> For the complete documentation index, see [llms.txt](https://docs.avis.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.avis.net/api-reference/overview.md). # Overview ### AVIS API in general: The AVIS API provides a consistent application programming interface designed for seamless compatibility with industry-standard AI protocols, including the OpenAI Chat Completions and Anthropic APIs. Architecture & Schema Highlights * Industry-Standard Compatibility: Request and response schemas strictly adhere to established industry standards, minimizing integration overhead and enabling effortless migration from existing systems. * Cross-Platform Schema Normalization: AVIS automatically synchronizes and normalizes data structures across all supported models and providers. * Unified Developer Experience: Thanks to comprehensive schema normalization, developers only need to refer to a single integration guide to configure and run any model powered by AVIS. Avis exposes a single generation backend through three drop-in API surfaces: **OpenAI**, **Anthropic**, and **Google Gemini**. Point your existing SDK or integration at the matching Avis base URL — no code changes beyond the base URL and API key. Every surface accepts any model in the shared Avis model catalog, regardless of which protocol you call it through (e.g. you can call an Anthropic model via the OpenAI-compatible endpoint). --- # 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.avis.net/api-reference/overview.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.