# MongoDB for Workflow (/database/providers/mongo/usage/mongodb-for-workflow)



`MongoDb` stores a Workflow's sessions and run history in MongoDB.

## Usage [#usage]

Start in a [virtual environment](/sdk/setup).

Provide either `db_url` or `db_client`. The default database name is `agno`; set `db_name` to use another database. The following example uses `db_url`.

Install the `agno`, `pymongo`, `openai`, and `ddgs` packages:

```shell
uv pip install agno pymongo openai ddgs
```

### Run MongoDB [#run-mongodb]

Install [Docker Desktop](https://docs.docker.com/get-started/get-docker/), then start MongoDB on port `27017`:

```bash
docker run -d \
  --name local-mongo \
  -p 27017:27017 \
  -e MONGO_INITDB_ROOT_USERNAME=mongoadmin \
  -e MONGO_INITDB_ROOT_PASSWORD=secret \
  mongo
```

Set your OpenAI API key:

## Set OpenAI Key [#set-openai-key]

Set your `OPENAI_API_KEY` as an environment variable. You can get one [from OpenAI](https://platform.openai.com/account/api-keys).

<CodeBlockTabs defaultValue="Mac">
  <CodeBlockTabsList>
    <CodeBlockTabsTrigger value="Mac">
      Mac
    </CodeBlockTabsTrigger>

    <CodeBlockTabsTrigger value="Windows PowerShell">
      Windows PowerShell
    </CodeBlockTabsTrigger>
  </CodeBlockTabsList>

  <CodeBlockTab value="Mac">
    ```bash
    export OPENAI_API_KEY=sk-***
    ```
  </CodeBlockTab>

  <CodeBlockTab value="Windows PowerShell">
    ```powershell
    $env:OPENAI_API_KEY = "your-openai-api-key"
    ```
  </CodeBlockTab>
</CodeBlockTabs>

```python title="mongodb_for_workflow.py"
from agno.agent import Agent
from agno.db.mongo import MongoDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.websearch import WebSearchTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow

db_url = "mongodb://mongoadmin:secret@localhost:27017"

db = MongoDb(db_url=db_url)

# Define agents
hackernews_agent = Agent(
    name="HackerNews Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[HackerNewsTools()],
    role="Extract key insights and content from HackerNews posts",
)
web_agent = Agent(
    name="Web Agent",
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[WebSearchTools()],
    role="Search the web for the latest news and trends",
)

# Define research team for complex analysis
research_team = Team(
    name="Research Team",
    members=[hackernews_agent, web_agent],
    instructions="Research tech topics from HackerNews and the web",
)

content_planner = Agent(
    name="Content Planner",
    model=OpenAIResponses(id="gpt-5.2"),
    instructions=[
        "Plan a content schedule over 4 weeks for the provided topic and research content",
        "Ensure there are 3 posts per week",
    ],
)

# Define steps
research_step = Step(
    name="Research Step",
    team=research_team,
)

content_planning_step = Step(
    name="Content Planning Step",
    agent=content_planner,
)

# Create and use workflow
if __name__ == "__main__":
    content_creation_workflow = Workflow(
        name="Content Creation Workflow",
        description="Automated content creation from blog posts to social media",
        db=db,
        steps=[research_step, content_planning_step],
    )
    content_creation_workflow.print_response(
        input="Recent AI trends",
        markdown=True,
    )
```

## Run the example [#run-the-example]

Save the code as `mongodb_for_workflow.py`, start the database described above, and run:

```shell
python mongodb_for_workflow.py
```

## Parameters [#parameters]

| Parameter                  | Type                    | Default | Description                                                                                   |
| -------------------------- | ----------------------- | ------- | --------------------------------------------------------------------------------------------- |
| `id`                       | `Optional[str]`         | -       | Database ID. Derived deterministically from connection/client and database name when omitted. |
| `db_client`                | `Optional[MongoClient]` | -       | The MongoDB client to use.                                                                    |
| `db_name`                  | `Optional[str]`         | -       | The name of the database to use.                                                              |
| `db_url`                   | `Optional[str]`         | -       | The database URL to connect to.                                                               |
| `session_collection`       | `Optional[str]`         | -       | Name of the collection to store sessions.                                                     |
| `runs_collection`          | `Optional[str]`         | -       | Name of the collection to store individual runs.                                              |
| `memory_collection`        | `Optional[str]`         | -       | Name of the collection to store memories.                                                     |
| `metrics_collection`       | `Optional[str]`         | -       | Name of the collection to store metrics.                                                      |
| `eval_collection`          | `Optional[str]`         | -       | Name of the collection to store evaluation runs.                                              |
| `knowledge_collection`     | `Optional[str]`         | -       | Name of the collection to store knowledge documents.                                          |
| `traces_collection`        | `Optional[str]`         | -       | Name of the collection to store traces.                                                       |
| `spans_collection`         | `Optional[str]`         | -       | Name of the collection to store spans.                                                        |
| `schedules_collection`     | `Optional[str]`         | -       | Name of the collection to store cron schedules.                                               |
| `schedule_runs_collection` | `Optional[str]`         | -       | Name of the collection to store schedule run history.                                         |
| `learnings_collection`     | `Optional[str]`         | -       | Name of the collection to store learnings.                                                    |