List of the Top 25 AI Agent Frameworks for Enterprise in 2026

Lyzr Agent Studio offers a low-code/no-code environment that empowers organizations to design, implement, and expand AI agents with minimal technical skills. This innovative platform is founded on Lyzr’s unique Agent Framework, which is distinguished as the first and only agent framework that integrates safe and dependable AI directly into its core structure. By utilizing this platform, both technical and non-technical individuals can create AI-driven solutions that enhance automation, boost operational effectiveness, and elevate customer interactions without needing deep programming knowledge. Additionally, Lyzr Agent Studio facilitates the development of sophisticated, industry-specific applications across fields such as Banking, Financial Services, and Insurance (BFSI), and enables the deployment of AI agents tailored for Sales, Marketing, Human Resources, or Finance. This flexibility makes it an invaluable tool for businesses looking to innovate and streamline their processes.

Rasa stands out as a pioneer in the field of generative conversational AI, providing businesses with the tools to enhance their customer service operations while cutting expenses through advanced AI assistant creation and management on a large scale. By integrating both pro-code and no-code solutions, our platform fosters collaboration among teams, leading to the development of more intelligent and efficient AI assistants, ultimately speeding up the realization of value. This innovative approach not only streamlines the building process but also ensures that companies can adapt quickly to changing customer needs.

LangChain is a versatile framework that simplifies the process of building, deploying, and managing LLM-based applications, offering developers a suite of powerful tools for creating reasoning-driven systems. The platform includes LangGraph for creating sophisticated agent-driven workflows and LangSmith for ensuring real-time visibility and optimization of AI agents. With LangChain, developers can integrate their own data and APIs into their applications, making them more dynamic and context-aware. It also provides fault-tolerant scalability for enterprise-level applications, ensuring that systems remain responsive under heavy traffic. LangChain’s modular nature allows it to be used in a variety of scenarios, from prototyping new ideas to scaling production-ready LLM applications, making it a valuable tool for businesses across industries.

LangGraph empowers users to achieve greater accuracy and control by facilitating the development of agents that can adeptly handle complex tasks. It serves as a robust platform for building and scaling applications driven by these intelligent agents. The platform’s versatile structure supports a range of control strategies, such as single-agent, multi-agent, hierarchical, and sequential flows, effectively meeting the demands of complicated real-world scenarios. To ensure dependability, simple integration of moderation and quality loops allows agents to stay aligned with their goals. Moreover, LangGraph provides the tools to create customizable templates for cognitive architecture, enabling straightforward configuration of tools, prompts, and models through LangGraph Platform Assistants. With a built-in stateful design, LangGraph agents collaborate with humans by preparing work for review and waiting for consent before proceeding with actions. Users have the capability to oversee the decision-making processes of the agents, while the "time-travel" function offers the ability to revert and modify prior actions for enhanced accuracy. This adaptability not only ensures effective task execution but also allows agents to respond to evolving needs and constructive feedback, fostering continuous improvement in their performance. As a result, LangGraph stands out as a powerful ally in navigating the complexities of task management and optimization.

Solace specializes in Event-Driven Architecture (EDA) and boasts two decades of expertise in delivering highly dependable, robust, and scalable data transfer solutions that utilize the publish & subscribe (pub/sub) model. Their technology facilitates the instantaneous data exchange that underpins many daily conveniences, such as prompt loyalty rewards from credit cards, weather updates on mobile devices, real-time tracking of aircraft on the ground and in flight, as well as timely inventory notifications for popular retail stores and grocery chains. Additionally, the technology developed by Solace is instrumental for numerous leading stock exchanges and betting platforms worldwide. Beyond their reliable technology, exceptional customer service is a significant factor that attracts clients to Solace and fosters long-lasting relationships. The combination of innovative solutions and dedicated support ensures that customers not only choose Solace but also continue to rely on their services over time.

The Multi-Agent Framework enables the conversion of a brief requirement into a detailed array of outputs, which includes PRD, design specifications, tasks, and repository information. By designating different roles to individual GPTs, a cohesive software entity is formed that can adeptly handle complex projects. MetaGPT takes a single-line requirement and produces user stories, competitive analyses, requirements, data structures, APIs, and documentation. Its design incorporates roles such as product managers, architects, project managers, and engineers, which support the entire workflow of a software organization through well-structured Standard Operating Procedures (SOPs). This cohesive methodology not only improves collaboration but also optimizes the development process, ensuring that every facet of software production is addressed effectively. Ultimately, such a streamlined approach empowers teams to respond rapidly to changes and enhances overall project success.

Riff is a next-generation AI agent platform designed to automate complex, high-impact business workflows across enterprise environments. It enables organizations to build intelligent agents that can read, write, and act within real systems such as ERP, CRM, and data platforms. These agents handle critical processes like order management, procurement, finance operations, and exception resolution. Riff integrates with major enterprise tools including SAP, Oracle, Salesforce, ServiceNow, Microsoft 365, and cloud data platforms. The platform is designed to deliver production-ready solutions in weeks, not months, accelerating time-to-value. It follows a unique model where business teams define outcomes, IT ensures governance, and domain experts build and scale solutions. Riff generates full-stack AI workflows, including agents, APIs, and tools, using standard technologies for flexibility and control. It supports secure, audit-ready deployments with enterprise-grade compliance such as SOC 2, ISO 27001, and GDPR readiness. The platform includes governance features like access controls, audit logs, and lifecycle management. It allows organizations to continuously improve and scale AI capabilities by enabling internal teams to build future agents independently. Riff helps reduce manual workload, improve accuracy, and accelerate decision-making across operations. Overall, it empowers enterprises to transform core business processes through scalable, secure, and production-ready AI automation.

AutoGen is an open-source programming framework specifically crafted for agent-based artificial intelligence. This framework offers a high-level abstraction for facilitating multi-agent dialogues, enabling users to effortlessly design workflows that incorporate large language models (LLMs). AutoGen includes a wide variety of functional systems that address multiple applications across different sectors and complexities. Furthermore, it enhances LLM inference APIs to improve performance while reducing costs, proving to be an indispensable resource for developers. With its user-friendly features, individuals can now expedite the creation of sophisticated intelligent agent systems like never before, making development processes more efficient and accessible. As a result, AutoGen not only simplifies the technical aspects of AI development but also encourages innovation in the field.

Semantic Kernel serves as a versatile open-source toolkit that streamlines the development of AI agents and allows for the incorporation of advanced AI models into applications developed in C#, Python, or Java. This middleware not only speeds up the deployment of comprehensive enterprise solutions but also attracts major corporations, including Microsoft and various Fortune 500 companies, thanks to its flexibility, modular design, and enhanced observability features. Developers benefit from built-in security measures like telemetry support, hooks, and filters, enabling them to deliver responsible AI solutions at scale confidently. The toolkit's compatibility with versions 1.0 and above across C#, Python, and Java underscores its reliability and commitment to avoiding breaking changes. Furthermore, existing chat-based APIs can be easily upgraded to support additional modalities, such as voice and video, enhancing its overall adaptability. Semantic Kernel is designed with a forward-looking approach, ensuring it can seamlessly integrate with new AI models as technology progresses, thus preserving its significance in the fast-evolving realm of artificial intelligence. This innovative framework empowers developers to explore new ideas and create without the concern of their tools becoming outdated, fostering an environment of continuous growth and advancement.

Letta empowers you to create, deploy, and manage agents on a substantial scale, facilitating the development of production applications that leverage agent microservices through REST APIs. By embedding memory functionalities into your LLM services, Letta significantly boosts their advanced reasoning capabilities and offers transparent long-term memory via the cutting-edge technology developed by MemGPT. We firmly believe that the core of programming agents is centered around the programming of memory itself. This innovative platform, crafted by the creators of MemGPT, features self-managed memory specifically tailored for LLMs. Within Letta's Agent Development Environment (ADE), you have the ability to unveil the comprehensive sequence of tool calls, reasoning procedures, and decisions that shape the outputs produced by your agents. Unlike many tools limited to prototyping, Letta is meticulously designed by systems experts for extensive production, ensuring that your agents can evolve and enhance their efficiency over time. The system allows you to interrogate, debug, and refine your agents' outputs, steering clear of the opaque, black box solutions often provided by major closed AI corporations, thus granting you total control over the development journey. With Letta, you are set to embark on a transformative phase in agent management, where transparency seamlessly integrates with scalability. This advancement not only enhances your ability to optimize agents but also fosters innovation in application development.

Phidata is an open-source platform dedicated to the development, deployment, and management of AI agents. It empowers users to design tailored agents that possess memory, knowledge, and the capability to access external tools, thereby enhancing the performance of AI across a wide range of applications. The platform supports a variety of large language models and seamlessly integrates with multiple databases, vector storage systems, and APIs. To accelerate the development process, Phidata provides users with pre-built templates that allow for a smooth transition from creating agents to preparing them for production. Additionally, it includes features such as real-time monitoring, evaluations of agent performance, and tools for optimization, ensuring that AI implementations are reliable and scalable. Developers have the flexibility to integrate their own cloud infrastructure, enabling personalized configurations to meet specific needs. Furthermore, Phidata places a strong emphasis on solid enterprise support, offering security protocols, agent guardrails, and automated DevOps workflows to streamline the deployment process. This all-encompassing strategy guarantees that teams can fully leverage AI technology while effectively managing their individual requirements and maintaining oversight of their systems. In doing so, Phidata not only enhances the user experience but also fosters innovation in AI applications.

PydanticAI is a cutting-edge framework designed in Python, aiming to streamline the development of top-notch applications that harness the power of generative AI technologies. Created by the developers behind Pydantic, this framework easily integrates with major AI models like OpenAI, Anthropic, and Gemini. It employs a type-safe structure that allows for real-time debugging and performance monitoring through the Pydantic Logfire system. By leveraging Pydantic for output validation, PydanticAI ensures that responses from models are both structured and consistent. Furthermore, the framework includes a dependency injection system that supports an iterative approach to development and testing, while also facilitating the streaming of LLM outputs for rapid validation. Ideal for projects centered around AI, PydanticAI encourages a flexible and efficient assembly of agents, all while following best practices in Python development. Ultimately, PydanticAI aspires to deliver a seamless experience akin to FastAPI in the context of generative AI application creation, thus improving the overall workflow for developers significantly. With its robust features and user-friendly design, PydanticAI is set to become an essential tool for those looking to excel in the AI development landscape.

Genspark is a cutting-edge AI platform that simplifies the generation of content and the automation of tasks, offering powerful features like video and image creation, and deep research. The Genspark Super Agent plays a pivotal role, assisting users with a wide array of tasks such as selecting gifts, booking travel, making restaurant reservations, and generating comprehensive reports. With its user-friendly interface, Genspark allows you to automate and streamline workflows, creating high-quality, insightful content in a fraction of the time.

Mastra is a developer-friendly TypeScript framework designed to create advanced AI agents that can perform tasks, manage knowledge bases, and persist memory within workflows. By utilizing TypeScript, Mastra offers a robust solution for building scalable AI agents with full control over task execution, user interactions, and data storage. Developers can create intelligent agents that remember past interactions and make informed decisions based on real-time data, making Mastra a perfect tool for building everything from AI assistants to sophisticated automation systems. Its easy setup, scalability, and powerful integration features ensure efficient development cycles for AI-powered solutions.

The Agent Development Kit (ADK) is a modular, open-source framework that empowers developers to create, test, and deploy AI agents using Google’s cutting-edge technologies. Built for seamless integration with Gemini models, ADK supports the creation of simple, task-oriented agents or complex multi-agent systems capable of sophisticated collaboration and coordination. The platform offers advanced features like dynamic routing, pre-built tools for common tasks, and an ecosystem that supports third-party libraries. With flexible deployment options such as Gemini Enterprise Agent Platform, Cloud Run, or local environments, ADK is a robust solution for building scalable, production-ready AI systems.

Agno is an innovative framework tailored for the development of agents that possess memory, knowledge, tools, and reasoning abilities. It enables developers to create a wide array of agents, including those that reason, operate multimodally, collaborate in teams, and execute complex workflows. With an appealing user interface, Agno not only facilitates seamless interaction with agents but also includes features for monitoring and assessing their performance. Its model-agnostic nature guarantees a uniform interface across over 23 model providers, effectively averting the challenges associated with vendor lock-in. Agents can be instantiated in approximately 2 microseconds on average, which is around 10,000 times faster than LangGraph, while utilizing merely 3.75KiB of memory—50 times less than LangGraph. The framework emphasizes reasoning, allowing agents to engage in "thinking" and "analysis" through various reasoning models, ReasoningTools, or a customized CoT+Tool-use strategy. In addition, Agno's native multimodality enables agents to process a range of inputs and outputs, including text, images, audio, and video. The architecture of Agno supports three distinct operational modes: route, collaborate, and coordinate, which significantly enhances agent interaction flexibility and effectiveness. Overall, by integrating these advanced features, Agno establishes a powerful platform for crafting intelligent agents capable of adapting to a multitude of tasks and environments, promoting innovation in agent-based applications.

Swarm represents a cutting-edge educational framework developed by OpenAI, focusing on the exploration of lightweight, ergonomic multi-agent systems. Its architecture emphasizes both scalability and customization, making it particularly suitable for scenarios where multiple independent tasks and instructions are challenging to manage through a single prompt. Operating exclusively on the client side, Swarm functions with a stateless design similar to the Chat Completions API it utilizes, facilitating the creation of scalable and user-friendly solutions without requiring extensive training. While they share a similar name for simplicity, Swarm agents operate independently and are not connected to the assistants found in the assistants API. The framework includes a variety of examples that illustrate key concepts such as setup, function execution, handoffs, and context variables, along with more complex applications like a multi-agent setup tailored to handle a wide range of customer service inquiries in the airline sector. This adaptability empowers users to effectively leverage the capabilities of multi-agent interactions across different environments and use cases. Ultimately, Swarm enhances the approach to managing complex tasks by allowing for a more distributed and efficient method of operation in diverse applications.

The OpenAI Agents SDK empowers developers to build agent-based AI applications in an efficient and intuitive way, reducing unnecessary complications. This SDK is an advanced iteration of our previous project, Swarm, aimed at agent experimentation. It includes a streamlined collection of essential components: agents, which are sophisticated language models equipped with specific directives and tools; handoffs, which support the distribution of tasks among agents; and guardrails, which ensure that inputs from agents are accurately validated. By utilizing Python in conjunction with these components, developers can create complex interactions between tools and agents, enabling the creation of effective applications without facing a steep learning curve. Additionally, the SDK features built-in tracing capabilities that allow users to visualize, debug, and evaluate their agent workflows, as well as to fine-tune models to meet their unique requirements. This comprehensive array of functionalities positions the Agents SDK as an indispensable tool for developers looking to effectively tap into the potential of AI. Ultimately, it fosters a more accessible environment for innovation in AI development.

The Transformers library is an adaptable tool that provides pretrained models for a variety of tasks, including natural language processing, computer vision, audio processing, and multimodal applications, allowing users to perform both inference and training seamlessly. By utilizing the Transformers library, you can train models that are customized to fit your specific datasets, develop applications for inference, and harness the power of large language models for generating text content. To begin exploring suitable models and harnessing the capabilities of Transformers for your projects, visit the Hugging Face Hub without delay. This library features an efficient inference class that is applicable to numerous machine learning challenges, such as text generation, image segmentation, automatic speech recognition, and question answering from documents. Moreover, it comes equipped with a powerful trainer that supports advanced functionalities like mixed precision, torch.compile, and FlashAttention, making it well-suited for both standard and distributed training of PyTorch models. The library guarantees swift text generation via large language models and vision-language models, with each model built on three essential components: configuration, model, and preprocessor, which facilitate quick deployment for either inference or training purposes. In addition, Transformers is designed to provide users with an intuitive interface that simplifies the process of developing advanced machine learning applications, ensuring that even those new to the field can leverage its full potential. Overall, Transformers equips users with the necessary tools to effortlessly create and implement sophisticated machine learning solutions that can address a wide range of challenges.

Cua is a computer-use agent platform purpose-built for AI systems that need to operate real software environments end to end. It enables agents to control full operating systems in secure cloud sandboxes, executing tasks through visual understanding and precise UI actions. Cua supports parallel agent execution, multi-turn workflows, and cross-platform environments including macOS, Windows, and Linux. The platform includes tools for generating UI datasets, recording agent trajectories, and running standardized benchmarks. Developers can deploy agents in minutes using a simple CLI or SDK without managing infrastructure. Cua integrates with leading vision-language models and automatically routes requests for optimal performance. It is designed to help teams ship, scale, and continuously improve computer-use agents.

Notte is a sophisticated framework designed for the development, deployment, and scaling of customized full-stack web AI agents through a unified API. It transforms the digital landscape into a user-friendly environment for agents, allowing websites to be navigated as coherent maps articulated in natural language. Users benefit from on-demand headless browser instances that come with standard and customizable proxy settings, as well as features like CDP, cookie integration, and session replay capabilities. This platform enables autonomous agents, powered by large language models (LLMs), to perform complex tasks across the internet with ease. For scenarios requiring enhanced precision, Notte offers a comprehensive web browser interface specifically designed for LLM agents. In addition, it includes a secure vault and a credential management system that guarantees the safe sharing of authentication details with AI agents. Notte also features an advanced perception layer that improves the infrastructure for agents by simplifying the conversion of websites into structured, easily digestible maps for LLM analysis. This capability not only boosts operational efficiency but also expands the range of tasks that agents can handle effectively. As a result, Notte stands at the forefront of web AI innovation, providing tools that empower developers to create highly capable and versatile AI agents.

OpenAdapt offers a complimentary desktop automation tool designed to enhance your efficiency by learning from your interactions with your desktop and online activities. It monitors your screen, keyboard, mouse actions, and even audio from your microphone if you choose, with all data securely kept on your device. This software processes the gathered information through advanced algorithms to generate tailored instructions and prompts for AI language models. Importantly, before any data leaves your device, it undergoes a thorough cleansing process to eliminate any Personally Identifiable Information (PII) and Protected Health Information (PHI), allowing you to review the sanitized data to confirm that it contains no sensitive information. We emphasize your privacy by ensuring that no personal data, files, or recordings of your activities are stored or collected by us. Additionally, OpenAdapt incorporates strong security measures within its framework to safeguard API keys and payment information, giving users confidence while utilizing the software. This dedication to maintaining security and privacy allows you to automate your tasks effectively, all while protecting your personal data from potential risks. With OpenAdapt, you can streamline your workflow seamlessly, knowing that your information remains secure and confidential.

AgentSea is a groundbreaking open-source platform that simplifies the creation, deployment, and sharing of AI agents. It offers a comprehensive array of libraries and tools for building AI applications while following the UNIX principle of specialization. These tools can operate on their own or be integrated into a larger agent application, ensuring they work seamlessly with well-known frameworks like LlamaIndex and LangChain. Some of its standout features include SurfKit, which serves as a Kubernetes-style orchestrator for agents; DeviceBay, a system designed for the integration of pluggable devices such as file systems and desktops; ToolFuse, which allows users to encapsulate scripts, third-party applications, and APIs as Tool implementations; AgentD, a daemon that enables bots to access a Linux desktop environment; and AgentDesk, which supports virtual machines powered by AgentD. In addition, Taskara helps with task management, while ThreadMem is built to create persistent threads that can handle multiple roles effectively. MLLM simplifies interactions with various LLMs and multimodal LLMs. Moreover, AgentSea includes experimental agents like SurfPizza and SurfSlicer, which effectively leverage multimodal strategies to interact with graphical user interfaces. This platform not only enhances the development experience but also expands the potential applications of AI agents across diverse fields, paving the way for innovative solutions and advancements in technology.

Agent Squad is a dynamic and powerful open-source framework developed by AWS that aids in the management of multiple AI agents and the navigation of complex conversations. This innovative framework supports the orchestration of multiple agents, allowing for effective collaboration and the use of various AI entities within a single cohesive system. It is built to function in both Python and TypeScript, demonstrating its dual language capability. By employing intelligent intent classification, it skillfully routes inquiries to the most suitable agent, taking into account the context and content of each query. Furthermore, Agent Squad accommodates a variety of output types, supporting both streaming and non-streaming responses from different agents, thus offering flexible interaction options. It also ensures that conversation context is maintained and utilized across various agents, which helps to keep interactions coherent and relevant. The framework's architecture is designed to be highly extensible, making it easy to integrate new agents or modify existing ones to address specific needs. Additionally, Agent Squad's deployment versatility allows it to function smoothly on a range of platforms, from AWS Lambda to local setups or any cloud-based service, making it a versatile option for numerous applications. This design not only fosters enhanced collaboration among agents but also significantly improves the overall user experience through more effective dialogue management, ultimately leading to more meaningful interactions.

Strands Agents SDK is a powerful open-source framework built to help developers design, control, and deploy AI agents with greater flexibility and reliability. Supporting both Python and TypeScript, it enables developers to build agents using familiar programming paradigms without relying on complex orchestration systems. The SDK allows tools to be defined as simple functions, which the AI model can call dynamically during execution. This approach removes the need for rigid pipelines and gives developers more control over how agents behave. It is compatible with any AI model or cloud provider, making it highly adaptable for different environments and enterprise needs. A key feature of Strands is its steering system, which allows developers to intercept and guide agent actions before and after execution. This improves accuracy, safety, and compliance by ensuring that agents follow defined rules. The SDK also supports multi-agent architectures, enabling collaboration between agents to solve complex tasks. Built-in memory management helps maintain context across extended conversations, reducing the need for manual token handling. Observability tools provide insights into agent performance, including tool usage, model calls, and execution flow. Additionally, the evaluation SDK allows developers to test and refine agent behavior before deploying to production. Overall, Strands Agents SDK delivers a modern, developer-friendly approach to building scalable, intelligent, and controllable AI agents.