Agent API Launch - PraisonAI Documentation

PraisonAI Agents can be deployed as HTTP APIs or MCP (Model Context Protocol) servers, enabling seamless integration with web applications, microservices, and other systems.

Quick Start

Install Package

Install PraisonAI Agents:

pip install praisonaiagents

For MCP support, also install:

pip install praison-mcp

Create an Agent

Create an agent to deploy as an API:

from praisonaiagents import Agent

# Create your agent
agent = Agent(
    name="API Assistant",
    role="API helper",
    goal="Answer questions and perform tasks via API",
    backstory="An AI assistant accessible through HTTP endpoints",
    llm="gpt-4o-mini"
)

Launch as API

Deploy the agent as an HTTP API:

# Launch as HTTP API
agent.launch(
    protocol="http",  # Default protocol
    host="0.0.0.0",
    port=8000,
    path="/assistant"  # Available at http://localhost:8000/assistant
)

Test the API

Test your deployed agent API:

curl -X POST http://localhost:8000/assistant \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello, how can you help me?"}'

Launch Methods

HTTP API Server

The most common deployment method is as an HTTP API server using FastAPI:

from praisonaiagents import Agent

# Create agent
agent = Agent(
    name="Customer Support",
    role="Support specialist", 
    goal="Help customers with their inquiries",
    tools=["web_search", "knowledge_base"]  # Optional tools
)

# Launch as HTTP API
agent.launch(
    protocol="http",     # Protocol type (default: "http")
    host="0.0.0.0",      # Listen on all interfaces
    port=8000,           # Port number
    path="/support",     # API endpoint path
    debug=False          # Debug mode for development
)

# API will be available at:
# POST http://localhost:8000/support

MCP Server

For Model Context Protocol integration:

from praisonaiagents import Agent

# Create agent
agent = Agent(
    name="SearchAgent",
    instructions="Search the internet for information",
    llm="gpt-4o-mini"
)

# Launch as MCP server
agent.launch(
    protocol="mcp",      # MCP protocol
    port=8080,           # Port number
    host="0.0.0.0"       # Host address
)

# MCP server will create SSE endpoints
# Tool will be named: execute_SearchAgent_task

API Usage

When launched as an HTTP API, agents expose a single endpoint that accepts POST requests:

HTTP API Endpoint

POST /path Send messages to the agent and receive responses.

# Request
{
    "message": "What's the weather like?"
}

# Response
{
    "response": "I'd be happy to help with weather information..."
}

The endpoint also supports form data:

curl -X POST http://localhost:8000/assistant \
  -F "message=Hello, how are you?"

Complete Examples

Example 1: Single Agent HTTP API

from praisonaiagents import Agent

# Create an agent with specific instructions
agent = Agent(
    name="Research Assistant",
    instructions="You are a helpful research assistant. Help users find and summarize information.",
    llm="gpt-4o-mini"
)

# Launch the agent
agent.launch(
    path="/research",
    port=3030,
    host="0.0.0.0"
)

# The agent is now available at http://localhost:3030/research

Example 2: Multi-Agent HTTP API System

from praisonaiagents import Agent, Agents

# Create multiple specialized agents
research_agent = Agent(
    name="Research",
    instructions="Research and gather information on topics"
)

summarize_agent = Agent(
    name="Summarize", 
    instructions="Create concise summaries of provided information"
)

# Create an agents collection
agents = Agents(
    name="ResearchTeam",
    agents=[research_agent, summarize_agent]
)

# Launch all agents on the same endpoint
agents.launch(
    path="/team",
    port=8000,
    host="0.0.0.0"
)

# All agents available at http://localhost:8000/team

Example 3: MCP Server with Tools

from praisonaiagents import Agent

# Create agent with tools
agent = Agent(
    name="TweetAgent",
    instructions="Create engaging tweets based on the topic provided",
    tools=["web_search"]  # Can include tools
)

# Launch as MCP server
agent.launch(
    protocol="mcp",
    port=8080,
    host="127.0.0.1"
)

# MCP tool available as: execute_TweetAgent_task

Example 4: Multiple Endpoints on Same Port

from praisonaiagents import Agent

# Create different agents
sales_agent = Agent(
    name="Sales",
    instructions="Help with product information and sales"
)

support_agent = Agent(
    name="Support",
    instructions="Provide technical support"
)

# Launch on different paths but same port
sales_agent.launch(
    path="/sales",
    port=8000
)

support_agent.launch(
    path="/support", 
    port=8000
)

# Both available on port 8000:
# - http://localhost:8000/sales
# - http://localhost:8000/support

Example 5: Debug Mode for Development

from praisonaiagents import Agent

# Create agent
agent = Agent(
    name="Dev Assistant",
    instructions="Help with development tasks",
    llm="gpt-4o-mini"
)

# Launch with debug mode enabled
agent.launch(
    path="/dev",
    port=8000,
    debug=True  # Enables auto-reload and detailed logging
)

Launch Parameters

Parameter	Type	Description	Default
`protocol`	str	Launch protocol: “http” or “mcp"	"http”
`host`	str	Host to bind to	”0.0.0.0”
`port`	int	Port number	8000
`path`	str	API endpoint path (HTTP) or base path (MCP)	”/“
`debug`	bool	Enable debug mode with auto-reload	False

Client Integration

Python Client

import requests
from typing import Dict, Any

class AgentAPIClient:
    def __init__(self, base_url: str):
        self.base_url = base_url.rstrip('/')
        self.headers = {"Content-Type": "application/json"}
    
    def chat(self, message: str) -> Dict[str, Any]:
        response = requests.post(
            self.base_url,
            headers=self.headers,
            json={"message": message}
        )
        response.raise_for_status()
        return response.json()

# Usage
client = AgentAPIClient("http://localhost:8000/assistant")
response = client.chat("Hello!")
print(response["response"])

JavaScript Client

class AgentAPIClient {
    constructor(baseUrl) {
        this.baseUrl = baseUrl.replace(/\/$/, '');
    }
    
    async chat(message) {
        const response = await fetch(this.baseUrl, {
            method: 'POST',
            headers: {
                'Content-Type': 'application/json'
            },
            body: JSON.stringify({ message })
        });
        
        if (!response.ok) {
            throw new Error(`API error: ${response.statusText}`);
        }
        
        return response.json();
    }
}

// Usage
const client = new AgentAPIClient('http://localhost:8000/assistant');
const response = await client.chat('Hello!');
console.log(response.response);

MCP Client Integration

For MCP servers, use an MCP-compatible client:

from mcp import Client

# Connect to MCP server
client = Client("http://localhost:8080")

# Call the agent tool
result = await client.call_tool(
    "execute_TweetAgent_task",
    arguments={"input": "Create a tweet about AI"}
)

Deployment Best Practices

Performance

Use appropriate worker processes
Enable connection pooling
Monitor resource usage
Consider horizontal scaling for high load

Security

Deploy behind a reverse proxy (nginx, Apache)
Implement authentication at proxy level
Use HTTPS in production
Validate and sanitize inputs
Set up rate limiting

Production Deployment

Docker Deployment

# Dockerfile
FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install praisonaiagents

# Copy agent code
COPY agent.py .

# Expose port
EXPOSE 8000

# Run agent API
CMD ["python", "agent.py"]

# docker-compose.yml
version: '3.8'
services:
  agent-api:
    build: .
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    restart: unless-stopped

Systemd Service

# /etc/systemd/system/agent-api.service
[Unit]
Description=PraisonAI Agent API
After=network.target

[Service]
Type=simple
User=www-data
WorkingDirectory=/opt/agent-api
Environment="OPENAI_API_KEY=your-key"
ExecStart=/usr/bin/python3 /opt/agent-api/agent.py
Restart=always

[Install]
WantedBy=multi-user.target

Nginx Reverse Proxy

server {
    listen 80;
    server_name api.example.com;

    location / {
        proxy_pass http://localhost:8000;
        proxy_http_version 1.1;
        proxy_set_header Upgrade $http_upgrade;
        proxy_set_header Connection 'upgrade';
        proxy_set_header Host $host;
        proxy_cache_bypass $http_upgrade;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
    }
}

Important Notes

Threading: The launch() method uses threading to run servers in the background
Blocking: The last launch() call in your script will block the main thread
Multiple Agents: You can run multiple agents on the same port with different paths (HTTP mode only)
Dependencies: HTTP mode requires FastAPI and uvicorn, MCP mode requires praison-mcp
API Documentation: HTTP APIs automatically get FastAPI documentation at /docs

Troubleshooting

Port already in use

Check if another process is using the port: lsof -i :8000
Kill the process or use a different port
Ensure previous agent instances are properly stopped

Missing dependencies

For HTTP: pip install fastapi uvicorn
For MCP: pip install praison-mcp mcp
Check error messages for specific missing packages

Agent not responding

Check console for error messages
Verify API key is set correctly
Test with debug=True for more detailed logs
Ensure agent initialization is successful

Connection refused

Verify the host and port settings
Check firewall rules
Ensure the agent is actually running
Try connecting from localhost first

Next Steps

API Reference

Detailed Agent API documentation

MCP Integration

Learn more about Model Context Protocol

Getting Started

Core Concepts

Workflows

Features

Models

Tools

Other Features

Monitoring

Developers

Configuration

Best Practices

Getting Started (No Code)

API Reference

​Quick Start

​Launch Methods

​HTTP API Server

​MCP Server

​API Usage

​HTTP API Endpoint

​Complete Examples

​Example 1: Single Agent HTTP API

​Example 2: Multi-Agent HTTP API System

​Example 3: MCP Server with Tools

​Example 4: Multiple Endpoints on Same Port

​Example 5: Debug Mode for Development

​Launch Parameters

​Client Integration

​Python Client

​JavaScript Client

​MCP Client Integration

​Deployment Best Practices

Performance

Security

​Production Deployment

​Docker Deployment

​Systemd Service

​Nginx Reverse Proxy

​Important Notes

​Troubleshooting

​Next Steps

API Reference

MCP Integration

Quick Start

Launch Methods

HTTP API Server

MCP Server

API Usage

HTTP API Endpoint

Complete Examples

Example 1: Single Agent HTTP API

Example 2: Multi-Agent HTTP API System

Example 3: MCP Server with Tools

Example 4: Multiple Endpoints on Same Port

Example 5: Debug Mode for Development

Launch Parameters

Client Integration

Python Client

JavaScript Client

MCP Client Integration

Deployment Best Practices

Production Deployment

Docker Deployment

Systemd Service

Nginx Reverse Proxy

Important Notes

Troubleshooting

Next Steps