knowledge.py
"""Upload, monitor, search, and delete AgentOS knowledge content.
Upload processing is asynchronous, so this example polls the concrete
content-status endpoint before listing and searching.
Prerequisites: start ``_server.py`` and set OPENAI_API_KEY.
Run: .venvs/demo/bin/python cookbook/05_agent_os/03_python_client/04_knowledge.py
Try: watch processing move from processing to completed before search runs.
"""
import asyncio
from agno.client import AgentOSClient
from agno.os.routers.knowledge.schemas import ContentStatus
BASE_URL = "http://localhost:7778"
# ---------------------------------------------------------------------------
# Create the Client
# ---------------------------------------------------------------------------
async def wait_until_processed(client: AgentOSClient, content_id: str) -> ContentStatus:
"""Poll an uploaded content item until processing reaches a terminal state."""
for _ in range(60):
status = await client.get_knowledge_content_status(content_id)
print(f"Content status: {status.status.value}")
if status.status is ContentStatus.COMPLETED:
return status.status
if status.status is ContentStatus.FAILED:
raise RuntimeError(status.status_message or "Knowledge processing failed")
await asyncio.sleep(0.5)
raise TimeoutError("Knowledge content did not finish processing")
async def manage_knowledge() -> None:
"""Exercise the full knowledge content lifecycle."""
client = AgentOSClient(base_url=BASE_URL)
uploaded = await client.upload_knowledge_content(
name="Python client notes",
description="Small document uploaded by the Python client cookbook.",
text_content=(
"AgentOS exposes agents, teams, workflows, sessions, memory, "
"knowledge, and evaluations through one HTTP API."
),
metadata={"source": "03_python_client"},
)
print(f"Upload accepted: {uploaded.id}")
await wait_until_processed(client, uploaded.id)
content = await client.list_knowledge_content()
print(f"Knowledge items: {len(content.data)}")
results = await client.search_knowledge(
query="What does AgentOS expose?",
limit=5,
)
print(f"Search results: {len(results.data)}")
for result in results.data:
print(f"- {result.content}")
if result.reranking_score is not None:
print(f" Reranking score: {result.reranking_score}")
deleted = await client.delete_knowledge_content(uploaded.id)
print(f"Deleted content: {deleted.id}")
# ---------------------------------------------------------------------------
# Run the Example
# ---------------------------------------------------------------------------
if __name__ == "__main__":
asyncio.run(manage_knowledge())
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U "agno[os]" chromadb openai
3
Export your API keys
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Clone Agno
Clone the pinned Agno source and run the remaining commands from its root:
git clone https://github.com/agno-agi/agno.git
cd agno
git checkout v3.0.4
5
Start the AgentOS server
Open another terminal in the parent directory where
.venv and the cloned agno directory are siblings, then start the shared server on port 7778:source .venv/bin/activate
export OPENAI_API_KEY="your_openai_api_key_here"
cd agno
python cookbook/05_agent_os/03_python_client/_server.py
.venv\Scripts\activate
$Env:OPENAI_API_KEY="your_openai_api_key_here"
Set-Location agno
python cookbook/05_agent_os/03_python_client/_server.py
6
Run the example
Run the example from the repository root:
python cookbook/05_agent_os/03_python_client/04_knowledge.py