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Basic Information

EvoAgentX is an open-source framework for automatically generating, executing, evaluating and evolving multi-agent workflows driven by large language models. It is designed to convert natural language goals into structured workflows, instantiate agents, assign tools, run coordinated actions and collect outputs. The repository targets researchers and developers who build agentic systems and need systematic tooling for workflow generation, benchmarking and optimization. It bundles LLM integration, examples and tutorials to help users configure models, create agents, visualize graphs and persist workflows. EvoAgentX also integrates evolution algorithms to optimize prompts and workflow structures and provides utilities for human-in-the-loop interactions, tool-enabled agents and benchmark evaluation across tasks such as multi-hop QA, code generation and reasoning.

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App Details

Features
Core components include WorkFlowGenerator for automatic workflow synthesis from goals, WorkFlowGraph for visualizing and saving workflows, AgentManager for creating and managing agents, and WorkFlow for execution. The project provides LLM integration with configurable model settings and streaming support. Tool-enabled workflows let agents access toolkits such as an Arxiv toolkit. Human-in-the-loop is supported via HITLManager and specialized HITL agents for approval and input collection. EvoAgentX integrates evolution optimizers including TextGrad, MIPRO and AFlow and includes benchmarking and evaluation code. The repo supplies examples, demo videos, tutorials, Colab notebooks, a pip-installable package and fixtures for reproducible experiments.
Use Cases
EvoAgentX helps teams automate the end-to-end lifecycle of agentic workflows: from goal specification to automated design, execution and iterative optimization. It reduces manual engineering by synthesizing multi-agent plans from plain language and by assigning tool access where appropriate. Integrated optimizers enable prompt and structural improvements to increase task performance on standard benchmarks. HITL features permit human approvals and data collection at critical steps for safer deployments. The framework provides reproducible evaluation scripts, visual inspection of workflows, examples for common use cases and tutorials to shorten onboarding for researchers and practitioners who want to build, test and refine multi-agent systems.

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