Stepflow was designed to provide a common runtime for GenAI workflow frameworks. We've been working on a proof-of-concept converting flows from Langflow to Stepflow and executing them using Stepflow. This post demonstrates the integration and provides implementation details.

In the rapidly evolving landscape of AI applications, the ability to orchestrate complex workflows that combine language models with practical tools has become essential. Today, we'll explore how Stepflow, a modern open source workflow orchestration engine, seamlessly integrates LangChain's AI capabilities with common tools via the Model Context Protocol (MCP) to create a powerful research assistant.

While invoking MCP tools from within an agent is nothing new, in this post we are building a practical, modular "flow" on top of Stepflow's declarative workflow engine. This system will generate research questions, analyze text, create structured notes, and save everything to an organized file structure. All this will be orchestrated through a declarative YAML workflow that's easy to understand and modify.

We're excited to announce the initial release of Stepflow, an open-source protocol and runtime designed to make building, executing, and scaling GenAI workflows simple, secure, and portable. Whether you're prototyping locally or deploying to production, Stepflow provides the foundation for reliable AI workflow execution.