cosmo-wander-ai/cosmo-edge

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CosmoEdge goes beyond model serving with a complete application layer for model import, visual orchestration, alarms, and event delivery. The core engine and console in this repository are released under Apache-2.0; certified hardware, commercial preset models, and Model Guard distribution protection have separate boundaries.

Choose Your Starting Point

Your task	Start here	Project and version
Deploy video AI applications, configure models, scenes, and alarms	Quick Start · Platforms	Engine and console in this repository; stable v1.2.0
Customize an object detector from your data	CosmoEdge Training Skill	Independent project; published v0.1.0-preview.1, with later revisions on its main
Let an AI assistant query alarms, retrieve images, and request confirmed detection start/stop	CosmoEdge Connect	Independent local MCP / WorkBuddy integration; v0.1.0-alpha.1 source preview

The training Skill and Connect are installed and released separately. See Companion Projects for prerequisites, validation scope, and version links.

Releases and Recent Development

Stable release: v1.2.0. Provides separate Open packages for BM1688, CV186X, RK3576, and RV1126B, plus the x86 Docker development path and Apple Silicon macOS Docker Preview. Download packages from the GitHub Release and check the 1.2 release notes for the platform boundaries and validation results.

Changes since v1.1.0 include:

• Video access: ONVIF discovery and authenticated access, plus GB28181 device management with TCP/UDP signaling and video access.
• Models and visual workflows: YOLO26 and oriented bounding-box (OBB) handling; native BM1688 Laya-V model integration, alarm review, and multi-ROI question configuration for images and video. Implementation and completed validation are described in PR #219.
• Platform integration: Management APIs for versioned resource delivery, resumable uploads, task start/stop, and state readback.
• Alarms and console: Alarm-record deletion, target labels and measured confidence, live overlay diagnostics, and refreshed console layouts and themes.
• Deployment and engineering quality: RK3576 protected-model handling, model-package validation, video-sample event measurement and VLM evaluation tools, and fixes to task recovery, media handling, log retention, and x86 Docker startup. See the 1.2.0 changelog.

Consult each feature's documentation for required models and target platforms. The v1.1 report remains historical evidence for its recorded candidates and workloads; it does not measure v1.2.0.

Choose a Platform

CosmoEdge provides one engine architecture and orchestration experience, but each build selects one inference backend and uses models generated for that target platform. The table distinguishes the v1.2.0 package scope from device validation in this release run.

Platform	Status	Runtime / model artifact	Current scope and evidence
Sophon BM1688	v1.2 Open package; device installation, upgrade, and rollback checked	BMRT / .nn	The package-bound device result is recorded in the 1.2 release manifest; prior VLM and workload measurements remain in the v1.1 report
Rockchip RK3576 / RV1126B	Separate v1.2 Open packages built and audited	RKNN / target-specific .rknn; RKLLM on RK3576	No device validation in this release run; RV1126B does not claim VLM support
Sophon CV186X	v1.2 Open package built and audited	BMRT / target-specific .nn	No CV186X device validation in this release run
x86 Linux / Windows; Apple Silicon macOS	x86 Docker development path; macOS Preview	ONNX Runtime / .onnx	Host-specific results are recorded in the 1.2 release manifest; macOS uses amd64 emulation, not native arm64 performance
Sophon BM1684X	Planned	—	Not part of the current release scope

Quick Start

The commands below clone and build current main. For the stable release, use the v1.2.0 packages, or check out the v1.2.0 tag before building and follow that version's documentation.

Get the Source

The host needs Git and working Docker; Windows uses Docker Desktop's Linux container mode. For a new checkout, run the following. For an existing checkout, enter its root instead:

git clone https://github.com/cosmo-wander-ai/cosmo-edge.git
cd cosmo-edge

Run all commands below at the repository root on the host. Select one group for your host and target chip. Edge-device builds default to Open packages and export artifacts without starting the application on the build host. See the Build Guide for preparation and full steps.

Try locally on x86

No edge hardware is required. The x86 mode uses the same UI and workflow as edge deployments; throughput depends on the CPU.

Linux x86_64 / Bash:

./scripts/docker-compose.sh -f docker-compose.x86.yml up -d --build

Windows x86_64 / PowerShell:

docker compose -f docker-compose.x86.windows.yml up -d --build

Open http://localhost:8080 after startup. Artifacts are exported to build_output/. Use the Scenario Configuration tutorial to create your first AI detection task.

Apple Silicon macOS Preview

Read the Mac Preview requirements and scope first, then run in the Mac terminal:

./scripts/macos-docker-preview.sh doctor && ./scripts/macos-docker-preview.sh up
./scripts/macos-docker-preview.sh status

Open http://127.0.0.1:8080 locally. Artifacts are in build_output/macos-x86/. This path uses amd64 emulation for a local, single-video trial.

Build for Sophon

Select the chip, then the host. The script selects the model resource directory automatically. The Linux wrapper selects Compose V2/V1 and requests elevation only when Docker access requires it. The Windows PowerShell entry requires Docker Desktop and Compose V2.

BM1688

Linux x86_64 / Bash:

./scripts/docker-compose.sh -f docker-compose.sophon.yml \
  run --rm cosmo-sophon-package --chip bm1688

Windows x86_64 / PowerShell:

.\scripts\build_sophon_package.ps1 -Chip bm1688

Output: build_output/public-runtime/bm1688/.

CV186X

Linux x86_64 / Bash:

./scripts/docker-compose.sh -f docker-compose.sophon.yml \
  run --rm cosmo-sophon-package --chip cv186x

Windows x86_64 / PowerShell:

.\scripts\build_sophon_package.ps1 -Chip cv186x

Output: build_output/public-runtime/cv186x/.

Each target directory contains one package, TARGET_CHIP, and SHA256SUMS. Default Open packages use plaintext models without device authorization. Verify through the Build Guide, then install through the Deployment Guide.

Build for Rockchip

On a Linux x86_64 host in Bash, first pull the shared builder:

./scripts/docker-compose.sh -f docker-compose.rockchip.yml pull cosmo-rockchip-package

Then select one target chip.

RK3576

COSMO_TARGET_CHIP=rk3576 ./scripts/docker-compose.sh \
  -f docker-compose.rockchip.yml run --rm cosmo-rockchip-package

Output: build_output/rk3576/, using the tracked RK3576 model resources.

RV1126B

COSMO_TARGET_CHIP=rv1126b ./scripts/docker-compose.sh \
  -f docker-compose.rockchip.yml run --rm cosmo-rockchip-package

Output: build_output/rv1126b/. Default include automatically generates and verifies the model overlay from the platform manifest; no manual preparation is needed. See the Build Guide for default community-model licenses and usage restrictions.

The shared image selects isolated MPP/RGA configurations by chip; only RK3576 packages include RKLLM v1.3.0. Both targets export chip markers, media-runtime markers, and checksums. Verify through the Build Guide, then use the shared installation entry.

Controlled Protected builds support BM1688, CV186X, and RK3576; RV1126B is currently unsupported. See advanced build options for commands and prerequisites. Use CV186X Quick Start for identity checks and first detection, and RK3576 Integration for RKNN model and device boundaries.

What You Can Build

• Real-time video analytics: detection, classification, tracking, zones, counters, OSD, and alarm snapshots.
• Prompt-driven vision: VLM state judgment and GroundingDINO open-vocabulary detection alongside conventional CV pipelines.
• Visual application workflows: connect models, rules, events, and output actions in the browser.
• Edge integrations: operate managed tasks and deliver structured events through REST, WebSocket, MQTT, or HTTP webhooks.

Core Capabilities

Capability	What it covers	Go deeper
Native runtime	C++17 engine for multi-channel media, inference scheduling, OSD, tasks, and events	Architecture
Visual orchestration	Browser-based pipeline composition, task binding, parameter validation, and live feedback	Pipeline tutorial
Inference and media	Platform backends for Sophon, RKNN, and x86; platform-specific builds and model artifacts	Build Guide
VLM and DINO	Prompt-based judgment, open-vocabulary detection, and optional VLM review before a detection alarm is reported	VLM Guide
Operations and integration	Model management, alarms, event history, REST, WebSocket, MQTT, and webhooks	API Overview
Model onboarding and protection	Model conversion, import, validation, and the Open/Protected distribution boundary	Model Porting Guide

▶ Watch: compose a complete visual pipeline

https://github.com/user-attachments/assets/94b9418b-36c8-47b6-a730-ad8f508a6709

▶ Watch: GroundingDINO and VLM visual workflows

https://github.com/user-attachments/assets/212a33a8-e662-4678-9945-02c78d808e4d

Agent-Assisted Development

Already have a model-porting, integration, or UI task? Give your usual coding agent the business goal, available materials, target device or test environment, and acceptance criteria. The repository provides task entry points, examples, checks, and evidence boundaries so the result can include importable artifacts, scoped code changes, and a verifiable conclusion.

Start with Agent-Assisted Development, then use the Model Porting Guide or Contributor Guide for the task at hand.

Validation

CosmoEdge 1.1 multi-platform performance report

The v1.1 report covers BM1688, CV186X, RK3576, and RV1126B. It includes 49 independent small-model case reports for person detection, no-safety-helmet analysis, and a concurrent mixed workload at 5 FPS per business task, a controlled 72-hour dual-CV configured profile, and validated VLM performance for BM1688, CV186X, and RK3576.

• English benchmark index
• 中文基准报告索引
• English primary report (rendered documentation site)
• 72-hour dual-CV report (rendered documentation site)
• Methodology and reproduction

Expand v1.1 candidate identities, performance tables, and test conditions

The short-run capacity refresh uses source commit 89c73a7464a81ef378686447d7c1eeb88b988455, tree 6857fbcce72c7af64e6cb23a27e66a405e9df9af, one fixed 1080p24 input, 30-second steps, and the gates recorded in the release manifest. The 72-hour observation uses source commit 44209759f450e96cda265acfa8bc6d17a1138888, tree 5cbdefeaefaf642407356c22c271ccc7d57935b0, and the same controlled input.

Platform	72-hour configured channels	Task bindings	Sample coverage	Min / avg FPS	Result
BM1688	8	16	4316 / 4320	4.68 / 5.086	PASS
CV186X	8	16	4316 / 4320	4.54 / 5.085	PASS
RK3576	8	16	4316 / 4320	5.00 / 5.098	PASS
RV1126B	4	8	4316 / 4320	4.85 / 5.230	PASS

These results describe the tested fixed-channel profiles under controlled local-loop input; they are not maximum-capacity or RTSP-resilience measurements. Execution details are documented in the methodology.

Each channel concurrently runs two business tasks across three model stages: person detection has one detector stage, while no-safety-helmet analysis has a detector followed by a classifier.

Platform	Workload per channel	Model stages/ch	Target FPS/task	Passing channels	Business-task bindings
BM1688	Person detection + no-safety-helmet analysis	3	5	≥16	32/32
CV186X	Person detection + no-safety-helmet analysis	3	5	≥8	16/16
RK3576	Person detection + no-safety-helmet analysis	3	5	≥8	16/16
RV1126B	Person detection + no-safety-helmet analysis	3	5	≥4	8/8

The three VLM platforms use one final protocol: each newly added route completes task-local readiness before a 60-second measurement window, and every active route must pass the executed 80% threshold against the 0.1 FPS-per-channel target. Each platform's fixed candidate package also passed model load, task creation, valid inference, event/alarm output, and task recovery after service restart; these claims do not extend to later rebuilt packages.

Platform	Target FPS/ch	Last passing channels	First failure
BM1688	0.1	6 channels	7-channel FPS ratio 69.5%, below the 80% gate
CV186X	0.1	6 channels	7-channel FPS ratio 69.2%, below the 80% gate
RK3576	0.1	4 channels	5-channel FPS ratio 69.7%, below the 80% gate
RV1126B	—	—	Outside this VLM validation

The capacity values are exact short-run gate boundaries under the recorded conditions—not maximum-capacity certification or production recommendations. The separate 72-hour table verifies only its configured profile. See the v1.1 benchmark for the complete matrices. Previously published data remains available through one v1.0 historical archive link.

Architecture

+------------------------------------------------------------------+
| Web Console | Visual Orchestration | REST / WebSocket / MQTT      |
+--------------------------------+---------------------------------+
                                 |
+--------------------------------v---------------------------------+
| C++ Engine Core                                                   |
| Media | Inference | Tasks | Rules | Alarms | Events | Models      |
+--------------------------------+---------------------------------+
                                 |
+--------------------------------v---------------------------------+
| Inference and Media Backend Interfaces                            |
+--------------------+----------------------+----------------------+
| Sophon BMRT/VPU    | RKNN + MPP/RGA       | ONNX Runtime/FFmpeg  |
| BM1688; CV186X          | RK3576          | x86 Linux / Windows  |
+--------------------+----------------------+----------------------+

One build selects one inference backend. Model artifacts are generated for the target platform, and feature/model coverage and capacity remain platform-specific. The public v1.2.0 packages use the Open profile; Model Guard Protected distribution remains a separate path, including the RK3576 source implementation described in PR #151.

Documentation, Devices, and Community

Start here	Best for
Documentation Home	Full documentation index and learning path
Quick Start Guide	First setup and scenario run
Scenario Configuration	Building scene-level workflows
VLM Guide	Prompt-based visual judgment and events
Model Porting Guide	Importing your own model
Agent-Assisted Development	Delegating an extension task with verifiable results
Companion Projects	Training Skill and Connect / MCP prerequisites and independent versions
Changelog	Release history and unreleased main-branch changes
Build Guide	x86, Sophon, and RK3576 build/package paths
API Overview	REST, WebSocket, MQTT, and webhook integration

Certified devices add preconfigured acceleration, validated commercial model packages, and dedicated deployment support; they do not unlock separate software features. Devices are available in mainland China from the Taobao store; contact [email protected] for other regions or project support.

Contributions are welcome through scoped bug reports, documentation improvements, scenarios, and integration notes. Read CONTRIBUTING.md before opening a pull request.

GitHub Discussions is the official searchable English Q&A and community-support channel for v1.2, and general reproducible defects belong in GitHub Issues. Users in mainland China can use Gitee Issues for repository access, installation, usage, release downloads, and device-adaptation questions. Code changes and pull requests remain on GitHub; report vulnerabilities privately through SECURITY.md. No Discord channel is operated for this release.

FAQ

Can I try CosmoEdge without Sophon or Rockchip hardware?

Yes. Use x86 developer mode on Linux or Windows. Apple Silicon Macs can use the Docker Preview for the console, pipeline, model-management, and integration workflow with one local video. The Mac path uses amd64 emulation and does not enable Model Guard. Edge NPU hardware is still required for target-platform acceleration and capacity validation.

What is the boundary between the Open and Protected packages?

They expose the same application features and use the same MD5 upgrade lifecycle. Open uses plaintext models without device authorization; Sophon Protected packages can carry encrypted commercial preset models and provisioning tooling that require a device-bound certificate. Application archives themselves are not signed.

Can I use my own trained models?

Yes. For training from data, use the independent Training Skill. For an existing model, use the model-porting path to validate the tensor, preprocessing, post-processing, target runtime, and business accuracy contract. A model artifact must be generated for the platform where it will run.

How production-ready is CosmoEdge?

v1.2.0 provides Open packages for BM1688, CV186X, RK3576, and RV1126B, with a scoped x86 Docker path and macOS Preview. This release run checked BM1688 installation, upgrade, rollback, and data preservation; other chips received build and package audits. The v1.1 report records earlier measured workloads and does not qualify v1.2.0 capacity. Production sizing still requires validation with your own models, streams, accuracy requirements, and deployment conditions.

License

CosmoEdge is licensed under the Apache License 2.0. Copyright 2026 CosmoEdge Contributors.

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