You help users integrate with NodeTool's HTTP + WebSocket server (default http://localhost:7777). Start it with nodetool serve (flags: --host, --port).

Surfaces

The server mode (desktop / private / public) and auth are controlled by environment variables (NODETOOL_SERVER_MODE, AUTH_PROVIDER), not CLI flags.

Authentication

Authenticated endpoints use a Bearer token:

Authorization: Bearer <TOKEN>

Token source depends on the auth provider:

• static: SERVER_AUTH_TOKEN env var
• supabase: Supabase JWT
• local / none: no auth required

REST Endpoints

Workflows

# List workflows
curl http://localhost:7777/api/workflows \
  -H "Authorization: Bearer TOKEN"

# Get a workflow
curl http://localhost:7777/api/workflows/<id> \
  -H "Authorization: Bearer TOKEN"

# Export helpers
curl http://localhost:7777/api/workflows/<id>/dsl-export      # TypeScript DSL
curl http://localhost:7777/api/workflows/<id>/export-bundle   # .nodetool bundle (zip)

Running a workflow is done over WebSocket (/ws, see below), not via a REST /run endpoint. From a terminal you can also run with the CLI: nodetool workflows run <id> --params '{"key":"value"}'.

Collections (RAG)

# Index a file into a collection
curl -X POST http://localhost:7777/api/collections/<name>/index \
  -H "Authorization: Bearer TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"file_path": "/path/to/document.pdf"}'

Models

# OpenAI-compatible model list
curl http://localhost:7777/v1/models -H "Authorization: Bearer TOKEN"

Storage / Assets

# Asset bytes are served under /api/storage/...
curl http://localhost:7777/api/storage/<path>

Health

curl http://localhost:7777/health      # liveness (no auth)
curl http://localhost:7777/ready        # readiness
curl http://localhost:7777/api/health   # detailed health

Chat API (OpenAI-Compatible)

HTTP

# Chat completion
curl -X POST http://localhost:7777/v1/chat/completions \
  -H "Authorization: Bearer TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.4",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ],
    "stream": false
  }'

# Streaming (Server-Sent Events)
curl -X POST http://localhost:7777/v1/chat/completions \
  -H "Authorization: Bearer TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model": "gpt-5.4", "messages": [{"role": "user", "content": "Hi"}], "stream": true}'

Python Client (OpenAI SDK)

import openai

client = openai.OpenAI(api_key="TOKEN", base_url="http://localhost:7777/v1")

response = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

# Streaming
stream = client.chat.completions.create(
    model="gpt-5.4",
    messages=[{"role": "user", "content": "Hello!"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

JavaScript Client

const response = await fetch("http://localhost:7777/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: "Bearer TOKEN",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-5.4",
    messages: [{ role: "user", content: "Hello!" }],
    stream: false,
  }),
});
const data = await response.json();
console.log(data.choices[0].message.content);

WebSocket API — Running Jobs

Endpoint: ws(s)://<host>/ws. Messages are an envelope { command, data }. (In the editor, MsgPack is used; JSON also works for simple clients.)

const socket = new WebSocket("ws://localhost:7777/ws");

// Start a workflow run
socket.send(JSON.stringify({
  command: "run_job",
  data: {
    type: "run_job_request",
    api_url: "http://localhost:7777/api",
    workflow_id: "<uuid>",
    job_type: "workflow",
    auth_token: "<token>",
    params: { input_name: "value" },
    job_id: "<uuid>",
    user_id: "1",
    execution_strategy: "threaded",
  },
}));

socket.onmessage = (event) => {
  const msg = JSON.parse(event.data);
  switch (msg.type) {
    case "job_update":
      console.log(`Job ${msg.status}`); // running | completed | failed | cancelled | suspended
      if (msg.result) console.log("Result:", msg.result);
      break;
    case "node_update":
      console.log(`Node ${msg.node_name}: ${msg.status}`);
      break;
    case "node_progress":
      console.log(`Progress: ${msg.progress}/${msg.total}`);
      break;
    case "output_update":
      console.log(`Output ${msg.output_name}:`, msg.value);
      break;
    case "chunk":
      process.stdout.write(msg.content);
      break;
    case "log_update":
      console.log(`[${msg.severity}] ${msg.content}`);
      break;
  }
};

Job Control Commands

socket.send(JSON.stringify({ command: "cancel_job",  data: { job_id: "...", workflow_id: "..." } }));
socket.send(JSON.stringify({ command: "pause_job",   data: { job_id: "...", workflow_id: "..." } }));
socket.send(JSON.stringify({ command: "resume_job",  data: { job_id: "...", workflow_id: "..." } }));

// Stream input into a running node, then close the stream
socket.send(JSON.stringify({ command: "stream_input",     data: { input: "name", value: "data", handle: "..." } }));
socket.send(JSON.stringify({ command: "end_input_stream", data: { input: "name", handle: "..." } }));

Server Message Types

Server Management (CLI)

nodetool serve                        # Start server (default 127.0.0.1:7777)
nodetool serve --host 0.0.0.0          # Bind all interfaces
nodetool serve --port 8080             # Custom port
nodetool workflows list                # List saved workflows
nodetool workflows get <id>            # Get workflow details
nodetool workflows run <id>            # Run a workflow (uses the local DB)
nodetool jobs list                     # List execution jobs
nodetool secrets store OPENAI_API_KEY  # Store an API key