> ## 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.

# Gemini Interactions - Deep Research multi-turn

> Continue a Deep Research interaction across turns.

Continue a Deep Research interaction across turns. Each response carries an interaction\_id; the next turn references it via `previous_interaction_id` so the API only receives the new user message (the server already has the prior research and its citations).

```python deep_research_multi_turn.py theme={null}
"""
Gemini Interactions - Deep Research multi-turn
===============================================

Continue a Deep Research interaction across turns. Each response carries an
interaction_id; the next turn references it via `previous_interaction_id`
so the API only receives the new user message (the server already has the
prior research and its citations).

Persisting the interaction_id requires a db (e.g. SqliteDb): the assistant
message stores it under provider_data, and the next turn reads it back.

A common Deep Research multi-turn flow:
  1. Turn 1: ask for a plan (collaborative_planning=True returns just the plan)
  2. Turn 2: approve or refine the plan
  3. Turn 3+: drill into specific sections of the report

For the dedicated plan/approve flow see deep_research_collaborative_planning.py.
"""

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.google import GeminiInteractions

agent = Agent(
    model=GeminiInteractions(
        agent="deep-research-preview-04-2026",
        thinking_summaries="auto",
    ),
    add_history_to_context=True,
    db=SqliteDb(db_file="tmp/data.db"),
    markdown=True,
)

if __name__ == "__main__":
    # Turn 1 - kick off the research task.
    agent.print_response(
        "Research the current state of solid-state battery commercialization "
        "and summarize the leading approaches."
    )

    # Turn 2 - drill into one approach. The server has the prior research;
    # only this question is sent on the wire.
    agent.print_response(
        "Dive deeper into the sulfide-electrolyte approach: who the leading "
        "labs and companies are, and what their reported milestones look like."
    )

    # Turn 3 - synthesize across turns.
    agent.print_response(
        "Based on everything we've covered, which approach has the clearest "
        "path to mass-market EV deployment in the next five years?"
    )
```

## Run the Example

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

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

  <Step title="Export your Google API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export GOOGLE_API_KEY="your_google_api_key_here"
      ```

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

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

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

Full source: [cookbook/90\_models/google/gemini\_interactions/deep\_research\_multi\_turn.py](https://github.com/agno-agi/agno/blob/main/cookbook/90_models/google/gemini_interactions/deep_research_multi_turn.py)