Decision Guide

When to use which integration model

Pick the integration model that fits your stack, latency budget, and operational constraints.

python

from praisonaiagents import Agent

agent = Agent(
    name="Integration Advisor",
    instructions="Recommend a PraisonAI recipe integration model for the described app.",
)
agent.start("We need recipes from a Node.js microservice on one machine.")

The user describes their environment; you map it to the right recipe integration pattern below.

Diagram

Quick Start

Simple Usage

Describe your environment and let the advisor recommend a model.

python

from praisonaiagents import Agent

agent = Agent(name="Integration Advisor", instructions="Recommend a recipe integration model.")
agent.start("We need recipes from a Node.js microservice on one machine.")

With Configuration
Once you pick the sidecar model, serve recipes locally.
bash
```
praisonai serve recipe --config agents.yaml
```

How It Works

Diagram

Quick Decision Tree

Diagram

Comparison Matrix

Criteria	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Latency	Lowest	Low	Medium	Medium	Variable	Low
Language	Python	Any	Any	Any	Any	Platform
Complexity	Low	Low	Medium	High	High	Medium
Multi-tenant	❌	❌	❌	✅	✅	❌
Async	❌	❌	❌	❌	✅	❌
Auth built-in	❌	❌	✅	✅	✅	❌
Streaming	✅	✅	✅	✅	❌	✅

By Use Case

Use Case	Primary Model	Alternative
SaaS support replies	Model 3/4	Model 1
Meeting notes	Model 2/5	Model 1
SQL assistant	Model 1	Model 3
Code review	Model 6/2	Model 3
Marketing content	Model 3	Model 5
Customer onboarding	Model 4	Model 3
Fraud detection	Model 5	Model 4
Document Q&A	Model 1/3	Model 4
Sales coaching	Model 5	Model 2
Data migration	Model 2	Model 1
Multi-agent router	Model 1/3	Model 4
Localization	Model 5/2	Model 3

By Environment

Development

Recommended: Model 1 (SDK) or Model 2 (CLI)
Why: Fast iteration, easy debugging

Staging

Recommended: Model 3 (HTTP Sidecar)
Why: Mimics production, tests HTTP integration

Production

Recommended: Model 4 (Remote Runner) or Model 5 (Event-Driven)
Why: Scalable, secure, monitored

Configuration Consolidation

Using agents.yaml for All Config

You can consolidate serve configuration into your existing agents.yaml file instead of maintaining a separate serve.yaml:

yaml

# agents.yaml - unified configuration
framework: praisonai
topic: My Application

# Agent definitions
roles:
  assistant:
    role: AI Assistant
    goal: Help users
    tasks:
      respond:
        description: Respond to {query}

# Server configuration (optional section)
serve:
  host: 127.0.0.1
  port: 8765
  auth: api-key
  preload: true
  recipes:
    - my-recipe
    - another-recipe

Then start the server:

bash

praisonai serve recipe --config agents.yaml

This approach:

✅ Single configuration file
✅ Agent definitions + server config together
✅ Easier to manage
✅ Version control friendly

Best Practices

Let language decide the first branch

Python apps get the Embedded SDK’s lowest latency; anything else starts at CLI, Sidecar, or Remote Runner.

Match the model to the environment, not just the app

Dev favours SDK/CLI for fast iteration; production favours Remote Runner or Event-Driven for scale, auth, and monitoring.

Consolidate config in agents.yaml

A serve: section in agents.yaml keeps agent and server settings in one version-controlled file instead of a separate serve.yaml.

Reach for async models for batch work

Event-Driven is the only model with built-in async, so choose it when throughput and queued processing matter more than latency.

Integration Models

Detailed setup for each model

Use Cases

See which model fits each scenario

Decision Guide

Quick Start

How It Works

Quick Decision Tree

Comparison Matrix

By Use Case

By Environment

Development

Staging

Production

Configuration Consolidation

Using agents.yaml for All Config

Best Practices

Related