Spaces:
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| title: Agentic Workflow Node Lab | |
| emoji: 🤖 | |
| colorFrom: indigo | |
| colorTo: yellow | |
| sdk: docker | |
| app_port: 7860 | |
| pinned: false | |
| # Agentic Workflow Lab | |
| A deliberately small educational workflow simulator. Build a graph with **Input**, **AI Worker**, **Decision Rules**, **Human Review**, **Simulated Tool**, and **Output** nodes, then send one item through it at a time. Actions and integrations are simulations only. | |
| ## Local setup (Conda + VS Code) | |
| 1. Install [Miniconda](https://docs.conda.io/projects/miniconda/en/latest/) and Node.js 20+. | |
| 2. In this folder, create the environment: | |
| ```bash | |
| conda env create -f environment.yml | |
| conda activate workflow-lab | |
| cp .env.example .env | |
| ``` | |
| 3. Put your server-side API key in `.env`. The browser never receives it. | |
| 4. In VS Code, run **Tasks: Run Task → Start app**, or use two terminals: | |
| ```bash | |
| uvicorn backend.main:app --reload --port 8000 | |
| cd frontend && npm install && npm run dev | |
| ``` | |
| Open <http://localhost:5173>. The frontend proxies `/api` calls to FastAPI. | |
| ## LLM settings | |
| The backend uses the OpenAI Python SDK and supports OpenAI-compatible endpoints: | |
| ```dotenv | |
| OPENAI_API_KEY=... | |
| OPENAI_MODEL=gpt-4.1-mini | |
| # OPENAI_BASE_URL=https://your-compatible-endpoint/v1 | |
| ``` | |
| Without a key, the app runs in deterministic demo mode so the workflow UI can still be taught and tested. | |
| ## How execution works | |
| - Exactly one item enters through an Input node. | |
| - Solid workflow connections determine what runs next. | |
| - Purple dashed tool connections grant an AI Worker access to a Simulated Tool; they do not advance the workflow. | |
| - AI Workers see each attached tool's name, description, expected input, and prior results. They decide at runtime whether to call a tool, may call up to five tools, and then produce their final work. | |
| - Simulated Tools return prompt-generated information or simulated actions. They never invoke a real integration. | |
| - Decision Rules return the ID of one connected target using route labels and explicit plain-English rules. | |
| - Human Review is a dedicated node that pauses on the server; the learner replies in English, then execution resumes. | |
| - Output ends the run and displays the accumulated result and event trace. | |
| - Safety limits of 50 workflow steps and five tool calls per worker prevent accidental loops. | |
| ### The two connection types | |
| ```text | |
| Input ──flow──> AI Worker ──flow──> Decision Rules ──flow──> Output | |
| │ | |
| └──tool access (dashed)──> Simulated Tool | |
| ``` | |
| Select a workflow connection to give it a route label such as `High risk` or `Standard response`. Decision Rules uses these labels when applying its English rules. | |
| Workflow JSON can be downloaded and loaded in the browser. The JSON stores the canvas, connections, node settings, and input text—never API keys. | |
| ## Hugging Face Spaces deployment | |
| Create a new **Docker Space**, upload this repository, and add `OPENAI_API_KEY` as a Space **Secret**. Optionally add `OPENAI_MODEL` and `OPENAI_BASE_URL` as variables/secrets. The included `Dockerfile` builds React and serves it from FastAPI on port 7860. | |
| For Git-based deployment: | |
| ```bash | |
| git remote add space https://huggingface.co/spaces/YOUR_NAME/YOUR_SPACE | |
| git push space main | |
| ``` | |
| Do not commit `.env`; it is ignored. | |
| ## Project map | |
| ```text | |
| backend/ FastAPI API, execution engine, prompt templates | |
| frontend/ React + React Flow visual editor | |
| .vscode/tasks.json one-click local development task | |
| environment.yml Conda environment | |
| Dockerfile Hugging Face Spaces build | |
| ``` | |