SerpKite Integrations

Zapier is an AI-powered automation platform designed to help teams safely scale workflows, agents, and AI-driven processes. It connects over 8,000 apps into a single ecosystem, allowing businesses to automate work across tools without writing code. Zapier enables teams to build AI workflows, custom AI agents, and chatbots that handle real tasks automatically. The platform brings AI, data, and automation together in one place for faster execution. Zapier supports enterprise-grade security, compliance, and observability for mission-critical workflows. With pre-built templates and AI-assisted setup, teams can start automating in minutes. Trusted by leading global companies, Zapier turns AI from hype into measurable business results.

Make is a visual platform for anyone to design, build, and automate anything—from tasks and workflows to apps and systems—without coding. SMBs, startups, scaleups, teams, and enterprises around the world use Make to scale their business faster than ever. Make enables people to connect and create workflows at the speed of their ideas. With Make, anyone can build like a developer, launching solutions across all industries and business areas at a fraction of the cost and time. Make allows teams to visualize, modify, and collaborate on processes that scale as quickly as their organization. Whether you’re integrating sales and marketing tools, automating a customer journey, improving business operations, or building a custom back-end system—creating on Make is powerful, intuitive, and playful. As our Maker community has shown us, when the experience of building sparks as much joy as the solution, there are no limits to what's possible.

Build complex automations 10x faster, without fighting APIs. Your days spent slogging through a spaghetti of scripts are over. Use JavaScript when you need flexibility and UI for everything else. n8n allows you to build flexible workflows focused on deep data integration. And with sharable templates and a user-friendly UI, the less technical people on your team can collaborate on them too. Unlike other tools, complexity is not a limitation. So you can build whatever you want — without stressing over budget. Connect APIs with no code to automate basic tasks. Or write vanilla Javascript when you need to manipulate complex data. You can implement multiple triggers. Branch and merge your workflows. And even pause flows to wait for external events. Interface easily with any API or service with custom HTTP requests. Avoid breaking live workflows by separating dev and prod environments with unique sets of auth data.

Flowise is an open-source platform that enables developers and teams to build AI agents and LLM-powered applications through a visual interface. The platform provides modular building blocks that allow users to create everything from simple chatbot workflows to complex multi-agent systems. With its drag-and-drop design environment, developers can rapidly prototype and deploy AI-powered applications without extensive coding. Flowise supports integrations with more than 100 large language models, embeddings, and vector databases. It also includes features such as human-in-the-loop workflows, observability tools, and execution tracing for monitoring agent behavior. Developers can extend applications through APIs, SDKs, and embedded chat interfaces using TypeScript or Python. By combining visual development tools with scalable infrastructure, Flowise simplifies the process of building and deploying production-ready AI agents.

Model Context Protocol (MCP) is an open protocol designed to standardize how applications provide context to large language models (LLMs). It acts as a universal connector, similar to a USB-C port, allowing LLMs to seamlessly integrate with various data sources and tools. MCP supports a client-server architecture, enabling programs (clients) to interact with lightweight servers that expose specific capabilities. With growing pre-built integrations and flexibility to switch between LLM vendors, MCP helps users build complex workflows and AI agents while ensuring secure data management within their infrastructure.

LangChain is a powerful, composable framework designed for building, running, and managing applications powered by large language models (LLMs). It offers an array of tools for creating context-aware, reasoning applications, allowing businesses to leverage their own data and APIs to enhance functionality. LangChain’s suite includes LangGraph for orchestrating agent-driven workflows, and LangSmith for agent observability and performance management. Whether you're building prototypes or scaling full applications, LangChain offers the flexibility and tools needed to optimize the LLM lifecycle, with seamless integrations and fault-tolerant scalability.

Gain precision and control with LangGraph to build agents that reliably handle complex tasks. Build and scale agentic applications with LangGraph Platform. LangGraph's flexible framework supports diverse control flows – single agent, multi-agent, hierarchical, sequential – and robustly handles realistic, complex scenarios. Ensure reliability with easy-to-add moderation and quality loops that prevent agents from veering off course. Use LangGraph Platform to templatize your cognitive architecture so that tools, prompts, and models are easily configurable with LangGraph Platform Assistants. With built-in statefulness, LangGraph agents seamlessly collaborate with humans by writing drafts for review and awaiting approval before acting. Easily inspect the agent’s actions and "time-travel" to roll back and take a different action to correct course.

Next.js gives you the best developer experience with all the features you need for production: hybrid static & server rendering, TypeScript support, smart bundling, route pre-fetching, and more. The interactive course with quizzes will guide you through everything you need to know to use Next.js. Next.js has built-in support for internationalized (i18n) routing since v10.0.0. You can provide a list of locales, the default locale, and domain-specific locales and Next.js will automatically handle the routing.

PydanticAI is a Python-based agent framework designed to simplify the development of production-grade applications using generative AI. Built by the team behind Pydantic, the framework integrates seamlessly with popular AI models such as OpenAI, Anthropic, Gemini, and others. It offers type-safe design, real-time debugging, and performance monitoring through Pydantic Logfire. PydanticAI also provides structured responses by leveraging Pydantic to validate model outputs, ensuring consistency. The framework includes a dependency injection system to support iterative development and testing, as well as the ability to stream LLM outputs for rapid validation. It is ideal for AI-driven projects that require flexible and efficient agent composition using standard Python best practices. We built PydanticAI with one simple aim: to bring that FastAPI feeling to GenAI app development.

Mastra is a powerful TypeScript framework for building intelligent AI agents that can execute tasks, access knowledge bases, and maintain memory persistently within workflows. This framework simplifies the process of creating and deploying AI-powered agents by leveraging TypeScript’s capabilities to streamline development. With features like customizable agent instructions, memory, and task orchestration, Mastra provides developers with the tools to build and scale AI agents for various applications, from personal assistants to specialized domain experts.

​The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm. The Agents SDK has a very small set of primitives, agents, which are LLMs equipped with instructions and tools; handoffs, which allow agents to delegate to other agents for specific tasks; and guardrails, which enable the inputs to agents to be validated. In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in tracing that lets you visualize and debug your agentic flows, evaluate them, and even fine-tune models for your application.

The Vercel AI SDK is a free, open source TypeScript toolkit from the creators of Next.js that gives developers unified, high-level primitives to build AI-powered features quickly across any model provider by changing a single line of code. It abstracts common complexities like streaming responses, multi-turn tool execution, error handling and recovery, and model switching while remaining framework-agnostic so builders can go from idea to working application in minutes. With a unified provider API, developers can generate typed objects, compose generative UIs, and deliver instant, streamed AI responses without reinventing plumbing, and the SDK includes documentation, cookbooks, a playground, and community-driven extensibility to accelerate development. It handles the hard parts under the hood while exposing enough control to get under the hood when needed, making integration with multiple LLMs seamless.

Dify is an open-source platform designed to streamline the development and operation of generative AI applications. It offers a comprehensive suite of tools, including an intuitive orchestration studio for visual workflow design, a Prompt IDE for prompt testing and refinement, and enterprise-level LLMOps capabilities for monitoring and optimizing large language models. Dify supports integration with various LLMs, such as OpenAI's GPT series and open-source models like Llama, providing flexibility for developers to select models that best fit their needs. Additionally, its Backend-as-a-Service (BaaS) features enable seamless incorporation of AI functionalities into existing enterprise systems, facilitating the creation of AI-powered chatbots, document summarization tools, and virtual assistants.

LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs.

CrewAI is a leading multi-agent platform that enables organizations to streamline workflows across various industries by building and deploying automated processes using any Large Language Model (LLM) and cloud platform. It offers a comprehensive suite of tools, including a framework and UI Studio, to facilitate the rapid development of multi-agent automations, catering to both coding professionals and those seeking no-code solutions. The platform supports flexible deployment options, allowing users to move their created 'crews'—teams of AI agents—to production with confidence, utilizing powerful tools for different deployment types and autogenerated user interfaces. CrewAI also provides robust monitoring capabilities, enabling users to track the performance and progress of their AI agents on both simple and complex tasks. Additionally, it offers testing and training tools to continually enhance the efficiency and quality of outcomes produced by these AI agents.