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Agentic Cycle

The agentic cycle is the pattern where the LLM repeatedly calls tools, receives results, and continues reasoning until it can provide a final answer. This enables complex multi-step operations.

To send a turn to a named child agent, see Subagents.

Tip: Code Mode can reduce agent loop iterations by letting the LLM write a program that calls multiple tools in a single execution. See Code Mode.

mermaid
graph TD
    A[User sends message] --> B[LLM analyzes request]
    B --> C{Does task need tools?}
    C -->|No| D[Generate text response]
    C -->|Yes| E[Call appropriate tool]
    E --> F{Where does<br/>tool execute?}
    F -->|Server| G[Execute on server]
    F -->|Client| H[Execute on client]
    G --> I[Tool returns result]
    H --> I
    I --> J[Add result to conversation]
    J --> K[LLM analyzes result]
    K --> L{Task complete?}
    L -->|No| E
    L -->|Yes| D
    D --> M[Stream response to user]
    M --> N[Done]
    
    style E fill:#e1f5ff
    style G fill:#ffe1e1
    style H fill:#ffe1e1
    style L fill:#fff4e1

Detailed Agentic Flow

mermaid
sequenceDiagram
    participant User
    participant Client
    participant Server
    participant LLM
    participant Tools
    
    User->>Client: "What's the weather in SF and LA?"
    Client->>Server: Send message
    Server->>LLM: Message + tool definitions
    
    Note over LLM: Cycle 1: Call first tool
    
    LLM->>Server: tool_call: get_weather(SF)
    Server->>Tools: Execute get_weather
    Tools-->>Server: {temp: 65, conditions: "sunny"}
    Server->>LLM: tool_result
    
    Note over LLM: Cycle 2: Call second tool
    
    LLM->>Server: tool_call: get_weather(LA)
    Server->>Tools: Execute get_weather
    Tools-->>Server: {temp: 75, conditions: "clear"}
    Server->>LLM: tool_result
    
    Note over LLM: Cycle 3: Generate answer
    
    LLM-->>Server: content: "SF is 65°F..."
    Server-->>Client: Stream response
    Client->>User: Display answer

Multi-Step Example

Here's a real-world example of the agentic cycle:

User: "Find me flights to Paris under $500 and book the cheapest one"

Cycle 1: LLM calls searchFlights({destination: "Paris", maxPrice: 500})

  • Tool returns: [{id: "F1", price: 450}, {id: "F2", price: 480}]

Cycle 2: LLM analyzes results and calls bookFlight({flightId: "F1"})

  • Tool requires approval (sensitive operation) — see Tool Approval
  • User approves
  • Tool returns: {bookingId: "B123", confirmed: true}

Cycle 3: LLM generates final response

  • "I found 2 flights under $500. I've booked the cheapest one (Flight F1) for $450. Your booking ID is B123."

Code Example: Agentic Weather Assistant

ts
import { chat, toolDefinition, toServerSentEventsResponse } from "@tanstack/ai";
import { openaiText } from "@tanstack/ai-openai";
import { z } from "zod";

// Tool definitions
const getWeatherDef = toolDefinition({
  name: "get_weather",
  description: "Get current weather for a city",
  inputSchema: z.object({
    city: z.string(),
  }),
});

const getClothingAdviceDef = toolDefinition({
  name: "get_clothing_advice",
  description: "Get clothing recommendations based on weather",
  inputSchema: z.object({
    temperature: z.number(),
    conditions: z.string(),
  }),
});

// Server implementations
const getWeather = getWeatherDef.server(async ({ city }) => {
  const response = await fetch(`https://api.weather.com/v1/${city}`);
  return await response.json();
});

const getClothingAdvice = getClothingAdviceDef.server(async ({ temperature, conditions }) => {
  // Business logic for clothing recommendations
  if (temperature < 50) {
    return { recommendation: "Wear a warm jacket" };
  }
  return { recommendation: "Light clothing is fine" };
});

// Server route
export async function POST(request: Request) {
  const { messages } = await request.json();

  const stream = chat({
    adapter: openaiText("gpt-5.5"),
    messages,
    tools: [getWeather, getClothingAdvice],
  });

  return toServerSentEventsResponse(stream);
}

User: "What should I wear in San Francisco today?"

Agentic Cycle:

  1. LLM calls get_weather({city: "San Francisco"}) → Returns {temp: 62, conditions: "cloudy"}
  2. LLM calls get_clothing_advice({temperature: 62, conditions: "cloudy"}) → Returns {recommendation: "Light jacket recommended"}
  3. LLM generates: "The weather in San Francisco is 62°F and cloudy. I recommend wearing a light jacket."

The loop continues only while the model's finish reason is tool_calls (with pending tool calls) and the agent loop strategy permits another iteration. It ends as soon as the model returns a normal stop finish reason. An answer that hits the output limit is a special case.

Controlling the loop

By default, the loop stops after five model turns (maxIterations(5)), even if the model calls more tools. It also stops after an answer that hits the output limit (untilFinishReason(["length"])). Override this with the agentLoopStrategy option.

Other built-in strategies:

  • untilFinishReason([...]) — continue until the model returns one of the given finish reasons (e.g. untilFinishReason(["stop", "length"])).
  • combineStrategies([...]) — combine multiple strategies with AND logic; the loop continues only while every strategy agrees.

A strategy is just a function that receives { iterationCount, finishReason, messages, toolCallCount, lastTurnToolCallCount } and returns true to allow another iteration or false to stop, so you can also write your own:

ts
import { chat, combineStrategies, maxIterations, toServerSentEventsResponse } from "@tanstack/ai";
import { openaiText } from "@tanstack/ai-openai";
import type { AgentLoopState } from "@tanstack/ai";
import { getWeather, getClothingAdvice } from "./tools";

export async function POST(request: Request) {
  const { messages } = await request.json();
  const stream = chat({
    adapter: openaiText("gpt-5.5"),
    messages,
    tools: [getWeather, getClothingAdvice],
    agentLoopStrategy: combineStrategies([
      maxIterations(10),
      ({ messages }: AgentLoopState) => messages.length < 100,
    ]),
  });
  return toServerSentEventsResponse(stream);
}

When the output limit cuts a tool call

A model can run out of output tokens in the middle of a tool call, for example while it writes a long file. The provider stops with the finish reason length, and the input of the call is not complete. chat() never runs such a call. It asks for no approval and starts no client tool. Each cut call gets an error tool result instead.

By default, the run then ends with the finish reason length. To let the model send the cut call again, use a strategy that does not stop on length:

ts
import { chat, maxIterations, toServerSentEventsResponse } from "@tanstack/ai";
import { openaiText } from "@tanstack/ai-openai";
import { writeFile } from "./tools";

export async function POST(request: Request) {
  const { messages } = await request.json();
  const stream = chat({
    adapter: openaiText("gpt-6.1-sol"),
    messages,
    tools: [writeFile],
    agentLoopStrategy: maxIterations(5),
  });
  return toServerSentEventsResponse(stream);
}

Then this happens after a cut answer:

  1. The loop calls the model again. The model reads the error result and sends the call again.
  2. maxIterations counts the cut answer as one model turn. A model that hits the limit every time stops at the iteration limit.

The client needs no change. When the loop goes on, the RUN_FINISHED event of the cut answer has the finish reason tool_calls, so useChat keeps the stream open for the next model turn.

The default error text tells the model to send the calls again and to split a large input. To use your own text, return a new result from the onAfterToolCall middleware hook. The error of a cut call has the code max_tokens:

ts
import { defineChatMiddleware } from "@tanstack/ai";

export const cutToolCallText = defineChatMiddleware({
  onAfterToolCall(_ctx, info) {
    const cut =
      info.error instanceof Error &&
      "code" in info.error &&
      info.error.code === "max_tokens";
    if (!cut) return;
    return {
      type: "replaceResult",
      result: `Your call to ${info.toolName} was cut off. Write the file in parts of 200 lines or less.`,
    };
  },
});

Add it with middleware: [cutToolCallText] next to agentLoopStrategy.

On the server, each assistant message that chat() stores keeps metadata.tanstack.finishReason. A cut answer has length. If a transcript has a cut call with no result, chat() does not run that call.

Tool-call budgets (middleware recipe)

Iterations ≠ tool calls. One model turn can emit many parallel tool calls. maxIterations only bounds model turns. Strategies run between turns, so without a per-turn cap a single runaway turn can still fan out unbounded.

There is no built-in maxToolCalls strategy. Cap tools with middleware:

  • onBeforeToolCall — skip excess calls inside one turn (maxPerTurn)
  • onShouldContinue — stop further turns once cumulative emitted tools hit a budget (max); skipped calls still count toward toolCallCount
ts
import {
  chat,
  maxIterations,
  toServerSentEventsResponse,
  type ChatMiddleware,
} from "@tanstack/ai";
import { openaiText } from "@tanstack/ai-openai";
import { getWeather, getClothingAdvice } from "./tools";

/** App-owned policy — not a library export. */
function toolCallBudget(options: {
  max?: number;
  maxPerTurn?: number;
}): ChatMiddleware {
  const { max, maxPerTurn } = options;
  let perTurn = 0;

  return {
    name: "tool-call-budget",
    onIteration() {
      perTurn = 0;
    },
    // Fresh per-turn budget for pending/resume batches (no onIteration).
    onToolPhaseComplete() {
      perTurn = 0;
    },
    onBeforeToolCall() {
      if (maxPerTurn == null) return undefined;
      perTurn += 1;
      if (perTurn > maxPerTurn) {
        return {
          type: "skip",
          result: {
            error: `Skipped: exceeded maxToolCallsPerTurn (${maxPerTurn})`,
          },
        };
      }
      return undefined;
    },
    onShouldContinue(_ctx, state) {
      if (max != null && state.toolCallCount >= max) return false;
      return undefined;
    },
  };
}

export async function POST(request: Request) {
  const { messages } = await request.json();
  const stream = chat({
    adapter: openaiText("gpt-5.5"),
    messages,
    tools: [getWeather, getClothingAdvice],
    agentLoopStrategy: maxIterations(20), // model turns
    middleware: [
      toolCallBudget({
        maxPerTurn: 10, // cap parallel fan-out inside one turn
        max: 20, // stop further turns once cumulative emitted tools hit 20
      }),
    ],
  });
  return toServerSentEventsResponse(stream);
}

Place this before toolCacheMiddleware so over-budget skips win over cache hits. See onShouldContinue for the hook contract.

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Subagents
Thinking & Reasoning