Agent API Launch

Deploy AI agents as HTTP APIs or MCP servers for integration with external applications.

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

from praisonaiagents import Agent

agent = Agent(name="api-agent", instructions="Answer HTTP API requests.")
agent.launch(port=8000)

The user deploys an agent as an HTTP or MCP service; external apps call the API instead of importing the SDK directly.

How It Works

An external client calls the launched endpoint, and the agent runs the request before returning the response.

Choose a Protocol

Pick the launch protocol that matches the caller.

Quick Start

1. Install Package

   Install PraisonAI Agents:

   pip install praisonaiagents
   

   For MCP support, also install:

   pip install praison-mcp
   
2. 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"
   )
   
3. 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
   )
   
4. 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, AgentTeam

# 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 = AgentTeam(
    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

Multiple Agent / Agents instances may call .launch(port=N) concurrently from different threads — registration is atomic. If two launch calls use the same path on the same port, the second gets an auto-suffixed path (/path_abc123) and a warning is logged. Server readiness is signalled deterministically (no fixed sleep); .launch() returns only after the port is accepting connections. The wait defaults to 5 seconds and is configurable via the PRAISONAI_SERVER_READY_TIMEOUT environment variable. If the server doesn’t become ready in time, .launch() still returns and a warning is logged — check server logs for startup errors.

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

Environment Variables

Variable	Default	Description
PRAISONAI_SERVER_READY_TIMEOUT	5.0	Seconds .launch() waits for the HTTP server to be ready. On timeout, a warning is logged and execution continues.

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=%OPENAI_API_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

1. Threading: The launch() method uses threading to run servers in the background
2. Blocking: The last launch() call in your script will block the main thread
3. Multiple Agents: You can run multiple agents on the same port with different paths (HTTP mode only)
4. Dependencies: HTTP mode requires FastAPI and uvicorn, MCP mode requires praison-mcp
5. 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

Agent server slow to start

If you see Agent server on port N did not become ready within 5.0s in the logs, the FastAPI server took longer than the default 5s to come up. This is usually safe (.launch() still returns and the server keeps starting), but you can raise the wait:

export PRAISONAI_SERVER_READY_TIMEOUT=15  # seconds

Common causes: slow imports, cold-start on resource-constrained machines, blocking startup hooks.

Next Steps

API Reference

Detailed Agent API documentation

MCP Integration

Learn more about Model Context Protocol

Best Practices

Pick the protocol that matches your caller

Use protocol="http" for web apps, cURL, and microservices that speak REST, and protocol="mcp" when the caller is an MCP-compatible agent host. HTTP is the default, so agent.launch(port=8000) gives you a POST endpoint with automatic FastAPI docs at /docs. Switch only when the consumer requires MCP tools.

Share a port with distinct paths, not duplicate ones

Multiple agents can run on the same port when each uses a unique path. Registration is atomic across threads, but if two .launch() calls reuse the same path on the same port the second is auto-suffixed (/path_abc123) and a warning is logged. Assign explicit, distinct paths (/sales, /support) so clients always hit the agent they expect.

Tune the readiness timeout for slow cold starts

.launch() returns only after the port accepts connections, waiting up to 5 seconds by default. On resource-constrained machines or with heavy imports, raise the wait so startup logs aren’t misread as failures:

export PRAISONAI_SERVER_READY_TIMEOUT=15

A timeout logs a warning but does not abort — the server keeps starting in the background.

Terminate TLS and auth at the edge

The launch server is intentionally minimal. In production, place it behind a reverse proxy (nginx) that handles HTTPS, authentication, and rate limiting rather than exposing 0.0.0.0:8000 directly. Bind to 127.0.0.1 when the proxy runs on the same host.

Related

Run a persistent multi-agent server with richer routing and lifecycle control.

Connect agents to Model Context Protocol tools and servers.