Release Notes

These Release Notes describe the key features, software enhancements and improvements, and known issues for the VSS release product package.

VSS 3.3.0

Key Features and Enhancements

• Removed the vss-deploy-profile Agent Skill and replaced it with vss-build-vision-ai, which deploys the same base, search, lvs and alerts developer profiles plus the warehouse industry profile, and can additionally extend a running deployment with a delta overlay.

  vss-build-vision-ai takes no -p / -m flags — describe the profile in the request instead:

  vss-deploy-profile	vss-build-vision-ai
  -p base / -p search / -p lvs	the matching stock developer workflow
  -p alerts -m verification / -p alerts -m real-time	the stock Alerts workflow in 2d_cv / 2d_vlm mode
  -p warehouse	the warehouse industry profile (variant selected during intake)

  The /deploy pre-GA alias now resolves to /vss-build-vision-ai.

Agent Workflows

Known Issues and Limitations

• Video Management does not refresh after adding an RTSP stream through chat: In the Search profile, a stream added through the VSS UI chat sidebar may not appear in Video Management until the browser tab is refreshed. Workaround: after the agent confirms that the stream was added, refresh the browser tab to update the stream list.
• RTSP searches return no results for queries without visual attributes: In the Search profile, in-stack VSS Agent query decomposition applies a 2025-01-01 time window to queries that describe an action or event but no visual attributes such as clothing or color, for example show person climbing ladder. The window excludes the stream’s recordings, so the search returns no results even when matching footage exists. Queries that include visual descriptors and searches over uploaded videos are unaffected, as are the vss CLI and the vss-search-archive skill.
• Attribute-only searches scoped to an uploaded video return no results: In the Search profile, the VSS Agent returns no results when a query describes visual attributes but no action and names an uploaded video as the video source, such as show person with green jacket in video source warehouse_sample. Queries without a video source, fusion searches that combine an action with visual attributes, and searches over RTSP streams are unaffected, as are the vss CLI and the vss-search-archive skill. Workaround: omit the video source from the query.
• Human-in-the-Loop (HITL) dialog content is clipped when zoomed in: The in-stack VSS Agent’s HITL dialog may not resize to fit the available viewport when browser zoom is increased, leaving content clipped or truncated. Suggested workaround: reduce browser zoom until the full dialog is visible.
• Empty structured-event results during file summarization: CA-RAG may fail to parse RT-VLM output that does not match the expected event schema, leaving empty event results even when captions are generated and stored and the summarize API reports success.
• Live RTSP caption gaps under overload: When RT-VLM inference cannot keep up with incoming video chunks, including at high stream concurrency, the bounded decoder-to-VLM queues drop decoded chunks to limit queued-frame memory growth. Affected chunks may produce empty captions while the live HTTP/SSE request remains active; logs report Live decoder backlog exceeded the bounded decoder-to-VLM transport. Separately, the GPU-memory admission guard may reject new live-stream requests with HTTP 503 ServerBusy when free GPU memory falls below its configured threshold. Reduce concurrent streams or the inference workload to match the platform’s sustainable throughput; increasing queue capacity does not resolve sustained overload.
• Live RTSP backend OOM on Jetson AGX Orin: Prolonged concurrent live captioning with Cosmos Reason 3 Edge has also encountered a backend out-of-memory termination during burst requests, followed by HTTP 503 ServiceUnavailable. Bounded decoder queues do not cap total service memory or guarantee protection against backend OOM. Reduce concurrency and restart the service if the backend has terminated.
• Memory pressure under concurrent summarization on DGX Spark and Jetson Thor: RT-VLM memory usage with Cosmos Reason 3 Super NVFP4 varies with request concurrency. Concurrent file-summarization workloads may exceed available system memory, causing stalls, RT-VLM service termination, and HTTP 503 errors.

VSS 3.2.1

VSS 3.2.1 is a bug fix release for the VSS 3.2.0 release.

Key Features and Enhancements

• Updated the default VLM model for the Real-Time Video Language Model (RT-VLM) microservice and the agent workflows to Cosmos Reason 3 Nano.
• Updated the Smart City Industry profile to use 3.2.0 microservices.
• Updated CPU-based multimedia support.

Note: Users should use the VSS 3.2.1 GitHub release/branch and the container tags referenced by the 3.2.1 deployment assets. The 3.2.1 manifests may intentionally reference a mix of new 3.2.1 tags and unchanged 3.2 tags; this is expected. Only containers requiring a remediation or feature delta were republished.

Older affected 2.x, 3.0, and 3.1 container images are deprecated and should not be used for new deployments. These older images will be removed from NGC on September 30, 2026.

VSS 3.2.0

These are the VSS 3.2.0 Release Notes. This is the first 3.X General Availability (GA) release. It hardens the modular architecture and documents the developer profiles.

Key Features and Enhancements

• Source code released for all microservices and agent workflows on GitHub.

• Agent Skills (Agent Skills) (Early Access) are now available for agent-driven deployment and operations with support for multiple agent harnesses. Skills are provided for:

  • Profile deployment.
  • Agent workflow use cases such as video summarization, search, and report generation.
  • Deployment and management of RTVI, Alerts, and Behavior Analytics microservices.
  • Camera and stream management.

• Added Helm chart support for all of the agent workflows.

• Added optional audio-in-video support on the base agent profile: remote Nemotron 3 Nano Omni VLM with audio-enabled prompts and configurations.

• Added a Real-time Computer Vision 3D (RT-CV-3D) microservice with support for MV3DT-based 3D multi-camera detection and tracking, and integrated it into a new Warehouse Blueprint application.

• Added the Automatic Calibration microservice for automated camera position and orientation calibration.

• NemoClaw + VSS (Early Access): The deploy_nemoclaw_vss.ipynb notebook for deploying and operating VSS from the NemoClaw / OpenClaw chat UI, bundled with the VSS OpenClaw plugin, VSS Agent Skills, a VSS policy preset, and the host-side VSS Orchestrator MCP server.

Agent Workflows

System Components

Known Issues and Limitations

• The OpenClaw chat UI can display a duplicate assistant response for a single user query. The duplicate is a rendering issue only; the underlying agent processes the query once and the tool invocations and results are unaffected. Refresh the chat or ignore the duplicated bubble.

• The OpenClaw UI can stop rendering agent responses after running for some time and appear stale, for example while periodic VSS deployment status polling continues in the background. Workaround: refresh the UI to see the latest responses.

• Docker Engine 29.5.0 and later can fail to pull some NGC-hosted image tags after the image layers download, with an error from registry: Incorrect Repository Format message. Use a supported Docker Engine version earlier than 29.5.0. If you must use Docker Engine 29.5.0 or later, add or merge the following daemon-side override in /etc/docker/daemon.json and restart Docker:

  {
    "features": {
      "containerd-snapshotter": false
    }
  }

  This disables the containerd snapshotter image store path for the Docker daemon and uses the legacy Docker graphdriver image store. Preserve any existing daemon settings, such as the required exec-opts cgroup driver configuration.

• Agent with audio-enabled configuration for Nemotron 3 Nano Omni model is known to encounter multiple retry issues with videos that are missing audio or non-compatible audio streams.

• After deploying with the Brev Launchable, Brev can occasionally report services unhealthy even though Jupyter Notebook, VSS deployment, and VSS skills continue to function. This appears to be an intermittent Brev status issue and is harmless for VSS operations.

• Phoenix must be accessed through HAProxy on port 7777 (https://7777-<id>.brevlab.com/phoenix), not directly on port 6006. Phoenix is configured for reverse-proxy access with PHOENIX_HOST_ROOT_PATH=/phoenix; HAProxy strips that prefix before forwarding, while direct access on 6006 leaves paths such as /phoenix/graphql unchanged and returns 405 Method Not Allowed, which breaks the UI. Port 6006 does not need a Brev secure link for Phoenix.

• (Search profile, Brev 2-GPU environments): When using local VLM, the Search critic is automatically disabled with a warning to avoid invalid local VLM GPU assignment. As a result, critic-based verification is unavailable unless you use a remote VLM or a host with more than 2 GPUs.

  [WARN] Brev environment has 2 GPU(s). Disabling Search critic to avoid starting the local VLM on GPU 2.

VSS 3.1.0

These are the VSS 3.1.0 Release Notes. This is an early access release including a refactored architecture and new features. There are some features in an alpha state and should not be used in production.

Key Features and Enhancements

• Updated the Search Agent Workflow to introduce attribute search, multi-embedding fusion search, and a critic agent to review search results.
• Updated the Real-Time Computer Vision (RT-CV) microservice to support embedding generation for detected objects. This release supports two embedding models: RADIO-CLIP and SigLIP2.
• Updated Brev Launchable deployment to support the 3.X architecture and deploy all of the agent workflows.
• Added support for AGX Thor and DGX Spark with hybrid deployment (remote LLM) of the Base and Alerts profiles.
• Added additional deployment options for undefined hardware profiles.

VSS 3.0.0

These are the VSS 3.0.0 Release Notes. This is an early access release including a refactored architecture and new features. There are some features in an alpha state and should not be used in production.

Key Features and Enhancements

• Updated the out-of-the-box-experience which includes launching a minimal vision agent and allowing developers to add on agent workflows using a combination of microservices. Agent workflows available in this release are:

  • Report generation and Q&A: The agent can generate templated reports and answer questions using the VLM. This is part of the base agent profile in Quickstart.
  • Video summarization: The agent can generate long video summaries with time-stamped highlights.
  • Alert verification: Augment existing CV pipelines with VLMs to verify events and extract additional insights.
  • Real-Time VLM alerts: Generate tail-end alerts using VLM.
  • Search: Open vocabulary search for actions and events. This is an alpha feature.

• Introduced 2 industry-specific, large scale, blueprint examples for smart cities and warehouses.

• Modularized the VSS architecture, introducing new microservices and APIs.

• Introduced a top-level agent, capable of planning and executing vision-based workflows leveraging the new microservices.

• Introduced Real-Time Video Intelligence (RTVI) microservices for accelerated feature extraction from stored and streamed video. Three microservices are included in this release:

  • Real-Time VLM (RT-VLM): Generates captions and alerts for live streams using Vision Language Models.
  • Real-Time Embedding (RT-Embedding): Generates embeddings for live streams and video files.
  • Real-Time Computer Vision (RT-CV): Detects and tracks objects in live streams and video files.

• Refactored video summarization workflow into the Video Summarization microservice.

• Introduced Video IO and Storage (VIOS) microservices, to manage video (stored and streamed), recording, and playback.

• Introduced Behavior Analytics microservice, to set up heuristics for event creation based on computer vision metadata.

• Introduced calibration microservices, to calibrate the camera position and orientation for 3D and multi-view applications.

• Integrated a new API Gateway / MCP (Model Context Protocol) server to route requests to the appropriate microservices.