EvoAgentX/EvoAgentX
EvoAgentX
EvoAgentX is an agent platform that combines human-in-the-loop checkpoints, MCP tool integration, multi-agent workflow orchestration, and self-improvement capabilities. It functions as a self-improving agent framework that connects to MCP-compatible servers and orchestrates multi-agent workflows using natural-language goals, while also serving as a platform that discovers, configures, and manages tools from MCP servers for use in automated agent workflows.
The platform distinguishes itself through a dual-memory agent architecture that maintains short-term and persistent memory stores, enabling agents to recall context and improve behavior across sessions. It features evolutionary workflow optimization that improves agent workflows by applying mutation, guided search, and retrieval-augmented evaluation across successive generations. A human-in-the-loop checkpoint system pauses workflow execution at configurable points to collect structured input, approvals, or corrections from a human operator, while a prompt-to-workflow compilation capability translates natural-language goals into structured multi-agent workflow graphs through automated planning and decomposition.
The system provides a provider-agnostic LLM adapter that routes agent interactions to multiple language model backends through a unified interface supporting OpenAI, Qwen, Claude, and local deployments. It includes a plugin-style built-in tool library offering a modular collection of tools for code execution, file I/O, databases, search, and browser automation without external dependencies. The MCP-based tool abstraction layer connects agents to external tools via a standardized protocol using stdio and HTTP servers with automatic discovery and lifecycle management.