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    panda-agi

    panda-agi

    sinaptik-ai May 28, 2025
    323 copies 0 downloads

    PandaAGI provides a simple, intuitive API for building general AI agents in just a few lines of code

    Agent Definition
    # 🐼 PandaAGI SDK - An SDK for AGI (Agentic General Intelligence)
    
    [![Release](https://img.shields.io/pypi/v/panda-agi?label=Release&style=flat-square)](https://pypi.org/project/panda-agi/)
    [![Discord](https://dcbadge.vercel.app/api/server/kF7FqH2FwS?style=flat&compact=true)](https://discord.gg/KYKj9F2FRH)
    [![Downloads](https://static.pepy.tech/badge/panda-agi)](https://pepy.tech/project/panda-agi)
    [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
    [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1XEbeTeOgqUKKWsujgkDLKz23FPTPEmjM?usp=sharing)
    
    The PandaAGI SDK provides a simple, intuitive API for building general AI agents in just a few lines of code. It abstracts away the complexity of Agentic Loops and provides a powerful interface for you to build autonomous agents.
    Each agent can be configured to run in a custom environment, interacting with the web, your file system, writing code, and running shell commands.
    
    ## Installation
    
    ```bash
    pip install panda-agi
    ```
    
    Or with uv:
    
    ```bash
    uv add panda-agi
    ```
    
    ## 🔧 Getting started
    
    First of all, make sure you have a API key. You can get one for free [here](https://agi.pandas-ai.com/).
    Make sure to set it as an environment variable:
    
    ```bash
    export PANDA_AGI_KEY=your_api_key
    ```
    
    or set it in the .env file:
    
    ```bash
    PANDA_AGI_KEY=your_api_key
    ```
    
    Once you have the API key, you can start using the SDK:
    
    ```python
    import asyncio
    from panda_agi import Agent
    from panda_agi.envs import LocalEnv
    
    async def main():
        # Create a custom environment for the agent
        agent_env = LocalEnv("./my_agent_workspace")
        
        # Create the agent
        agent = Agent(environment=agent_env)
        
        # Run the agent with a task
        response = agent.run("Tell me a joke about pandas")
        print(response.output)
    
        # Other possible tasks
        response = agent.run("Make a report of the real estate market in Germany")
     

    Tags

    agentagentic-aiagentic-workflowagentsagicursordata-analysisdata-visualizationllmlovable

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