Order the stream parts emitted by streamText for one tool call plus a final text response

Attempt it

1finish, closing part with finish reason and usage
2tool-call, model emits a tool invocation with arguments
3text-delta(s), final natural-language tokens after the tool result
4tool-result, your server-side handler returns the tool output
5text-delta(s), initial reasoning / preamble tokens (if any)
6start, opening part with stream metadata

**Vercel AI SDK's `streamText`** emits a sequence of typed stream parts that a chat UI consumes in order. For a single tool call followed by a final text answer, the canonical sequence is: 1. **start**. Opening part with stream metadata (run ID, message ID). 2. **text-delta(s)**. Preamble or reasoning tokens the model emits before deciding to call a tool. May be zero in tight tool-using flows. 3. **tool-call**. The model emits a tool invocation with arguments. Your UI can show 'calling search(query=...)' as soon as this arrives. 4. **tool-result**. Your server runs the tool and the result arrives. The model has not seen this yet. 5. **text-delta(s)**. The model now reads the tool-result and emits the final natural-language answer, token by token. Typewriter rendering happens here. 6. **finish**. Closing part with the finish reason and usage tokens. The two ordering rules that catch most candidates: tool-result always comes after tool-call (the model emits the request first, your runtime fulfills it), and the final answer text comes after the tool-result (the model has to read the result before composing the answer). Anything else is broken.

**The canonical sequence is start → preamble text-deltas → tool-call → tool-result → final text-deltas → finish.** Each part is a typed object on the stream, and the order is enforced by the request-response pattern of tool use. **start** opens the stream with metadata. **text-delta** parts before the tool call are optional. They show up when the model emits reasoning or a preamble like 'Let me search for that' before calling the tool. In many tool-using flows there are zero preamble deltas; the model goes straight to the tool-call. **tool-call** is the model's request: tool name and arguments, often streamed as deltas itself for argument by argument visibility. **tool-result** comes from your server-side handler after it runs the tool; this part carries the data the model needs to compose the final answer. There is always a tool-call before a tool-result, because the result is the response to the call. After the tool-result, the model resumes generation. The final **text-delta** parts are the natural-language answer that grounds in the tool's output, streamed token by token to the chat UI. Finally **finish** closes the stream with the finish reason (`stop`, `length`, `tool_calls`) and usage stats. The failure modes worth knowing: placing final text before tool-result (the model has nothing to ground in), placing tool-result before tool-call (logically impossible), or omitting finish (the UI never knows the response is complete). The order in the question is the only legal one for this scenario.

Quick reference

TL;DR

start opens the stream, then preamble text-deltas, then the model's tool-call, then your tool-result, then the final text-deltas after the tool returns, then finish closes the stream.

Memory aid

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Easy to grasp

Imagine a waiter taking your order. First they say 'good evening' (start). They suggest 'maybe an appetizer' (preamble text). They walk to the kitchen and shout the order (tool-call). The kitchen sends back a plate (tool-result). Then they describe the dish to you (final text after the result). Finally they bow and step away (finish). Reordering steps in real life would be confusing; the same is true for a streamText pipeline driving a chat UI.

Key concepts

LLM Frameworks

Detailed answer & concept explanation

~5 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.

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Quick scan · 2–4 min

Everything important, quickly.

6 to 8 min: the six canonical parts + why each ordering rule is forced + tool-call argument streaming + multi-tool extensions + UI rendering pattern with useChat.

Real-world examples

Real products, models, and research that use this idea.

Next.js AI chatbot template streams exactly this sequence into the UI useChat hook for tool-using flows.
Vercel's v0.dev assistant uses streamText with code-execution tools, the user sees tool-call chips before the tool-result arrives.
Perplexity-style answer engines follow this pattern, search tool-call then tool-result then grounded answer text-deltas.
Linear's AI duplicate detection follows the same shape, retrieval tool-call then result then ranked-answer text-deltas.
OpenAI Responses API streaming uses an analogous shape with response.output_text.delta and tool events, the AI SDK normalizes both providers' streams into the same stream-part types.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QWalk through what changes when the model decides to call two tools in parallel.

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QHow would you render a tool-call chip in the UI before the tool finishes?

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QWhat goes wrong if you append the tool-result to the message history but skip rendering the final text-deltas?

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Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Placing tool-result before tool-call, or placing the final text-deltas before the tool runs. Final text always follows the tool-result because the model needs to read it.

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

Primary sources. Skim if you want the original framing.