<div align="center">
<img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/fulllogo_transparent.png?raw=True" width="350">
<br/>
**SuperAgentX**: A lightweight autonomous true multi-agent framework with AGI capabilities.
<br/>
[](https://www.python.org/downloads/release/python-3100/)
[](https://github.com/superagentxai/superagentX)
[](https://github.com/superagentxai/superagentX/blob/master/LICENSE)
</div>
## Key Features
🚀 **Open-Source Framework**: A lightweight, open-source AI framework built for multi-agent applications with Artificial General Intelligence (AGI) capabilities.
🎯 **Goal-Oriented Multi-Agents**: This technology enables the creation of agents with retry mechanisms to achieve set goals. Communication between agents is Parallel, Sequential, or hybrid.
🏖️ **Easy Deployment**: Offers WebSocket, RESTful API, and IO console interfaces for rapid setup of agent-based AI solutions.
♨️ **Streamlined Architecture**: Enterprise-ready scalable and pluggable architecture. No major dependencies; built independently!
📚 **Contextual Memory**: Uses SQL + Vector databases to store and retrieve user-specific context effectively.
🧠 **Flexible LLM Configuration**: Supports simple configuration options of various Gen AI models.
🤝🏻 **Extendable Handlers**: Allows integration with diverse APIs, databases, data warehouses, data lakes, IoT streams, and more, making them accessible for function-calling features.
## Table of contents
- [What is SuperAgentX?](#what-is-superagentx)
- [Why SuperAgentX?](#why-superagentx)
- [Getting Started](#getting-started)
- [Installing Dependencies](#installing-dependencies)
- [Contribution](#contribution)
- [License](#license)
## What is SuperAgentX?
**The Ultimate Modular Autonomous Agentic AI Framework for Progressing Towards AGI.** <br/><br/>
SuperAgentX is an advanced agentic AI framework designed to accelerate the development of Artificial General Intelligence (AGI). It provides a powerful, modular, and flexible platform for building autonomous AI agents capable of executing complex tasks with minimal human intervention. By integrating cutting-edge AI technologies and promoting efficient, scalable agent behavior, SuperAgentX embodies a critical step forward in the path toward superintelligence and AGI. Whether for research, development, or deployment, SuperAgentX is built to push the boundaries of what's possible with autonomous AI systems.
## Why SuperAgentX?
SuperAgentX addresses the growing need for highly capable, autonomous AI systems that can perform complex tasks with minimal human intervention. As we approach the limits of narrow AI, there's a need for an adaptable and scalable framework to bridge the gap toward AGI (Artificial General Intelligence). Here’s why SuperAgentX stands out:
**Super**: Cutting-edge AI systems with exceptional capabilities, paving the way to **AGI** (Artificial General Intelligence) and **ASI** (Artificial Super Intelligence).</p>
**Agent**: Autonomous Multi AI agent framework designed to make decisions, act independently, and handle complex tasks. </p>
**X**: The unknown, the limitless, the extra factor that makes SuperAgentX revolutionary, futuristic, and transformative.</p>
### Getting Started
```shell
pip install superagentx
```
##### Usage - Example SuperAgentX Code
This SuperAgentX example utilizes two handlers, Amazon and Walmart, to search for product items based on user input from the IO Console.
1. It uses Parallel execution of handler in the agent
2. Memory Context Enabled
3. LLM configured to OpenAI
4. Pre-requisites
Set OpenAI Key:
```shell
export OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxx
```
Set Rapid API Key <a href="https://rapidapi.com/auth/sign-up" target="_blank">Free Subscription</a> for Amazon, Walmart Search APIs
```shell
export RAPID_API_KEY=XXXXXXXXXXXXXXXXXXXXXXXXX
```
```python
# Additional lib needs to install
# `pip install superagentx-handlers`
# python3 superagentx_examples/ecom_iopipe.py
import asyncio
from rich import print as rprint
from superagentx.memory import Memory
from superagentx.agent import Agent
from superagentx.engine import Engine
from superagentx.llm import LLMClient
from superagentx.agentxpipe import AgentXPipe
from superagentx.pipeimpl.iopipe import IOPipe
from superagentx.prompt import PromptTemplate
from superagentx_handlers.ecommerce.amazon import AmazonHandler
from superagentx_handlers.ecommerce.walmart import WalmartHandler
async def main():
"""
Launches the e-commerce pipeline console client for processing requests and handling data.
"""
# LLM Configuration
llm_config = {'llm_type': 'openai'}
llm_client: LLMClient = LLMClient(llm_config=llm_config)
# Enable Memory
memory = Memory(memory_config={"llm_client": llm_client})
# Add Two Handlers (Tools) - Amazon, Walmart
amazon_ecom_handler = AmazonHandler()
walmart_ecom_handler = WalmartHandler()
# Prompt Template
prompt_template = PromptTemplate()
# Amazon & Walmart Engine to execute handlers
amazon_engine = Engine(
handler=amazon_ecom_handler,
llm=llm_client,
prompt_template=prompt_template
)
walmart_engine = Engine(
handler=walmart_ecom_handler,
llm=llm_client,
prompt_template=prompt_template
)
# Create Agent with Amazon, Walmart Engines execute in Parallel - Search Products from user prompts
ecom_agent = Agent(
name='Ecom Agent',
goal="Get me the best search results",
role="You are the best product searcher",
llm=llm_client,
prompt_template=prompt_template,
engines=[[amazon_engine, walmart_engine]]
)
# Pipe Interface to send it to public accessible interface (Cli Console / WebSocket / Restful API)
pipe = AgentXPipe(
agents=[ecom_agent],
memory=memory
)
# Create IO Cli Console - Interface
io_pipe = IOPipe(
search_name='SuperAgentX Ecom',
agentx_pipe=pipe,
read_prompt=f"\n[bold green]Enter your search here"
)
await io_pipe.start()
if __name__ == '__main__':
try:
asyncio.run(main())
except (KeyboardInterrupt, asyncio.CancelledError):
rprint("\nUser canceled the [bold yellow][i]pipe[/i]!")
```
##### Usage - Example SuperAgentX Result
SuperAgentX searches for product items requested by the user in the console, validates them against the set goal, and returns the result. It retains the context, allowing it to respond to the user's next prompt in the IO Console intelligently.
## Architecture
<img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/architecture.png?raw=True" title="SuperAgentX Architecture"/>
## Large Language Models
| Icon | LLM Name | Status |
|-----------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/openai.png?raw=True" title="OpenAI" height="20" width="20"/> | **OpenAI** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/checkmark.png?raw=True" title="Tested" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/azure-icon.png?raw=True" title="Azure OpenAI" height="20" width="20"/> | **Azure OpenAI** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/checkmark.png?raw=True" title="Tested" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/awsbedrock.png?raw=True" title="AWS Bedrock" height="20" width="20"/> | **AWS Bedrock** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/checkmark.png?raw=True" title="Tested" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/gemini.png?raw=True" title="Google Gemini" height="20" width="20"/> | **Google Gemini** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/wip.png?raw=True" title="Development Inprogress" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/meta.png?raw=True" title="Google Gemini" height="20" width="20"/> | **Meta Llama** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/wip.png?raw=True" title="Development Inprogress" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/ollama.png?raw=True" title="Ollama" height="20" width="20"/> | **Ollama** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/wip.png?raw=True" title="Development Inprogress" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/claude-ai-logo.png?raw=True" title="Claude AI" height="20" width="20"/> | **Claude AI** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/todo.png?raw=True" title="TODO" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/mistral-ai-logo.png?raw=True" title="Mistral AI" height="20" width="30"/> | **Mistral AI** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/todo.png?raw=True" title="TODO" height="20" width="20"/> |
| <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/ibm.png?raw=True" title="IBM WatsonX AI" height="20" width="30"/> | **IBM WatsonX** | <img src="https://github.com/superagentxai/superagentX/blob/master/docs/images/todo.png?raw=True" title="TODO" height="20" width="20"/> |
## Environment Setup
```shell
$ cd <path-to>/superagentx
$ python3.12 -m venv venv
$ source venv/bin/activate
(venv) $ pip install poetry
(venv) $ poetry install
```
## Documentation (_Coming Soon_)
## License
SuperAgentX is released under the [MIT](https://github.com/superagentxai/superagentX/blob/master/LICENSE) License.
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"description": "<div align=\"center\">\n\n<img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/fulllogo_transparent.png?raw=True\" width=\"350\">\n\n\n<br/>\n\n\n**SuperAgentX**: A lightweight autonomous true multi-agent framework with AGI capabilities.\n\n<br/>\n\n[](https://www.python.org/downloads/release/python-3100/)\n[](https://github.com/superagentxai/superagentX)\n[](https://github.com/superagentxai/superagentX/blob/master/LICENSE)\n\n</div>\n\n## Key Features\n\n\ud83d\ude80 **Open-Source Framework**: A lightweight, open-source AI framework built for multi-agent applications with Artificial General Intelligence (AGI) capabilities.\n\n\ud83c\udfaf **Goal-Oriented Multi-Agents**: This technology enables the creation of agents with retry mechanisms to achieve set goals. Communication between agents is Parallel, Sequential, or hybrid.\n\n\ud83c\udfd6\ufe0f **Easy Deployment**: Offers WebSocket, RESTful API, and IO console interfaces for rapid setup of agent-based AI solutions.\n\n\u2668\ufe0f **Streamlined Architecture**: Enterprise-ready scalable and pluggable architecture. No major dependencies; built independently!\n\n\ud83d\udcda **Contextual Memory**: Uses SQL + Vector databases to store and retrieve user-specific context effectively.\n\n\ud83e\udde0 **Flexible LLM Configuration**: Supports simple configuration options of various Gen AI models.\n\n\ud83e\udd1d\ud83c\udffb **Extendable Handlers**: Allows integration with diverse APIs, databases, data warehouses, data lakes, IoT streams, and more, making them accessible for function-calling features.\n\n\n## Table of contents\n- [What is SuperAgentX?](#what-is-superagentx)\n- [Why SuperAgentX?](#why-superagentx)\n- [Getting Started](#getting-started)\n- [Installing Dependencies](#installing-dependencies)\n- [Contribution](#contribution)\n- [License](#license)\n\n## What is SuperAgentX?\n\n**The Ultimate Modular Autonomous Agentic AI Framework for Progressing Towards AGI.** <br/><br/>\nSuperAgentX is an advanced agentic AI framework designed to accelerate the development of Artificial General Intelligence (AGI). It provides a powerful, modular, and flexible platform for building autonomous AI agents capable of executing complex tasks with minimal human intervention. By integrating cutting-edge AI technologies and promoting efficient, scalable agent behavior, SuperAgentX embodies a critical step forward in the path toward superintelligence and AGI. Whether for research, development, or deployment, SuperAgentX is built to push the boundaries of what's possible with autonomous AI systems.\n\n## Why SuperAgentX?\n\nSuperAgentX addresses the growing need for highly capable, autonomous AI systems that can perform complex tasks with minimal human intervention. As we approach the limits of narrow AI, there's a need for an adaptable and scalable framework to bridge the gap toward AGI (Artificial General Intelligence). Here\u2019s why SuperAgentX stands out:\n\n**Super**: Cutting-edge AI systems with exceptional capabilities, paving the way to **AGI** (Artificial General Intelligence) and **ASI** (Artificial Super Intelligence).</p>\n**Agent**: Autonomous Multi AI agent framework designed to make decisions, act independently, and handle complex tasks. </p>\n**X**: The unknown, the limitless, the extra factor that makes SuperAgentX revolutionary, futuristic, and transformative.</p>\n\n### Getting Started\n\n```shell\npip install superagentx\n```\n##### Usage - Example SuperAgentX Code\nThis SuperAgentX example utilizes two handlers, Amazon and Walmart, to search for product items based on user input from the IO Console.\n\n1. It uses Parallel execution of handler in the agent \n2. Memory Context Enabled\n3. LLM configured to OpenAI\n4. Pre-requisites\n\nSet OpenAI Key: \n```shell\nexport OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxx\n```\n\nSet Rapid API Key <a href=\"https://rapidapi.com/auth/sign-up\" target=\"_blank\">Free Subscription</a> for Amazon, Walmart Search APIs\n```shell\nexport RAPID_API_KEY=XXXXXXXXXXXXXXXXXXXXXXXXX\n```\n\n```python \n# Additional lib needs to install\n# `pip install superagentx-handlers`\n# python3 superagentx_examples/ecom_iopipe.py\n\nimport asyncio\n\nfrom rich import print as rprint\n\nfrom superagentx.memory import Memory\nfrom superagentx.agent import Agent\nfrom superagentx.engine import Engine\nfrom superagentx.llm import LLMClient\nfrom superagentx.agentxpipe import AgentXPipe\nfrom superagentx.pipeimpl.iopipe import IOPipe\nfrom superagentx.prompt import PromptTemplate\nfrom superagentx_handlers.ecommerce.amazon import AmazonHandler\nfrom superagentx_handlers.ecommerce.walmart import WalmartHandler\n\n\nasync def main():\n \"\"\"\n Launches the e-commerce pipeline console client for processing requests and handling data.\n \"\"\"\n\n # LLM Configuration\n llm_config = {'llm_type': 'openai'}\n llm_client: LLMClient = LLMClient(llm_config=llm_config)\n\n # Enable Memory\n memory = Memory(memory_config={\"llm_client\": llm_client})\n\n # Add Two Handlers (Tools) - Amazon, Walmart\n amazon_ecom_handler = AmazonHandler()\n walmart_ecom_handler = WalmartHandler()\n\n # Prompt Template\n prompt_template = PromptTemplate()\n\n # Amazon & Walmart Engine to execute handlers\n amazon_engine = Engine(\n handler=amazon_ecom_handler,\n llm=llm_client,\n prompt_template=prompt_template\n )\n walmart_engine = Engine(\n handler=walmart_ecom_handler,\n llm=llm_client,\n prompt_template=prompt_template\n )\n\n # Create Agent with Amazon, Walmart Engines execute in Parallel - Search Products from user prompts\n ecom_agent = Agent(\n name='Ecom Agent',\n goal=\"Get me the best search results\",\n role=\"You are the best product searcher\",\n llm=llm_client,\n prompt_template=prompt_template,\n engines=[[amazon_engine, walmart_engine]]\n )\n\n # Pipe Interface to send it to public accessible interface (Cli Console / WebSocket / Restful API)\n pipe = AgentXPipe(\n agents=[ecom_agent],\n memory=memory\n )\n\n # Create IO Cli Console - Interface\n io_pipe = IOPipe(\n search_name='SuperAgentX Ecom',\n agentx_pipe=pipe,\n read_prompt=f\"\\n[bold green]Enter your search here\"\n )\n await io_pipe.start()\n\n\nif __name__ == '__main__':\n try:\n asyncio.run(main())\n except (KeyboardInterrupt, asyncio.CancelledError):\n rprint(\"\\nUser canceled the [bold yellow][i]pipe[/i]!\")\n\n```\n##### Usage - Example SuperAgentX Result\nSuperAgentX searches for product items requested by the user in the console, validates them against the set goal, and returns the result. It retains the context, allowing it to respond to the user's next prompt in the IO Console intelligently. \n\n\n\n## Architecture\n<img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/architecture.png?raw=True\" title=\"SuperAgentX Architecture\"/>\n\n## Large Language Models\n\n| Icon | LLM Name | Status |\n|-----------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/openai.png?raw=True\" title=\"OpenAI\" height=\"20\" width=\"20\"/> | **OpenAI** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/checkmark.png?raw=True\" title=\"Tested\" height=\"20\" width=\"20\"/> |\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/azure-icon.png?raw=True\" title=\"Azure OpenAI\" height=\"20\" width=\"20\"/> | **Azure OpenAI** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/checkmark.png?raw=True\" title=\"Tested\" height=\"20\" width=\"20\"/> | \n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/awsbedrock.png?raw=True\" title=\"AWS Bedrock\" height=\"20\" width=\"20\"/> | **AWS Bedrock** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/checkmark.png?raw=True\" title=\"Tested\" height=\"20\" width=\"20\"/> |\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/gemini.png?raw=True\" title=\"Google Gemini\" height=\"20\" width=\"20\"/> | **Google Gemini** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/wip.png?raw=True\" title=\"Development Inprogress\" height=\"20\" width=\"20\"/> |\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/meta.png?raw=True\" title=\"Google Gemini\" height=\"20\" width=\"20\"/> | **Meta Llama** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/wip.png?raw=True\" title=\"Development Inprogress\" height=\"20\" width=\"20\"/> |\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/ollama.png?raw=True\" title=\"Ollama\" height=\"20\" width=\"20\"/> | **Ollama** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/wip.png?raw=True\" title=\"Development Inprogress\" height=\"20\" width=\"20\"/> |\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/claude-ai-logo.png?raw=True\" title=\"Claude AI\" height=\"20\" width=\"20\"/> | **Claude AI** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/todo.png?raw=True\" title=\"TODO\" height=\"20\" width=\"20\"/> |\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/mistral-ai-logo.png?raw=True\" title=\"Mistral AI\" height=\"20\" width=\"30\"/> | **Mistral AI** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/todo.png?raw=True\" title=\"TODO\" height=\"20\" width=\"20\"/> |\n| <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/llms/ibm.png?raw=True\" title=\"IBM WatsonX AI\" height=\"20\" width=\"30\"/> | **IBM WatsonX** | <img src=\"https://github.com/superagentxai/superagentX/blob/master/docs/images/todo.png?raw=True\" title=\"TODO\" height=\"20\" width=\"20\"/> |\n\n## Environment Setup\n```shell\n$ cd <path-to>/superagentx\n$ python3.12 -m venv venv\n$ source venv/bin/activate\n(venv) $ pip install poetry\n(venv) $ poetry install\n```\n\n## Documentation (_Coming Soon_)\n\n## License\n\nSuperAgentX is released under the [MIT](https://github.com/superagentxai/superagentX/blob/master/LICENSE) License.\n",
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