Async MongoDB for Workflow

AsyncMongoDb stores a Workflow's sessions and run history asynchronously in MongoDB. Use async Workflow methods such as arun() and aprint_response().

Usage

Start in a virtual environment and set the model key before running the example.

Set OpenAI Key

Set your OPENAI_API_KEY as an environment variable. You can get one from OpenAI.

export OPENAI_API_KEY=sk-***

Install the pymongo (4.9 or later), openai, and ddgs packages:

uv pip install agno "pymongo>=4.9" openai ddgs

motor clients are also supported, but motor is deprecated. Use PyMongo's async client instead.

Run MongoDB

Install Docker Desktop, then start MongoDB on port 27017:

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

async_mongodb_for_workflow.py

import asyncio

from agno.agent import Agent
from agno.db.mongo import AsyncMongoDb
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 = AsyncMongoDb(db_url=db_url)

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",
)

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 that I have posts for 3 posts per week",
    ],
)

research_step = Step(
    name="Research Step",
    team=research_team,
)

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


async def 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],
    )
    try:
        await content_creation_workflow.aprint_response(
            input="AI trends in 2024",
            markdown=True,
        )
    finally:
        await db.close()


if __name__ == "__main__":
    asyncio.run(main())

Parameters

Parameter	Type	Default	Description
`id`	`Optional[str]`	-	Database ID. Derived deterministically from connection/client and database name when omitted.
`db_client`	`Optional[Union[AsyncIOMotorClient, AsyncMongoClient]]`	-	The MongoDB async client to use. Supports Motor and PyMongo async clients.
`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 session 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.
`learnings_collection`	`Optional[str]`	-	Name of the collection to store learnings.
`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.

Run the Example

Save the code as async_mongodb_for_workflow.py, complete the prerequisites above, then run:

python async_mongodb_for_workflow.py