EvoAgentX

What does this agent do, and when should you use it?

EvoAgentX is an open-source framework for building, evaluating, and evolving LLM-based agents and agentic workflows. A natural-language goal is passed to WorkFlowGenerator to create a WorkFlowGraph; AgentManager instantiates its agents, and WorkFlow.execute() runs the result. The project includes short- and long-term memory, human-in-the-loop controls through HITLManager, and toolkits for search, code execution, files, databases, images, and browsers. It integrates TextGrad, MIPRO, AFlow, and EvoPrompt as workflow or prompt evolution algorithms, alongside benchmark and evaluation material. It is installed and used as a Python library rather than delivered as a hosted chat product; model calls and external capabilities depend on the configured providers, tools, and credentials.

A developer configures OpenAILLMConfig and OpenAILLM, then calls WorkFlowGenerator(llm=llm).generate_workflow(goal) to create a workflow graph from a goal. AgentManager.add_agents_from_workflow() creates agents from that graph, and WorkFlow(graph=..., agent_manager=..., llm=...).execute() runs the workflow and returns output. The generator can receive a tools list, such as ArxivToolkit, and may assign those tools to relevant agents; the repository also provides toolkits for search, HTTP requests, file operations, command execution, databases, images, and browser automation. HITLInterceptorAgent can pause execution before a named action by a target agent and request a human approval or rejection; workflow graphs can also be display()ed and saved or loaded with save_module() / from_file().

1. A developer who wants to turn a goal such as generating HTML for a Tetris game into a multi-agent workflow.
2. A researcher optimizing prompts or workflow structure for HotPotQA, MBPP, or MATH with TextGrad, AFlow, MIPRO, or EvoPrompt.
3. A research team that needs a workflow to retrieve and summarize arXiv papers for selected keywords and time ranges, using ArxivToolkit or MCP tools.
4. An application developer who needs a human approval checkpoint before a sensitive action such as sending email, using HITLInterceptorAgent.
5. A Python team building workflows that call search, HTTP, filesystem, PostgreSQL, MongoDB, FAISS, or browser tools.

How do you install or deploy this agent?

Install with pip:

pip install evoagentx

Or install directly from the repository:

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

The local-development example uses Python 3.11: create and activate a conda environment, then run pip install -r requirements.txt or pip install -e ..

How do you use this agent?

First set OpenAI credentials, for example on Linux/macOS:

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

Then create a model and run a first workflow:

import os
from evoagentx.models import OpenAILLMConfig, OpenAILLM
from evoagentx.workflow import WorkFlowGenerator, WorkFlow
from evoagentx.agents import AgentManager

openai_config = OpenAILLMConfig(

model="gpt-4o-mini",
openai_key=os.getenv("OPENAI_API_KEY"),
stream=True,

output_response=True

)

llm = OpenAILLM(config=openai_config)
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)

To make tools available, pass an instance such as ArxivToolkit() to the tools arguments of both WorkFlowGenerator and AgentManager. Beyond OpenAI, the README states that Qwen, Claude, DeepSeek, and Kimi can be integrated through native adapters or LiteLLM, SiliconFlow, and OpenRouter; complete configuration examples for those paths are not included in the supplied material.

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