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

Terminal

pip install sac-sdk

2. Run

bash

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)

Terminal

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)

Terminal

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

Setup details →

OpenClaw (Skill)

Terminal

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)

server.ts

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

Code

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:

server.ts

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

Code

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