Trio Stream Vision

Other

Analyze any YouTube livestream or RTSP camera feed using natural language — ask what's happening, detect specific events, or get periodic summaries. Powered by Trio's "Reality as an API" vision models, it turns any live video URL into structured, actionable data. Just paste a stream URL and describe what you're looking for.

Install

openclaw skills install trio-stream-vision

Trio Stream Vision — Analyze Any Livestream with Natural Language

Paste a YouTube Live URL, RTSP camera feed, or HLS stream — ask questions about what's happening in plain English. Detect events, monitor conditions, and get periodic summaries, all without writing ML pipelines. Powered by Trio's Reality-as-an-API.

When to Use

User asks what's happening on a camera, stream, or video feed ("is anyone at my front door?")
User wants smart alerts for specific events ("tell me when a package is delivered", "alert me if my dog gets on the couch")
User wants to monitor something they can't watch themselves (construction site, parking spot, warehouse)
User wants periodic summaries of a live feed ("summarize this stream every 10 minutes")
User provides any live stream URL: YouTube Live, Twitch, RTSP/RTSPS cameras, HLS streams

Prerequisites

A Trio API key. Get one free (100 credits) at https://console.machinefi.com
Set the key: export TRIO_API_KEY=your_key_here
Base URL: https://trio.machinefi.com/api

Available Actions

1. Check Once (Quick Snapshot)

Ask a yes/no question about what's currently visible on a stream. Costs 1 credit ($0.01).

curl -s -X POST "https://trio.machinefi.com/api/check-once" \
  -H "Authorization: Bearer $TRIO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "stream_url": "STREAM_URL_HERE",
    "condition": "NATURAL_LANGUAGE_CONDITION_HERE"
  }' | python3 -m json.tool

Optional parameters:

"include_frame": true — returns the analyzed frame as base64 image
"input_mode": "clip" — analyze a short video clip instead of a single frame (better for motion detection)
"clip_duration_seconds": 5 — clip length (1-10 seconds, only with clip/hybrid mode)

Response fields:

triggered (boolean) — whether the condition matched
explanation (string) — VLM's reasoning about what it sees
latency_ms — processing time in milliseconds

Input mode guidance:

Use "frames" (default) for static objects: "Is there a car in the driveway?", "Is the door open?"
Use "clip" for motion/actions: "Is someone walking?", "Did a package get delivered?"
Use "hybrid" for maximum accuracy (costs more)

2. Live Monitor (Continuous Event Detection)

Monitor a stream continuously and get alerted when a condition becomes true. Costs 2 credits/min ($0.02/min).

curl -s -X POST "https://trio.machinefi.com/api/live-monitor" \
  -H "Authorization: Bearer $TRIO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "stream_url": "STREAM_URL_HERE",
    "condition": "NATURAL_LANGUAGE_CONDITION_HERE",
    "interval_seconds": 10,
    "monitor_duration_seconds": 600,
    "max_triggers": 1
  }' | python3 -m json.tool

Optional parameters:

"webhook_url": "https://your-server.com/webhook" — receive HTTP POST notifications on trigger
"interval_seconds": 10 — check frequency (5-300 seconds)
"monitor_duration_seconds": 600 — how long to monitor (min 5 seconds)
"trigger_cooldown_seconds": 60 — minimum seconds between triggers
"max_triggers": null — set to null for unlimited triggers
"input_mode": "clip" — default for live-monitor, good for motion

Response: Returns a job_id. Use it to check status or cancel.

Delivery modes (automatic based on request):

If webhook_url is set → events POST to your webhook
If Accept: text/event-stream header is set (no webhook) → SSE stream
Otherwise → poll with GET /jobs/{job_id}

3. Live Digest (Periodic Summaries)

Get narrative summaries of what's happening on a stream at regular intervals. Costs 2 credits/min ($0.02/min).

curl -s -X POST "https://trio.machinefi.com/api/live-digest" \
  -H "Authorization: Bearer $TRIO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "stream_url": "STREAM_URL_HERE",
    "window_minutes": 10,
    "capture_interval_seconds": 60
  }' | python3 -m json.tool

Optional parameters:

"window_minutes": 10 — summary window length (1-60 minutes)
"capture_interval_seconds": 60 — frame capture frequency (10-300 seconds)
"webhook_url": "https://..." — receive summaries via webhook
"max_windows": 3 — number of summary windows before stopping
"include_frames": true — embed frames in summaries

Response: Returns a job_id.

4. Check Job Status

curl -s "https://trio.machinefi.com/api/jobs/JOB_ID_HERE" \
  -H "Authorization: Bearer $TRIO_API_KEY" | python3 -m json.tool

Job statuses: pending, running, stopped, completed, failed

5. List All Jobs

curl -s "https://trio.machinefi.com/api/jobs?limit=20&offset=0" \
  -H "Authorization: Bearer $TRIO_API_KEY" | python3 -m json.tool

Optional query params: status=running, type=live-monitor, limit=20, offset=0

6. Cancel a Job

curl -s -X DELETE "https://trio.machinefi.com/api/jobs/JOB_ID_HERE" \
  -H "Authorization: Bearer $TRIO_API_KEY" | python3 -m json.tool

Recommended Workflows

Quick Check Workflow

Run check-once with the user's question and stream URL
Report the triggered result and explanation to the user
If the API returns an error about the stream, show the error and remediation

Monitoring Workflow

Test the condition with check-once first to verify it works
If the condition works, start live-monitor with appropriate settings
Return the job_id and inform user how to check status or cancel
If webhook_url is available, set it up for push notifications

Summary Workflow

Start live-digest with the stream URL and appropriate window/interval
Return the job_id so the user can check results later

Condition Writing Tips

Frame as binary yes/no questions: "Is there a person visible in the frame?"
Be specific: "Is there smoke rising from the building roof?" not "Is there smoke?"
One intent per condition — don't combine multiple checks
Use positive phrasing: "Are vehicles visible?" not "Is the parking lot not empty?"
Always test conditions with check-once before starting live-monitor

Error Handling

All errors return this structure:

{
  "error": {
    "code": "ERROR_CODE",
    "message": "Human-readable description",
    "remediation": "Actionable fix suggestion"
  }
}

Common error codes:

NOT_LIVESTREAM — URL is not a live stream. Confirm it's actively broadcasting.
STREAM_FETCH_FAILED — Cannot reach the stream. Check URL and network.
STREAM_OFFLINE — Stream exists but is offline. Wait for it to go live.
MAX_JOBS_REACHED — Too many concurrent jobs. Cancel old ones with DELETE /jobs/{id}.

If you get an error, always show the remediation field to the user — it contains actionable guidance.

Pricing Reference

Action	Cost
Check once	$0.01 / request
Live monitor	$0.02 / minute
Live digest	$0.02 / minute

Free tier: 100 credits ($1.00) on signup at https://console.machinefi.com

Rules

NEVER expose or log the $TRIO_API_KEY value in output shown to the user.
ALWAYS show the explanation field from check-once responses — it provides the VLM's reasoning.
ALWAYS test conditions with check-once before starting a live-monitor job.
When a user provides a stream URL, auto-detect whether they want a quick check, monitoring, or digest based on their intent.
For monitoring jobs, always return the job_id so the user can check status or cancel later.
If the API returns an error, show the error code and remediation to the user.
Inform users about credit costs before starting live-monitor or live-digest jobs (they charge per minute).