Skip to main content
AgentOS is where your company’s AI agents run. You write an agent in whatever language or framework your team prefers — plain Python, TypeScript, LangGraph, the OpenAI Agents SDK, Agno, Mastra, the Vercel AI SDK, Upsonic, anything that can serve HTTP — package it with a Dockerfile, and upload it. AgentOS builds it, runs it, and puts the same controls around every agent:
  • Model access through one gateway, with each environment’s own providers and models, and tokens and cost recorded per run.
  • Guardrails that check what goes to and comes from the models (national IDs, IBANs, card numbers, secrets, your own patterns), and network guardrails that decide which hosts an agent may reach.
  • Human approvals: an agent can stop and ask a person, and continue with their decision.
  • Memory, state and sessions kept by the platform, so agents stay stateless.
  • Evals that score a version on a dataset before it takes traffic, and compare models and prompts.
  • An audit log of every action, and webhooks to tell your other systems what happened.
AgentOS is framework and language agnostic by design. Everything an agent does with the platform is a plain HTTP call, so the Python and TypeScript SDKs are a convenience, never a requirement.

​
How it works

  1. You upload a bundle: a zip with your code, a Dockerfile and an agentos.yaml that names the agent’s capabilities and their input and output schemas.
  2. AgentOS builds an image, starts a container and waits until it answers GET /health. That is a new version of the agent.
  3. A caller starts a run of a capability. AgentOS checks the input against the schema and calls your container’s POST /invoke with it, plus a short-lived run token.
  4. With that token your code calls models through the gateway, keeps state, saves memory, uploads files, starts child runs or asks a person for a decision.
  5. Your code answers completed with an output, failed with an error, or suspended while a person decides. The run, its model calls, tokens, cost and every event are on the run page.

​
Key concepts

​
Roles

Everyone works inside environments with one of two roles:
  • Members run agents, read runs, decide approvals they are allowed to, and work on evals.
  • Admins also deploy agents and change models and providers, guardrails, secrets, webhooks and network rules. Members can see these settings (never a secret’s value) but not change them.
Scripts and other systems use a service account with an API key. See Users and API keys.

​
Next steps

Quickstart

Deploy your first agent and run it, in the panel and with the API.

Build an agent

The two endpoints every agent serves, and what it gets on each run.

SDKs

Python and TypeScript helpers for approvals, state, memory and models.

API reference

Deploy, run, decide and evaluate from your own code.