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Run recipes inside your Python process for the lowest latency and direct access to results. 
from praisonaiagents import Agent

agent = Agent(
    name="In-Process Runner",
    instructions="Execute recipe logic in-process for a notebook workflow.",
)
agent.start("Run the summarisation recipe on this paragraph.")
The user calls the SDK from application code; results return in memory with no network hop.
When to Use: Python applications, Jupyter notebooks, data pipelines, or any scenario where you need the lowest possible latency and direct memory access to results.

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How It Works

The Embedded SDK runs recipes directly in your Python process. No network calls, no serialization overhead—just direct function calls.

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Pros & Cons

  • Zero network latency - Direct in-process execution
  • Direct memory access - Work with Python objects directly
  • Simplest integration - Just import and call
  • Full Python ecosystem - Use any Python library alongside recipes
  • Streaming support - Real-time event streaming

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Step-by-Step Tutorial

1

Install PraisonAI

pip install praisonai
2

Set API Keys

export OPENAI_API_KEY=your-key
3

List Available Recipes

from praisonai import recipe

# List all available recipes
recipes = recipe.list_recipes()
for r in recipes:
    print(f"{r.name}: {r.description}")
4

Run a Recipe (Sync)

from praisonai import recipe

result = recipe.run(
    "my-recipe",
    input={"query": "Summarize this document"},
    options={"timeout_sec": 60}
)

if result.ok:
    print(f"Success: {result.output}")
else:
    print(f"Error: {result.error}")
5

Run a Recipe (Streaming)

from praisonai import recipe

for event in recipe.run_stream("my-recipe", input={"query": "Hello"}):
    if event.event_type == "progress":
        print(f"[{event.data.get('step')}] {event.data.get('message')}")
    elif event.event_type == "completed":
        print(f"Done: {event.data.get('output')}")

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Production-Ready Example

import logging
from praisonai import recipe
from praisonai.recipe import RecipeError, RecipeResult

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def process_with_recipe(
    recipe_name: str,
    input_data: dict,
    timeout_sec: int = 60,
    retries: int = 3
) -> RecipeResult:
    """
    Production-ready recipe invocation with retries and error handling.
    """
    last_error = None
    
    for attempt in range(retries):
        try:
            result = recipe.run(
                recipe_name,
                input=input_data,
                options={
                    "timeout_sec": timeout_sec,
                    "trace_id": f"req-{attempt}",
                }
            )
            
            if result.ok:
                logger.info(f"Recipe {recipe_name} completed: run_id={result.run_id}")
                return result
            else:
                logger.warning(f"Recipe failed (attempt {attempt + 1}): {result.error}")
                last_error = result.error
                
        except RecipeError as e:
            logger.error(f"Recipe error (attempt {attempt + 1}): {e}")
            last_error = str(e)
    
    # All retries exhausted
    raise RuntimeError(f"Recipe {recipe_name} failed after {retries} attempts: {last_error}")


# Usage
if __name__ == "__main__":
    result = process_with_recipe(
        "support-reply-drafter",
        input_data={"ticket_id": "T-123", "message": "I need help"},
        timeout_sec=30,
        retries=3
    )
    print(result.output)

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Troubleshooting

Ensure PraisonAI is installed in your Python environment:
pip install praisonai
If using a virtual environment, make sure it’s activated.
Check available recipes:
praisonai recipe list
Recipes are discovered from:
  1. ~/.praisonai/templates
  2. ~/.config/praison/templates
  3. ./.praison/templates (current directory)
Verify your API key is set:
import os
print(os.environ.get("OPENAI_API_KEY", "NOT SET"))
Set it in your environment:
export OPENAI_API_KEY=sk-...
Increase the timeout or check if the recipe is hanging:
result = recipe.run(
    "my-recipe",
    input=data,
    options={"timeout_sec": 120}  # 2 minutes
)

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Security & Ops Notes

Security Considerations
  • Process isolation: Recipes run in your process—malicious recipes could access your memory
  • API keys: Ensure API keys are not logged or exposed
  • Input validation: Validate inputs before passing to recipes
  • Resource limits: Consider memory/CPU limits for long-running recipes

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API Reference

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recipe.run()

def run(
    name: str,
    input: dict = None,
    config: dict = None,
    session_id: str = None,
    options: dict = None
) -> RecipeResult
ParameterTypeDescription
namestrRecipe name
inputdictInput data for the recipe
configdictRecipe configuration overrides
session_idstrSession ID for stateful recipes
optionsdictExecution options (timeout_sec, trace_id, etc.)

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recipe.run_stream()

def run_stream(
    name: str,
    input: dict = None,
    config: dict = None,
    session_id: str = None,
    options: dict = None
) -> Iterator[RecipeEvent]
Returns an iterator of RecipeEvent objects with:
  • event_type: “started”, “progress”, “completed”, “error”
  • data: Event-specific data dictionary

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Next Steps