PraisonAI 0.0.47

PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration.

Praison AI

Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.

	Cookbook	Open in Colab
Basic	PraisonAI
Include Tools	PraisonAI Tools

TL;DR

pip install praisonai
export OPENAI_API_KEY="Enter your API key"
praisonai --init create a movie script about dog in moon
praisonai

Table of Contents

• Installation
• Initialise
• Run
• Full Automatic Mode
• User Interface
• Praison AI Chat
• Create Custom Tools
• Agents Playbook
• Include praisonai package in your project
• Commands to Install Dev Dependencies
• Other Models
• Contributing
• Star History

Installation

pip install praisonai

Initialise

export OPENAI_API_KEY="Enter your API key"

Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys

Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.

praisonai --init create a movie script about dog in moon

This will automatically create agents.yaml file in the current directory.

To initialse with a specific agent framework (Optional):

praisonai --framework autogen --init create movie script about cat in mars

Run

praisonai

or

python -m praisonai

Specify the agent framework (Optional):

praisonai --framework autogen

Full Automatic Mode

praisonai --auto create a movie script about Dog in Moon

User Interface

pip install -U "praisonai[ui]"
export OPENAI_API_KEY="Enter your API key"
chainlit create-secret
export CHAINLIT_AUTH_SECRET=xxxxxxxx
praisonai ui

or

python -m praisonai ui

Praison AI Chat

• https://docs.praison.ai/chat/

pip install "praisonai[chat]"
export OPENAI_API_KEY="Enter your API key"
praisonai chat

Create Custom Tools

• https://docs.praison.ai/tools/custom/

Step 1: Pre-requisite to Create a Custom Tool

agents.yaml file should be present in the current directory.

If it doesn't exist, create it by running the command praisonai --init research about the latest AI News and prepare a detailed report.

Step 2: to Create a Custom Tool

Create a file called tools.py in the same directory as the agents.yaml file.

# example tools.py
from duckduckgo_search import DDGS
from praisonai_tools import BaseTool

class InternetSearchTool(BaseTool):
    name: str = "InternetSearchTool"
    description: str = "Search Internet for relevant information based on a query or latest news"

    def _run(self, query: str):
        ddgs = DDGS()
        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
        return results

Step 3: to Create a Custom Tool

Add the tool to the agents.yaml file as show below under the tools section - InternetSearchTool.

framework: crewai
topic: research about the latest AI News and prepare a detailed report
roles:
  research_analyst:
    backstory: Experienced in gathering and analyzing data related to AI news trends.
    goal: Analyze AI News trends
    role: Research Analyst
    tasks:
      gather_data:
        description: Conduct in-depth research on the latest AI News trends from reputable
          sources.
        expected_output: Comprehensive report on current AI News trends.
    tools:
    - InternetSearchTool

Agents Playbook

Simple Playbook Example

framework: crewai
topic: Artificial Intelligence
roles:
  screenwriter:
    backstory: 'Skilled in crafting scripts with engaging dialogue about {topic}.'
    goal: Create scripts from concepts.
    role: Screenwriter
    tasks:
      scriptwriting_task:
        description: 'Develop scripts with compelling characters and dialogue about {topic}.'
        expected_output: 'Complete script ready for production.'

Use 100+ Models

• https://docs.praison.ai/models/

Include praisonai package in your project

• https://docs.praison.ai/developers/wrapper
• https://docs.praison.ai/developers/wrapper-tools/

Option 1: Using RAW YAML

from praisonai import PraisonAI

# Example agent_yaml content
agent_yaml = """
framework: "crewai"
topic: "Space Exploration"

roles:
  astronomer:
    role: "Space Researcher"
    goal: "Discover new insights about {topic}"
    backstory: "You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos."
    tasks:
      investigate_exoplanets:
        description: "Research and compile information about exoplanets discovered in the last decade."
        expected_output: "A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth."
"""

# Create a PraisonAI instance with the agent_yaml content
praisonai = PraisonAI(agent_yaml=agent_yaml)

# Run PraisonAI
result = praisonai.run()

# Print the result
print(result)

Option 2: Using separate agents.yaml file

Note: Please create agents.yaml file before hand.

from praisonai import PraisonAI

def basic(): # Basic Mode
    praisonai = PraisonAI(agent_file="agents.yaml")
    praisonai.run()

if __name__ == "__main__":
    basic()

Commands to Install Dependencies:

1. Install all dependencies, including dev dependencies:

   poetry install
   

2. Install only documentation dependencies:

   poetry install --with docs
   

3. Install only test dependencies:

   poetry install --with test
   

4. Install only dev dependencies:

   poetry install --with dev
   

This configuration ensures that your development dependencies are correctly categorized and installed as needed.

Contributing

• Fork on GitHub: Use the "Fork" button on the repository page.
• Clone your fork: git clone https://github.com/yourusername/praisonAI.git
• Create a branch: git checkout -b new-feature
• Make changes and commit: git commit -am "Add some feature"
• Push to your fork: git push origin new-feature
• Submit a pull request via GitHub's web interface.
• Await feedback from project maintainers.

Star History