> For the complete documentation index, see [llms.txt](https://docs.rierino.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rierino.com/introduction.md). # Introduction - [Low-Code Microservices & AI Agent Platform](https://docs.rierino.com/introduction/rierino-overview.md): Rierino is a low-code microservices and AI agent development platform with an architecture designed for scale and flexibility - [Rierino Use Cases](https://docs.rierino.com/introduction/rierino-use-cases.md): Rierino can boost development of any large scale backend heavy application, such as information management, decision automation, BPM, agentic operations & orchestration - [Platform Architecture](https://docs.rierino.com/introduction/platform-architecture.md): Headless, multi-tier microservices architecture with full control over security, scalability, integrations and deployment model - [Built with ML & AI](https://docs.rierino.com/introduction/built-with-ml-and-ai.md): Rierino natively supports embedding GenAI as well as traditional ML models and features into any UI, workflow or API through low-code configurations - [Rierino Claude Plugin](https://docs.rierino.com/introduction/built-with-ml-and-ai/rierino-claude-plugin.md): Connect Claude Code and Claude Desktop to Rierino with platform-aware skills, specialist agents, slash commands, and an authenticated MCP proxy. - [Rierino FAQ](https://docs.rierino.com/introduction/rierino-faq.md): This page lists frequently asked questions about Rierino platform positioning and architecture --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.rierino.com/introduction.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.