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Agno supports using MongoDB as a storage backend for Workflows using the MongoDBDb class.

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Usage

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Run MongoDB

Install docker desktop and run MongoDB on port 27017 using: 
docker run -d \
  --name local-mongo \
  -p 27017:27017 \
  -e MONGO_INITDB_ROOT_USERNAME=mongoadmin \
  -e MONGO_INITDB_ROOT_PASSWORD=secret \
  mongo
mongodb_for_workflow.py
from agno.agent import Agent
from agno.db.mongodb import MongoDBDb
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.hackernews import HackerNewsTools
from agno.workflow.step import Step
from agno.workflow.workflow import Workflow

db_url = "mongodb://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=[HackerNewsTools()],
    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 that I have posts for 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="AI trends in 2024",
        markdown=True,
    )

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Params

ParameterTypeDefaultDescription
idOptional[str]-The ID of the database instance. UUID by default.
db_clientOptional[MongoClient]-The MongoDB client to use.
db_nameOptional[str]-The name of the database to use.
db_urlOptional[str]-The database URL to connect to.
session_collectionOptional[str]-Name of the collection to store sessions.
memory_collectionOptional[str]-Name of the collection to store memories.
metrics_collectionOptional[str]-Name of the collection to store metrics.
eval_collectionOptional[str]-Name of the collection to store evaluation runs.
knowledge_collectionOptional[str]-Name of the collection to store knowledge documents.
traces_collectionOptional[str]-Name of the collection to store traces.
spans_collectionOptional[str]-Name of the collection to store spans.