Serve

# Agents server
praisonai serve agents --file agents.yaml --port 8000

# UI-Gateway (Pattern C)
praisonai serve ui-gateway --style dashboard --port 8765

# Unified server (all providers)
praisonai serve unified --port 8765

# Legacy syntax (still supported)
praisonai serve agents.yaml

# Start server with default settings (port 8005, host 127.0.0.1)
praisonai serve agents.yaml

📄 Loading agents from: agents.yaml
  ✓ Loaded: Researcher
  ✓ Loaded: Writer
  ✓ Loaded: Editor

🚀 Starting PraisonAI API server...
   Host: 127.0.0.1
   Port: 8005
   Agents: 3
🚀 Multi-Agent HTTP API available at http://127.0.0.1:8005/agents
📊 Available agents for this endpoint (3): Researcher, Writer, Editor
🔗 Per-agent endpoints: /agents/researcher, /agents/writer, /agents/editor
✅ FastAPI server started at http://127.0.0.1:8005
📚 API documentation available at http://127.0.0.1:8005/docs

# Custom port (localhost default — no key needed)
praisonai serve agents.yaml --port 9000

# Public bind — REQUIRES an api key (or SystemExit at startup)
export PRAISONAI_SERVE_API_KEY="$(openssl rand -hex 32)"
praisonai serve agents.yaml --host 0.0.0.0

# Equivalent using the CLI flag
praisonai serve agents.yaml --port 8080 --host 0.0.0.0 --api-key "$PRAISONAI_SERVE_API_KEY"

# Using --serve flag instead of serve command
praisonai agents.yaml --serve

# With options
praisonai agents.yaml --serve --port 8005

# Basic UI-Gateway
praisonai serve ui-gateway

# With custom settings  
praisonai serve ui-gateway --style chat --port 8765

# With agents file and custom title
praisonai serve ui-gateway --agents agents.yaml --title "My App"

curl -X POST http://127.0.0.1:8005/agents \
  -H "Content-Type: application/json" \
  -d '{"query": "Research AI trends and write a summary"}'

{
  "query": "Research AI trends and write a summary",
  "results": [
    {"agent": "Researcher", "response": "...research findings..."},
    {"agent": "Writer", "response": "...written summary..."},
    {"agent": "Editor", "response": "...edited content..."}
  ],
  "final_response": "...final edited content..."
}

# Run only the researcher agent
curl -X POST http://127.0.0.1:8005/agents/researcher \
  -H "Content-Type: application/json" \
  -d '{"query": "What are the latest AI trends?"}'

{
  "agent": "Researcher",
  "query": "What are the latest AI trends?",
  "response": "...research findings..."
}

curl http://127.0.0.1:8005/agents/list

{
  "agents": [
    {"name": "Researcher", "id": "researcher"},
    {"name": "Writer", "id": "writer"},
    {"name": "Editor", "id": "editor"}
  ]
}

name: Content Creation Pipeline
description: Research, write, and edit content

agents:
  researcher:
    name: Researcher
    role: Research Specialist
    goal: Find accurate and relevant information
    backstory: Expert at finding and synthesizing information
    llm: gpt-4o-mini

  writer:
    name: Writer
    role: Content Writer
    goal: Create engaging content from research
    backstory: Skilled writer who transforms research into readable content
    llm: gpt-4o-mini

  editor:
    name: Editor
    role: Content Editor
    goal: Polish and improve written content
    backstory: Meticulous editor ensuring quality and clarity
    llm: gpt-4o-mini

# Terminal 1: Start the API server
praisonai serve agents.yaml --port 8005

# Terminal 2: Create n8n workflow
praisonai agents.yaml --n8n

from praisonaiagents import Agent, AgentTeam
import yaml

# Load agents from YAML
with open('agents.yaml', 'r') as f:
    config = yaml.safe_load(f)

agents = []
for agent_id, cfg in config['agents'].items():
    agent = Agent(
        name=cfg.get('name', agent_id),
        role=cfg.get('role', ''),
        goal=cfg.get('goal', ''),
        llm=cfg.get('llm', 'gpt-4o-mini')
    )
    agents.append(agent)

# Start server
praison = AgentTeam(agents=agents)
praison.launch(port=8005, host='127.0.0.1')

curl http://localhost:8765/__praisonai__/discovery

{
  "schema_version": "1.0.0",
  "server_name": "praisonai-unified",
  "providers": [
    {"type": "agents-api", "name": "Agents API"},
    {"type": "mcp", "name": "MCP Server"}
  ],
  "endpoints": [
    {"name": "agents", "provider_type": "agents-api"},
    {"name": "mcp/tools", "provider_type": "mcp"}
  ]
}

# Start A2A server
praisonai serve a2a --file agents.yaml --port 8082

# Test agent card
curl http://localhost:8082/.well-known/agent.json

# Send A2A message
curl -X POST http://localhost:8082/a2a \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","method":"message/send","id":"1","params":{"message":{"role":"user","parts":[{"type":"text","text":"Hello!"}]}}}'

# Start A2U event stream server
praisonai serve a2u --port 8083

# Get info
curl http://localhost:8083/a2u/info

# Subscribe to events (SSE)
curl -N http://localhost:8083/a2u/events/events

# stdio transport (default) — local pipe for Claude Desktop / Cursor
praisonai serve mcp --transport stdio

# SSE transport
praisonai serve mcp --transport sse --port 8080

# HTTP (Streamable HTTP) transport — use the `mcp serve` command
praisonai mcp serve --transport http-stream --port 8080

# List tools
curl http://localhost:8080/mcp/tools

# Start tools as MCP server
praisonai serve tools --port 8081

# SSE endpoint for Claude Desktop
curl http://localhost:8081/sse

# Start the async jobs API (default: 127.0.0.1:8005)
praisonai serve jobs

# Custom port + persistent SQLite store for jobs and idempotency keys
praisonai serve jobs --port 8005 --db /var/lib/praisonai/jobs.db

# Bind publicly with auto-reload
praisonai serve jobs --host 0.0.0.0 --port 8005 --reload

Starting jobs server on 127.0.0.1:8005
  Submit: POST /api/v1/runs
  Health: /health

# Submit a run
curl -X POST http://127.0.0.1:8005/api/v1/runs \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Summarize the last 24h of arxiv AI papers"}'