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mcp server

software-as-content-sdk

dynsoft-lab/software-as-content-sdkcommunitylistedunreviewed
Use tool

Set up

Runs on your computer. Pick your agent and operating system.

Before you start

  • uv (`curl -LsSf https://astral.sh/uv/install.sh | sh`)
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "dynsoft-lab-software-as-content-sdk": {
      "command": "uvx",
      "args": [
        "sac-sdk"
      ],
      "env": {
        "SAC_API_KEY": "<your SAC_API_KEY>"
      }
    }
  }
}

same format Cursor, GitHub Copilot CLI, Junie, JetBrains AI Assistant, Augment (Auggie CLI), Factory Droid, Kiro, Devin CLI +12

Values in angle brackets are placeholders. Restart Claude Desktop after editing its config.Claude Desktop docs

README

synced Oct 17, 2026

SaC SDK

Interaction layer between you and your agents.

PyPI version Python License

AI agents can reason, code, and call APIs — but when they need to communicate back to you, all they have is text. SaC (Software as Content) is the missing interaction layer: your agent responds with a live, persistent, interactive app that evolves as the conversation continues. Not a screenshot, not a markdown wall — a real UI you click, explore, and shape together with your agent.

Quickstart

1. Install

pip install sac-sdk

2. Run

sac serve

First time? It'll ask for your API key and save it. Then open http://localhost:18420, type "3-day Tokyo trip planner with budget", and watch a live React app stream in. Click buttons. Ask it to evolve. This is SaC running a built-in agent loop — no external agent needed.

Connect to your agent

SaC plugs into the agent you already use — through MCP, Skill, or code.

Claude Code (MCP)

pip install sac-sdk
sac setup claude-code        # registers SaC as an MCP server

Restart Claude Code. Then try:

"Help me understand this codebase using a visualized and interactive app using SaC MCP."

Setup details →

Codex (Skill)

pip install sac-sdk
sac setup codex              # installs the SaC skill
sac serve                    # keep running in a terminal

Setup details →

OpenClaw (Skill)

pip install sac-sdk
sac setup openclaw           # installs the SaC skill
sac serve                    # keep running in a terminal

Setup details →

Python (build your own agent)

from sac import SaC

sac = SaC()
conv = sac.conversation()
app = await conv.generate("3-day Tokyo itinerary")
print(app.url)   # user opens this
# app.code contains the generated TSX

How it works

Your agent ──▶ SaC ──▶ User sees a live app at a URL
                   ◀── User clicks a button / types a message
Your agent ──▶ SaC ──▶ Same URL, app evolves in place
                   ◀── ...

One URL, one conversation. The agent doesn't generate a new page every turn — it evolves the existing app. Users keep their context; the agent keeps its state.

Two channels, one loop: every response is either a UI update (the app evolves) or a chat reply (a text bubble). Users can click buttons in the app OR type in the chat — both go back to the agent through the same callback.

When to use SaC

SaC is for tasks where exploration and interaction matter more than a final answer.

Good fit: trip planning, data analysis dashboards, comparison shopping, project planning, research, financial reviews, decision aids, internal tools

Not the right tool for: simple Q&A, one-shot automations ("set an alarm"), conversations that are purely text

Customize

Every layer is pluggable:

from sac import SaC, FileStore

sac = SaC(
    llm=YourLLMProvider(...),       # any class implementing LLMProvider
    search=YourSearchProvider(...), # any class implementing SearchProvider
    store=FileStore(".sac"),
)

Prompts live in src/sac/runtime/prompts/ and the default design system is in src/sac/renderer/design-systems/default/.

Architecture

src/sac/
├── sac.py / conversation.py    Entry + Conversation primitive
├── runtime/                    Generate + Evolve pipeline, prompts, providers
├── server/
│   ├── http/                   FastAPI + SSE streaming + viewer
│   └── mcp/                    MCP stdio server (Claude Code integration)
└── renderer/                   iframe sandbox + design system

Full architecture →

Project status

v0.1.2 — alpha. The core protocol (generate → evolve → callback loop) is stable and runs in production at sac.dynsoft.ai. The SDK surface is being polished toward v1.0.

Contributing

Issues and PRs welcome. Highest-leverage contributions right now:

For local dev: pip install -e .

License

Apache-2.0

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