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

• Installation
• Configuration
• Examples: Automatic WorkFlow Generation
• Demo Video
• Tutorial and Use Cases

We recommend installing EvoAgentX using pip:

pip install evoagentx

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=3.10

# 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.

• Linux/macOS:

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

• Windows Command Prompt:

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

• Windows PowerShell:

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

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

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

• Create a .env file in your project root:

OPENAI_API_KEY=<your-openai-api-key>

Then load it in Python:

from dotenv import load_dotenv 
import os 

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

🔐 Tip: Don’t forget to add .env to your .gitignore to avoid committing secrets.

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

🎥 Demo video coming soon – stay tuned!

In the meantime, check out the EvoAgentX Quickstart Guide for a step-by-step guide to get started with EvoAgentX.

Explore how to effectively use EvoAgentX with the following resources:

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

💡 Discover real-world applications and unleash the potential of EvoAgentX in your projects!

📢 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. 🤝🚀

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