เอเจนต์ Deep Research ของ Gemini

import time
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    input="Research the history of Google TPUs.",
    agent="deep-research-preview-04-2026",
    background=True,
)

print(f"Research started: {interaction.id}")

while True:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.steps[-1].content[0].text)
        break
    elif interaction.status == "failed":
        print(f"Research failed: {interaction.error}")
        break
    time.sleep(10)

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    input: 'Research the history of Google TPUs.',
    agent: 'deep-research-preview-04-2026',
    background: true
});

console.log(`Research started: ${interaction.id}`);

while (true) {
    const result = await client.interactions.get(interaction.id);
    if (result.status === 'completed') {
        console.log(result.steps.at(-1).content[0].text);
        break;
    } else if (result.status === 'failed') {
        console.log(`Research failed: ${result.error}`);
        break;
    }
    await new Promise(resolve => setTimeout(resolve, 10000));
}

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.GetInteractionByIdRequest;
import java.util.Collections;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Research the history of Google TPUs."))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println("Research started: " + interaction.id().orElse(""));

while (true) {
  interaction =
      client.interactions
          .get(GetInteractionByIdRequest.builder().id(interaction.id().get()).build())
          .interaction()
          .get();
  if (InteractionStatus.COMPLETED.equals(interaction.status().orElse(null))) {
    System.out.println(interaction.outputText().orElse(""));
    break;
  } else if (InteractionStatus.FAILED.equals(interaction.status().orElse(null))) {
    System.out.println("Research failed: " + interaction.errors().orElse(Collections.emptyList()));
    break;
  }
  Thread.sleep(10000);
}

# 1. Start the research task
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": "Research the history of Google TPUs.",
    "agent": "deep-research-preview-04-2026",
    "background": true
}'

# 2. Poll for results (Replace INTERACTION_ID)
# curl -X GET "https://generativelanguage.googleapis.com/v1beta/interactions/INTERACTION_ID" \
# -H "x-goog-api-key: $GEMINI_API_KEY"

from google import genai

client = genai.Client()

# First interaction: request a research plan
plan_interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Do some research on Google TPUs.",
    agent_config={
        "type": "deep-research",
        "thinking_summaries": "auto",
        "collaborative_planning": True,
    },
    background=True,
)

# Wait for and retrieve the plan
while (result := client.interactions.get(id=plan_interaction.id)).status != "completed":
    time.sleep(5)
print(result.steps[-1].content[0].text)

const planInteraction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Do some research on Google TPUs.',
    agent_config: {
        type: 'deep-research',
        thinking_summaries: 'auto',
        collaborative_planning: true
    },
    background: true
});

let result;
while ((result = await client.interactions.get(planInteraction.id)).status !== 'completed') {
    await new Promise(r => setTimeout(r, 5000));
}
console.log(result.steps.at(-1).content[0].text);

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DeepResearchAgentConfig;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.ThinkingSummaries;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.GetInteractionByIdRequest;

Client client = new Client();

// First interaction: request a research plan
CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Do some research on Google TPUs."))
        .agentConfig(
            DeepResearchAgentConfig.builder()
                .thinkingSummaries(ThinkingSummaries.AUTO)
                .collaborativePlanning(true)
                .build())
        .background(true)
        .build();

Interaction planInteraction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

// Wait for and retrieve the plan
Interaction result;
while (true) {
  result =
      client.interactions
          .get(GetInteractionByIdRequest.builder().id(planInteraction.id().get()).build())
          .interaction()
          .get();
  if (InteractionStatus.COMPLETED.equals(result.status().orElse(null))) {
    break;
  }
  Thread.sleep(5000);
}
System.out.println(result.outputText().orElse(""));

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": "Do some research on Google TPUs.",
    "agent_config": {
        "type": "deep-research",
        "thinking_summaries": "auto",
        "collaborative_planning": true
    },
    "background": true
}'

# Second interaction: refine the plan
refined_plan = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Focus more on the differences between Google TPUs and competitor hardware, and less on the history.",
    agent_config={
        "type": "deep-research",
        "thinking_summaries": "auto",
        "collaborative_planning": True,
    },
    previous_interaction_id=plan_interaction.id,
    background=True,
)

while (result := client.interactions.get(id=refined_plan.id)).status != "completed":
    time.sleep(5)
print(result.steps[-1].content[0].text)

const refinedPlan = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Focus more on the differences between Google TPUs and competitor hardware, and less on the history.',
    agent_config: {
        type: 'deep-research',
        thinking_summaries: 'auto',
        collaborative_planning: true
    },
    previous_interaction_id: planInteraction.id,
    background: true
});

let result;
while ((result = await client.interactions.get(refinedPlan.id)).status !== 'completed') {
    await new Promise(r => setTimeout(r, 5000));
}
console.log(result.steps.at(-1).content[0].text);

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DeepResearchAgentConfig;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.ThinkingSummaries;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.GetInteractionByIdRequest;

Client client = new Client();
String planInteractionId = "PLAN_INTERACTION_ID";

// Second interaction: refine the plan
CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(
            InteractionsInput.of(
                "Focus more on the differences between Google TPUs and competitor hardware, and less on the history."))
        .agentConfig(
            DeepResearchAgentConfig.builder()
                .thinkingSummaries(ThinkingSummaries.AUTO)
                .collaborativePlanning(true)
                .build())
        .previousInteractionId(planInteractionId)
        .background(true)
        .build();

Interaction refinedPlan =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

Interaction result;
while (true) {
  result =
      client.interactions
          .get(GetInteractionByIdRequest.builder().id(refinedPlan.id().get()).build())
          .interaction()
          .get();
  if (InteractionStatus.COMPLETED.equals(result.status().orElse(null))) {
    break;
  }
  Thread.sleep(5000);
}
System.out.println(result.outputText().orElse(""));

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": "Focus more on the differences between Google TPUs and competitor hardware, and less on the history.",
    "agent_config": {
        "type": "deep-research",
        "thinking_summaries": "auto",
        "collaborative_planning": true
    },
    "previous_interaction_id": "PREVIOUS_INTERACTION_ID",
    "background": true
}'

# Third interaction: approve the plan and kick off research
final_report = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Plan looks good!",
    agent_config={
        "type": "deep-research",
        "thinking_summaries": "auto",
        "collaborative_planning": False,
    },
    previous_interaction_id=refined_plan.id,
    background=True,
)

while (result := client.interactions.get(id=final_report.id)).status != "completed":
    time.sleep(5)
print(result.steps[-1].content[0].text)

const finalReport = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Plan looks good!',
    agent_config: {
        type: 'deep-research',
        thinking_summaries: 'auto',
        collaborative_planning: false
    },
    previous_interaction_id: refinedPlan.id,
    background: true
});

let result;
while ((result = await client.interactions.get(finalReport.id)).status !== 'completed') {
    await new Promise(r => setTimeout(r, 5000));
}
console.log(result.steps.at(-1).content[0].text);

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DeepResearchAgentConfig;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.ThinkingSummaries;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.GetInteractionByIdRequest;

Client client = new Client();
String refinedPlanId = "REFINED_PLAN_ID";

// Third interaction: approve the plan and kick off research
CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Plan looks good!"))
        .agentConfig(
            DeepResearchAgentConfig.builder()
                .thinkingSummaries(ThinkingSummaries.AUTO)
                .collaborativePlanning(false)
                .build())
        .previousInteractionId(refinedPlanId)
        .background(true)
        .build();

Interaction finalReport =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

Interaction result;
while (true) {
  result =
      client.interactions
          .get(GetInteractionByIdRequest.builder().id(finalReport.id().get()).build())
          .interaction()
          .get();
  if (InteractionStatus.COMPLETED.equals(result.status().orElse(null))) {
    break;
  }
  Thread.sleep(5000);
}
System.out.println(result.outputText().orElse(""));

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": "Plan looks good!",
    "agent_config": {
        "type": "deep-research",
        "thinking_summaries": "auto",
        "collaborative_planning": false
    },
    "previous_interaction_id": "PREVIOUS_INTERACTION_ID",
    "background": true
}'

import base64
import time

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Analyze global semiconductor market trends. Include graphics showing market share changes.",
    agent_config={
        "type": "deep-research",
        "visualization": "auto",
    },
    background=True,
)

print(f"Research started: {interaction.id}")

while (result := client.interactions.get(id=interaction.id)).status != "completed":
    time.sleep(5)

for step in result.steps:
    if step.type == "model_output":
        for content_item in step.content:
            if content_item.type == "text":
                print(content_item.text)
            elif content_item.type == "image" and content_item.data:
                image_bytes = base64.b64decode(content_item.data)
                print(f"Received image: {len(image_bytes)} bytes")

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Analyze global semiconductor market trends. Include graphics showing market share changes.',
    agent_config: {
        type: 'deep-research',
        visualization: 'auto'
    },
    background: true
});

console.log(`Research started: ${interaction.id}`);

let result;
while ((result = await client.interactions.get(interaction.id)).status !== 'completed') {
    await new Promise(r => setTimeout(r, 5000));
}

for (const step of result.steps) {
    if (step.type === 'model_output') {
        for (const contentItem of step.content) {
            if (contentItem.type === 'text') {
                console.log(contentItem.text);
            } else if (contentItem.type === 'image' && contentItem.data) {
                console.log(`[Image Output: ${contentItem.data.substring(0, 20)}...]`);
            }
        }
    }
}

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DeepResearchAgentConfig;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.ModelOutputStep;
import com.google.genai.gaos.models.interactions.Step;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.Visualization;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.GetInteractionByIdRequest;
import java.util.Base64;
import java.util.Collections;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(
            InteractionsInput.of(
                "Analyze global semiconductor market trends. Include graphics showing market share changes."))
        .agentConfig(DeepResearchAgentConfig.builder().visualization(Visualization.AUTO).build())
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println("Research started: " + interaction.id().orElse(""));

Interaction result;
while (true) {
  result =
      client.interactions
          .get(GetInteractionByIdRequest.builder().id(interaction.id().get()).build())
          .interaction()
          .get();
  if (InteractionStatus.COMPLETED.equals(result.status().orElse(null))) {
    break;
  }
  Thread.sleep(5000);
}

for (Step step : result.steps().orElse(Collections.emptyList())) {
  if (step instanceof ModelOutputStep) {
    for (Content contentItem : ((ModelOutputStep) step).content().orElse(Collections.emptyList())) {
      if (contentItem instanceof TextContent) {
        System.out.println(((TextContent) contentItem).text().orElse(""));
      } else if (contentItem instanceof ImageContent) {
        ImageContent img = (ImageContent) contentItem;
        if (img.data().isPresent()) {
          byte[] imageBytes = Base64.getDecoder().decode(img.data().get());
          System.out.println("Received image: " + imageBytes.length + " bytes");
        }
      }
    }
  }
}

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": "Analyze global semiconductor market trends. Include graphics showing market share changes.",
    "agent_config": {
        "type": "deep-research",
        "visualization": "auto"
    },
    "background": true
}'

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="What are the latest developments in quantum computing?",
    tools=[{"type": "google_search"}],
    background=True,
)

const interaction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'What are the latest developments in quantum computing?',
    tools: [{ type: 'google_search' }],
    background: true
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.GoogleSearch;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("What are the latest developments in quantum computing?"))
        .tools(Arrays.asList(GoogleSearch.builder().build()))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": "What are the latest developments in quantum computing?",
    "tools": [{"type": "google_search"}],
    "background": true
}'

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Summarize the content of https://www.wikipedia.org/.",
    tools=[{"type": "url_context"}],
    background=True,
)

const interaction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Summarize the content of https://www.wikipedia.org/.',
    tools: [{ type: 'url_context' }],
    background: true
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.URLContext;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Summarize the content of https://www.wikipedia.org/."))
        .tools(Arrays.asList(URLContext.builder().build()))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": "Summarize the content of https://www.wikipedia.org/.",
    "tools": [{"type": "url_context"}],
    "background": true
}'

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Calculate the 50th Fibonacci number.",
    tools=[{"type": "code_execution"}],
    background=True,
)

const interaction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Calculate the 50th Fibonacci number.',
    tools: [{ type: 'code_execution' }],
    background: true
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CodeExecution;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Calculate the 50th Fibonacci number."))
        .tools(Arrays.asList(CodeExecution.builder().build()))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": "Calculate the 50th Fibonacci number.",
    "agent": "deep-research-preview-04-2026",
    "tools": [{"type": "code_execution"}],
    "background": true
}'

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Check the status of my last server deployment.",
    tools=[
        {
            "type": "mcp_server",
            "name": "Deployment Tracker",
            "url": "https://mcp.example.com/mcp",
            "headers": {"Authorization": "Bearer my-token"},
        }
    ],
    background=True,
)

const interaction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Check the status of my last server deployment.',
    tools: [
        {
            type: 'mcp_server',
            name: 'Deployment Tracker',
            url: 'https://mcp.example.com/mcp',
            headers: { Authorization: 'Bearer my-token' }
        }
    ],
    background: true
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.MCPServer;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;
import java.util.Collections;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Check the status of my last server deployment."))
        .tools(
            Arrays.asList(
                MCPServer.builder()
                    .name("Deployment Tracker")
                    .url("https://mcp.example.com/mcp")
                    .headers(Collections.singletonMap("Authorization", "Bearer my-token"))
                    .build()))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": "Check the status of my last server deployment.",
    "tools": [
        {
            "type": "mcp_server",
            "name": "Deployment Tracker",
            "url": "https://mcp.example.com/mcp",
            "headers": {"Authorization": "Bearer my-token"}
        }
    ],
    "background": true
}'

import time
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    input="Compare our 2025 fiscal year report against current public web news.",
    agent="deep-research-preview-04-2026",
    background=True,
    tools=[
        {
            "type": "file_search",
            "file_search_store_names": ['fileSearchStores/my-store-name']
        }
    ]
)

const interaction = await client.interactions.create({
    input: 'Compare our 2025 fiscal year report against current public web news.',
    agent: 'deep-research-preview-04-2026',
    background: true,
    tools: [
        { type: 'file_search', file_search_store_names: ['fileSearchStores/my-store-name'] },
    ]
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.FileSearch;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(
            InteractionsInput.of(
                "Compare our 2025 fiscal year report against current public web news."))
        .tools(
            Arrays.asList(
                FileSearch.builder()
                    .fileSearchStoreNames(Arrays.asList("fileSearchStores/my-store-name"))
                    .build()))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": "Compare our 2025 fiscal year report against current public web news.",
    "agent": "deep-research-preview-04-2026",
    "background": true,
    "tools": [
        {"type": "file_search", "file_search_store_names": ["fileSearchStores/my-store-name"]},
    ]
}'

prompt = """
Research the competitive landscape of EV batteries.

Format the output as a technical report with the following structure:
1. Executive Summary
2. Key Players (Must include a data table comparing capacity and chemistry)
3. Supply Chain Risks
"""

interaction = client.interactions.create(
    input=prompt,
    agent="deep-research-preview-04-2026",
    background=True
)

const prompt = `
Research the competitive landscape of EV batteries.

Format the output as a technical report with the following structure:
1. Executive Summary
2. Key Players (Must include a data table comparing capacity and chemistry)
3. Supply Chain Risks
`;

const interaction = await client.interactions.create({
    input: prompt,
    agent: 'deep-research-preview-04-2026',
    background: true,
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;

Client client = new Client();

String prompt =
    "Research the competitive landscape of EV batteries.\n\n"
        + "Format the output as a technical report with the following structure:\n"
        + "1. Executive Summary\n"
        + "2. Key Players (Must include a data table comparing capacity and chemistry)\n"
        + "3. Supply Chain Risks";

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of(prompt))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": "Research the competitive landscape of EV batteries.\n\nFormat the output as a technical report with the following structure: \n1. Executive Summary\n2. Key Players (Must include a data table comparing capacity and chemistry)\n3. Supply Chain Risks",
    "agent": "deep-research-preview-04-2026",
    "background": true
}'

import time
from google import genai

client = genai.Client()

prompt = """Analyze the interspecies dynamics and behavioral risks present
in the provided image of the African watering hole. Specifically, investigate
the symbiotic relationship between the avian species and the pachyderms
shown, and conduct a risk assessment for the reticulated giraffes based on
their drinking posture relative to the specific predator visible in the
foreground."""

interaction = client.interactions.create(
    input=[
        {"type": "text", "text": prompt},
        {
            "type": "image",
            "mime_type": "image/jpeg",
            "uri": "https://storage.googleapis.com/generativeai-downloads/images/generated_elephants_giraffes_zebras_sunset.jpg"
        }
    ],
    agent="deep-research-preview-04-2026",
    background=True
)

print(f"Research started: {interaction.id}")

while True:
    interaction = client.interactions.get(interaction.id)
    if interaction.status == "completed":
        print(interaction.steps[-1].content[0].text)
        break
    elif interaction.status == "failed":
        print(f"Research failed: {interaction.error}")
        break
    time.sleep(10)

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const prompt = `Analyze the interspecies dynamics and behavioral risks present
in the provided image of the African watering hole. Specifically, investigate
the symbiotic relationship between the avian species and the pachyderms
shown, and conduct a risk assessment for the reticulated giraffes based on
their drinking posture relative to the specific predator visible in the
foreground.`;

const interaction = await client.interactions.create({
    input: [
        { type: 'text', text: prompt },
        {
            type: 'image',
            mime_type: "image/jpeg",
            uri: 'https://storage.googleapis.com/generativeai-downloads/images/generated_elephants_giraffes_zebras_sunset.jpg'
        }
    ],
    agent: 'deep-research-preview-04-2026',
    background: true
});

console.log(`Research started: ${interaction.id}`);

while (true) {
    const result = await client.interactions.get(interaction.id);
    if (result.status === 'completed') {
        console.log(result.steps.at(-1).content[0].text);
        break;
    } else if (result.status === 'failed') {
        console.log(`Research failed: ${result.error}`);
        break;
    }
    await new Promise(resolve => setTimeout(resolve, 10000));
}

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.ImageContent;
import com.google.genai.gaos.models.interactions.ImageContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.GetInteractionByIdRequest;
import java.util.Arrays;
import java.util.Collections;

Client client = new Client();

String prompt =
    "Analyze the interspecies dynamics and behavioral risks present "
        + "in the provided image of the African watering hole. Specifically, investigate "
        + "the symbiotic relationship between the avian species and the pachyderms "
        + "shown, and conduct a risk assessment for the reticulated giraffes based on "
        + "their drinking posture relative to the specific predator visible in the "
        + "foreground.";

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(
            InteractionsInput.ofContent(
                Arrays.asList(
                    TextContent.builder().text(prompt).build(),
                    ImageContent.builder()
                        .mimeType(ImageContentMimeType.IMAGE_JPEG)
                        .uri(
                            "https://storage.googleapis.com/generativeai-downloads/images/generated_elephants_giraffes_zebras_sunset.jpg")
                        .build())))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println("Research started: " + interaction.id().orElse(""));

while (true) {
  interaction =
      client.interactions
          .get(GetInteractionByIdRequest.builder().id(interaction.id().get()).build())
          .interaction()
          .get();
  if (InteractionStatus.COMPLETED.equals(interaction.status().orElse(null))) {
    System.out.println(interaction.outputText().orElse(""));
    break;
  } else if (InteractionStatus.FAILED.equals(interaction.status().orElse(null))) {
    System.out.println("Research failed: " + interaction.errors().orElse(Collections.emptyList()));
    break;
  }
  Thread.sleep(10000);
}

# 1. Start the research task with image input
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": [
        {"type": "text", "text": "Analyze the interspecies dynamics and behavioral risks present in the provided image of the African watering hole. Specifically, investigate the symbiotic relationship between the avian species and the pachyderms shown, and conduct a risk assessment for the reticulated giraffes based on their drinking posture relative to the specific predator visible in the foreground."},
        {"type": "image", "mime_type": "image/jpeg", "uri": "https://storage.googleapis.com/generativeai-downloads/images/generated_elephants_giraffes_zebras_sunset.jpg"}
    ],
    "agent": "deep-research-preview-04-2026",
    "background": true
}'

# 2. Poll for results (Replace INTERACTION_ID)
# curl -X GET "https://generativelanguage.googleapis.com/v1beta/interactions/INTERACTION_ID" \
# -H "x-goog-api-key: $GEMINI_API_KEY"

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input=[
        {"type": "text", "text": "What is this document about?"},
        {
            "type": "document",
            "uri": "https://arxiv.org/pdf/1706.03762",
            "mime_type": "application/pdf",
        },
    ],
    background=True,
)

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: [
        { type: 'text', text: 'What is this document about?' },
        {
            type: 'document',
            uri: 'https://arxiv.org/pdf/1706.03762',
            mime_type: 'application/pdf'
        }
    ],
    background: true
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DocumentContent;
import com.google.genai.gaos.models.interactions.DocumentContentMimeType;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import java.util.Arrays;

Client client = new Client();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(
            InteractionsInput.ofContent(
                Arrays.asList(
                    TextContent.builder().text("What is this document about?").build(),
                    DocumentContent.builder()
                        .uri("https://arxiv.org/pdf/1706.03762")
                        .mimeType(DocumentContentMimeType.APPLICATION_PDF)
                        .build())))
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

# 1. Start the research task with document input
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "deep-research-preview-04-2026",
    "input": [
        {"type": "text", "text": "What is this document about?"},
        {"type": "document", "uri": "https://arxiv.org/pdf/1706.03762", "mime_type": "application/pdf"}
    ],
    "background": true
}'

from google import genai

client = genai.Client()

interaction_id = None
last_event_id = None
is_complete = False

def process_stream(stream):
    global interaction_id, last_event_id, is_complete
    for event in stream:
        if event.event_type == "interaction.created":
            interaction_id = event.interaction.id
        if event.event_id:
            last_event_id = event.event_id
        if event.event_type == "step.delta":
            if event.delta.type == "text":
                print(event.delta.text, end="", flush=True)
            elif event.delta.type == "thought":
                print(f"Thought: {event.delta.text}", flush=True)
        elif event.event_type in ("interaction.completed", "interaction.error"):
            is_complete = True

stream = client.interactions.create(
    input="Research the history of Google TPUs.",
    agent="deep-research-preview-04-2026",
    background=True,
    stream=True,
    agent_config={"type": "deep-research", "thinking_summaries": "auto"},
)
process_stream(stream)

# Reconnect if the connection drops
while not is_complete and interaction_id:
    status = client.interactions.get(interaction_id)
    if status.status != "in_progress":
        break
    stream = client.interactions.get(
        id=interaction_id, stream=True, last_event_id=last_event_id,
    )
    process_stream(stream)

import { GoogleGenAI } from '@google/genai';

const client = new GoogleGenAI({});

let interactionId;
let lastEventId;
let isComplete = false;

async function processStream(stream) {
    for await (const event of stream) {
        if (event.type === 'interaction.created') {
            interactionId = event.interaction.id;
        }
        if (event.event_id) lastEventId = event.event_id;
        if (event.type === 'step.delta') {
            if (event.delta.type === 'text') {
                process.stdout.write(event.delta.text);
            } else if (event.delta.type === 'thought') {
                console.log(`Thought: ${event.delta.text}`);
            }
        } else if (['interaction.completed', 'interaction.error'].includes(event.type)) {
            isComplete = true;
        }
    }
}

const stream = await client.interactions.create({
    input: 'Research the history of Google TPUs.',
    agent: 'deep-research-preview-04-2026',
    background: true,
    stream: true,
    agent_config: { type: 'deep-research', thinking_summaries: 'auto' },
});
await processStream(stream);

// Reconnect if the connection drops
while (!isComplete && interactionId) {
    const status = await client.interactions.get(interactionId);
    if (status.status !== 'in_progress') break;
    const resumeStream = await client.interactions.get(interactionId, {
        stream: true, last_event_id: lastEventId,
    });
    await processStream(resumeStream);
}

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.Content;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DeepResearchAgentConfig;
import com.google.genai.gaos.models.interactions.ErrorEvent;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionCompletedEvent;
import com.google.genai.gaos.models.interactions.InteractionCreatedEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEEvent;
import com.google.genai.gaos.models.interactions.InteractionSSEStreamEvent;
import com.google.genai.gaos.models.interactions.InteractionStatus;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.StepDelta;
import com.google.genai.gaos.models.interactions.TextContent;
import com.google.genai.gaos.models.interactions.TextDelta;
import com.google.genai.gaos.models.interactions.ThinkingSummaries;
import com.google.genai.gaos.models.interactions.ThoughtSummaryDelta;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;
import com.google.genai.gaos.models.operations.GetInteractionByIdRequest;
import com.google.genai.gaos.utils.EventStream;

class StreamProcessor {
  String interactionId = null;
  String lastEventId = null;
  boolean isComplete = false;

  void processStream(EventStream<InteractionSSEStreamEvent> stream) {
    for (InteractionSSEStreamEvent streamEvent : stream) {
      InteractionSSEEvent event = streamEvent.data().orElse(null);
      if (event instanceof InteractionCreatedEvent) {
        InteractionCreatedEvent created = (InteractionCreatedEvent) event;
        interactionId = created.interaction().flatMap(i -> i.id()).orElse(null);
        if (created.eventId().isPresent()) {
          lastEventId = created.eventId().get();
        }
      } else if (event instanceof StepDelta) {
        StepDelta stepDelta = (StepDelta) event;
        if (stepDelta.eventId().isPresent()) {
          lastEventId = stepDelta.eventId().get();
        }
        if (stepDelta.delta().isPresent()) {
          if (stepDelta.delta().get() instanceof TextDelta) {
            System.out.print(((TextDelta) stepDelta.delta().get()).text().orElse(""));
            System.out.flush();
          } else if (stepDelta.delta().get() instanceof ThoughtSummaryDelta) {
            ThoughtSummaryDelta thought = (ThoughtSummaryDelta) stepDelta.delta().get();
            Content content = thought.content().orElse(null);
            if (content instanceof TextContent) {
              System.out.println("Thought: " + ((TextContent) content).text().orElse(""));
            }
          }
        }
      } else if (event instanceof InteractionCompletedEvent || event instanceof ErrorEvent) {
        isComplete = true;
      }
    }
  }
}

Client client = new Client();
StreamProcessor processor = new StreamProcessor();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Research the history of Google TPUs."))
        .background(true)
        .stream(true)
        .agentConfig(
            DeepResearchAgentConfig.builder().thinkingSummaries(ThinkingSummaries.AUTO).build())
        .build();

try (EventStream<InteractionSSEStreamEvent> stream =
    client.interactions.create(CreateInteractionRequestBody.of(params)).events()) {
  processor.processStream(stream);
}

// Reconnect if the connection drops
while (!processor.isComplete && processor.interactionId != null) {
  Interaction status =
      client.interactions
          .get(GetInteractionByIdRequest.builder().id(processor.interactionId).build())
          .interaction()
          .get();
  if (!InteractionStatus.IN_PROGRESS.equals(status.status().orElse(null))) {
    break;
  }
  try (EventStream<InteractionSSEStreamEvent> stream =
      client.interactions
          .get(
              GetInteractionByIdRequest.builder()
                  .id(processor.interactionId)
                  .stream(true)
                  .lastEventId(processor.lastEventId)
                  .build())
          .events()) {
    processor.processStream(stream);
  }
}

# 1. Start the stream (save the INTERACTION_ID from the interaction.start event
#    and the last "event_id" you receive)
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": "Research the history of Google TPUs.",
    "agent": "deep-research-preview-04-2026",
    "background": true,
    "stream": true,
    "agent_config": {
        "type": "deep-research",
        "thinking_summaries": "auto"
    }
}'

# 2. If the connection drops, reconnect with your saved IDs
curl -X GET "https://generativelanguage.googleapis.com/v1beta/interactions/INTERACTION_ID?stream=true&last_event_id=LAST_EVENT_ID" \
-H "x-goog-api-key: $GEMINI_API_KEY"

import time
from google import genai

client = genai.Client()

interaction = client.interactions.create(
    input="Can you elaborate on the second point in the report?",
    model="gemini-3.1-pro-preview",
    previous_interaction_id="COMPLETED_INTERACTION_ID"
)

print(interaction.steps[-1].content[0].text)

const interaction = await client.interactions.create({
    input: 'Can you elaborate on the second point in the report?',
    model: 'gemini-3.1-pro-preview',
    previous_interaction_id: 'COMPLETED_INTERACTION_ID'
});
console.log(interaction.steps.at(-1).content[0].text);

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateModelInteraction;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;

Client client = new Client();

CreateModelInteraction params =
    CreateModelInteraction.builder()
        .model("gemini-3.1-pro-preview")
        .input(InteractionsInput.of("Can you elaborate on the second point in the report?"))
        .previousInteractionId("COMPLETED_INTERACTION_ID")
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

System.out.println(interaction.outputText().orElse(""));

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": "Can you elaborate on the second point in the report?",
    "model": "gemini-3.1-pro-preview",
    "previous_interaction_id": "COMPLETED_INTERACTION_ID"
}'

agent_config = {
    "type": "deep-research",
    "thinking_summaries": "auto",
    "visualization": "auto",
    "collaborative_planning": False,
}

interaction = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Research the competitive landscape of cloud GPUs.",
    agent_config=agent_config,
    background=True,
)

const interaction = await client.interactions.create({
    agent: 'deep-research-preview-04-2026',
    input: 'Research the competitive landscape of cloud GPUs.',
    agent_config: {
        type: 'deep-research',
        thinking_summaries: 'auto',
        visualization: 'auto',
        collaborative_planning: false,
    },
    background: true,
});

import com.google.genai.Client;
import com.google.genai.gaos.models.interactions.CreateAgentInteraction;
import com.google.genai.gaos.models.interactions.DeepResearchAgentConfig;
import com.google.genai.gaos.models.interactions.Interaction;
import com.google.genai.gaos.models.interactions.InteractionsInput;
import com.google.genai.gaos.models.interactions.ThinkingSummaries;
import com.google.genai.gaos.models.interactions.Visualization;
import com.google.genai.gaos.models.operations.CreateInteractionRequestBody;

Client client = new Client();

DeepResearchAgentConfig agentConfig =
    DeepResearchAgentConfig.builder()
        .thinkingSummaries(ThinkingSummaries.AUTO)
        .visualization(Visualization.AUTO)
        .collaborativePlanning(false)
        .build();

CreateAgentInteraction params =
    CreateAgentInteraction.builder()
        .agent("deep-research-preview-04-2026")
        .input(InteractionsInput.of("Research the competitive landscape of cloud GPUs."))
        .agentConfig(agentConfig)
        .background(true)
        .build();

Interaction interaction =
    client.interactions.create(CreateInteractionRequestBody.of(params)).interaction().get();

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "input": "Research the competitive landscape of cloud GPUs.",
    "agent": "deep-research-preview-04-2026",
    "agent_config": {
        "type": "deep-research",
        "thinking_summaries": "auto",
        "visualization": "auto",
        "collaborative_planning": false
    },
    "background": true
}'