AgentOS: Serve agents over API, MCP, and interfaces like Slack

AgentOS is a durable agent runtime that serves agents over API, MCP, and chat interfaces like Slack. Build customer-facing agents and serve them to your users from your product, through AI apps like Claude and ChatGPT, or interfaces like Slack. AgentOS gives you one agent backend for every frontend.

Three ways to build agents.

1. Coding agent. Point a coding agent at the skills in .agents/skills/ and it can create, improve and evaluate your agents for you.
2. Natural language. Ask the built-in Platform Builder to build agents for you.
3. No-code Studio. Build agents visually using the AgentOS Studio.

Three ways to serve your agents to your users.

1. Your product. Call the AgentOS REST API from your product.
2. AI apps. Connect your agents to Claude and ChatGPT using the AgentOS MCP server.
3. Chat interfaces. Distribute your agents through Slack, WhatsApp (and more) using AgentOS Interfaces.

Monitor and govern your agents.

The AgentOS Control Plane gives you a unified view of your agent platform. Trace every action. Enforce agent- and tool-level permissions.

Everything runs in your cloud, your data lives in your database.

Get Started

Copy this prompt into your favorite coding agent. It sets up the platform and builds your first agent for you:

Help me set up my agent platform and build my first agent.

Clone https://github.com/agno-agi/agentos-railway into a folder called agent-platform, cd in, and run the setup-platform skill (in .agents/skills/).

Your coding agent checks Docker, sets up .env, boots the platform, verifies the MCP endpoint, connects to the AgentOS UI, then builds your first agent. Prefer to drive yourself? See Manual Setup.

Manual Setup

Step 1: Run locally

Prerequisite: Docker installed and running.

git clone https://github.com/agno-agi/agentos-railway agentos
cd agentos

# Configure credentials
cp example.env .env
# Open .env and set OPENAI_API_KEY

# Run the platform on docker
docker compose up -d --build

Confirm your AgentOS is running at http://localhost:8000/docs.

Step 2: Connect the AgentOS UI

1. Open os.agno.com and sign in.
2. Click Connect OS, enter http://localhost:8000 as the URL, name it Local AgentOS, and connect.

Step 3: Build your first agent using natural language

1. Click Chat under the Agno team and tell it what you're working on: "Help me build an agent for my product".
2. Give it the docs URL for your product, or for a product you like — docs.agno.com, say.
3. Click the Refresh button on the top right. You should now see your new agent in the Agents dropdown. Chat with it directly, or just ask Agno to run it for you.

Make the platform yours

Your cloned repo points at this public template. Create your own GitHub repo and point your platform at it:

git remote rename origin upstream    # keep the template connected for updates
git remote add origin <your-private-repo-url>
git push -u origin main

Heads up. Create the private repo first (github.com/new, or gh repo create <name> --private). Keep upstream connected, so that git pull upstream main brings in template updates in the future.

Run in production

You can run the platform anywhere that supports containers. This codebase comes with scripts to deploy the platform to Railway — and a coding-agent skill, /deploy-platform, that will help you deploy it.

Prerequisite: Railway CLI installed and railway login completed.

1. Set up your production env

Create a new .env.production file for production credentials.

cp .env .env.production          # or cp example.env .env.production
# Edit .env.production with production values

Keeping a separate .env.production lets us use different values for local and production: different OpenAI keys, production-only credentials, a different Slack workspace.

2. Deploy

./scripts/railway/up.sh

This provisions the AgentOS service and Postgres on the same private network. The script pauses and asks for a JWT verification key for authentication (see next section).

3. Production Auth

Token-Based Authorization is on by default. Without a JWT_VERIFICATION_KEY or JWT_JWKS_FILE, the app refuses to serve traffic in production. The platform's job is to keep your data private, so the safe default is "refuse to start" without an authentication token.

Token-Based Auth gives you three things:

1. No public access. The server rejects requests without a valid token.
2. Per-request identity. Middleware parses the token and extracts the user_id, session_id, and custom claims. Each request is tied to a user and session, giving you auditability and traceability.
3. Granular permissions. Scopes on the token decide what each caller can do — run agents, read sessions, manage the platform. Admin tokens can do everything; scoped tokens get exactly what their claims grant.

During ./scripts/railway/up.sh, the script creates your Railway domain and pauses so you can mint the key before the app starts.

1. Open os.agno.com, click Connect OS → Live, and enter your Railway domain.
2. Name it Live AgentOS, flip Token-Based Authorization (JWT) on and connect. The UI generates your public key. (Ran into an issue? Go to Settings → OS & Security → Token-Based Authorization (JWT) to get the key from the settings page.)
3. Copy the public key.
4. Paste the full public key into the up.sh prompt. The script saves it into your env file for future syncs:

JWT_VERIFICATION_KEY="-----BEGIN PUBLIC KEY-----
MIIBIjANBgkq...
-----END PUBLIC KEY-----"

If you get something wrong, you can re-sync environment variables with ./scripts/railway/env-sync.sh.

4. Verify

You can check the logs on the Railway dashboard, or by running the following command:

railway logs --service agent-os

5. Connect your AgentOS to MCP clients

AgentOS comes with an MCP server at /mcp (wired via mcp=MCPConfig(...) in app/main.py), where Agno itself is published as a first-class agno tool — clients just call it, no id discovery. There are two ways to connect your AgentOS to MCP clients:

1. AI Apps like Claude and ChatGPT connect to your AgentOS over the internet using OAuth. Add https://<railway-domain>/mcp as a custom connector in the chat app's connector settings. Leave the form's optional OAuth fields (client ID / client secret) empty. Click Connect and, on the consent page, enter the MCP_CONNECT_SECRET that up.sh generated during deploy (saved in .env.production).
2. Coding agents like Claude Code, Claude Desktop, Codex, and Cursor connect to your AgentOS via the MCP URL. Register your AgentOS with the MCP clients on your machine:

uvx agno connect --url https://<railway-domain>

After a successful connection, open one of these apps and ask:

can you access my agentos mcp?

Redeploy after code changes

To redeploy your AgentOS, run the following command:

./scripts/railway/redeploy.sh

Recommended: Auto-deploy on merge to main using:

1. Open the Railway dashboard, your project, the agent-os service, Settings.
2. Under Source, click Connect Repo and pick your repo.
3. Set the deploy branch to main and save.

Push to main triggers a build and rolling deploy. ./scripts/railway/env-sync.sh is still how you sync env changes.

Sync environment variables

To re-sync environment variables, run the following command:

./scripts/railway/env-sync.sh

Tear down

./scripts/railway/down.sh

Deletes the Railway project: the agent-os service, the pgvector database, and its volume, including all data. It also comments out the Railway-minted AGENTOS_URL and JWT_VERIFICATION_KEY in your env file, so a future up.sh derives a fresh domain and re-runs its guided key step. Custom domains are preserved.

Opting out of JWT (not recommended)

Change authorization=runtime_env != "dev" to authorization=False in app/main.py and redeploy. Use this only inside a private VPC behind another auth layer. Without it, anyone who guesses your Railway domain can access your platform.

Using the platform

This platform is designed so that coding agents can drive the entire create → improve → evaluate → maintain lifecycle for you.

Create

Open your coding agent of choice (Claude Code, Codex, Cursor) and run:

/create-agent

It asks a few questions, generates the agent file in agents/, registers it in app/main.py, adds its description and quick prompts to app/config.yaml, restarts the container, and smoke-tests it for you.

Improve

Improve your agents by running the following skills:

• /extend-agent — Add a tool, add a capability, refine the instructions, fix a known bug.
• /improve-agent — Claude simulates scenarios from the agent's INSTRUCTIONS and its real usage recorded in the database, runs them against the live container, judges the responses, and edits until they pass.

Evaluate

Run the eval suite to check for regressions. The evals live in evals/cases.py, and run history shows up in the AgentOS UI next to your sessions and traces.

The evals run on the host machine, so set up the venv with ./scripts/venv_setup.sh && source .venv/bin/activate, then run:

python -m evals --tag smoke      # fast checks of the self-driving surfaces
python -m evals --tag release    # broader pre-release confidence
python -m evals --name <case>    # one case while iterating
python -m evals -v               # stream the full run with rich panels

If a case fails, run /eval-and-improve — it diagnoses each failure, fixes what's in scope, and loops until green.

Maintain

Because the repo is managed by coding agents, it moves fast. Run /review-and-improve before a release or after a refactor: it sweeps for drift between docs, code, and config, auto-fixes mechanical drift like stale paths and missing env vars, and flags anything bigger.

Environment variables

Learn more

• Agno documentation
• AgentOS introduction
• Agno on GitHub. Drop a star if this is useful.