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Guide

How to Deploy an AI Agent to Production in 2026

Deploy your AI agent as an API service. Package it as a standard HTTP server (Express, FastAPI, Flask), deploy with a CLI command, and get a live URL. The agent becomes a callable endpoint that other applications, users, or even other agents can interact with.

The problem

You built an AI agent with LangChain, CrewAI, AutoGen, or the Claude Agent SDK. It works on your laptop. Now what? Deploying an agent means running a persistent process that handles API calls, manages state, and stays online. Most tutorials stop at the local demo.

Why deploying an agent is different from deploying an app

AI agents are stateful, long-running, and resource-intensive. They make external API calls (to OpenAI, Anthropic, etc.), manage conversation history, and often need persistent storage. A standard static site deployment will not work. You need a server runtime that stays alive, handles concurrent requests, and manages secrets like API keys securely.

The simplest deployment pattern

Wrap your agent in an HTTP server. FastAPI for Python agents, Express for TypeScript agents, or any framework that exposes a POST endpoint. The endpoint receives a prompt, runs the agent, and returns the response. This turns any agent into a standard API that can be deployed like any other web service.

What your agent deployment needs

A production agent deployment requires: a persistent server process (not serverless, since agents are often long-running), environment variables for API keys (OpenAI, Anthropic, database URLs), enough memory for model inference or API orchestration (512MB-1024MB typical), and HTTPS for secure communication. Optional but valuable: cron jobs for scheduled agent tasks, log streaming for debugging, and scaling for handling concurrent requests.

Approaches compared

Platform CLI (CreateOS, Railway, Render)

Pros

  • One-command deploy
  • Environment variables built in
  • Cron jobs for scheduled agent tasks
  • Log streaming for debugging
  • Scaling from 1-3 replicas

Cons

  • Platform dependency

Best for: Developers who want to ship agents fast without managing infrastructure

Docker on a VPS (DigitalOcean, Hetzner)

Pros

  • Full control
  • Cheap for persistent workloads
  • GPU access possible

Cons

  • Manual SSL, monitoring, and updates
  • No built-in scaling
  • DevOps knowledge required

Best for: Developers comfortable with server management who need GPU access

Serverless (AWS Lambda, Vercel Functions)

Pros

  • No server management
  • Auto-scales to zero

Cons

  • Timeout limits (often 30s-60s)
  • Cold starts degrade UX
  • Not designed for long-running agent tasks
  • Stateless between invocations

Best for: Simple, stateless agent wrappers with fast responses only

Agent platforms (Dedalus Labs, MuleRun)

Pros

  • Purpose-built for agents
  • MCP integration
  • Marketplace distribution

Cons

  • Early stage, smaller ecosystems
  • Agent-specific, not general purpose

Best for: Teams building MCP-native agents for AI tool ecosystems

Deploy an AI agent with CreateOS CLI

Here is how to do it step by step using CreateOS CLI.

1

Install the CLI

$ brew install createos

Single binary for macOS and Linux.

2

Set up your agent as an API

$ # Example: FastAPI agent # main.py from fastapi import FastAPI app = FastAPI() @app.post("/agent") async def run_agent(prompt: str): # Your agent logic here return {"response": result}

Wrap your agent in an HTTP framework. Expose a POST endpoint that accepts a prompt and returns the response.

3

Deploy

$ createos login && createos init && createos deploy

Three commands. The CLI auto-detects your Python project, builds it, and deploys. You get a live URL.

4

Set API keys as environment variables

$ createos env set OPENAI_API_KEY=sk-... ANTHROPIC_API_KEY=sk-ant-...

Store API keys securely. Never hardcode them in your source code.

5

Schedule agent tasks (optional)

$ createos cronjobs create --name daily-report --schedule "0 9 * * *" --path /agent/daily-report --method POST

Run your agent on a schedule. Great for daily summaries, monitoring, or data collection agents.

Frequently asked questions

Can I deploy a LangChain agent?
Yes. Wrap your LangChain agent in a FastAPI or Flask server, expose it as an HTTP endpoint, and deploy with any CLI tool. CreateOS auto-detects Python projects and handles the rest.
How much memory does an AI agent need?
Most API-calling agents (LangChain, CrewAI, AutoGen) need 256MB-512MB since the heavy computation happens at the LLM provider. Agents running local models need 1GB+ depending on model size. CreateOS supports 500MB-1024MB per instance.
Can I deploy a multi-agent system?
Yes. Deploy each agent as a separate service with its own URL, or run all agents in a single service. For multi-agent orchestration frameworks like CrewAI or AutoGen, deploy the orchestrator as a single service that manages all agents internally.
How do I handle long-running agent tasks?
Use an async pattern: the initial request starts the task and returns a task ID. The agent runs in the background. A separate status endpoint returns results when done. This avoids HTTP timeouts for tasks that take minutes.
Can agents call other deployed agents?
Yes. Once deployed, each agent has a URL. Agents can call each other via HTTP. This is how multi-agent systems work in production: each agent is an independent service that other agents can invoke.

Related guides

How to Deploy from Your Terminal

Modern CLI tools let you deploy directly from the terminal with a single command. No browser, no dashboard, no clicking. Push code, see build logs stream in real time, and get a live URL printed back to your terminal.

Read guide

How to Schedule Cron Jobs Without Crontab

Modern deployment platforms include cron job scheduling as a built-in feature, managed through the CLI. You create jobs with a single command, get execution history with status codes and duration, and suspend or resume without deleting. No server access, no external service, no silent failures.

Read guide

How to Deploy an MCP Server

Deploy your MCP server as a standard HTTP service with SSE (Server-Sent Events) or Streamable HTTP transport. Any deployment platform that supports persistent processes can host it. The server becomes a remote endpoint that any MCP-compatible AI client can connect to.

Read guide

Try it yourself

$ brew install createos
Full documentationGitHub

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