Skip to main content

A flexible and powerful multi-agent AI framework for building advanced AI applications

Project description

MedhaAI

MedhaAI is a flexible and powerful multi-agent AI framework for building advanced AI applications. It provides a set of tools and abstractions that allow developers to create complex, collaborative AI systems with ease.

Features

  • Multi-agent system with prioritized agents
  • Support for multiple LLM providers (OpenAI, Anthropic)
  • Flexible memory systems
  • Advanced planning and execution modules
  • Customizable toolkit system with advanced web scraping capabilities
  • Live streaming of agent activities
  • Performance monitoring and tracing
  • Asynchronous operations for improved performance

Installation

To install MedhaAI, simply use pip:

pip install medhaai

Quick Start

Here's a simple example to get you started with MedhaAI:

import asyncio
from medhaai import MedhaConfig, BaseLLM, BaseAgent, MedhaMultiAgentSystem

async def main():
    config = MedhaConfig()
    llm = BaseLLM.create("openai", api_key=config.openai_api_key)
    agent = BaseAgent.create("general", name="GeneralAgent", role="assistant", llm=llm)
    mas = MedhaMultiAgentSystem(task_decomposition_llm=llm)
    mas.add_agent(agent)
    result = await mas.run_task("Research and summarize the latest advancements in quantum computing")
    print(result)

asyncio.run(main())

Documentation

For more detailed information and advanced usage, please refer to our documentation.

Contributing

Contributions are welcome! Please see our Contributing Guide for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Project details


Download files

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

Source Distribution

medhaai-0.4.0.tar.gz (14.6 kB view details)

Uploaded Source

Built Distribution

medhaai-0.4.0-py3-none-any.whl (19.0 kB view details)

Uploaded Python 3

File details

Details for the file medhaai-0.4.0.tar.gz.

File metadata

  • Download URL: medhaai-0.4.0.tar.gz
  • Upload date:
  • Size: 14.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.4

File hashes

Hashes for medhaai-0.4.0.tar.gz
Algorithm Hash digest
SHA256 a9929dbfaf351fed528c5b7a66b3268deba2ad4d2e5e86db0d3c39e484cd920a
MD5 8ffb7588259e7234a3dddb84e67f9e3c
BLAKE2b-256 01a48f31baf65c6af58a2b191a07495ff8e2f2504a65c37470de18b7221af5b4

See more details on using hashes here.

File details

Details for the file medhaai-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: medhaai-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 19.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.11.4

File hashes

Hashes for medhaai-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 376a0451d25e3b7f41f89ecc3aab0e4200e674f45b5b172517283051f5624c7a
MD5 65b0ec3562fdc4d914a5b6c0b61d684e
BLAKE2b-256 f4504094446c189d6f68b92a6be83fcd3a6081b3a10ebdd0042b8578f93ab635

See more details on using hashes here.

Supported by

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