Loading...
Loading...
Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. This skill covers the Interactions API, the recommended way to use Gemini models and agents in Python and TypeScript.
npx skill4agent add google-gemini/gemini-skills gemini-interactions-apigenerateContentprevious_interaction_idgemini-3.1-pro-previewgemini-3-flash-previewgemini-3.1-flash-lite-previewgemini-3-pro-image-previewgemini-3.1-flash-image-previewgemini-2.5-progemini-2.5-flashdeep-research-pro-preview-12-2025[!IMPORTANT] Models like,gemini-2.0-*are legacy and deprecated. Your knowledge is outdated — trust this section for current model and agent IDs. If a user asks for a deprecated model, usegemini-1.5-*or pro instead and note the substitution. Never generate code that references a deprecated model ID.gemini-3-flash-preview
google-genai1.55.0pip install -U google-genai@google/genai1.33.0npm install @google/genaifrom google import genai
client = genai.Client()
interaction = client.interactions.create(
model="gemini-3-flash-preview",
input="Tell me a short joke about programming."
)
print(interaction.outputs[-1].text)import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const interaction = await client.interactions.create({
model: "gemini-3-flash-preview",
input: "Tell me a short joke about programming.",
});
console.log(interaction.outputs[interaction.outputs.length - 1].text);from google import genai
client = genai.Client()
# First turn
interaction1 = client.interactions.create(
model="gemini-3-flash-preview",
input="Hi, my name is Phil."
)
# Second turn — server remembers context
interaction2 = client.interactions.create(
model="gemini-3-flash-preview",
input="What is my name?",
previous_interaction_id=interaction1.id
)
print(interaction2.outputs[-1].text)import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
// First turn
const interaction1 = await client.interactions.create({
model: "gemini-3-flash-preview",
input: "Hi, my name is Phil.",
});
// Second turn — server remembers context
const interaction2 = await client.interactions.create({
model: "gemini-3-flash-preview",
input: "What is my name?",
previous_interaction_id: interaction1.id,
});
console.log(interaction2.outputs[interaction2.outputs.length - 1].text);import time
from google import genai
client = genai.Client()
# Start background research
interaction = client.interactions.create(
agent="deep-research-pro-preview-12-2025",
input="Research the history of Google TPUs.",
background=True
)
# Poll for results
while True:
interaction = client.interactions.get(interaction.id)
if interaction.status == "completed":
print(interaction.outputs[-1].text)
break
elif interaction.status == "failed":
print(f"Failed: {interaction.error}")
break
time.sleep(10)import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
// Start background research
const initialInteraction = await client.interactions.create({
agent: "deep-research-pro-preview-12-2025",
input: "Research the history of Google TPUs.",
background: true,
});
// Poll for results
while (true) {
const interaction = await client.interactions.get(initialInteraction.id);
if (interaction.status === "completed") {
console.log(interaction.outputs[interaction.outputs.length - 1].text);
break;
} else if (["failed", "cancelled"].includes(interaction.status)) {
console.log(`Failed: ${interaction.status}`);
break;
}
await new Promise(resolve => setTimeout(resolve, 10000));
}from google import genai
client = genai.Client()
stream = client.interactions.create(
model="gemini-3-flash-preview",
input="Explain quantum entanglement in simple terms.",
stream=True
)
for chunk in stream:
if chunk.event_type == "content.delta":
if chunk.delta.type == "text":
print(chunk.delta.text, end="", flush=True)
elif chunk.event_type == "interaction.complete":
print(f"\n\nTotal Tokens: {chunk.interaction.usage.total_tokens}")import { GoogleGenAI } from "@google/genai";
const client = new GoogleGenAI({});
const stream = await client.interactions.create({
model: "gemini-3-flash-preview",
input: "Explain quantum entanglement in simple terms.",
stream: true,
});
for await (const chunk of stream) {
if (chunk.event_type === "content.delta") {
if (chunk.delta.type === "text" && "text" in chunk.delta) {
process.stdout.write(chunk.delta.text);
}
} else if (chunk.event_type === "interaction.complete") {
console.log(`\n\nTotal Tokens: ${chunk.interaction.usage.total_tokens}`);
}
}Interactionoutputstypetexttextthoughtsignaturesummaryfunction_callidnameargumentsfunction_resultcall_idnameresultgoogle_search_callgoogle_search_resultcode_execution_callcode_execution_resulturl_context_callurl_context_resultmcp_server_tool_callmcp_server_tool_resultfile_search_callfile_search_resultimagedatamime_typeuri{
"id": "v1_abc123",
"model": "gemini-3-flash-preview",
"status": "requires_action",
"object": "interaction",
"role": "model",
"outputs": [
{
"type": "function_call",
"id": "gth23981",
"name": "get_weather",
"arguments": { "location": "Boston, MA" }
}
],
"usage": {
"total_input_tokens": 100,
"total_output_tokens": 25,
"total_thought_tokens": 0,
"total_tokens": 125,
"total_tool_use_tokens": 50
}
}completedin_progressrequires_actionfailedcancelledstartChat()previous_interaction_idsendMessage()interactions.create(previous_interaction_id=...)response.textinteraction.outputs[-1].textbackground=Trueagent="deep-research-pro-preview-12-2025"store=truestore=falseprevious_interaction_idbackground=truetoolssystem_instructiongeneration_configbackground=Trueprevious_interaction_id