AgentOS

Deploy AI agents as production web services

Deploy agents, teams, and flows as production-ready APIs.

---

Quick Start

1. Install

   pip install praisonai[os]
   
2. Python Code

   from praisonai import AgentOS
   from praisonaiagents import Agent
   
   app = AgentOS(agents=[
       Agent(instructions="You are a helpful assistant")
   ])
   app.serve(port=8080)
   
3. CLI

   praisonai app --port 8080
   

---

What AgentOS Creates

Endpoint	Method	Purpose
/	GET	App status
/health	GET	Health check
/api/agents	GET	List agents
/api/chat	POST	Chat with agent
/docs	GET	API documentation

---

Configuration

Defaults

from praisonai import AgentOS
from praisonaiagents import Agent

app = AgentOS(agents=[Agent(instructions="Help me")])
app.serve()  # Port 8000

Inline Options

app = AgentOS(
    name="My AI App",
    agents=[agent1, agent2],
    teams=[my_team],
    flows=[my_flow]
)
app.serve(port=9000, reload=True)

Config Class

from praisonaiagents import AgentOSConfig

config = AgentOSConfig(
    name="Production App",
    host="0.0.0.0",
    port=8000,
    reload=False,
    cors_origins=["https://myapp.com"],
    api_prefix="/api",
    docs_url="/docs",
    debug=False,
    log_level="info",
    workers=4,
    timeout=60
)

app = AgentOS(agents=[...], config=config)
app.serve()

---

All Configuration Options

Option	Type	Default	Description
name	str	"PraisonAI App"	Application name
host	str	"0.0.0.0"	Host address
port	int	8000	Port number
reload	bool	False	Auto-reload (dev mode)
cors_origins	list[str]	["*"]	Allowed CORS origins
api_prefix	str	"/api"	API route prefix
docs_url	str	"/docs"	API docs URL
openapi_url	str	"/openapi.json"	OpenAPI schema URL
debug	bool	False	Debug mode
log_level	str	"info"	Logging level
workers	int	1	Worker processes
timeout	int	60	Request timeout (seconds)

---

CLI Commands

Command	Description
praisonai app	Start with defaults
praisonai app --port 9000	Custom port
praisonai app --host 127.0.0.1	Custom host
praisonai app --config agents.yaml	Load agents from file
praisonai app --reload	Dev mode with auto-reload
praisonai app --debug	Enable debug logging
praisonai app --name "My API"	Custom app name

---

YAML Config File

agents.yaml

agents:
  - name: Assistant
    instructions: You are a helpful assistant
    llm: gpt-4o-mini
    
  - name: Researcher
    instructions: You research topics thoroughly
    llm: gpt-4o

praisonai app --config agents.yaml --port 8080

---

Architecture

---

Deployment Patterns

Single Agent

from praisonai import AgentOS
from praisonaiagents import Agent

app = AgentOS(agents=[
    Agent(name="support", instructions="Customer support agent")
])
app.serve(port=8000)

Multiple Agents

app = AgentOS(agents=[
    Agent(name="support", instructions="Customer support"),
    Agent(name="sales", instructions="Sales assistance"),
    Agent(name="tech", instructions="Technical help")
])
app.serve()

With Teams

from praisonaiagents import Agent, AgentTeam, Task

researcher = Agent(name="researcher", instructions="Research")
writer = Agent(name="writer", instructions="Write")

task1 = Task(description="Research topic", agent=researcher)
task2 = Task(description="Write article", agent=writer, context=[task1])

team = AgentTeam(agents=[researcher, writer], tasks=[task1, task2])

app = AgentOS(teams=[team])
app.serve()

With Flows

from praisonaiagents import Agent, AgentFlow

flow = AgentFlow(steps=[
    Agent(instructions="Step 1"),
    Agent(instructions="Step 2")
])

app = AgentOS(flows=[flow])
app.serve()

---

Chat API Request

curl -X POST http://localhost:8000/api/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello!", "agent_name": "support"}'

Response:

{
  "response": "Hello! How can I help you today?",
  "agent_name": "support",
  "session_id": null
}

---

Best Practices

Use for all production deployments

Even for single agents, AgentOS provides proper API structure, CORS, health checks, and auto-generated docs.

Enable reload only in development

Use --reload during development. Disable in production for better performance.

Configure CORS for security

Replace ["*"] with specific origins in production: cors_origins=["https://myapp.com"]

Use workers for scaling

Increase workers for production: workers=4 (typically 2-4x CPU cores)

---

Related

Core Components

Compare Agent, AgentTeam, AgentFlow, AgentOS

Agents

Agent configuration reference