List of the Top 13 Agentic AI Platforms for Apify in 2026

Enhance the efficiency of your projects with a customized approach through Airtable. This adaptable collaboration platform combines the strengths of a relational database with the simplicity of a spreadsheet interface. It works seamlessly on multiple devices and platforms, providing real-time data updates to keep everyone on the same page. Users can choose from various visualization options like grid, calendar, Kanban, gallery, and forms, simplifying task management and improving workflow organization. Furthermore, Airtable’s flexible customization features empower users to create specific solutions that cater to a wide array of project requirements, ultimately boosting productivity and collaboration. With its intuitive design, Airtable makes project management not just effective but also enjoyable.

Zapier is a powerful AI and automation platform built to help teams orchestrate workflows across their entire tech stack. It connects more than 8,000 apps, enabling seamless automation between tools, data, and AI systems. Zapier allows users to create advanced multi-step workflows enhanced with AI decision-making. Teams can build custom AI agents that handle tasks like lead qualification, IT support, and customer responses. AI chatbots and AI-assisted workflow builders make automation accessible to non-technical users. Zapier centralizes data using tables and forms to power intelligent automations. The platform provides enterprise-grade security with SOC 2 and SOC 3 compliance. Full observability ensures teams can monitor, audit, and optimize every automation. Zapier supports role-based access controls and single sign-on for enterprise governance. It scales from personal productivity to company-wide automation programs. Zapier helps organizations move faster without sacrificing control or security. It enables real-world AI adoption that delivers measurable ROI.

Craft intricate automations at incredible speed, removing the burden of managing APIs. The long hours spent untangling scripts are now a thing of the past. By harnessing JavaScript, you gain improved flexibility while the user-friendly interface takes care of the rest. n8n allows you to create versatile workflows that focus on thorough data integration. Furthermore, with readily available templates and an easy-to-use interface, even those with less technical knowledge can engage and collaborate effectively within the team. Unlike many other platforms, complexity will not stifle your creativity; you can bring to life any concept your mind envisions—without the stress of financial constraints. Effortlessly link APIs using no-code methods for straightforward task automation, or explore vanilla JavaScript for more complex data handling. You have the capability to establish various triggers, diverge, combine workflows, and even pause actions to wait for external events. Engage with any API or service through tailored HTTP requests, while also protecting your live workflows by keeping distinct development and production environments with separate authentication keys. Embrace the limitless possibilities for innovation, and enjoy the satisfaction of realizing your unique ideas without barriers. The platform encourages exploration and experimentation, paving the way for groundbreaking solutions.

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.

Lindy is an AI-driven assistant crafted to enhance efficiency in professional environments. It integrates effortlessly with a variety of platforms such as Gmail, Zoom, and Twilio. Users can automate various workflows, including email handling, customer service interactions, scheduling, and data entry for customer relationship management. With its user-friendly interface, individuals can develop AI agents in just a few minutes, simplifying intricate tasks without needing any programming knowledge. Additionally, Lindy provides customizable templates tailored to distinct requirements. A notable feature is its ability to transform real-time communications between healthcare providers and patients into precise and detailed notes. By utilizing Lindy, professionals can prioritize critical elements of their roles while allowing routine tasks to be managed effectively and effortlessly. This not only boosts productivity but also enhances overall job satisfaction.

Flowise is an adaptable open-source platform that streamlines the process of developing customized Large Language Model (LLM) applications through an easy-to-use drag-and-drop interface, tailored for low-code development. It supports connections to various LLMs like LangChain and LlamaIndex, along with offering over 100 integrations to aid in the creation of AI agents and orchestration workflows. Furthermore, Flowise provides a range of APIs, SDKs, and embedded widgets that facilitate seamless integration into existing systems, guaranteeing compatibility across different platforms. This includes the capability to deploy applications in isolated environments utilizing local LLMs and vector databases. Consequently, developers can efficiently build and manage advanced AI solutions while facing minimal technical obstacles, making it an appealing choice for both beginners and experienced programmers.

The Model Context Protocol (MCP) serves as a versatile and open-source framework designed to enhance the interaction between artificial intelligence models and various external data sources. By facilitating the creation of intricate workflows, it allows developers to connect large language models (LLMs) with databases, files, and web services, thereby providing a standardized methodology for AI application development. With its client-server architecture, MCP guarantees smooth integration, and its continually expanding array of integrations simplifies the process of linking to different LLM providers. This protocol is particularly advantageous for developers aiming to construct scalable AI agents while prioritizing robust data security measures. Additionally, MCP's flexibility caters to a wide range of use cases across different industries, making it a valuable tool in the evolving landscape of AI technologies.

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 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.

Amazon Bedrock serves as a robust platform that simplifies the process of creating and scaling generative AI applications by providing access to a wide array of advanced foundation models (FMs) from leading AI firms like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. Through a streamlined API, developers can delve into these models, tailor them using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and construct agents capable of interacting with various corporate systems and data repositories. As a serverless option, Amazon Bedrock alleviates the burdens associated with managing infrastructure, allowing for the seamless integration of generative AI features into applications while emphasizing security, privacy, and ethical AI standards. This platform not only accelerates innovation for developers but also significantly enhances the functionality of their applications, contributing to a more vibrant and evolving technology landscape. Moreover, the flexible nature of Bedrock encourages collaboration and experimentation, allowing teams to push the boundaries of what generative AI can achieve.

LlamaIndex functions as a dynamic "data framework" aimed at facilitating the creation of applications that utilize large language models (LLMs). This platform allows for the seamless integration of semi-structured data from a variety of APIs such as Slack, Salesforce, and Notion. Its user-friendly yet flexible design empowers developers to connect personalized data sources to LLMs, thereby augmenting application functionality with vital data resources. By bridging the gap between diverse data formats—including APIs, PDFs, documents, and SQL databases—you can leverage these resources effectively within your LLM applications. Moreover, it allows for the storage and indexing of data for multiple applications, ensuring smooth integration with downstream vector storage and database solutions. LlamaIndex features a query interface that permits users to submit any data-related prompts, generating responses enriched with valuable insights. Additionally, it supports the connection of unstructured data sources like documents, raw text files, PDFs, videos, and images, and simplifies the inclusion of structured data from sources such as Excel or SQL. The framework further enhances data organization through indices and graphs, making it more user-friendly for LLM interactions. As a result, LlamaIndex significantly improves the user experience and broadens the range of possible applications, transforming how developers interact with data in the context of LLMs. This innovative framework fundamentally changes the landscape of data management for AI-driven applications.

CrewAI distinguishes itself as a leading multi-agent platform that assists enterprises in enhancing workflows across diverse industries by developing and executing automated processes utilizing any Large Language Model (LLM) and cloud technologies. It offers a rich suite of tools, including a robust framework and a user-friendly UI Studio, which facilitate the rapid development of multi-agent automations, catering to both seasoned developers and those who prefer to avoid coding. The platform presents flexible deployment options, allowing users to seamlessly transition their created 'crews'—made up of AI agents—into production settings, supported by sophisticated tools designed for various deployment needs and automatically generated user interfaces. Additionally, CrewAI encompasses thorough monitoring capabilities that enable users to evaluate the effectiveness and advancement of their AI agents in handling both simple and complex tasks. It also provides resources for testing and training, aimed at consistently enhancing the efficiency and quality of the outputs produced by these AI agents. By doing so, CrewAI not only streamlines processes but also enables organizations to fully leverage the transformative power of automation in their daily operations. This comprehensive approach positions CrewAI as a vital asset for any business looking to innovate and improve its operational efficiencies.

Langflow is a low-code platform designed for AI application development that empowers users to harness agentic capabilities alongside retrieval-augmented generation. Its user-friendly visual interface allows developers to construct complex AI workflows effortlessly through drag-and-drop components, facilitating a more efficient experimentation and prototyping process. Since it is based on Python and does not rely on any particular model, API, or database, Langflow offers seamless integration with a broad spectrum of tools and technology stacks. This flexibility enables the creation of sophisticated applications such as intelligent chatbots, document processing systems, and multi-agent frameworks. The platform provides dynamic input variables, fine-tuning capabilities, and the option to create custom components tailored to individual project requirements. Additionally, Langflow integrates smoothly with a variety of services, including Cohere, Bing, Anthropic, HuggingFace, OpenAI, and Pinecone, among others. Developers can choose to utilize pre-built components or develop their own code, enhancing the platform's adaptability for AI application development. Furthermore, Langflow includes a complimentary cloud service, allowing users to swiftly deploy and test their projects, which promotes innovation and rapid iteration in AI solution creation. Overall, Langflow emerges as an all-encompassing solution for anyone eager to effectively utilize AI technology in their projects. This comprehensive approach ensures that users can maximize their productivity while exploring the vast potential of AI applications.