#Need help to connect to talk with openclaw

1 messages · Page 1 of 1 (latest)

robust aurora Mar 23, 2026, 8:23 AM

@golden trail tell me exactly how I can send and image or wenrtc video stream to openclaw without using whatsapp, telegram or any other chat app, I need to know the exact api, or endpoint/s, or webhook/s, or MCP, or gateway in order to connect my openclaw. now called oc to my own application that can take pictures or video and I need to send/stream it to oc so it can receive the data to interpret the img and vid data live, of course using a multi-model llm thathas vison abilities. Tell in great detail at a grade five level with real code examples and sources so I can make these connections giving me step by step guide.

eager forumBOT Mar 23, 2026, 8:23 AM

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robust aurora Mar 23, 2026, 8:37 AM

golden trailBOT Mar 23, 2026, 8:40 AM

Yes — you can do this without WhatsApp/Telegram.

The honest answer first

Think of OpenClaw like a house with doors:

Image door (works now): POST /v1/responses ✅
Video stream door (true WebRTC ingest): not a built-in public endpoint today ❌
Webhook door (/hooks/*): good for event text triggers, not direct live media upload ⚠️

So for “live video” today, the practical pattern is:

your app captures WebRTC frames
send frames as images to POST /v1/responses every X ms
keep same user id so context stays continuous

Step-by-step (ELI5 style)

0) Check your version first

openclaw --version
openclaw status

1) Enable the HTTP Responses API in OpenClaw

Edit ~/.openclaw/openclaw.json and add:

{
gateway: {
auth: {
mode: "token",
token: "REPLACE_WITH_LONG_RANDOM_TOKEN"
},
http: {
endpoints: {
responses: { enabled: true }
}
}
}
}

Then restart:

openclaw gateway restart

2) Send one image (proof it works)

curl -sS http://127.0.0.1:18789/v1/responses \
-H "Authorization: Bearer REPLACE_WITH_LONG_RANDOM_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"model": "openclaw:main",
"user": "camera-lab-1",
"input": [{

"type": "message",
"role": "user",
"content": [
{ "type": "input_text", "text": "Describe this frame in simple words." },
{ "type": "input_image", "source": { "type": "url", "url": "https://example.com/frame.jpg" } }
]
}]
}'

You can also send base64 instead of URL:

{
"type": "input_image",

"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": "....base64...."
}
}

3) “Live video” pattern (frame-by-frame)

Your app does this loop:

grab frame from WebRTC video element (e.g. every 500–1000ms)
JPEG encode it

POST to your backend
backend forwards to OpenClaw /v1/responses

Browser (capture frames)

<video id="v" autoplay playsinline></video>
<script>
const v = document.getElementById("v");
const stream = await navigator.mediaDevices.getUserMedia({ video: true, audio: false });
v.srcObject = stream;

const canvas = document.createElement("canvas");
const ctx = canvas.getContext("2d");

setInterval(async () => {

canvas.width = v.videoWidth;
canvas.height = v.videoHeight;
ctx.drawImage(v, 0, 0);
const jpegBase64 = canvas.toDataURL("image/jpeg", 0.7).split(",")[1];

await fetch("/api/analyze-frame", {
method: "POST",
headers: {"Content-Type": "application/json"},
body: JSON.stringify({ cameraId: "cam-1", jpegBase64 })
});
}, 1000);
</script>

Backend (keeps token secret)

import express from "express";
const app = express();
app.use(express.json({ limit: "15mb" }));

const OC_URL = "http://127.0.0.1:18789/v1/responses";
const OC_TOKEN = process.env.OPENCLAW_GATEWAY_TOKEN;

app.post("/api/analyze-frame", async (req, res) => {
const { cameraId, jpegBase64 } = req.body;

const body = {
model: "openclaw:main",
user: `live-${cameraId}`, // keep same value for rolling context
input: [{
type: "message",

role: "user",
content: [
{ type: "input_text", text: "What changed since last frame? Keep it short." },
{ type: "input_image", source: { type: "base64", media_type: "image/jpeg", data: jpegBase64 } }
]
}]
};

const r = await fetch(OC_URL, {
method: "POST",
headers: {
"Authorization": `Bearer ${OC_TOKEN}`,
"Content-Type": "application/json"
},
body: JSON.stringify(body)

});

const json = await r.json();
res.json(json);
});

app.listen(3000, () => console.log("Backend on :3000"));

4) Important limits you should know

POST /v1/responses officially supports input_image and input_file
default input_file is for text/pdf types, not true video analysis input
so native WebRTC video stream ingest is not first-class yet

If you need true continuous video intelligence, do one of these:

frame sampling (best now, easiest)
build a custom OpenClaw channel/plugin for your app transport

5) Security (super important)

Your Gateway bearer token is basically a master key.
Do not put it in frontend/mobile app code.

Use:

backend proxy (as above)
private network / loopback / tailnet
strict auth token

Good docs / sources

OpenResponses API: https://docs.openclaw.ai/gateway/openresponses-http-api
OpenAI-compatible chat endpoint: https://docs.openclaw.ai/gateway/openai-http-api
Gateway protocol (WS): https://docs.openclaw.ai/gateway/protocol
Webhooks: https://docs.openclaw.ai/automation/webhook
Config reference (gateway.http.endpoints.responses.*): https://docs.openclaw.ai/gateway/configuration-reference
Channel plugin guide (if you want native custom app integration): https://docs.openclaw.ai/plugins/sdk-channel-plugins

If you want, I can give you a copy-paste minimal repo layout next (frontend + backend + .env + test curl) so you can run this in 10 minutes.

OpenClaw

OpenResponses API - OpenClaw

OpenClaw

OpenAI Chat Completions - OpenClaw

OpenClaw

Gateway Protocol - OpenClaw

OpenClaw

Webhooks - OpenClaw

OpenClaw

Configuration Reference - OpenClaw

robust aurora Mar 23, 2026, 8:54 AM

@golden trail you said " base64 instead of URL:" tell me the pros vs cons for each possible method and which is the fastest, lowest latency and the most efficient with token usage, elaborate in detail as I need as much information as possible to be the first one then to make this for you and have it work amazingly well and as fast as possible

golden trailBOT Mar 23, 2026, 8:56 AM

Perfect question. You’re now optimizing the right thing.

TL;DR (for your use case: live camera/WebRTC)

Best method today:
✅ Base64 frames → POST /v1/responses
with smart frame skipping + downscaling.

Why: lowest moving parts, lowest end-to-end delay, most predictable.

───

Base64 vs URL (deep comparison)

| Method                  | Latency                                      | Bandwidth                                                | Reliability                                              | Security/Privacy                        | Token usage                        |

| ----------------------- | -------------------------------------------- | -------------------------------------------------------- | -------------------------------------------------------- | --------------------------------------- | ---------------------------------- |
| input_image with base64 | Fastest for live frames (no extra fetch hop) | Bigger request body (~33% base64 overhead)               | High (no URL expiry/DNS/403 issues)                      | Better (media stays app↔gateway path)   | Same as URL if same pixels/quality |
| input_image with URL    | Slower for live (gateway must fetch URL)     | Tiny request body, but image still transferred elsewhere | More failure points (expired URL, timeout, blocked host) | More exposure (signed URLs/storage/CDN) | Same as base64 if same final image |

───

Important truth about token usage

For vision, tokens are driven mainly by image content/size, not by “URL vs base64 transport”.

So for token efficiency, focus on:

smaller resolution
lower frame rate
send only changed frames
tighter prompt (“describe only changes”)

───

Which one is fastest in practice?

If you are sending live WebRTC frames

Winner: base64.

Because URL flow usually becomes:

app uploads frame to storage
app sends URL to OpenClaw
OpenClaw downloads frame
model runs

That extra upload+download path adds delay and failure points.

───

Which one is cheapest (infra/network)?

• URL can be cheaper on your app→gateway request size, but often adds storage + egress costs.
• Base64 has bigger JSON payloads, but simpler architecture and often lower ops cost for real-time pipelines.

For “build fast + stable + low latency”, base64 wins.

───

Best-performance recipe (what to actually do)

Use this pipeline:

Capture video frame
Downscale to 512px–768px long edge

JPEG quality 0.55–0.7
Send max 1–2 FPS (not 30 FPS)
Only send when frame changed enough
Keep one in-flight request; drop stale frames (“latest frame wins”)

───

Practical ranking for your goal (“amazingly fast”)

🥇 Base64 frame stream to /v1/responses
🥈 URL images (good for already-hosted images, not best for live)
🥉 Webhooks (/hooks/*) for media ingestion (not ideal; they’re event triggers)

───

Micro-optimizations that matter a lot

• Keep same user id in requests (session continuity)
• Keep prompt short and fixed
• Ask for delta: “What changed since last frame?”
• Crop ROI (region of interest) instead of whole frame when possible
• Send high-res frame only every N seconds, low-res for frequent checks

───

Final recommendation for your exact objective

If your goal is first + fastest + production-ready:

• Use POST /v1/responses
• Use base64 for live frames
• Build adaptive frame sending (motion-based)
• Keep vision prompt tiny + change-focused

That gives best real-time feel and best token efficiency for live monitoring.

If you want, next I can give you an adaptive sender algorithm (with motion threshold + dynamic FPS + backpressure handling) you can drop straight into Node.