> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# 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.
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
subgraph "Agent API Launch"
Agent[🤖 Agent] --> Launch[🚀 agent.launch]
Launch --> HTTP[🌐 HTTP API]
Launch --> MCP[🔌 MCP Server]
HTTP --> Client[📱 Web Client]
MCP --> MCPClient[🤖 MCP Client]
Client --> Response[📤 Response]
MCPClient --> Response
end
classDef agent fill:#8B0000,color:#fff
classDef tool fill:#189AB4,color:#fff
class Agent agent
class Launch,HTTP,MCP,Client,MCPClient,Response tool
```
## How It Works
An external client calls the launched endpoint, and the agent runs the request before returning the response.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
participant User
participant Client
participant AgentServer
participant Agent
User->>Client: Send message
Client->>AgentServer: POST /path
AgentServer->>Agent: Run task
Agent-->>AgentServer: Result
AgentServer-->>Client: JSON response
Client-->>User: Answer
```
## Choose a Protocol
Pick the launch protocol that matches the caller.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph TB
Q{Who calls the agent?} -->|Web app / cURL / REST| H[protocol=http]
Q -->|MCP-compatible host| M[protocol=mcp]
classDef config fill:#6366F1,stroke:#7C90A0,color:#fff
classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff
class Q config
class H,M tool
```
## Quick Start
Install PraisonAI Agents:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents
```
For MCP support, also install:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praison-mcp
```
Create an agent to deploy as an API:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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"
)
```
Deploy the agent as an HTTP API:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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 your deployed agent API:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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.
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Request
{
"message": "What's the weather like?"
}
# Response
{
"response": "I'd be happy to help with weather information..."
}
```
The endpoint also supports form data:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
curl -X POST http://localhost:8000/assistant \
-F "message=Hello, how are you?"
```
## Complete Examples
### Example 1: Single Agent HTTP API
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```javascript theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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: ${response.statusText}`);
}
return response.json();
}
}
// Usage
const client = new AgentAPIClient('http://localhost:8000/assistant');
const response = await client.chat('Hello!');
console.log(response.response);
```
### MCP Client Integration
For MCP servers, use an MCP-compatible client:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from mcp import Client
# Connect to MCP server
client = Client("http://localhost:8080")
# Call the agent tool
result = await client.call_tool(
"execute_TweetAgent_task",
arguments={"input": "Create a tweet about AI"}
)
```
## Deployment Best Practices
* Use appropriate worker processes
* Enable connection pooling
* Monitor resource usage
* Consider horizontal scaling for high load
* Deploy behind a reverse proxy (nginx, Apache)
* Implement authentication at proxy level
* Use HTTPS in production
* Validate and sanitize inputs
* Set up rate limiting
## Production Deployment
### Docker Deployment
```dockerfile theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Dockerfile
FROM python:3.11-slim
WORKDIR /app
# Install dependencies
COPY requirements.txt .
RUN pip install praisonaiagents
# Copy agent code
COPY agent.py .
# Expose port
EXPOSE 8000
# Run agent API
CMD ["python", "agent.py"]
```
```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# docker-compose.yml
version: '3.8'
services:
agent-api:
build: .
ports:
- "8000:8000"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
restart: unless-stopped
```
### Systemd Service
```ini theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# /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
```nginx theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
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:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export PRAISONAI_SERVER_READY_TIMEOUT=15 # seconds
```
Common causes: slow imports, cold-start on resource-constrained machines, blocking startup hooks.
## Next Steps
Detailed Agent API documentation
Learn more about Model Context Protocol
## Best Practices
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.
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.
`.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:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export PRAISONAI_SERVER_READY_TIMEOUT=15
```
A timeout logs a warning but does not abort — the server keeps starting in the background.
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.