• [July 2025] 🎉 EvoAgentX is on arxiv!
• [July 2025] 🎉 EvoAgentX has achieved 1,000 stars!
• [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
• Tool-Enabled Workflows Generation:
• Demo Video
  • ✨ Final Results
• Evolution Algorithms
  • 📊 Results
• Applications
• Tutorial and Use Cases
• 🎯 Roadmap
• 🙋 Support
  • Join the Community
  • Add the meeting to your calendar
  • Contact Information
  • Community Call
• 🙌 Contributing to EvoAgentX
• 📖 Citation
• 📚 Acknowledgements
• 📄 License

We recommend installing EvoAgentX using pip:

pip install git+https://github.com/EvoAgentX/EvoAgentX.git

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

git clone https://github.com/EvoAgentX/EvoAgentX.git
cd EvoAgentX
# Create a new conda environment
conda create -n evoagentx python=3.11

# Activate the environment
conda activate evoagentx

# Install the package
pip install -r requirements.txt
# OR install in development mode
pip install -e .

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

export OPENAI_API_KEY=<your-openai-api-key>

set OPENAI_API_KEY=<your-openai-api-key>

$env:OPENAI_API_KEY="<your-openai-api-key>" # " is required 

import os
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

OPENAI_API_KEY=<your-openai-api-key>

from dotenv import load_dotenv 
import os 

load_dotenv() # Loads environment variables from .env file
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

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

from evoagentx.models import OpenAILLMConfig, OpenAILLM

# Load the API key from environment
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

# Define LLM configuration
openai_config = OpenAILLMConfig(
    model="gpt-4o-mini",       # Specify the model name
    openai_key=OPENAI_API_KEY, # Pass the key directly
    stream=True,               # Enable streaming response
    output_response=True       # Print response to stdout
)

# Initialize the language model
llm = OpenAILLM(config=openai_config)

# Generate a response from the LLM
response = llm.generate(prompt="What is Agentic Workflow?")

📖 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:

from evoagentx.workflow import WorkFlowGenerator, WorkFlowGraph, WorkFlow
from evoagentx.agents import AgentManager

goal = "Generate html code for the Tetris game"
workflow_graph = WorkFlowGenerator(llm=llm).generate_workflow(goal)

agent_manager = AgentManager()
agent_manager.add_agents_from_workflow(workflow_graph, llm_config=openai_config)

workflow = WorkFlow(graph=workflow_graph, agent_manager=agent_manager, llm=llm)
output = workflow.execute()
print(output)

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

In more advanced scenarios, your workflow agents may need to use external tools. EvoAgentX allows Automatic tool integration: Provide a list of toolkits to WorkFlowGenerator. The generator will consider these and include them in the agents if appropriate.

For instance, to enable an Arxiv toolkit:

from evoagentx.tools import ArxivToolkit

# Initialize a command-line toolkit for file operations
arxiv_toolkit = ArxivToolkit()

# Generate a workflow with the toolkit available to agents
wf_generator = WorkFlowGenerator(llm=llm, tools=[arxiv_toolkit])
workflow_graph = wf_generator.generate_workflow(goal="Find and summarize the latest research on AI in the field of finance on arXiv")

# Instantiate agents with access to the toolkit
agent_manager = AgentManager(tools=[arxiv_toolkit])
agent_manager.add_agents_from_workflow(workflow_graph, llm_config=openai_config)

workflow = WorkFlow(graph=workflow_graph, agent_manager=agent_manager, llm=llm)
output = workflow.execute()
print(output)

In this setup, the workflow generator may assign the ArxivToolkit to relevant agents, enabling them to execute shell commands as part of the workflow (e.g. creating directories and files)

In advanced scenarios, EvoAgentX supports integrating human-in-the-loop interactions within your agent workflows. This means you can pause an agent’s execution for manual approval or inject user-provided input at key steps, ensuring critical decisions are vetted by a human when needed.

All human interactions are managed through a central HITLManager instance. The HITL module includes specialized agents like HITLInterceptorAgent for approval gating and HITLUserInputCollectorAgent for collecting user data.

For instance, to require human approval before an email-sending agent executes its action:

from evoagentx.hitl import HITLManager, HITLInterceptorAgent, HITLInteractionType, HITLMode

hitl_manager = HITLManager()
hitl_manager.activate()  # Enable HITL (disabled by default)

# Interceptor agent to approve/reject the DummyEmailSendAction of DataSendingAgent
interceptor = HITLInterceptorAgent(
    target_agent_name="DataSendingAgent",
    target_action_name="DummyEmailSendAction",
    interaction_type=HITLInteractionType.APPROVE_REJECT,
    mode=HITLMode.PRE_EXECUTION    # ask before action runs
)
# Map the interceptor’s output field back to the workflow’s input field for continuity
hitl_manager.hitl_input_output_mapping = {"human_verified_data": "extracted_data"}

# Add the interceptor to the AgentManager and include HITL in the workflow execution
agent_manager.add_agent(interceptor)
workflow = WorkFlow(graph=workflow_graph, agent_manager=agent_manager, llm=llm, hitl_manager=hitl_manager)

When this interceptor triggers, the workflow will pause and prompt in the console for [a]pprove or [r]eject before continuing. If approved, the flow proceeds using the human-verified data; if rejected, the action is skipped or handled accordingly.

📂 For a complete working example, check out the tutorial /hitl.md

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

Application 1: Job Recommendation

Application 2: Stock Visual Analysis

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.

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.

Open Deep Research

OWL Agent

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.

📅 Click the link below to add the EvoAgentX Weekly Meeting (Sundays, 16:30–17:30 GMT+8) to your calendar:

👉 Add to your Google Calendar

👉 Add to your Tencent Meeting

👉 Download the EvoAgentX_Weekly_Meeting.ics file

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

• Email: evoagentx.ai@gmail.com

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

• Bilibili
• Youtube

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. 🤝🚀

Please consider citing our work if you find EvoAgentX helpful:

📄 EvoAgentX 📄 Survey Paper

@article{wang2025evoagentx,
  title={EvoAgentX: An Automated Framework for Evolving Agentic Workflows},
  author={Wang, Yingxu and Liu, Siwei and Fang, Jinyuan and Meng, Zaiqiao},
  journal={arXiv preprint arXiv:2507.03616},
  year={2025}
}
@article{fang202survey,
      title={A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems}, 
      author={Jinyuan Fang and Yanwen Peng and Xi Zhang and Yingxu Wang and Xinhao Yi and Guibin Zhang and Yi Xu and Bin Wu and Siwei Liu and Zihao Li and Zhaochun Ren and Nikos Aletras and Xi Wang and Han Zhou and Zaiqiao Meng},
      year={2025},
      journal={arXiv preprint arXiv:2508.07407},
      url={https://arxiv.org/abs/2508.07407}, 
}

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.