Documentation Index Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
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PraisonAI supports three integration patterns for different deployment scenarios.
Pattern Decision Guide Pattern B: In-Process Host Embed PraisonAIUI directly in your Python application.
from praisonai . integration import build_host_app app = build_host_app ( title = " My Agent App " , pages =[ " chat " , " agents " , " sessions " ] ) # Run with uvicorn main:app
When to Use:
Single application deployment
Direct Python integration needed
Simple agent interactions
Development and testing
When NOT to Use:
Need separate UI and API processes
Complex microservice architecture
High-scale production deployments
Pattern C: Integrated Gateway Single process serving UI, REST API, and WebSocket on one port.
import asyncio from praisonai . integration import run_integrated_gateway async def main (): await run_integrated_gateway ( port = 8080 , host = " 0.0.0.0 " , title = " Agent Gateway " , agents =[{ " name " : " Assistant " , " llm " : " gpt-4o " }] ) asyncio . run ( main ())
When to Use:
Need unified endpoint for UI + API
WebSocket real-time communication required
Single-port deployment constraints
Container deployment scenarios
When NOT to Use:
Want separate scaling of UI vs API
Complex routing requirements
Need multiple protocol support
Deferred (P3) - Connect to PraisonAI Cloud via Platform Client
Connect PraisonAIUI to PraisonAI Cloud for managed agents.
# Future implementation from praisonai . integration import configure_host configure_host ( title = " Cloud Agents " , platform_token = os . getenv ( " PRAISONAI_PLATFORM_TOKEN " ) )
When Available:
Cloud-managed agent infrastructure
Multi-tenant deployments
Enterprise security requirements
Global agent orchestration
Comparison Matrix Feature Pattern B Pattern C Pattern D Complexity Low Medium High Setup Time Minutes Minutes TBD Scalability Application-bound Single-process Cloud-scale Real-time Via callbacks WebSocket Platform streams Deployment Embedded Standalone Distributed Use Cases Apps, Prototypes Gateways, APIs Enterprise, SaaS
Pattern Examples FastAPI Integration (Pattern B) from fastapi import FastAPI from praisonai . integration import build_host_app main_app = FastAPI () # Your API routes @ main_app . get ( " /api/health " ) def health (): return { " status " : " healthy " } # Mount agent UI agent_ui = build_host_app ( title = " Agent Dashboard " , pages =[ " chat " , " agents " ] ) main_app . mount ( " /agents " , agent_ui ) # Access at: http://localhost:8000/agents
Container Deployment (Pattern C) FROM python:3.11 COPY . /app WORKDIR /app RUN pip install praisonai[ui] EXPOSE 8080 CMD [ "python" , "-c" , "import asyncio; from praisonai.integration import run_integrated_gateway; asyncio.run(run_integrated_gateway(port=8080))" ]
Development vs Production # Quick development setup from praisonai . integration import build_host_app app = build_host_app ( title = " Dev Agent " , pages =[ " chat " , " agents " , " logs " ], agent_kwargs ={ " llm " : " gpt-4o-mini " } )
# Production-ready gateway import os from praisonai . integration import run_integrated_gateway await run_integrated_gateway ( port = int ( os . getenv ( " PORT " , " 8080 " )), host = " 0.0.0.0 " , title = os . getenv ( " APP_TITLE " , " Agent Gateway " ), agents =[{ " name " : " Production Assistant " , " llm " : os . getenv ( " PRAISONAI_MODEL " , " gpt-4o " ), " instructions " : " You are a production assistant. " }], ui_config ={ " analytics " : os . getenv ( " ANALYTICS_ID " ), " theme " : " production " } )
Migration Path Moving between patterns as your needs evolve:
# Start with Pattern B (embedded) app = build_host_app ( title = " MVP " ) # Migrate to Pattern C (gateway) # Extract configuration: config = { " title " : " MVP " , " agents " : [ ... ], " pages " : [ ... ] } # Use in gateway: await run_integrated_gateway ( ** config ) # Future: Pattern D (cloud) # Same config, different backend configure_host ( platform_token = " ... " , ** config )
Host Integration Implementation details
Backend Injection Custom backend services
