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The Interactions API is now generally available. We recommend using this API for access to all the latest features and models.
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Text generation
Note: This version of the page covers the Interactions API. You can use the toggle on this page to switch to the generateContent API version of this page.
The Gemini API can generate text output from text, images, video, and audio
inputs.
Here's a basic example:
Python
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.5-flash",
input="How does AI work?"
)
print(interaction.output_text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const interaction = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "How does AI work?",
});
console.log(interaction.output_text);
}
await main();
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"input": "How does AI work?"
}'
The Google GenAI SDKs provide convenience properties directly
on the returned Interaction object to access the model's response.
The most common helper is interaction.output_text (String), which returns
the last text blocks in the model's response. If the response is split
across multiple consecutive TextContent blocks, it automatically joins them.
Note that .output_text does not include earlier text blocks separated by
non-text content (such as thoughts, images, audio, or tool calls). For complex
or interleaved multimodal responses, you must manually iterate over steps
instead. To learn more about other media convenience properties, see the
Interactions overview.
Thinking with Gemini
Gemini models often have "thinking"
enabled by default which allows the model to reason before responding to a
request.
Each model supports different thinking configurations which gives you control
over cost, latency, and intelligence. For more details, see the
thinking guide.
Python
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.5-flash",
input="How does AI work?",
generation_config={
"thinking_level": "low"
}
)
print(interaction.output_text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const interaction = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "How does AI work?",
generation_config: {
thinking_level: "low",
},
});
console.log(interaction.output_text);
}
await main();
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"input": "How does AI work?",
"generation_config": {
"thinking_level": "low"
}
}'
System instructions and other configurations
You can guide the behavior of Gemini models with system instructions. Pass
a system_instruction parameter to configure the model's behavior.
Python
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.5-flash",
system_instruction="You are a cat. Your name is Neko.",
input="Hello there"
)
print(interaction.output_text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const interaction = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "Hello there",
system_instruction: "You are a cat. Your name is Neko.",
});
console.log(interaction.output_text);
}
await main();
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"system_instruction": "You are a cat. Your name is Neko.",
"input": "Hello there"
}'
You can also override default generation parameters, such as
temperature, using the generation_config parameter.
Python
from google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3.5-flash",
input="Explain how AI works",
generation_config={
"temperature": 1.0
}
)
print(interaction.output_text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const interaction = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "Explain how AI works",
generation_config: {
temperature: 1.0,
},
});
console.log(interaction.output_text);
}
await main();
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"input": "Explain how AI works",
"generation_config": {
"temperature": 1.0
}
}'
Refer to the Interactions API reference
for a complete list of configurable parameters and their
descriptions.
Multimodal inputs
The Gemini API supports multimodal inputs, allowing you to combine text with
media files. The following example demonstrates providing an image:
Python
from google import genai
client = genai.Client()
uploaded_file = client.files.upload(file="path/to/organ.jpg")
interaction = client.interactions.create(
model="gemini-3.5-flash",
input=[
{"type": "text", "text": "Tell me about this instrument"},
{
"type": "image",
"uri": uploaded_file.uri,
"mime_type": uploaded_file.mime_type
}
]
)
print(interaction.output_text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const uploadedFile = await ai.files.upload({
file: "path/to/organ.jpg",
config: { mimeType: "image/jpeg" }
});
const interaction = await ai.interactions.create({
model: "gemini-3.5-flash",
input: [
{type: "text", text: "Tell me about this instrument"},
{
type: "image",
uri: uploadedFile.uri,
mime_type: uploadedFile.mimeType
}
],
});
console.log(interaction.output_text);
}
await main();
REST
# First upload the file using the Files API, then use the URI:
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"input": [
{"type": "text", "text": "Tell me about this instrument"},
{
"type": "image",
"uri": "YOUR_FILE_URI",
"mime_type": "image/jpeg"
}
]
}'
For alternative methods of providing images and more advanced image processing,
see our image understanding guide.
The API also supports document, video, and
audio inputs and understanding.
Streaming responses
By default, the model returns a response only after the entire generation
process is complete.
For more fluid interactions, use streaming to handle response chunks
as they're generated. For a comprehensive guide covering event types,
streaming with tools, thinking, agents, and image generation, see the
dedicated Streaming interactions
guide.
Python
from google import genai
client = genai.Client()
stream = client.interactions.create(
model="gemini-3.5-flash",
input="Explain how AI works",
stream=True
)
for event in stream:
if event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="")
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const stream = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "Explain how AI works",
stream: true,
});
for await (const event of stream) {
if (event.event_type === "step.delta") {
if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
}
}
}
}
await main();
REST
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions?alt=sse" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
--no-buffer \
-d '{
"model": "gemini-3.5-flash",
"input": "Explain how AI works",
"stream": true
}'
Multi-turn conversations
The Interactions API supports multi-turn conversations by chaining interactions
together using previous_interaction_id. Each turn is a separate interaction,
and the API automatically manages conversation history.
Note: Unlike other APIs where you might manage conversation history manually,
the Interactions API handles conversation state server-side. You
pass the id from the previous interaction to continue the conversation.
Python
from google import genai
client = genai.Client()
interaction1 = client.interactions.create(
model="gemini-3.5-flash",
input="I have 2 dogs in my house.",
)
print(interaction1.output_text)
interaction2 = client.interactions.create(
model="gemini-3.5-flash",
input="How many paws are in my house?",
previous_interaction_id=interaction1.id,
)
print(interaction2.output_text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const interaction1 = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "I have 2 dogs in my house.",
});
console.log("Response 1:", interaction1.output_text);
const interaction2 = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "How many paws are in my house?",
previous_interaction_id: interaction1.id,
});
console.log("Response 2:", interaction2.output_text);
}
await main();
REST
RESPONSE1=$(curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"input": "I have 2 dogs in my house."
}')
INTERACTION_ID=$(echo "$RESPONSE1" | jq -r '.id')
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"input": "I have two dogs in my house. How many paws are in my house?",
"previous_interaction_id": "'$INTERACTION_ID'"
}'
Streaming can also be used for multi-turn conversations by combining
previous_interaction_id with the streaming methods.
Python
from google import genai
client = genai.Client()
interaction1 = client.interactions.create(
model="gemini-3.5-flash",
input="I have 2 dogs in my house.",
)
print(interaction1.output_text)
stream = client.interactions.create(
model="gemini-3.5-flash",
input="How many paws are in my house?",
previous_interaction_id=interaction1.id,
stream=True
)
for event in stream:
if event.event_type == "step.delta":
if event.delta.type == "text":
print(event.delta.text, end="")
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const interaction1 = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "I have 2 dogs in my house.",
});
console.log("Response 1:", interaction1.output_text);
const stream = await ai.interactions.create({
model: "gemini-3.5-flash",
input: "How many paws are in my house?",
previous_interaction_id: interaction1.id,
stream: true,
});
for await (const event of stream) {
if (event.event_type === "step.delta") {
if (event.delta.type === "text") {
process.stdout.write(event.delta.text);
}
}
}
}
await main();
REST
RESPONSE1=$(curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"input": "I have 2 dogs in my house."
}')
INTERACTION_ID=$(echo "$RESPONSE1" | jq -r '.id')
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions?alt=sse" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
--no-buffer \
-d '{
"model": "gemini-3.5-flash",
"input": "How many paws are in my house?",
"previous_interaction_id": "'$INTERACTION_ID'",
"stream": true
}'
Stateless conversations
By default, the Interactions API manages conversation state server-side when you use previous_interaction_id. However, you can also operate in stateless mode by managing the conversation history yourself on the client side.
To use stateless mode:
1. Set store=false in your request to opt out of server-side storage.
2. Maintain the conversation history as an array of steps on the client side.
3. In subsequent requests, pass the accumulated steps in the input field, and append your new turn as a user_input step.
Note: If the model uses "thinking" or tools, you must preserve and resend all model-generated steps (such as thought and function_call steps) exactly as received, as they contain signatures required to continue the conversation.
Python
from google import genai
client = genai.Client()
history = [
{
"type": "user_input",
"content": [{"type": "text", "text": "I have 2 dogs in my house."}]
}
]
interaction1 = client.interactions.create(
model="gemini-3.5-flash",
store=False,
input=history
)
print("Response 1:", interaction1.steps[-1].content[0].text)
for step in interaction1.steps:
history.append(step.model_dump())
history.append({
"type": "user_input",
"content": [{"type": "text", "text": "How many paws are in my house?"}]
})
interaction2 = client.interactions.create(
model="gemini-3.5-flash",
store=False,
input=history
)
print("Response 2:", interaction2.steps[-1].content[0].text)
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
async function main() {
const history = [
{
type: "user_input",
content: [{ type: "text", text: "I have 2 dogs in my house." }]
}
];
const interaction1 = await ai.interactions.create({
model: "gemini-3.5-flash",
store: false,
input: history
});
console.log("Response 1:", interaction1.steps.at(-1).content[0].text);
history.push(...interaction1.steps);
history.push({
type: "user_input",
content: [{ type: "text", text: "How many paws are in my house?" }]
});
const interaction2 = await ai.interactions.create({
model: "gemini-3.5-flash",
store: false,
input: history
});
console.log("Response 2:", interaction2.steps.at(-1).content[0].text);
}
await main();
REST
# Turn 1: Send request with store: false
RESPONSE1=$(curl -s -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "gemini-3.5-flash",
"store": false,
"input": [
{
"type": "user_input",
"content": "I have 2 dogs in my house."
}
]
}')
# Extract the steps from response
MODEL_STEPS=$(echo "$RESPONSE1" | jq '.steps')
# Reconstruct the full history for Turn 2 by combining:
# 1. First user input
# 2. Model response steps
# 3. Second user input
HISTORY=$(jq -n \
--argjson first_input '[{"type": "user_input", "content": "I have 2 dogs in my house."}]' \
--argjson model_steps "$MODEL_STEPS" \
--argjson second_input '[{"type": "user_input", "content": "How many paws are in my house?"}]' \
"'"'"'$first_input + $model_steps + $second_input'"'"'")
# Turn 2: Send the full history
curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d "{
\"model\": \"gemini-3.5-flash\",
\"store\": false,
\"input\": $HISTORY
}"
Prompting tips
Consult our prompt engineering guide for
suggestions on getting the most out of Gemini.
What's next
Try Gemini in Google AI Studio.
Experiment with
structured outputs for
JSON-like responses.
Explore Gemini's image,
video,
audio and
document understanding
capabilities.
Learn about multimodal
file prompting strategies.
Send feedback
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-07-08 UTC.
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