Praison AI

Model 1: Embedded SDK

Direct Python integration with zero network overhead

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

python
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.

Diagram

Input

Agent

Output

How It Works

Diagram

Your Python Application

Application Code

recipe.run

Recipe Execution

Result Object

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

Pros & Cons

Pros

  • 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

Cons

  • Python-only – Not available for other languages
  • Shared resources – Recipe runs in same process (memory, CPU)
  • No built-in multi-tenancy – Single-tenant by design
  • No process isolation – Errors can affect host process

Step-by-Step Tutorial

  1. Install PraisonAI
    bash
    pip install praisonai
  2. Set API Keys
    bash
    export OPENAI_API_KEY=your-key
  3. List Available Recipes
    python
    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)
    python
    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)
    python
    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')}")

Production-Ready Example

python
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)

Troubleshooting

ImportError: No module named 'praisonai'

Ensure PraisonAI is installed in your Python environment:

bash
pip install praisonai

If using a virtual environment, make sure it’s activated.

Recipe not found

Check available recipes:

bash
praisonai recipe list

Recipes are discovered from:

  1. ~/.praisonai/templates
  2. ~/.config/praison/templates
  3. ./.praison/templates (current directory)
API key error

Verify your API key is set:

python
import os
print(os.environ.get("OPENAI_API_KEY", "NOT SET"))

Set it in your environment:

bash
export OPENAI_API_KEY=sk-...
Timeout errors

Increase the timeout or check if the recipe is hanging:

python
result = recipe.run(
    "my-recipe",
    input=data,
    options={"timeout_sec": 120}  # 2 minutes
)

Security & Ops Notes

  • 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

API Reference

recipe.run()

python
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.)

recipe.run_stream()

python
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

Next Steps