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LLM & Agentic

Messages API

Claude Messages API deep dive: request/response format, system prompts, multi-turn conversations, and best practices.

Last updated

Core5 min read

After this section you can

  • Make a Claude call and name what each of the five inputs controls
  • Read a response: content blocks, stop_reason, and the usage numbers that drive cost
  • Handle multi-turn history yourself, and the errors you cannot ignore
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Messages API: Your First Claude Call

Every Claude integration — chatbot, agent, RAG pipeline, batch job — bottoms out in one HTTP call to the Messages API. Master its five moving parts and the rest of this track is just elaboration.

THE CENTRAL IDEA

The Messages API is stateless request/response: you send the entire conversation every time, Claude replies once, and the server remembers nothing. That single fact explains almost everything downstream — why you manage messages[] yourself, why context windows fill up, why prompt caching exists, why memory is something you bolt on. Get the shape of one call in your head and agents, streaming, and tool use stop being magic.

Anatomy of one Messages API call — five inputs, one structured response
REQUEST model — which Claude system — role & rules messages[] — the whole conversation, user/assistant re-sent every call (stateless) max_tokens — output cap output_config — effort Messages API stateless RESPONSE content[] — text and/or tool_use blocks stop_reason — why it stopped: end_turn, tool_use, … usage — input_tokens, output_tokens — you pay for both append assistant reply → next turn re-sends the whole array

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