> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Team With Custom Retriever

> Demonstrates a custom team knowledge retriever that uses runtime dependencies.

```python team_with_custom_retriever.py theme={null}
"""
Team With Custom Retriever
==========================

Demonstrates a custom team knowledge retriever that uses runtime dependencies.
"""

from typing import Optional

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.openai import OpenAIResponses
from agno.run import RunContext
from agno.team import Team
from agno.vectordb.pgvector import PgVector

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
vector_db = PgVector(
    table_name="team-knowledge",
    embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    db_url=db_url,
)
knowledge = Knowledge(vector_db=vector_db)

knowledge.insert(
    url="https://docs.agno.com/llms-full.txt",
)


def knowledge_retriever(
    query: str,
    team: Optional[Team] = None,
    num_documents: int = 5,
    run_context: Optional[RunContext] = None,
    **kwargs,
) -> Optional[list[dict]]:
    """Custom team knowledge retriever that can inspect runtime dependencies."""
    dependencies = run_context.dependencies if run_context else None

    if dependencies:
        print(f"[Team Retriever] Dependencies received: {list(dependencies.keys())}")

        project_id = dependencies.get("project_id")
        user_role = dependencies.get("role")
        team_context = dependencies.get("team_context")

        if project_id:
            print(f"[Team Retriever] Project ID: {project_id}")

        if user_role:
            print(f"[Team Retriever] User role: {user_role}")

        if team_context:
            print(f"[Team Retriever] Team context: {team_context}")
    else:
        print("[Team Retriever] No dependencies available")

    try:
        docs = knowledge.search(
            query=query,
            max_results=num_documents,
        )
        print(f"[Team Retriever] Found {len(docs)} documents")
        return [doc.to_dict() for doc in docs]
    except Exception as e:
        print(f"[Team Retriever] Error: {e}")
        return []


# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
researcher = Agent(
    name="Researcher",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Research information from the knowledge base",
)

analyst = Agent(
    name="Analyst",
    model=OpenAIResponses(id="gpt-5.2"),
    role="Analyze and synthesize information",
)

# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
research_team = Team(
    name="Research Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[researcher, analyst],
    knowledge=knowledge,
    knowledge_retriever=knowledge_retriever,
    search_knowledge=True,
    add_knowledge_to_context=True,
    instructions="Work together to research and analyze information. Always search the knowledge base first using the search_knowledge_base tool before answering.",
)

# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    print("=== Example 1: Team Without Dependencies ===\n")
    response = research_team.run(
        "What are AI agents? Search the knowledge base for information.",
    )
    print(f"\nTeam Response: {response.content}\n")

    print("\n=== Example 2: Team With Runtime Dependencies ===\n")
    response = research_team.run(
        "What are AI agents? Search the knowledge base for information.",
        dependencies={
            "project_id": "project-123",
            "role": "researcher",
            "team_context": {
                "focus_area": "AI/ML",
                "priority": "high",
            },
        },
    )
    print(f"\nTeam Response: {response.content}\n")
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno "psycopg[binary]" beautifulsoup4 openai pgvector sqlalchemy
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Snippet file="run-pgvector-step.mdx" />

  <Step title="Run the example">
    Save the code above as `team_with_custom_retriever.py`, then run:

    ```bash theme={null}
    python team_with_custom_retriever.py
    ```
  </Step>
</Steps>

Full source: [cookbook/03\_teams/05\_knowledge/04\_team\_with\_custom\_retriever.py](https://github.com/agno-agi/agno/blob/main/cookbook/03_teams/05_knowledge/04_team_with_custom_retriever.py)