SaC — Software as Content

SaC SDK

Interaction layer between you and your agents.

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:

• Prompt improvements in src/sac/runtime/prompts/
• Design system contributions in src/sac/renderer/design-systems/

For local dev: pip install -e .

License

Apache-2.0