Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.praison.ai/llms.txt

Use this file to discover all available pages before exploring further.

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

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 (5s timeout).

​
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

ParameterTypeDescriptionDefault
protocolstrLaunch protocol: “http” or “mcp""http”
hoststrHost to bind to”0.0.0.0”
portintPort number8000
pathstrAPI endpoint path (HTTP) or base path (MCP)”/“
debugboolEnable debug mode with auto-reloadFalse

​
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

  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

  • 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
  • For HTTP: pip install fastapi uvicorn
  • For MCP: pip install praison-mcp mcp
  • Check error messages for specific missing packages
  • Check console for error messages
  • Verify API key is set correctly
  • Test with debug=True for more detailed logs
  • Ensure agent initialization is successful
  • 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