EvoAgentX: The First Self-Evolving AI Agent Framework

• [May 2025] 🎉 EvoAgentX has been officially released!

• 🔥 Latest News
• ⚡ Get Started
• Installation
• LLM Configuration
  • API Key Configuration
  • Configure and Use the LLM
• Automatic WorkFlow Generation
• Demo Video
  • ✨ Final Results
• Evolution Algorithms
  • 📊 Results
• Applications
• Tutorial and Use Cases
• 🎯 Roadmap
• 🙋 Support
  • Join the Community
  • Contact Information
• 🙌 Contributing to EvoAgentX
• 📚 Acknowledgements
• 📄 License

We recommend installing EvoAgentX using pip:

For local development or detailed setup (e.g., using conda), refer to the Installation Guide for EvoAgentX.

Example (optional, for local development):

To use LLMs with EvoAgentX (e.g., OpenAI), you must set up your API key.

Option 1: Set API Key via Environment Variable

• Linux/macOS:

• Windows Command Prompt:

• Windows PowerShell:

Once set, you can access the key in your Python code with:

Option 2: Use .env File

• Create a .env file in your project root and add the following:

Then load it in Python:

Once the API key is set, initialise the LLM with:

📖 More details on supported models and config options: LLM module guide.

Once your API key and language model are configured, you can automatically generate and execute multi-agent workflows in EvoAgentX.

🧩 Core Steps:

1. Define a natural language goal
2. Generate the workflow with WorkFlowGenerator
3. Instantiate agents using AgentManager
4. Execute the workflow via WorkFlow

💡 Minimal Example:

You can also:

• 📊 Visualise the workflow: workflow_graph.display()
• 💾 Save/load workflows: save_module() / from_file()

📂 For a complete working example, check out the workflow_demo.py

EvoAgentX_demo.mov

In this demo, we showcase the workflow generation and execution capabilities of EvoAgentX through two examples:

• Application 1: Intelligent Job Recommendation from Resume
• Application 2: Visual Analysis of A-Share Stocks

We have integrated some existing agent/workflow evolution algorithms into EvoAgentX, including TextGrad, MIPRO and AFlow.

To evaluate the performance, we use them to optimize the same agent system on three different tasks: multi-hop QA (HotPotQA), code generation (MBPP) and reasoning (MATH). We randomly sample 50 examples for validation and other 100 examples for testing.

Tip: We have integrated these benchmark and evaluation code in EvoAgentX. Please refer to the benchmark and evaluation tutorial for more details.

Method HotPotQA
(F1%) MBPP
(Pass@1 %) MATH
(Solve Rate %)

Original	63.58	69.00	66.00
TextGrad	71.02	71.00	76.00
AFlow	65.09	79.00	71.00
MIPRO	69.16	68.00	72.30

Please refer to the examples/optimization folder for more details.

We use our framework to optimize existing multi-agent systems on the GAIA benchmark. We select Open Deep Research and OWL, two representative multi-agent framework from the GAIA leaderboard that is open-source and runnable.

We apply EvoAgentX to optimize their prompts. The performance of the optimized agents on the GAIA benchmark validation set is shown in the figure below.

Full Optimization Reports: Open Deep Research and OWL.

💡 New to EvoAgentX? Start with the Quickstart Guide for a step-by-step introduction.

Explore how to effectively use EvoAgentX with the following resources:

🛠️ Follow the tutorials to build and optimize your EvoAgentX workflows.

🚀 We're actively working on expanding our library of use cases and optimization strategies. More coming soon — stay tuned!

• Modularize Evolution Algorithms: Abstract optimization algorithms into plug-and-play modules that can be easily integrated into custom workflows.
• Develop Task Templates and Agent Modules: Build reusable templates for typical tasks and standardized agent components to streamline application development.
• Integrate Self-Evolving Agent Algorithms: Incorporate more recent and advanced agent self-evolution across multiple dimensions, including prompt tuning, workflow structures, and memory modules.
• Enable Visual Workflow Editing Interface: Provide a visual interface for workflow structure display and editing to improve usability and debugging.

📢 Stay connected and be part of the EvoAgentX journey!
🚩 Join our community to get the latest updates, share your ideas, and collaborate with AI enthusiasts worldwide.

• Discord — Chat, discuss, and collaborate in real-time.
• X (formerly Twitter) — Follow us for news, updates, and insights.
• WeChat — Connect with our Chinese community.

If you have any questions or feedback about this project, please feel free to contact us. We highly appreciate your suggestions!

• Email: [email protected]

We will respond to all questions within 2-3 business days.

Thanks go to these awesome contributors

We appreciate your interest in contributing to our open-source initiative. We provide a document of contributing guidelines which outlines the steps for contributing to EvoAgentX. Please refer to this guide to ensure smooth collaboration and successful contributions. 🤝🚀

This project builds upon several outstanding open-source projects: AFlow, TextGrad, DSPy, LiveCodeBench, and more. We would like to thank the developers and maintainers of these frameworks for their valuable contributions to the open-source community.

Source code in this repository is made available under the MIT License.