Google Cloud Run Reference

The Cloud Run service is agent-os, the Cloud SQL instance is agentos-db, and the Artifact Registry repo is agentos. The scripts target the current gcloud project and us-central1; override them with GCP_PROJECT_ID and GCP_REGION.

Manage

Task	Command
Deploy code changes	`./scripts/gcp/redeploy.sh`
Sync env variables	`./scripts/gcp/env-sync.sh` (defaults to `.env.production`; pass `.env` to sync that instead)
Tail logs	`gcloud run services logs read agent-os --region us-central1 --limit 100`
Resize the service	Edit the `--cpu`/`--memory` flags in `scripts/gcp/up.sh` and rerun it
Tear down	`./scripts/gcp/down.sh` (add `--yes` to skip the confirmation)

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. Token-Based Auth gives you three things:

No public access. The server rejects requests without a valid token.
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.
Granular permissions. User tokens can run an agent and view their own sessions. Admin tokens read everyone’s sessions and test any agent.

To opt out (not recommended), set 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 Cloud Run URL can access your platform.

Customize

Add an agent

Ask your coding agent to run /create-new-agent, or do it by hand. Create agents/my_agent.py:

from agno.agent import Agent

from app.settings import default_model
from db import get_postgres_db

INSTRUCTIONS = """\
What the agent does, which tools it uses, the rules to follow when answering.
"""

my_agent = Agent(
    id="my-agent",
    name="My Agent",
    model=default_model(),
    db=get_postgres_db(),
    instructions=INSTRUCTIONS,
    enable_agentic_memory=True,
    add_datetime_to_context=True,
    add_history_to_context=True,
    num_history_runs=5,
)

from agents.my_agent import my_agent

agent_os = AgentOS(
    ...
    agents=[agent_builder, platform_manager, web_search, my_agent],
)

Local containers hot-reload on save. For production, run ./scripts/gcp/redeploy.sh.

Change the model

app/settings.py defines default_model(), used by every agent. Change it in one place:

from agno.models.anthropic import Claude

def default_model():
    return Claude(id="claude-sonnet-5")

Add anthropic to pyproject.toml, set the provider key in your env, and regenerate pins:

./scripts/generate_requirements.sh

Rebuild locally with docker compose up -d --build. For production:

./scripts/gcp/env-sync.sh
./scripts/gcp/redeploy.sh

Add tools

Agno ships 100+ toolkits. See Toolkits.

from agno.tools.slack import SlackTools

my_agent = Agent(
    ...
    tools=[SlackTools()],
)

Add dependencies

Edit pyproject.toml.
Regenerate pins: ./scripts/generate_requirements.sh (add upgrade to refresh every pin).
Rebuild locally with docker compose up -d --build, or redeploy with ./scripts/gcp/redeploy.sh.

Enable Slack

Set both variables in your env file:

SLACK_BOT_TOKEN=xoxb-...
SLACK_SIGNING_SECRET=...

Sync with ./scripts/gcp/env-sync.sh. The interface activates automatically and routes messages to Agent Builder; change the agent= argument in app/main.py to point at another agent. See Slack setup.

Toggle scheduled workflows

The deployment check runs daily by default (ENABLE_DEPLOY_CHECK=True); it is deterministic and free. Scheduled evals are off by default (ENABLE_SCHEDULED_EVALS=False) because they use model calls. Both workflows stay runnable on demand regardless.

Format, validate, and run evals

The format, validate, and eval scripts run on the host and need a venv. Set it up once:

./scripts/venv_setup.sh
source .venv/bin/activate

Task	Command
Format	`./scripts/format.sh`
Lint and type-check	`./scripts/validate.sh`
Run smoke evals	`python -m evals --tag smoke`

./scripts/mcp_check.sh runs inside the container, so it needs no venv.

Environment variables

Variable	Required	Default	Description
`OPENAI_API_KEY`	Yes	-	Models and embeddings.
`RUNTIME_ENV`	No	`prd`	`dev` disables JWT. Compose sets it for local. Never put it in an env file that syncs to Cloud Run, or production deploys unauthenticated.
`JWT_VERIFICATION_KEY`	Production	-	Public key from os.agno.com. Quote the value so the multi-line PEM parses as one variable.
`JWT_JWKS_FILE`	Production	-	Path to a JWKS file. Alternative to `JWT_VERIFICATION_KEY`.
`MCP_CONNECT_SECRET`	No	generated by `up.sh`	OAuth consent secret (16+ chars) for connecting claude.ai and ChatGPT to `/mcp`. `up.sh` generates one on deploy and writes it to `.env.production`.
`AGENTOS_MCP_SIGNING_KEY`	No	generated	Optional high-entropy signing-key material (32+ chars) for OAuth tokens. Unset, a strong key is generated and persisted in the database. Rotating it invalidates outstanding tokens.
`AGENTOS_URL`	No	`http://127.0.0.1:8000`	Scheduler base URL. `up.sh` sets it to your Cloud Run URL. Scheduled jobs never fire if it stays at the default in production. Also the public origin OAuth metadata derives from when `MCP_CONNECT_SECRET` is set.
`ENABLE_DEPLOY_CHECK`	No	`True`	Daily deployment-check cron.
`ENABLE_SCHEDULED_EVALS`	No	`False`	Daily run-evals cron. Uses model calls.
`EVALS_TAG`	No	`smoke`	Eval tag the run-evals workflow runs.
`EVALS_CASE_TIMEOUT_SECONDS`	No	`90`	Per-case timeout for run-evals runs.
`EVALS_SUITE_TIMEOUT_SECONDS`	No	`900`	Whole-suite timeout for run-evals runs.
`PARALLEL_API_KEY`	No	-	WebSearch uses the Parallel SDK when set, keyless MCP otherwise.
`SLACK_BOT_TOKEN`	No	-	Set with the signing secret to enable Slack.
`SLACK_SIGNING_SECRET`	No	-	Set with the bot token to enable Slack.
`GCP_PROJECT_ID` / `GCP_REGION`	No	current project, `us-central1`	Project and region the `scripts/gcp/` scripts target. `up.sh` records `GCP_REGION` in your env file so `down.sh` finds the right region.
`DB_HOST` / `DB_PORT` / `DB_USER` / `DB_PASS` / `DB_DATABASE`	No	matches compose	Postgres connection.
`DB_DRIVER`	No	`postgresql+psycopg`	SQLAlchemy driver.
`AGNO_DEBUG`	No	`False`	Verbose Agno logs. Compose sets it for dev.
`WAIT_FOR_DB`	No	`False`	If `True`, the entrypoint blocks on the database before starting. Compose sets it.

Troubleshooting

gcloud: command not found

Install the Google Cloud SDK, then run gcloud auth login and gcloud config set project <id>.

up.sh pauses asking for a JWT key

Expected. The script deploys first because Cloud Run only reveals the URL once the service exists, then pauses so you can mint the key against it. Mint the key at os.agno.com: connect your OS (Connect OS → Live, enter your Cloud Run URL), then turn on Token-Based Authorization (JWT) under Settings → OS & Security and paste the full PEM. To do it later, skip the prompt, add JWT_VERIFICATION_KEY or JWT_JWKS_FILE to .env.production, and run ./scripts/gcp/env-sync.sh.

App refuses to serve in production

JWT auth is on whenever RUNTIME_ENV is not dev. Set JWT_VERIFICATION_KEY or JWT_JWKS_FILE and sync. To opt out inside a private VPC behind another auth layer, set authorization=False in app/main.py.

Every request returns 403

Your organization likely enforces Domain Restricted Sharing (constraints/iam.allowedPolicyMemberDomains), which silently rejects the allUsers binding that --allow-unauthenticated needs. The deploy still succeeds; the service just ships private. up.sh prints a warning when it detects this. Grant roles/run.invoker to specific principals, or ask an org admin for an allUsers exception.

Cloud SQL or Cloud Run creation fails

Check that billing is enabled on the project. up.sh warns when it can’t confirm billing; Cloud SQL and Cloud Run creation both fail without it.

Scheduled jobs never fire

AGENTOS_URL is still the localhost default. up.sh sets it to your Cloud Run URL automatically; for a custom domain or tunnel, set it by hand and run ./scripts/gcp/env-sync.sh.

down.sh says the service is not found

down.sh is targeting a different region than the one you deployed to. up.sh records GCP_REGION in your env file and down.sh reads it from there; if the file is gone, rerun with GCP_REGION=<region> ./scripts/gcp/down.sh. A wrong-region teardown looks clean while the real resources keep billing.

Get Started

Starters

Apps

Agno CLI

Google Cloud Run Reference

Manage

Production auth

Customize

Format, validate, and run evals

Environment variables

Troubleshooting

​Manage

​Production auth

​Customize

​Format, validate, and run evals

​Environment variables

​Troubleshooting

Manage

Production auth

Customize

Format, validate, and run evals

Environment variables

Troubleshooting