Knowledge Tool

Let an agent and team write new facts into a PgVector knowledge base with update_knowledge.

knowledge_tool.py
"""
Knowledge Tool
=============================

Demonstrates knowledge tool.
"""

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.team.team import Team
from agno.vectordb.pgvector import PgVector

# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------


db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

kb = Knowledge(
    vector_db=PgVector(
        table_name="documents",
        db_url=db_url,
    ),
)

agent = Agent(
    knowledge=kb,
    update_knowledge=True,
)

# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    agent.print_response(
        "Update your knowledge with the fact that cats and dogs are pets", markdown=True
    )

    team = Team(
        name="Knowledge Team",
        members=[agent],
        knowledge=kb,
        update_knowledge=True,
    )
    team.print_response(
        "Update your knowledge with the fact that cats don't like water", markdown=True
    )

update_knowledge=True exposes add_to_knowledge_base. The model decides when to call it, and it inserts the supplied query/result text into the shared knowledge store in this example. It does not verify facts or update model weights. The cat/water statement is demonstration input, not a universally true fact; use information you intend to retain. Agent and team share the same PgVector table, while no session-history database is configured.

Run the Example

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U agno "psycopg[binary]" openai pgvector sqlalchemy

Export your OpenAI API key

export OPENAI_API_KEY="your_openai_api_key_here"

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18

Run the example

Save the code above as knowledge_tool.py, then run:

python knowledge_tool.py

Full source: cookbook/91_tools/knowledge_tool.py