Redis for Workflows

Store workflow sessions in Redis with RedisDb.

Agno supports using Redis as a storage backend for Workflows using the RedisDb class.

Usage

Start in a virtual environment.

Install dependencies:

uv pip install agno redis openai ddgs

Run Redis

Install Docker Desktop, then start Redis on port 6379:

docker run --name my-redis -p 6379:6379 -d redis

Export your OpenAI API key:

export OPENAI_API_KEY="your_openai_api_key_here"
redis_for_workflow.py
"""
Run: `uv pip install agno redis openai ddgs` to install the dependencies
"""
from agno.agent import Agent
from agno.db.redis import RedisDb
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

# 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 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=RedisDb(
            session_table="workflow_session",
            db_url="redis://localhost:6379",
        ),
        steps=[research_step, content_planning_step],
    )
    content_creation_workflow.print_response(
        input="AI trends in 2024",
        markdown=True,
    )

Run the example

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

python redis_for_workflow.py

Params

ParameterTypeDefaultDescription
idOptional[str]-Database ID. Derived deterministically from the URL (or client representation) and key prefix when omitted.
redis_clientOptional[Union[Redis, RedisCluster]]-Redis client instance to use. If not provided a new client will be created from db_url. Required for Redis Cluster connections.
db_urlOptional[str]-Redis connection URL (e.g., "redis://localhost:6379/0" or "rediss://user:pass@host:port/db"). Supports single-node Redis only.
db_prefixstr"agno"Prefix for all Redis keys.
expireOptional[int]-TTL for Redis keys in seconds.
session_tableOptional[str]-Name of the table to store sessions.
runs_tableOptional[str]-Name of the table to store runs.
memory_tableOptional[str]-Name of the table to store memories.
metrics_tableOptional[str]-Name of the table to store metrics.
eval_tableOptional[str]-Name of the table to store evaluation runs.
knowledge_tableOptional[str]-Name of the table to store knowledge documents.
traces_tableOptional[str]-Name of the table to store traces.
spans_tableOptional[str]-Name of the table to store spans.