Skip to main content

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.

Project description

PraisonAI Logo

Total Downloads Latest Stable Version License

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.

PraisonAI Architecture

Different User Interfaces:

Interface Description URL
UI Multi Agents such as CrewAI or AutoGen https://docs.praison.ai/ui/ui
Chat Chat with 100+ LLMs, single AI Agent https://docs.praison.ai/ui/chat
Code Chat with entire Codebase, single AI Agent https://docs.praison.ai/ui/code
Other Features Description Docs
Train Fine-tune LLMs using your custom data https://docs.praison.ai/train

Google Colab Multi Agents

Cookbook Open in Colab
Basic PraisonAI Open In Colab
Include Tools PraisonAI Tools Open In Colab

Install

Installation
PraisonAI pip install praisonai
PraisonAI Code pip install "praisonai[code]"
PraisonAI Chat pip install "praisonai[chat]"
PraisonAI Train pip install "praisonai[train]"

Key Features

  • Automated AI Agents Creation
  • Use CrewAI or AutoGen Framework
  • 100+ LLM Support
  • Chat with ENTIRE Codebase
  • Interactive UIs
  • YAML-based Configuration
  • Custom Tool Integration

TL;DR Multi Agents

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 Multi Agents

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 initialise 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

PraisonAI User Interfaces:

Interface Description URL
UI Multi Agents such as CrewAI or AutoGen https://docs.praisonai.com/ui/ui
Chat Chat with 100+ LLMs, single AI Agent https://docs.praisonai.com/ui/chat
Code Chat with entire Codebase, single AI Agent https://docs.praisonai.com/ui/code
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

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

Praison AI Code

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

Create Custom Tools

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

Include praisonai package in your project

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

Star History Chart

License

Praison AI is an open-sourced software licensed under the MIT license.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

praisonai-0.0.72.tar.gz (152.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

praisonai-0.0.72-cp312-cp312-manylinux_2_35_x86_64.whl (162.5 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.35+ x86-64

File details

Details for the file praisonai-0.0.72.tar.gz.

File metadata

  • Download URL: praisonai-0.0.72.tar.gz
  • Upload date:
  • Size: 152.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.9.20

File hashes

Hashes for praisonai-0.0.72.tar.gz
Algorithm Hash digest
SHA256 f61b2622a9c5624a9e17db220dfd81718d846d4f1aa02b125f07fba73b8263b7
MD5 e7d0c332ab53d4119fc8af7756f4d052
BLAKE2b-256 cc112f1f90d75647a064c49af085e64c76047f9f1fb1b3aa0b0a74ddd2e84d21

See more details on using hashes here.

File details

Details for the file praisonai-0.0.72-cp312-cp312-manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for praisonai-0.0.72-cp312-cp312-manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 2d1d5b6af2777ef04150ef445d3635eb9a327cc65e5b79f706c8e904cf71510d
MD5 0847223d8cbe91887708015da52a0ceb
BLAKE2b-256 ec2e92c5e8eed76e4f3af7b42c7eccb36df4622c79d279ff2c1bef512eb86b4c

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page