine learning models, they adapt to new cyberattack patterns, enabling proactive security measures. It’s paying off too. Research has shown applying security AI and automation is paying off, lowering breach costs in some instances by an average of USD 2.2 million.

Improving customer and employee experiences

When AI is harnessed correctly in an organization, a better experience awaits employees and customers alike. AI agents are transforming customer service by automating responses, managing inquiries, and providing real-time support with contextual understanding, which can also be applied in HR and recruitment scenarios.

The outcomes of using AI agents have boosted productivity, reduced wait times, and improved overall satisfaction, whether that’s by delivering instant, accurate responses, personalized shopping recommendations, or supporting a job onboarding process. 

Choosing the right AI agent framework for your business

Selecting the proper AI agent framework is essential as this dictates how easy it is to develop and deploy your AI agent and its ability to optimize workflows or even integrate with existing systems. 

There are a few factors to consider. 

Complexity

Some frameworks excel in managing intricate, multi-step workflows (such as LangGraph for structured decision-making or CrewAI for team-based AI systems), while others are designed for simpler, task-based automation (such as Autogen for modular AI collaboration). Pick the level that actually suits your needs, ensuring you choose a framework that can handle your workflow without unnecessary complexity.

Scalability 

Scalability and integration are also important. You need to ensure a framework supports future growth, integrates with existing tools, and aligns with their infrastructure. Open Source allows more flexibility in the long term, but proprietary systems might make it easier to get started.  

Finally, is it a framework that’s proven, or just being developed? 

The frameworks with more tenure offer more documentation and a development community, which may help overall performance and fine-tuning.

Choosing the right AI agent framework

Some of the top AI agent frameworks for enterprises are LangChain, Semantic Kernel, CrewAI, and Autogen, which offer diverse capabilities. Some focus on modularity and simplicity, helping with Semantic search and information retrieval, while others offer self-reflective AI systems capable of improving their own performance and supporting the building of sophisticated multi-agent systems. 

However, the future is rapidly changing the outlook for AI agent frameworks.

As users expect more speed, human-like fluency, and competency from their interactions with AI Agents, frameworks must keep pace. This could see a move towards a self-learning framework comprising feedback collection, feedback interpretation, and learning mechanisms, moving away from modular systems that need supervised learning paradigms. 

Parallel computing processes (where multiple tasks simultaneously improve speed and efficiency rather than being completed one by one) are also increasing, and it’s likely that future AI frameworks will act as key schedulers for massively parallel computing, optimizing performance across hardware. 

Frequently Asked Questions (FAQs)