Build with Coding Agents

Run the setup skill inside a freshly cloned Starter:

/setup-platform

The coding agent configures the local environment, starts AgentOS with Docker, verifies the MCP endpoint, builds your first agent, and helps you connect the AgentOS UI. Each Starter gives coding agents access to the project, live AgentOS API, evals, traces in PostgreSQL, and container logs. They can use that context to diagnose behavior, edit the implementation, and verify the result.

Skills

The skills live in .agents/skills/. Claude Code discovers them through the committed .claude/skills symlink. Codex and Cursor can use the same folder. Invoke a skill by name or describe the task and let the coding agent select it.

Skill	What it does	Use when
`/setup-platform`	Configures a fresh clone, starts AgentOS locally, verifies MCP, and builds the first agent.	Starting with a new Starter
`/create-new-agent`	Scaffolds an agent, registers it in `app/main.py`, and smoke-tests it live.	Adding a new agent
`/extend-agent`	Adds a tool or capability, refines instructions, or fixes a known bug.	The required change is already clear
`/improve-agent`	Derives probes from the agent’s `INSTRUCTIONS`, judges responses, makes focused edits, and reruns affected probes.	Improving behavior through simulations and probes
`/eval-and-improve`	Runs the eval suite, classifies failures, proposes scoped fixes, and reruns affected cases.	Resolving an eval regression
`/review-and-improve`	Checks documentation, code, and configuration together and fixes straightforward inconsistencies.	Maintaining the project after changes

Connect the live platform

Coding agents can edit the project and call its live agents over MCP. Register the local AgentOS with supported clients:

uvx agno connect

The command detects Claude Code, Claude Desktop, Codex, and Cursor and registers http://localhost:8000/mcp. Your coding agent can then call run_agent, run_team, and run_workflow against the running platform. For a deployed platform, pass its URL:

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

AgentOS configuration	What `agno connect` does
MCP without authentication	Writes a tokenless client entry
Token-protected AgentOS with an admin credential	Mints a scoped service-account token for each client
MCP OAuth	Writes the connection and prints the client sign-in step
Headless client using MCP OAuth	Use `--pat` with an admin credential or existing admin PAT

See agno connect for client configuration, credential requirements, and conflict handling.

Evals

The eval suite lives in evals/. Set up the host environment once:

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

Run the suite by scope:

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

Cases use AgentAsJudgeEval for model-judged behavior and ReliabilityEval for tool-call assertions. Results are stored in PostgreSQL and appear in the AgentOS UI beside sessions and traces. If a case fails, run /eval-and-improve.

Agent patterns

The bundled agents demonstrate three patterns:

Pattern	Example	When to use
Direct tools	`agents/web_search.py`	The agent needs fine-grained control over each tool call.
Context provider	`agents/platform_manager.py`	Several tools can sit behind one `query_<thing>` interface managed by a sub-agent.
Studio builder	`agents/agent_builder.py`	Users create and refine components from chat with confirmation gates for destructive actions.

Teams and workflows

Pattern	When to use
Teams	Route to specialists, coordinate parallel work, and synthesize results.
Workflows	Run a process with defined, repeatable control flow.

Use agents for open questions, teams for routing, and workflows for processes.

Scheduled tasks

The scheduler is enabled by default. Two reference workflows are registered:

Workflow	What it does	Default
Deployment Check	Runs a daily readiness report for the database, authentication, scheduler, MCP, and Slack configuration. It uses fixed checks with no model calls.	On. Disable with `ENABLE_DEPLOY_CHECK=False`.
Run Evals	Runs the smoke-tag eval suite daily and uses model calls.	Off. Enable with `ENABLE_SCHEDULED_EVALS=True`.

See Scheduler for the cron API.

Get Started

Starters

Apps

Agno CLI

Build with Coding Agents

Skills

Connect the live platform

Evals

Agent patterns

Teams and workflows

Scheduled tasks

​Skills

​Connect the live platform

​Evals

​Agent patterns

​Teams and workflows

​Scheduled tasks

Skills

Connect the live platform

Evals

Agent patterns

Teams and workflows

Scheduled tasks