Modular Project Setup

Before talking about dependencies, we need to talk about project setup. Sigasi Visual HDL is not a single-file editor. It considers the project as a whole and performs real-time analysis using project context to provide accurate results. That’s why a project description is required.

The project description contains information such as library mappings for VHDL or include directories for SystemVerilog. Defining this information is referred to as project setup. The project setup effectively defines how the design should be compiled: which files belong to the project, how they are related, and where dependencies can be found.

If the project is not configured correctly, Sigasi notifies you with an error caused by a missing dependency. The project setup is important not only for accurate analysis, but also because it serves as the foundation for downstream tools. Based on the project description, a compilation order can be generated and consumed by simulators, synthesis tools, and other parts of the HDL tool flow.

A correctly configured Sigasi project helps ensure that analysis, compilation, simulation, and implementation all operate on the same understanding of the design. In our 2026.2 release, we introduced Sigasi Modular Project Setup. In Modular Projects, the entire project is described by a single project.sigasi file, and the .settings folder is replaced by the .sigasi folder. The Modular Project approach simplifies project configuration and provides a more structured way to manage dependencies especially in an agentic AI world.

Sigasi MCP

Missing library mappings, incorrect include directories, or incomplete project configuration can cause dependency-related errors. Identifying the root cause of these issues is often time consuming. Sigasi Visual HDL can detect these problems through its project analysis capabilities, providing detailed diagnostics about missing dependencies and configuration issues.

Through Sigasi MCP, an AI agent can access those diagnostics and determine what needs to be changed in the project configuration. The agent doesn’t work in isolation or try to infer the root cause on its own. Instead, it uses the diagnostics provided by Sigasi Visual HDL to fix project configuration.

Instead of modifying HDL source code, the agent can update the Sigasi project configuration itself, for example by adding missing library mappings or correcting include directories. Once the configuration has been updated, Sigasi Visual HDL analyzes the project again. If additional dependency issues remain, the agent can continue refining the configuration until the project is correctly configured.

In this workflow, Sigasi Visual HDL provides the analysis and diagnostics, while the agent uses that information to automatically resolve dependency-related issues.

Fixing Dependencies

In this short example we’ll use Copilot in VS Code. While dependency errors can be resolved manually through GUI actions, we will instead ask the agent to fix them automatically without modifying the HDL code. Thanks to Sigasi MCP, the agent is able to detect the missing library definitions in the project.sigasi file and fix the issue by updating the project configuration.

Built for engineers and agents

Whether code is handwritten or agent-generated, Sigasi helps teams move faster without sacrificing quality or maintainability.

Watch the screencast to see Copilot in VS Code fix dependency errors.

Watch our webinar on Agentic AI

Interested in learning more on chip design in an agentic world? Then watch our Agentic AI webinar webinar in which we cover how Sigasi Visual HDL supports agents through MCP in CS Code and Cursor.

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