team_with_agentic_knowledge_filters.py
"""
Team With Agentic Knowledge Filters
===================================
Demonstrates AI-driven dynamic knowledge filtering for team retrieval.
"""
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.utils.media import (
SampleDataFileExtension,
download_knowledge_filters_sample_data,
)
from agno.vectordb.lancedb import LanceDb
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
downloaded_cv_paths = download_knowledge_filters_sample_data(
num_files=5, file_extension=SampleDataFileExtension.PDF
)
vector_db = LanceDb(
table_name="recipes",
uri="tmp/lancedb",
)
knowledge = Knowledge(
vector_db=vector_db,
)
knowledge.insert_many(
[
{
"path": downloaded_cv_paths[0],
"metadata": {
"user_id": "jordan_mitchell",
"document_type": "cv",
"year": 2025,
},
},
{
"path": downloaded_cv_paths[1],
"metadata": {
"user_id": "taylor_brooks",
"document_type": "cv",
"year": 2025,
},
},
{
"path": downloaded_cv_paths[2],
"metadata": {
"user_id": "morgan_lee",
"document_type": "cv",
"year": 2025,
},
},
{
"path": downloaded_cv_paths[3],
"metadata": {
"user_id": "casey_jordan",
"document_type": "cv",
"year": 2025,
},
},
{
"path": downloaded_cv_paths[4],
"metadata": {
"user_id": "alex_rivera",
"document_type": "cv",
"year": 2025,
},
},
]
)
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
web_agent = Agent(
name="Knowledge Search Agent",
role="Handle knowledge search",
knowledge=knowledge,
model=OpenAIResponses(id="gpt-5-mini"),
instructions=["Always take into account filters"],
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
team_with_knowledge = Team(
name="Team with Knowledge",
members=[web_agent],
model=OpenAIResponses(id="gpt-5-mini"),
knowledge=knowledge,
show_members_responses=True,
markdown=True,
enable_agentic_knowledge_filters=True,
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
team_with_knowledge.print_response(
"Tell me about Jordan Mitchell's work and experience with user_id as jordan_mitchell"
)
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 lancedb openai pyarrow pypdf
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
team_with_agentic_knowledge_filters.py, then run:python team_with_agentic_knowledge_filters.py