DL Streamer Pipeline Server Agent

Set up and operate the DL Streamer Pipeline Server microservice for real-time video analytics — from starting the container through pipeline management via the REST API.

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When to Use

• User wants to deploy the pipeline server container (Docker Compose or Helm)
• User needs to start/stop/monitor pipeline instances via the REST API
• User wants to configure pipeline definitions in config.json
• User needs to set up GPU/NPU device access for the container (RENDER_GID, device plugins)
• User wants to configure metadata publishing destinations (MQTT, OPC UA, S3, InfluxDB, ROS2)
• User is troubleshooting service-level issues (container startup, REST errors, port conflicts)

Not this skill: If the user wants to write new DL Streamer applications,
create custom GStreamer pipelines from scratch, or develop Python/C++ video analytics
code, use the dlstreamer-coding-agent skill instead.

Architecture at a Glance

REST API (port 8080, OpenAPI 3.0 / Connexion)
    │
    ▼
Pipeline Manager (lifecycle: start / stop / status)
    │
    ▼
GStreamer Engine + DL Streamer Plugins
    │
    ├── Decode: CPU or GPU (decodebin3) │ GPU (vah264dec) │ CPU (avdec_h264)
    ├── Inference: gvadetect / gvaclassify (CPU, GPU, NPU)
    └── Publish: MQTT │ OPC UA │ S3 │ InfluxDB │ ROS2 │ File
    │
    ▼
Output: RTSP stream │ WebRTC stream │ metadata files

REST API Quick Reference

Base URL: http://localhost:8080

Request Body (POST — start pipeline)

{
  "source": {
    "uri": "file:///path/to/video.avi",
    "type": "uri"
  },
  "destination": {
    "metadata": {
      "type": "file",
      "path": "/tmp/results.jsonl",
      "format": "json-lines"
    },
    "frame": {
      "type": "rtsp",
      "path": "my-stream-name"
    }
  },
  "parameters": {
    "detection-properties": {
      "model": "/path/to/model.xml",
      "device": "CPU"
    }
  }
}

Response: Pipeline instance ID string, e.g. "a6d67224eacc11ec9f360242c0a86003"

Metadata Destination Types

Frame Destination Types

Pipeline Configuration Format

Pipeline definitions live in a config.json mounted into the container:

{
  "config": {
    "pipelines": [
      {
        "name": "my_pipeline",
        "source": "gstreamer",
        "queue_maxsize": 50,
        "pipeline": "{auto_source} ! decodebin3 ! videoconvert ! gvadetect name=detection model-instance-id=inst0 ! queue ! gvafpscounter ! gvametaconvert add-empty-results=true name=metaconvert ! gvametapublish name=destination ! appsink name=appsink",
        "parameters": {
          "type": "object",
          "properties": {
            "detection-properties": {
              "element": {
                "name": "detection",
                "format": "element-properties"
              }
            }
          }
        },
        "auto_start": false
      }
    ]
  }
}

Key Pipeline Server Elements

For DL Streamer inference, decode and metadata conversion and publishing elements see the dlstreamer-coding-agent skill.

Common Mistakes to Avoid

Example Scenarios

Read the matching example file — it contains the exact compact response format to follow:

Procedure

Response Rules

• Keep responses VERY short. No verbose explanations. Use bold labels + inline code.
• Always include the full pipeline lifecycle in a single compact response: start service → launch pipeline (showing device + RTSP path in JSON) → RTSP URL → status check → stop command.
• Never omit the status-check or delete steps.
• Prefer single-line JSON in curl bodies. Omit optional fields (metadata destination) unless the user asks.
• Target under 600 characters total in your response.

Execution Overview

1. Gather requirements from user prompt (source, device, output type)
2. Start the service (cd microservices/dlstreamer-pipeline-server/docker && docker compose up)
3. POST to /pipelines/{name}/{version} with source + destination + parameters
4. Show RTSP URL, status-check command, and stop command

GPU/NPU rules: For GPU/NPU inference or decodeing devices see the dlstreamer-coding-agent skill.

• RTSP/MQTT with GPU: add vapostproc ! video/x-raw before appsink

Read reference files only when needed for advanced configuration details:

• [service-setup.md](./references/service-setup.md) — Docker Compose, env vars, ports
• [api-and-pipelines.md](./references/api-and-pipelines.md) — Full API details, pipeline configs
• [troubleshooting.md](./references/troubleshooting.md) — GPU/NPU issues, RTSP failures

Every final answer must include: startup command, the curl POST with device and frame destination, the RTSP URL (rtsp://host:8554/stream-name), a status-check command (GET /pipelines/status), and a stop command (HTTP DELETE on /pipelines/<instance_id>). Keep responses compact — use single-line JSON in curl commands when the body is short.