EvoAgentX is an automated framework for evaluating and evolving agentic workflows, designed for researchers and developers working with multi-agent systems. It simplifies the creation, execution, and optimization of complex AI agent interactions by enabling automatic workflow generation and providing integrated evolution algorithms.

How It Works

EvoAgentX employs a modular architecture that allows for the automatic generation of agent workflows from natural language goals. It leverages LLMs to define workflow structures and agent roles, then uses an AgentManager to instantiate and orchestrate these agents. The framework integrates evolution algorithms like TextGrad, MIPRO, and AFlow to optimize agent prompts and workflow structures, aiming to improve performance on various tasks.

Quick Start & Requirements

• Install via pip: pip install git+https://github.com/EvoAgentX/EvoAgentX.git
• Requires Python 3.10+.
• API keys (e.g., OpenAI) are necessary for LLM interaction, configurable via environment variables or a .env file.
• Official documentation and tutorials are available: EvoAgentX Homepage, Documentation

Highlighted Details

• Supports automatic generation of multi-agent workflows from natural language goals.
• Integrates evolution algorithms (TextGrad, MIPRO, AFlow) for prompt and workflow optimization.
• Benchmarked performance improvements on tasks like HotPotQA, MBPP, and MATH.
• Applied to optimize existing multi-agent frameworks like Open Deep Research and OWL on the GAIA benchmark.

Maintenance & Community

• Active development with a roadmap including modularization of evolution algorithms and integration of self-evolving agent capabilities.
• Community support available via Discord: Discord.
• Follow on X: @EvoAgentX.

Licensing & Compatibility

• Licensed under the MIT License.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The project is actively under development, with a roadmap indicating planned features such as a visual workflow editing interface and more advanced self-evolving agent algorithms. Specific performance gains may vary depending on the task and chosen optimization algorithms.