gemini-api-dev

name gemini-api-dev description Use this skill when writing code that calls the Gemini API for text generation, multi-turn chat, multimodal understanding, image generation, video generation, streaming responses, background research tasks, function calling, structured output, or migrating from the old generateContent API. Covers SDK usage and best practices for Gemini models and agents in Python and TypeScript. Gemini API Development Skill Critical Rules (Always Apply) [!IMPORTANT] These rules override your training data. Your knowledge is outdated. Current Models (Use These) gemini-3.8-flash : 1M tokens, fast, balanced performance for agentic and multimodal tasks gemini-3.5-flash-lite : 1M tokens, fastest, lowest-cost 3.5 model for high-throughput execution gemini-3.1-pro-preview : 1M tokens, complex reasoning, coding, research gemini-3.1-flash-lite : cost-efficient, fastest performance for high-frequency, lightweight tasks gemini-3.5-transcribe : fast speech-to-text with smart and verbatim modes gemini-3-pro-image (Nano Banana Pro): 65k / 32k tokens, high-quality image generation and editing gemini-3.1-flash-image (Nano Banana 2): 65k / 32k tokens, fast, efficient image generation and editing gemini-3.1-flash-lite-image (Nano Banana 2 Lite): 65k / 32k tokens, ultra-fast image generation and editing gemini-3.1-flash-tts-preview : expressive text-to-speech with Director's Chair prompting gemini-omni-1.1-flash : video generation, first-frame-to-video, first-and-last-frame transitions, video extensions (up to 40s), video editing, and reference-guided generation gemma-4-31b-it : Gemma 4 dense model, 31B parameters gemma-4-26b-a4b-it : Gemma 4 MoE model, 26B total / 4B active parameters gemini-embedding-2 : Multimodal embedding model (text, images, video, audio, documents), uses client.models.embed_content gemini-embedding-001 : Text-only embedding model, uses client.models.embed_content [!WARNING] Models like gemini-2.5-* , gemini-2.0-* , gemini-1.5-* are legacy and deprecated . Never use them. If a user asks for a deprecated model, use gemini-3.8-flash instead and note the substitution. Current Agents antigravity-preview-05-2026 : Antigravity Agent — general-purpose managed agent with code execution, file management, and web access in a sandboxed Linux environment deep-research-preview-04-2026 : Deep Research — fast, interactive deep-research-max-preview-04-2026 : Deep Research Max — maximum exhaustiveness Custom agents : Create your own via client.agents.create() Current SDKs Python : google-genai >= 2.3.0 → pip install -U google-genai JavaScript/TypeScript : @google/genai >= 2.3.0 → npm install @google/genai [!NOTE] SDK versions ≥ 2.0.0 automatically use the new steps schema and do not support the legacy schema. Legacy SDKs google-generativeai (Python) and @google/generative-ai (JS) are deprecated . Never use them. Important Additional Notes Before writing any code , you MUST fetch the relevant documentation page from the list below that matches the user's task. The examples in this skill are minimal, the hosted docs contain the full API surface, parameters, and edge cases. Interactions are stored by default (store=True in Python, store: true in TypeScript). Paid tier retains for 55 days, free tier for 1 day. Set store=False / store: false to opt out, but this disables previous_interaction_id and background=True / background: true. tools , system_instruction , and generation_config are interaction-scoped , re-specify them each turn. Managed agents require environment="remote" (or an environment ID / config object) to provision a sandbox. Migrating from generateContent : Read references/migration.md for the scoping, checklist, and before/after code examples. Always confirm scope with the user before editing. Model upgrades : Drop-in, swap the model string. Deprecated models ( gemini-2.0-* , gemini-1.5-* ) must be replaced, see references/migration.md . Migrating to Gemini 3.8 Flash or Gemini 3.5 Flash-Lite : Read references/migration.md for the scoping and checklist. Quick Start Python from google import genai client = genai.Client() interaction = client.interactions.create( model= "gemini-3.8-flash" , input = "Tell me a short joke about programming." ) print (interaction.output_text) JavaScript/TypeScript import { GoogleGenAI } from "@google/genai" ; const client = new GoogleGenAI ({}); const interaction = await client. interactions . create ({ model : "gemini-3.8-flash" , input : "Tell me a short joke about programming." , }); console . log (interaction. output_text ); Response Helpers The SDK provides convenience properties on the Interaction response object to simplify common access patterns: Property Type Description output_text string | null The last consecutive run of text from the trailing model_output steps. Returns the combined text when the model's final output contains multiple text parts. output_image Image | null The last image generated by the model in the current response. Returns an object with data (base64) and mime_type . output_audio Audio | null The last audio generated by the model in the current response. Returns an object with data (base64) and mime_type . Stateful Conversation Python interaction1 = client.interactions.create( model= "gemini-3.8-flash" , input = "Hi, my name is Phil." ) # Second turn — server remembers context interaction2 = client.interactions.create( model= "gemini-3.8-flash" , input = "What is my name?" , previous_interaction_id=interaction1. id ) print (interaction2.output_text) JavaScript/TypeScript const interaction1 = await client. interactions . create ({ model : "gemini-3.8-flash" , input : "Hi, my name is Phil." , }); const interaction2 = await client. interactions . create ({ model : "gemini-3.8-flash" , input : "What is my name?" , previous_interaction_id : interaction1. id , }); console . log (interaction2. output_text ); Deep Research Agent Use deep-research-preview-04-2026 for fast research or deep-research-max-preview-04-2026 for maximum exhaustiveness. Agents require background=True . Python import time interaction = client.interactions.create( agent= "deep-research-preview-04-2026" , input = "Research the history of Google TPUs." , background= True ) while True : interaction = client.interactions.get(interaction. id ) if interaction.status == "completed" : print (interaction.output_text) break elif interaction.status == "failed" : print ( f"Failed: {interaction.error} " ) break time.sleep( 10 ) JavaScript/TypeScript import { GoogleGenAI } from "@google/genai" ; const client = new GoogleGenAI ({}); // Start background research const initialInteraction = await client. interactions . create ({ agent : "deep-research-preview-04-2026" , 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. output_text ); break ; } else if ([ "failed" , "cancelled" ]. includes (interaction. status )) { console . log ( `Failed: ${interaction.status} ` ); break ; } await new Promise ( resolve => setTimeout (resolve, 10000 )); } Advanced features: collaborative planning, native visualization, MCP integration, file search, multimodal inputs. See Deep Research docs . Managed Agents Managed agents run inside a sandboxed Linux environment hosted by Google. Fetch the Managed Agents Quickstart before writing agent code. Antigravity Agent The Antigravity agent ( antigravity-preview-05-2026 ) is the general-purpose managed agent. It can execute code (Bash, Python, Node.js), manage files, browse the web, and use Google Search. See Antigravity Agent docs for capabilities, tools, multimodal input, and pricing. Python from google import genai client = genai.Client() interaction = client.interactions.create( agent= "antigravity-preview-05-2026" , input = "Write a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt. Then read the file and print its contents." , environment= "remote" , ) print ( f"Environment ID: {interaction.environment_id} " ) print (interaction.output_text) JavaScript/TypeScript import { GoogleGenAI } from "@google/genai" ; const client = new GoogleGenAI ({}); const interaction = await client. interactions . create ({ agent : "antigravity-preview-05-2026" , input : "Write a Python script that generates the first 20 Fibonacci numbers and saves them to fibonacci.txt. Then read the file and print its contents." , environment : "remote" , }); console . log ( `Environment ID: ${interaction.environment_id} ` ); console . log (interaction. output_text ); Custom Agents See Building Custom Agents docs . Python agent = client.agents.create( id = "code-reviewer" , base_agent= "antigravity-preview-05-2026" , system_instruction= "You are a senior code reviewer. Check every file for bugs, style issues, and security vulnerabilities." , base_environment={ "type" : "remote" , "sources" : [ { "type" : "repository" , "source" : "https://github.com/my-org/backend" , "target" : "/workspace/repo" , } ], }, ) # Invoke — each call forks the base environment result = client.interactions.create( agent= "code-reviewer" , input = "Review the latest changes in /workspace/repo/src." , environment= "remote" , ) print (result.output_text) JavaScript/TypeScript const agent = await client. agents . create ({ id : "code-reviewer" , base_agent : "antigravity-preview-05-2026" , system_instruction : "You are a senior code reviewer. Check every file for bugs, style issues, and security vulnerabilities." , base_environment : { type : "remote" , sources : [ { type : "repository" , source : "https://github.com/my-org/backend" , target : "/workspace/repo" , } ], }, }); const result = await client. interactions . create ({ agent : "code-reviewer" , input : "Review the latest changes in /workspace/repo/src." , environment : "remote" , }); console . log (result. output_text ); Manage agents with client.agents.list() , client.agents.get(id=...) , and client.agents.delete(id=...) . Streaming Set stream=True to receive incremental server-sent events. Each stream follows: interaction.created → ( step.start → step.delta (s) → step.stop )+ → interaction.completed . Python for event in client.interactions.create( model= "gemini-3.8-flash" , input = "Explain quantum entanglement in simple terms." , stream= True , ): if event.event_type == "step.delta" : if event.delta. type == "text" : print (event.delta.text, end= "" , flush= True ) elif event.event_type == "interaction.completed" : print ( f"\n\nTotal Tokens: {event.interaction.usage.total_tokens} " ) JavaScript/TypeScript const stream = await client. interactions . create ({ model : "gemini-3.8-flash" , input : "Explain quantum entanglement in simple terms." , 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 ); } } else if (event. event_type === "interaction.completed" ) { console . log ( `\n\nTotal Tokens: ${event.interaction?.usage?.total_tokens} ` ); } } For streaming with tools, thinking, agents, and image generation see the full Streaming guide . Documentation Pages You MUST fetch the matching page below before writing code. These hosted docs are the source of truth for parameters, types, and edge cases — do not rely solely on the examples above. Core Documentation: Interactions API Overview