ViraStack Agent Bridge

An experimental, agent-neutral MCP/CLI adapter for Cadence ViraStack in Virtuoso IC26.1. Every design action is delegated to the native ViraStack agent and its permission system.

中文说明

flowchart LR
  A[Any agent] --> B[MCP or CLI]
  B --> C[SSH worker]
  C --> D[Native ViraStack task manager]
  D --> E[Virtuoso tools]
  D <--> F[JedAI and your chosen model service]

The bridge reuses the official MCP Python SDK and SkillBridge transport. ViraStack owns task scheduling, tool execution, confirmations, results, and cancellation. Its existing native task IDs are wrapped in tickets that detect stale or mismatched sessions.

Requirements

• A licensed, running ViraStack session with a working model service.

• Linux Python 3.9+ in the Virtuoso host for the native worker.

• Python 3.10+ in the agent host for MCP; an SSH client for remote operation.

Cadence software, model credentials, licenses, PDKs and design data are supplied by the user's environment. This project is an independent integration; IC26.1's replay interface is version-sensitive. See validation for the exact tested scope.

Related MCP server: virtuoso-skill-mcp

Install the native worker

On the Virtuoso host, install this source checkout into a dedicated environment:

python3 -m venv ~/.venvs/virastack-bridge
~/.venvs/virastack-bridge/bin/pip install '.[native]'
~/.venvs/virastack-bridge/bin/python -c \
  'from importlib.resources import files; print(files("skillbridge") / "server/python_server.il")'

Load the printed file in the intended Virtuoso session, then start its transport:

load("/absolute/path/skillbridge/server/python_server.il")
pyStartServer(?id "virastack" ?python "/home/you/.venvs/virastack-bridge/bin/python")

Reuse an existing SkillBridge server and its workspace ID when one is already running. Connect ViraStack to your model service and keep the intended chat open.

Install the agent front end

python -m pip install '.[mcp]'

Configure the SSH host alias through your normal SSH setup, then set:

export VIRASTACK_BRIDGE_SSH_TARGET=eda-server
export VIRASTACK_BRIDGE_REMOTE_PYTHON=/home/you/.venvs/virastack-bridge/bin/python
export VIRASTACK_BRIDGE_WORKSPACE=virastack

VIRASTACK_BRIDGE_SSH_IDENTITY and VIRASTACK_BRIDGE_KNOWN_HOSTS are optional file paths. Host-key checking and batch authentication are enforced. For a front end running directly on the Linux Virtuoso host, omit VIRASTACK_BRIDGE_SSH_TARGET.

Use from any agent

An MCP client's server configuration can launch:

{
  "mcpServers": {
    "virastack": {
      "command": "virastack-agent-bridge",
      "args": ["mcp"],
      "env": {
        "VIRASTACK_BRIDGE_SSH_TARGET": "eda-server",
        "VIRASTACK_BRIDGE_REMOTE_PYTHON": "/home/you/.venvs/virastack-bridge/bin/python",
        "VIRASTACK_BRIDGE_WORKSPACE": "virastack"
      }
    }
  }
}

Tools: virastack_status, virastack_sessions, virastack_submit, virastack_job, virastack_cancel, and virastack_reply.

Select a chat explicitly. Submit a request, retain its ticket, and poll virastack_job. A single foreground request is submitted at a time per Virtuoso application. A submission acknowledgement does not mean the task completed. Read the native result and evidence to assess whether the requested design goal was achieved. Native pending questions are handled through virastack_reply; permissions are not automatically granted.

Agents that use shell tools can use the same interface:

virastack-agent-bridge sessions
virastack-agent-bridge submit --chat 0 --prompt \
  'Use the native Virtuoso tool to read getVersion(). Do not change designs or run simulations.'
virastack-agent-bridge job TICKET_FROM_SUBMIT

For larger or private prompts, use --prompt-file file.txt or --prompt-file -. After a client timeout, inspect sessions for latest_ticket before resubmitting.

Model backends

The external agent and ViraStack's model backend are independent. Configure the model service in JedAI/ViraStack. The adapter contains no Codex-specific authentication code. The current live native-agent validation used gpt-6.1-sol; model selection affects delegation and tool-use reliability.

For personal Codex login reuse, a separately installed compatible proxy such as codex-openai-proxy can serve as the model backend through a loopback-only SSH tunnel. Follow that project's pinned runtime and authentication instructions. Keep account credentials on their original host.

Development

python -m unittest discover -s tests -v

Protocol tests use fakes; live validation is opt-in and uses a real licensed server. See third-party components and validation.