composio-praisonai 0.7.20

🚀🔗 Leveraging PraisonAI with Composio

Facilitate the integration of PraisonAI with Composio to empower Praison Agents to directly interact with external applications, broadening their capabilities and application scope.

Objective

• Automate starring a GitHub repository using conversational instructions via PraisonAI Agents.

Installation and Setup

Ensure you have the necessary packages installed and connect your GitHub account to allow your agents to utilize GitHub functionalities.

# Install Composio LangChain package
pip install composio-praisonai

# Connect your GitHub account
composio-cli add github

# View available applications you can connect with
composio-cli show-apps

Usage Steps

1. Import Base Packages

Prepare your environment by initializing necessary imports from Praison and setting up your client.

import os
import yaml
from praisonai import PraisonAI

Step 2: Write the Praison-supported Composio Tools ins tools.py file.

This step involves fetching and integrating GitHub tools provided by Composio, and writing them in Praison supported Format, returning the name of tools in a format, that should be added to agents.yml file.

from composio_praisonai import Action, ComposioToolSet

composio_toolset = ComposioToolSet()
tools = composio_toolset.get_actions(
    actions=[Action.GITHUB_STAR_A_REPOSITORY_FOR_THE_AUTHENTICATED_USER]
)

tool_section_str = composio_toolset.get_tools_section(tools)
print(tool_section_str)

Step 3: Define the 'agents_yml` either in a separate file, or in your script.

This step involves configuring and executing the agent to carry out actions, such as starring a GitHub repository.

agent_yaml = """
framework: "crewai"
topic: "Github Management"

roles:
  developer:
    role: "Developer"
    goal: "An expert programmer"
    backstory: "A developer exploring new codebases and have certain tools available to execute different tasks."
    tasks:
      star_github:
        description: "Star a repo composiohq/composio on GitHub"
        expected_output: "Response whether the task was executed."
""" + tool_section_str

print(agent_yaml)

Step 4: Run the Praison Agents to execute the goal/task.

Here you initialize PraisonAI class, and execute.

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

# Run PraisonAI
result = praison_ai.main()

# Print the result
print(result)