> ## Documentation Index > Fetch the complete documentation index at: https://docs.ziet.ai/llms.txt > Use this file to discover all available pages before exploring further. # Introduction > Ziet is the first serverless platform for building AI agents. Write Python code, deploy instantly, and scale automatically. No servers, databases, or infrastructure to manage. ## What is Ziet? Ziet is a **serverless platform** for building and deploying AI agents. Focus on writing Python code while Ziet handles all the infrastructure, scaling, and observability. No servers, databases, or DevOps. Just write Python and deploy. Scale from zero to millions of requests automatically. Google, OpenAI, Stripe, Twilio, and more—out of the box. Track every action, memory operation, and agent decision. ## How It Works Ziet provides two powerful approaches: **Agentic Approach**: Build intelligent agents with Actions, Memory, and Strategies\ **Application Approach**: Build serverless APIs with Endpoints and Database ```python theme={null} from ziet import Action, Agent, PickActionsStrategy, HandOffStrategy @Action(id="search_flights", name="Search Flights", description="Search for flights") def search_flights(origin: str, dest: str) -> list: # Your code here return results @PickActionsStrategy(picks=2, allow_repeats=False) @HandOffStrategy(agents=["review_agent"]) @Agent( id="search_agent", name="FlightSearcher", description="Search and analyze flight options", instructions=""" Search for flights using multiple sources, analyze options, and hand off to review agent for final recommendation. """, actions=["search_flights", "compare_prices", "check_availability"] ) class FlightSearchAgent: pass # Agent behavior is defined by instructions and strategies ``` Deploy and invoke: ```bash theme={null} ziet deploy curl -X POST https://api.ziet.ai/agents/search_agent/run \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{"origin": "SFO", "dest": "NYC"}' ``` ```python theme={null} from ziet import Endpoint, db @Endpoint( id="get_flights", method="GET", path="/flights" ) def get_flights(origin: str, dest: str) -> dict: """Fetch flights from database.""" flights = db.query("flights") \ .where("origin", origin) \ .where("dest", dest) \ .all() return { "status": 200, "body": {"flights": flights} } @Endpoint( id="create_booking", method="POST", path="/bookings" ) def create_booking(flight_id: str, user_id: str) -> dict: """Create booking in database.""" booking_id = db.insert("bookings", { "flight_id": flight_id, "user_id": user_id, "status": "confirmed" }) return { "status": 201, "body": {"booking_id": booking_id} } ``` Deploy and invoke: ```bash theme={null} ziet deploy curl https://api.ziet.ai/flights?origin=SFO&dest=NYC curl -X POST https://api.ziet.ai/bookings \ -d '{"flight_id": "f123", "user_id": "u456"}' ``` ## Why Ziet? * Set up servers (EC2, ECS, Kubernetes) * Configure databases (Postgres, Redis, Vector DB) * Implement auth, rate limiting, monitoring * Write boilerplate for retries, timeouts, queues * Manage deployments, scaling, and infrastructure **Result**: Weeks of work before writing agent logic. * Write Python functions with `@Action` decorator * Define agents with `@Agent` decorator * Run `ziet deploy` **Result**: Production-ready agents in minutes. ## Get Started ```bash theme={null} pip install ziet ``` Follow our [quickstart guide](/quickstart) to build a complete agent in 5 minutes. ```bash theme={null} ziet deploy ``` ## Key Features ### Actions Serverless functions with built-in retries, timeouts, and error handling. ```python theme={null} @Action(timeout=30, retries=3) def process_data(data: dict) -> dict: return transformed_data ``` [Learn more about Actions →](/core/actions) ### Agents Autonomous agents that orchestrate multiple actions to complete complex tasks. ```python theme={null} @PickActionsStrategy(picks=3, allow_repeats=False) @HandOffStrategy(agents=["summarizer_agent"]) @Agent( id="researcher_agent", name="ResearchAgent", description="Research topics and gather information", instructions=""" Research the given topic by searching multiple sources, picking the best 3 search actions, and gathering comprehensive data. Hand off to summarizer agent when complete. """, actions=["search_google", "scrape_websites", "search_news"], model="gpt-4o" ) class ResearchAgent: pass # Agent behavior is defined by instructions and strategies ``` [Learn more about Agents →](/core/agents) ### Memory Built-in key-value and semantic search for agent state. ```python theme={null} from ziet import memory memory.add(key="best_option", value={"price": 350}) best = memory.get("best_option") results = memory.search("cheapest flights") ``` [Learn more about Memory →](/core/memory) ### Endpoints Serverless API endpoints—like AWS Lambda, but simpler. ```python theme={null} @Endpoint(method="GET", path="/users/{user_id}") def get_user(user_id: str) -> dict: user = db.query("users").where("id", user_id).first() return {"status": 200, "body": user} ``` [Learn more about Endpoints →](/application/endpoints) ### Database Persistent storage across all runs, agents, and endpoints. ```python theme={null} from ziet import db # Store data permanently user_id = db.insert("users", {"email": "alice@example.com"}) # Query anywhere user = db.query("users").where("id", user_id).first() ``` [Learn more about Database →](/application/database) ### Integrations Pre-built integrations with popular services. ```python theme={null} from ziet.integrations import google, openai, stripe results = google.search("best restaurants SF") response = openai.chat([{"role": "user", "content": "Summarize"}]) payment = stripe.create_payment_intent(amount=5000) ``` [View all integrations →](/integrations/overview) ## Next Steps Serverless actions Intelligent agents Temporary storage Agent orchestration REST API endpoints Persistent storage Built-in integrations API documentation