To start using Claude Opus 4.5, you first need API access through Anthropic or a supported cloud platform. On Anthropic’s own platform, you create an API key in the Claude console and then call the /v1/messages (or equivalent) endpoint with the model name, which is currently documented as claude-opus-4-5-20251101. From there, integration looks like most modern LLM APIs: you send a list of messages (system, user, assistant), specify max_tokens, temperature, and optional tool definitions, and then parse the streamed or non-streamed response. If you’re on Azure AI Foundry or another partner environment, the steps are similar but you provision a “deployment” first and then hit the provider’s endpoint rather than Anthropic’s directly.
In practical app code, the easiest starting point is to wrap Opus 4.5 behind a small client library or service. For example, in a Python backend you might write a helper function call_opus(prompt, tools=None, effort="medium") that knows your API key, model name, and default parameters. Your app code (web handlers, job workers, CLI tools) then calls this helper rather than the Anthropic API directly. This gives you one place to handle retries, logging, token counting, and guardrails. For front-end or agentic use cases, you’ll usually have a backend service that the UI or orchestrator hits, and that backend is what actually calls Claude.
If you plan to use Claude Opus 4.5 with RAG or agent memory, the next step is to plug in a vector database such as Milvus or Zilliz Cloud. The standard pattern is: (1) embed your documents or codebase using an embedding model; (2) at query time, retrieve the top-K relevant chunks from Milvus/Zilliz Cloud; (3) assemble a prompt that includes those chunks plus the user query; and (4) send that prompt to Opus 4.5. This architecture lets you keep your domain data in your own infrastructure while using Opus for reasoning, planning, and generation, and is usually the right “first serious” setup beyond simple chat demos.