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

# Deep Nested Team History

> Use case: Customer reports a database issue.

Use case: Customer reports a database issue. Triage escalates to Escalation Team, which further escalates to Expert Team for deep investigation. Each team maintains its conversation history across the multi-turn investigation.

```python nested_team_deep_history.py theme={null}
"""
Deep Nested Team History
========================

Demonstrates 3-layer nested team history with a customer support use case.

Structure:
- Support Team (Level 1)
  - Triage Agent
  - Escalation Team (Level 2)
    - Technical Support Agent
    - Expert Team (Level 3)
      - Database Expert Agent
      - Security Expert Agent

Use case: Customer reports a database issue. Triage escalates to Escalation Team,
which further escalates to Expert Team for deep investigation. Each team maintains
its conversation history across the multi-turn investigation.
"""

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.team import Team

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
db = SqliteDb(db_file="tmp/nested_team_deep_history.db")

# ---------------------------------------------------------------------------
# Level 3: Expert Team (innermost)
# ---------------------------------------------------------------------------
db_expert = Agent(
    name="Database Expert",
    model=OpenAIResponses(id="gpt-5.6-sol"),
    role="Diagnose database performance and connectivity issues",
)

security_expert = Agent(
    name="Security Expert",
    model=OpenAIResponses(id="gpt-5.6-sol"),
    role="Investigate security concerns and access issues",
)

expert_team = Team(
    name="Expert Team",
    model=OpenAIResponses(id="gpt-5.6-sol"),
    members=[db_expert, security_expert],
    add_history_to_context=True,
    role="Deep technical investigation requiring specialized expertise",
)

# ---------------------------------------------------------------------------
# Level 2: Escalation Team (middle)
# ---------------------------------------------------------------------------
tech_support = Agent(
    name="Technical Support",
    model=OpenAIResponses(id="gpt-5.6-sol"),
    role="Handle technical issues and coordinate escalations",
)

escalation_team = Team(
    name="Escalation Team",
    model=OpenAIResponses(id="gpt-5.6-sol"),
    members=[tech_support, expert_team],
    add_history_to_context=True,
    role="Handle escalated issues requiring technical expertise",
)

# ---------------------------------------------------------------------------
# Level 1: Support Team (outermost)
# ---------------------------------------------------------------------------
triage_agent = Agent(
    name="Triage Agent",
    model=OpenAIResponses(id="gpt-5.6-sol"),
    role="Initial customer contact and issue classification",
)

support_team = Team(
    name="Support Team",
    model=OpenAIResponses(id="gpt-5.6-sol"),
    members=[triage_agent, escalation_team],
    db=db,
    add_history_to_context=True,
    mode="route",
    show_members_responses=True,
)

# ---------------------------------------------------------------------------
# Run Multi-Turn Support Scenario
# ---------------------------------------------------------------------------
if __name__ == "__main__":
    session_id = "support-case-001"

    # Turn 1: Customer reports issue
    support_team.print_response(
        "Our database queries are timing out. Users cant log in.",
        session_id=session_id,
        stream=True,
    )

    # Turn 2: Follow-up with more details
    support_team.print_response(
        "The timeouts started after we deployed a new feature yesterday.",
        session_id=session_id,
        stream=True,
    )

    # Turn 3: Request resolution status
    support_team.print_response(
        "What have you found so far and whats the recommended fix?",
        session_id=session_id,
        stream=True,
    )
```

## Run the Example

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

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno openai 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>

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

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

Full source: [cookbook/03\_teams/07\_session/nested\_team\_deep\_history.py](https://github.com/agno-agi/agno/blob/v3.0.4/cookbook/03_teams/07_session/nested_team_deep_history.py)