studio_runner_direct.py
Run the Example
1
Set up your virtual environment
2
Install dependencies
3
Export your OpenAI API key
4
Run the example
Save the code above as
studio_runner_direct.py, then run:Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
The runner’s tools are plain methods, so a platform can call them without a wielding model.
"""
StudioRunnerTools called directly: list, run, and refusal semantics
===================================================================
The runner's tools are plain methods, so a platform can call them without a
wielding model. This example builds two published components, lists them, runs
one by id, and shows the registry guard: a runner constructed without the
registry refuses to run a component whose stored config references
registry-backed resources, because the rebuild would silently drop them.
Dispatch resolves only published versions; creates pass publish=True here (the
draft-inert behavior is demonstrated in registry_and_components.py).
Prerequisites: OPENAI_API_KEY
Run: .venvs/demo/bin/python cookbook/05_agent_os/22_studio/studio_runner_direct.py
Try: pass registry=registry to the second runner and watch the refusal clear
"""
import json
from pathlib import Path
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.registry import Registry
from agno.tools.calculator import CalculatorTools
from agno.tools.studio import StudioTools
from agno.tools.studio_runner import StudioRunnerTools
# ---------------------------------------------------------------------------
# Create two components: one plain, one with registry-backed tools
# ---------------------------------------------------------------------------
DB_DIR = Path(__file__).parent / "tmp"
DB_DIR.mkdir(exist_ok=True)
DB_FILE = DB_DIR / "studio_runner_direct.db"
DB_FILE.unlink(missing_ok=True)
db = SqliteDb(
id="studio-runner-direct-db",
db_file=str(DB_FILE),
)
registry = Registry(
name="Direct Runner Registry",
models=[OpenAIResponses(id="gpt-5.5")],
tools=[CalculatorTools()],
dbs=[db],
)
builder = StudioTools(registry=registry, db=db, default_model_id="gpt-5.5")
# Every StudioTools result is a StudioResult envelope; branch on ok and
# error.code, never on message text.
for name, instructions, tool_names in (
("Greeter", "Greet the user in one short sentence.", None),
("Calculator Agent", "Solve arithmetic with the calculator tool.", ["calculator"]),
):
result = json.loads(
builder.create_agent(
name=name,
instructions=instructions,
model_id="gpt-5.5",
tool_names=tool_names,
publish=True,
)
)
if not result["ok"]:
raise RuntimeError(f"create_agent failed: {result['error']['code']}")
# ---------------------------------------------------------------------------
# Run them as plain methods
# ---------------------------------------------------------------------------
def main() -> None:
runner = StudioRunnerTools(registry=registry, db=db)
listing = json.loads(runner.list_agents())
print("Agents in the platform database:")
for row in listing["agents"]:
print(" -", row["id"], "|", row["name"])
result = json.loads(runner.run_agent("greeter", "Hello there."))
print("Run status:", result["status"])
print("Run content:", result["content"])
# Without the registry, the tool-bearing component is refused: rebuilding
# it would drop the calculator and run a silently degraded agent.
registry_less = StudioRunnerTools(db=db)
refusal = json.loads(registry_less.run_agent("calculator-agent", "What is 2 + 2?"))
print("Registry-less refusal:", refusal["error"])
if __name__ == "__main__":
main()
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Install dependencies
uv pip install -U "agno[scheduler]" openai sqlalchemy
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
Run the example
studio_runner_direct.py, then run:python studio_runner_direct.py