Personas

from praisonaiagents import Agent

agent = Agent(name="Persona Router", instructions="Direct readers to a persona guide.")
agent.start("I deploy recipe infrastructure—where do I start?")

from praisonaiagents import Agent

agent = Agent(
    name="Persona Router",
    instructions="Recommend a persona workflow from the described role and stack.",
)
agent.start("I build a Node.js app that calls recipes over HTTP.")

# List all recipes
praisonai recipe list

# Get recipe details
praisonai recipe info support-reply-drafter

# Dry run to validate
praisonai recipe run support-reply-drafter \
  --input '{"ticket_id": "T-123", "message": "Test"}' \
  --dry-run

# Full test run
praisonai recipe run support-reply-drafter \
  --input '{"ticket_id": "T-123", "message": "Test"}' \
  --json

from praisonai import recipe

def handle_support_ticket(ticket):
    result = recipe.run(
        "support-reply-drafter",
        input={
            "ticket_id": ticket.id,
            "message": ticket.customer_message
        },
        options={"timeout_sec": 30}
    )
    
    if result.ok:
        return result.output["draft"]
    else:
        logger.error(f"Recipe failed: {result.error}")
        return None

from praisonai.recipe import RecipeError, RecipeTimeoutError

try:
    result = recipe.run("my-recipe", input=data)
except RecipeTimeoutError:
    # Handle timeout
    return fallback_response()
except RecipeError as e:
    # Handle other errors
    logger.error(f"Recipe error: {e}")
    raise

# serve.yaml (or agents.yaml)
host: 0.0.0.0
port: 8765
auth: api-key
api_key: ${PRAISONAI_API_KEY}

# Recipe filtering
recipes:
  - support-reply-drafter
  - meeting-action-items

# Performance
preload: true

# CORS for web clients
cors_origins: "https://app.example.com"

FROM python:3.11-slim
RUN pip install praisonai[serve]
COPY serve.yaml /app/
WORKDIR /app
ENV PRAISONAI_API_KEY=${PRAISONAI_API_KEY}
ENV OPENAI_API_KEY=${OPENAI_API_KEY}
EXPOSE 8765
CMD ["praisonai", "recipe", "serve", "--config", "serve.yaml"]

# Health check endpoint
curl http://localhost:8765/health

# Prometheus metrics (if enabled)
curl http://localhost:8765/metrics

upstream recipe_servers {
    server recipe1:8765;
    server recipe2:8765;
    server recipe3:8765;
}

server {
    listen 443 ssl;
    location /v1/recipes {
        proxy_pass http://recipe_servers;
    }
}

# alertmanager rules
groups:
  - name: recipe-server
    rules:
      - alert: RecipeServerDown
        expr: up{job="recipe-server"} == 0
        for: 1m
      - alert: HighLatency
        expr: recipe_request_duration_seconds > 10
        for: 5m

praisonai recipe init my-new-recipe
cd my-new-recipe

# TEMPLATE.yaml
schema_version: "1.0"
name: my-new-recipe
version: "1.0.0"
description: |
  Detailed description of what this recipe does.
author: your-name
license: Apache-2.0
tags: [category, use-case]

requires:
  env: [OPENAI_API_KEY]
  packages: []

tools:
  allow: [web_search]
  deny: [shell.exec, file.write]

config:
  input:
    query:
      type: string
      required: true
      description: The user's query
    context:
      type: string
      required: false
      description: Optional context

defaults:
  model: gpt-4o-mini
  temperature: 0.7

outputs:
  - name: result
    type: text
    description: The generated response

# workflow.yaml
framework: praisonai
topic: My Recipe Workflow

roles:
  analyst:
    role: Data Analyst
    goal: Analyze the input and provide insights
    backstory: Expert data analyst
    tasks:
      analyze:
        description: Analyze {query} with context {context}
        expected_output: Structured analysis

praisonai recipe validate my-new-recipe

# Dry run
praisonai recipe run my-new-recipe \
  --input '{"query": "test"}' \
  --dry-run

# Full test
praisonai recipe run my-new-recipe \
  --input '{"query": "test"}' \
  --json

# Create bundle
praisonai recipe pack my-new-recipe --output my-recipe.praison

# Share or deploy