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.
<!-- TODO: add demo GIF here -->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:
<img src="./docs/example-claudecode.jpg" alt="Claude Code + SaC example" width="800" />"Help me understand this codebase using a visualized and interactive app using SaC MCP."
Codex (Skill)
pip install sac-sdk
sac setup codex # installs the SaC skill
sac serve # keep running in a terminal
OpenClaw (Skill)
pip install sac-sdk
sac setup openclaw # installs the SaC skill
sac serve # keep running in a terminal
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
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 .
Citation
@article{xie2026sac,
title = {Software as Content: Dynamic Applications as the Human-Agent Interaction Layer},
author = {Xie, Mulong},
year = {2026},
url = {https://arxiv.org/abs/2603.21334}
}
License
Apache-2.0 · © 2026 Mulong Xie / Dynsoft Lab
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Built by Dynsoft Lab. Questions: mulong@mulongxie.me
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