How to Get Started With the Claude API
A practical first run at Anthropic's Claude API — keys, the Messages endpoint, system prompts, streaming, and where it differs from other LLM APIs.
How do you get started with the Claude API?
You get started with the Claude API by creating an Anthropic account, generating an API key at console.anthropic.com, and calling the Messages endpoint from your backend with that key. You send a list of messages and a model name such as a current Claude Sonnet or Claude Opus identifier, and Claude returns its reply. As with any LLM provider, the key stays server-side and your application talks to Anthropic over HTTPS.
The official anthropic SDK exists for both Python and TypeScript, and Anthropic also supports the Messages API over plain HTTP if you prefer to call it directly. Either way the shape is the same: roles, content, and a model.
The Messages API in one pass
A Claude request centres on a messages array where each entry has a role of user or assistant and the text content for that turn. One detail trips up newcomers: unlike some other APIs, the system prompt is not an entry inside the messages array — it is a separate top-level system parameter on the request.
- Put behaviour, tone, and constraints in the system field at the top of the request.
- Put the actual conversation turns in the messages array, alternating user and assistant.
- Set max_tokens explicitly — Claude requires it, and it caps how long the reply can run.
- Read the reply from the content blocks in the response and return the text to your app.
Because the endpoint is stateless, you resend the conversation history each time you want Claude to stay in context. There is no server-side memory of prior calls.
Write a system prompt Claude can actually use
Claude responds well to clear, structured instructions. Tell it who it is, what the task is, and how to format the answer. If you need a specific shape — JSON, a bulleted summary, a particular tone — say so directly in the system prompt and give a short example. Vague instructions produce vague output from any model, and a few minutes spent sharpening the system prompt usually beats hours of post-processing.
Be explicit about what Claude should do when it is unsure. Asking it to say so plainly, rather than guess, makes its answers far more trustworthy in a product.
Stream responses so the app feels fast
For anything conversational, enable streaming. Instead of waiting for the whole reply, you receive the text in small chunks as the model generates it and render them as they arrive. The total time is the same, but the perceived speed is dramatically better because the user sees words appear immediately. The SDK exposes streaming as an event stream you iterate over; forward those chunks to your frontend over a streaming connection and append them to the message as they come.
Mind tokens, limits, and cost from day one
Claude bills per input and output token, and each model has a generous but finite context window. A few habits keep things healthy:
- Trim or summarise long histories rather than resending an ever-growing transcript on every call.
- Handle HTTP 429 rate-limit responses with backoff and retry, the same as any external API.
- Use a smaller, cheaper model for routine work and reserve a larger model for genuinely hard tasks.
- Log token usage early so cost never surprises you later.
One genuinely useful Claude feature is prompt caching: if you reuse a large, stable chunk of context — a long system prompt or a reference document — across many calls, you can cache it so you are not billed full price to resend it every time. On high-volume workloads that one change can cut cost substantially.
Use tools when Claude needs to act, not just talk
Out of the box, Claude can only produce text. To let it look up live data or perform an action, you use tool use, which is Anthropic's name for function calling. You describe the tools available — each with a name, a description, and an input schema — and when Claude decides one is needed, it returns a structured request to call it rather than a final answer.
- Define your tools with clear names and descriptions so Claude knows exactly when each applies.
- When Claude requests a tool, your code runs it and returns the result in a follow-up message.
- Claude then continues its answer using that result, grounded in real data instead of guessing.
This is the bridge from a chat box to a genuine assistant that checks a database, queries an API, or triggers a workflow. Start with one or two well-described tools; a small, reliable tool set is far easier for the model to use correctly than a sprawling one.
Prefer it built for you?
The Claude API is friendly to start with, but production integration means streaming, history management, caching, tool use, and graceful failure handling all working together. If you want that done properly the first time, talk to BSH Technologies about our software engineering services and we will build your Claude integration end to end.
Frequently asked questions
Where do I get a Claude API key?
Sign up at the Anthropic Console (console.anthropic.com), then create a key under the API keys section. Store it as an environment variable such as ANTHROPIC_API_KEY and read it from your backend. Never commit the key to source control or expose it in client-side code.
How is the Claude Messages API different from the OpenAI API?
The biggest difference newcomers hit is the system prompt: in Claude it is a separate top-level system parameter, not an entry in the messages array. Claude also requires you to set max_tokens on every request. The overall pattern of roles, content, and a model name is broadly similar.
Does the Claude API remember previous messages?
No. The Messages endpoint is stateless and keeps no memory between calls. To maintain a conversation, your application resends the relevant message history with each request. For long chats, summarise or trim older turns to stay within the context window and reduce token cost.
What is prompt caching and should I use it?
Prompt caching lets you reuse a large, stable block of context, such as a long system prompt or reference document, without paying full price to resend it on every call. If you make many requests that share the same big context, enabling caching can significantly cut both latency and cost.
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