Lesson 177 of 1596
Calling the Claude API With Streaming
Anthropic's SDK in 20 lines. Learn messages, streaming tokens, and basic error handling.
Creators · AI-Assisted Coding · ~27 min read
Messages In, Tokens Out
Claude's API takes a list of messages and returns a reply. Streaming yields tokens as they are generated so users see output immediately.
SDK + env var. That is the setup.
npm install @anthropic-ai/sdk export ANTHROPIC_API_KEY=sk-ant-messages.create returns content blocks. Narrow on type before accessing text.
import Anthropic from "@anthropic-ai/sdk"; const client = new Anthropic(); export async function ask(prompt: string): Promise<string> { const res = await client.messages.create({ model: "claude-opus-4-7", max_tokens: 1024, system: "You are a concise coding tutor.", messages: [{ role: "user", content: prompt }], }); const block = res.content[0]; if (block.type !== "text") throw new Error("expected text block"); return block.text; }Stream events are typed. Filter for text_delta and write tokens as they arrive.
export async function askStreaming(prompt: string) { const stream = client.messages.stream({ model: "claude-opus-4-7", max_tokens: 1024, messages: [{ role: "user", content: prompt }], }); for await (const event of stream) { if ( event.type === "content_block_delta" && event.delta.type === "text_delta" ) { process.stdout.write(event.delta.text); } } const final = await stream.finalMessage(); console.log("\nstop_reason:", final.stop_reason); }Understanding "Calling the Claude API With Streaming" in practice: AI-assisted coding shifts work from syntax recall to design thinking — models handle boilerplate so you focus on architecture. Anthropic's SDK in 20 lines. Learn messages, streaming tokens, and basic error handling — and knowing how to apply this gives you a concrete advantage.
- Apply messages API in your ai-coding workflow to get better results
- Apply streaming in your ai-coding workflow to get better results
- Apply system prompt in your ai-coding workflow to get better results
- Apply model id in your ai-coding workflow to get better results
- 1Use AI to generate unit tests for an existing function
- 2Ask AI to refactor a messy function and explain the changes
- 3Have AI suggest a code review for a recent pull request
Key terms in this lesson
The big idea: messages.create for batch, messages.stream for UI. Narrow on block types and handle 529 like a grown-up.
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