Agent-as-Config: Persisting Agents, Teams, and Workflows

This cookbook demonstrates how to save and load Agents, Teams, and Workflows to/from a database, enabling configuration-as-code patterns where your AI components can be versioned, shared, and restored.

Overview

The Agent-as-Config feature allows you to:

Save agents, teams, and workflows to PostgreSQL or SQLite
Load them back with full functionality restored
Version your configurations (each save creates a new version)
Delete configurations (soft or hard delete)
Use a Registry to handle non-serializable components (tools, custom functions, schemas)

Prerequisites

PostgreSQL database running (or SQLite for development)
Database URL configured

bash

# Start PostgreSQL with Docker
./cookbook/scripts/run_pgvector.sh

Cookbooks

File	Description
`save_agent.py`	Save an agent configuration to the database
`get_agent.py`	Load an agent from the database and run it
`save_team.py`	Save a team with member agents to the database
`get_team.py`	Load a team and run it with delegation
`save_workflow.py`	Save a multi-step workflow to the database
`get_workflow.py`	Load a workflow and execute its steps
`registry.py`	Use a registry for non-serializable components
`auto_populate_registry.py`	Inspect how AgentOS auto-discovers components from teams and workflows
`auto_populate_registry_os.py`	Serve an AgentOS and see the auto-discovered components over the API

Agents

Saving an Agent

python

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

agent = Agent(
    id="my-agent",
    name="My Agent",
    model=OpenAIChat(id="gpt-4o-mini"),
    db=db,
)

# Save to database - returns version number
version = agent.save()
print(f"Saved agent as version {version}")

Loading an Agent

python

from agno.agent import get_agent_by_id
from agno.db.postgres import PostgresDb

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

# Load agent by ID
agent = get_agent_by_id(db=db, id="my-agent")

# Run the agent
agent.print_response("Hello!")

Listing All Agents

python

from agno.agent import get_agents

agents = get_agents(db=db)
for agent in agents:
    print(f"Agent: {agent.name} (ID: {agent.id})")

Deleting an Agent

python

# Soft delete (marks as deleted but keeps in database)
agent.delete()

# Hard delete (permanently removes from database)
agent.delete(hard_delete=True)

Teams

Teams automatically save their member agents as linked components.

Saving a Team

python

from agno.agent import Agent
from agno.team import Team
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

# Define member agents
researcher = Agent(
    id="researcher-agent",
    name="Researcher",
    model=OpenAIChat(id="gpt-4o-mini"),
    role="Research and gather information",
)

writer = Agent(
    id="writer-agent",
    name="Writer",
    model=OpenAIChat(id="gpt-4o-mini"),
    role="Write content based on research",
)

# Create and save the team
team = Team(
    id="content-team",
    name="Content Creation Team",
    model=OpenAIChat(id="gpt-4o-mini"),
    members=[researcher, writer],
    description="A team that researches and creates content",
    db=db,
)

version = team.save()
print(f"Saved team as version {version}")

Loading a Team

python

from agno.team import get_team_by_id

team = get_team_by_id(db=db, id="content-team")

# Run the team - it will delegate to members
team.print_response("Write about AI trends", stream=True)

Listing All Teams

python

from agno.team import get_teams

teams = get_teams(db=db)
for team in teams:
    print(f"Team: {team.name} (ID: {team.id})")

Workflows

Workflows save their steps with links to the agents/teams that execute them.

Saving a Workflow

python

from agno.agent import Agent
from agno.workflow import Workflow, Step
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIChat

db = PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai")

# Define agents for each step
research_agent = Agent(
    id="research-agent",
    name="Research Agent",
    model=OpenAIChat(id="gpt-4o-mini"),
    role="Extract key insights from data",
)

content_agent = Agent(
    id="content-agent",
    name="Content Agent",
    model=OpenAIChat(id="gpt-4o-mini"),
    role="Create content based on research",
)

# Define workflow steps
research_step = Step(name="Research Step", agent=research_agent)
content_step = Step(name="Content Step", agent=content_agent)

# Create and save the workflow
workflow = Workflow(
    id="content-workflow",
    name="Content Creation Workflow",
    description="Research and create content",
    db=db,
    steps=[research_step, content_step],
)

version = workflow.save()
print(f"Saved workflow as version {version}")

Loading a Workflow

python

from agno.workflow import get_workflow_by_id

workflow = get_workflow_by_id(db=db, id="content-workflow")

# Run the workflow
workflow.print_response(input="AI trends in 2024", markdown=True)

Listing All Workflows

python

from agno.workflow import get_workflows

workflows = get_workflows(db=db)
for workflow in workflows:
    print(f"Workflow: {workflow.name} (ID: {workflow.id})")

Registry for Non-Serializable Components

Some components cannot be serialized to JSON (tools, custom functions, Pydantic schemas). Use a Registry to provide these when loading.

Creating a Registry

python

from agno.registry import Registry
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.models.openai import OpenAIChat
from pydantic import BaseModel

# Custom tool function
def my_custom_tool(query: str) -> str:
    return f"Results for: {query}"

# Custom schemas
class InputSchema(BaseModel):
    message: str

class OutputSchema(BaseModel):
    result: str
    confidence: float

# Create registry with all non-serializable components
registry = Registry(
    name="My Registry",
    tools=[DuckDuckGoTools(), my_custom_tool],
    models=[OpenAIChat(id="gpt-4o-mini")],
    schemas=[InputSchema, OutputSchema],
)

Loading with a Registry

python

from agno.agent import get_agent_by_id

# When loading an agent that uses tools or schemas,
# pass the registry to restore non-serializable components
agent = get_agent_by_id(
    db=db,
    id="my-agent",
    registry=registry,
)

# The agent now has its tools and schemas restored
agent.print_response("Search for AI news")

What Gets Restored from Registry

Component	Serialized	Restored via Registry
Agent ID, name, description	Yes	-
Model configuration	Yes	-
Instructions, prompts	Yes	-
Tools (Toolkit, Function)	Name only	Full callable
Custom functions	Name only	Full callable
Pydantic schemas	Name only	Full class

Auto-Populating the Registry

You do not have to declare components twice. When you construct an AgentOS, it recursively walks every agent, team, and workflow you pass in and adds the models, tools, databases, and vector databases they reference to the registry automatically. This keeps GET /registry consistent with what is actually wired into your OS.

python

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS
from agno.team import Team

db = SqliteDb(db_file="tmp/auto_registry.db", id="auto-registry-db")

researcher = Agent(id="researcher", model=OpenAIResponses(id="gpt-5.4"), db=db)
writer = Agent(id="writer", model=OpenAIResponses(id="gpt-5.4-mini"))
team = Team(id="content-team", members=[researcher, writer])

# No registry passed; components are discovered from the team members
agent_os = AgentOS(teams=[team])

print([f"{m.provider}:{m.id}" for m in agent_os.registry.models])
# -> ['OpenAI:gpt-5.4', 'OpenAI:gpt-5.4-mini']

Details:

The walk covers nested teams and every workflow step type (including Condition else-branches and Router choices).
Models include reasoning_model, parser_model, output_model, and fallback models. Vector databases and contents databases are pulled from knowledge.
Deduplication is by id/name, so a model shared across many agents is collected once, and components you pass to a Registry explicitly are preserved (the discovered ones are merged in, never duplicated).
User objects are only referenced, never mutated.

See auto_populate_registry.py (offline inspection) and auto_populate_registry_os.py (served app).

Database Configuration

PostgreSQL (Recommended for Production)

python

from agno.db.postgres import PostgresDb

db = PostgresDb(db_url="postgresql+psycopg://user:pass@host:port/dbname")

SQLite (Development Only)

python

from agno.db.sqlite import SqliteDb

db = SqliteDb(db_url="sqlite:///agents.db")

Versioning

Each save() call creates a new version of the configuration:

python

# First save - version 1
agent.save()

# Modify and save again - version 2
agent.instructions = ["Updated instructions"]
agent.save()

# Load specific version (coming soon)
# agent = get_agent_by_id(db=db, id="my-agent", version=1)

Running the Examples

bash

# Start PostgreSQL
./cookbook/scripts/run_pgvector.sh

# Run save examples first
python cookbook/93_components/save_agent.py
python cookbook/93_components/save_team.py
python cookbook/93_components/save_workflow.py

# Then run get examples
python cookbook/93_components/get_agent.py
python cookbook/93_components/get_team.py
python cookbook/93_components/get_workflow.py

# Registry example
python cookbook/93_components/registry.py