Postgres for Workflow
Store workflow runs in PostgreSQL with PostgresDb.
PostgresDb stores a Workflow's sessions and run history in PostgreSQL.
Usage
Start in a virtual environment.
Install dependencies:
uv pip install agno openai ddgs sqlalchemy "psycopg[binary]"Export your OpenAI API key:
export OPENAI_API_KEY="your_openai_api_key_here"Run PostgreSQL
Install Docker Desktop, then start PostgreSQL with pgvector on port 5532:
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql \
-v pgvolume:/var/lib/postgresql \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:18from agno.agent import Agent
from agno.db.postgres import PostgresDb
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 = "postgresql+psycopg://ai:ai@localhost:5532/ai"
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,
)
if __name__ == "__main__":
content_creation_workflow = Workflow(
name="Content Creation Workflow",
description="Automated content creation from blog posts to social media",
db=PostgresDb(
session_table="workflow_session",
db_url=db_url,
),
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 postgres_for_workflow.py, start the database described above, and run:
python postgres_for_workflow.pyParameters
| Parameter | Type | Default | Description |
|---|---|---|---|
id | Optional[str] | - | The ID of the database instance. UUID by default. |
db_url | Optional[str] | - | The database URL to connect to. |
db_engine | Optional[Engine] | - | The SQLAlchemy database engine to use. |
db_schema | Optional[str] | - | The database schema to use. |
session_table | Optional[str] | - | Name of the table to store Agent, Team and Workflow sessions. |
runs_table | Optional[str] | - | Name of the table to store the runs of each session. |
memory_table | Optional[str] | - | Name of the table to store memories. |
metrics_table | Optional[str] | - | Name of the table to store metrics. |
eval_table | Optional[str] | - | Name of the table to store evaluation runs data. |
knowledge_table | Optional[str] | - | Name of the table to store knowledge content. |
traces_table | Optional[str] | - | Name of the table to store traces. |
spans_table | Optional[str] | - | Name of the table to store spans. |
versions_table | Optional[str] | - | Name of the table to store schema versions. |
components_table | Optional[str] | - | Name of the table to store components. |
component_configs_table | Optional[str] | - | Name of the table to store component configurations. |
component_links_table | Optional[str] | - | Name of the table to store links between components. |
learnings_table | Optional[str] | - | Name of the table to store learnings. |
schedules_table | Optional[str] | - | Name of the table to store cron schedules. |
schedule_runs_table | Optional[str] | - | Name of the table to store schedule run history. |
job_table | Optional[str] | - | Name of the table to store durable background run jobs. |
approvals_table | Optional[str] | - | Name of the table to store human approval requests. |
auth_tokens_table | Optional[str] | - | Name of the table to store OAuth tokens for external services. |
service_accounts_table | Optional[str] | - | Name of the table to store service accounts. |
mcp_oauth_clients_table | Optional[str] | - | Name of the table to store MCP OAuth client registrations. |
mcp_oauth_transactions_table | Optional[str] | - | Name of the table to store MCP OAuth transactions. |
mcp_oauth_codes_table | Optional[str] | - | Name of the table to store MCP OAuth authorization codes. |
mcp_oauth_refresh_tokens_table | Optional[str] | - | Name of the table to store MCP OAuth refresh tokens. |
mcp_oauth_keys_table | Optional[str] | - | Name of the table to store MCP OAuth signing keys. |
create_schema | bool | True | Whether to create the database schema if it doesn't exist. Set to False when the schema is managed externally. |