Understand Docker incidents faster, get actionable guidance, and reduce operational guesswork.
OpsAuto is an operations copilot for Docker environments. It does not stop at showing metrics and statuses, it helps teams understand what is wrong, why it might be happening, and what they should do next.
Most Docker monitoring tools are good at telling you what happened. OpsAuto is designed to help you answer three harder questions:
- What should I pay attention to right now?
- What is the likely cause?
- What should I do next?
OpsAuto turns operational signals into a practical workflow:
Operational data → understanding → action suggestions → response
OpsAuto is built for teams that operate Docker-based services but do not have deep SRE maturity yet.
Typical users:
- Startups and SMBs without a dedicated DevOps or SRE team
- Backend developers who also handle production operations
- Junior operators who need guidance, not just raw dashboards
- Teams that want faster incident triage without building a full observability stack first
- Server and container status overview
- Running, exited, restarting, unhealthy state classification
- CPU, memory, restart count summary
- Risky containers highlighted first
- Detects common abnormal patterns with deterministic rules
- Summarizes recent log patterns
- Explains likely causes in human-readable language
- Helps users understand severity and priority
- Recommends 1 to 3 concrete actions
- Includes reason, risk level, and expected impact
- Keeps the product focused on decision support, not blind automation
- Analysis happens locally in the Agent
- Raw logs are not sent to the central Control Plane
- Only summarized issue metadata is transmitted
- Detects restart loops, exited containers, resource pressure, and repeated error patterns
- Sends notifications with incident summary and action guidance
- Keeps teams moving from alert to response faster
OpsAuto is opinionated about how modern ops support should work.
- Decision support first: MVP focuses on helping humans decide safely
- Rule-first analysis: deterministic detection before heavier AI layers
- Local-first processing: analyze close to the workload, transmit less
- Actionable over exhaustive: fewer charts, better guidance
- Outbound-only agent model: easier to adopt in constrained environments
Customer Environment
OpsAuto Agent
- Collect Docker state and recent logs
- Detect abnormal patterns
- Build issue summaries and action suggestions
- Keep local queue/cache
↓ HTTPS (outbound only)
OpsAuto Control Plane
- Register agents and servers
- Store issue metadata
- Manage notifications
- Provide Dashboard APIs
↓
PostgreSQL + Dashboard
- Agent performs first-pass analysis locally
- Control Plane stores metadata, not raw logs
- Outbound-only communication from customer environment
- Rule-based issue lifecycle and deduplication
- Docker state collection
- Rule-based anomaly detection
- Log pattern summarization
- Action suggestion generation
- Server and issue dashboard views
- Slack or email notifications
- Lightweight structure visualization
- Auto-remediation
- Kubernetes support
- Full observability pipeline replacement
- Advanced network topology visualization
- Broad team permission system
- Cost optimization engine
- Container exited unexpectedly
- Restart loop detected in the last 10 minutes
- Memory usage remains above threshold
- CPU usage remains above threshold
- Repeated log patterns such as:
Connection refusedOutOfMemoryfailed to connecttimeouttoo many connections
OpsAuto is not trying to be another metrics-heavy observability wall. It is built as an operations copilot for teams that need help with triage, interpretation, and next-step guidance.
- Easier for non-expert operators to use
- Focused on incident understanding, not just data display
- Action recommendations are part of the core product, not an afterthought
- Privacy-conscious architecture, raw logs stay local
- Lightweight starting point for Docker-centric environments
- Not a replacement for full-stack observability platforms
- MVP intentionally avoids automatic execution and remediation
- Kubernetes and large-scale enterprise scenarios are out of scope initially
- Rule-first approach is safer and more explainable, but less broad than fully adaptive systems
| Approach | What it does well | Where it falls short | How OpsAuto differs |
|---|---|---|---|
| Traditional monitoring tools | Metrics, dashboards, alerting | Leaves triage and next action mostly to the operator | Adds issue interpretation and action guidance |
| Log aggregation platforms | Centralized search and analysis | Expensive and noisy for smaller teams, still requires expertise | Avoids shipping raw logs and focuses on summarized issues |
| APM / observability suites | Deep visibility across systems | Often heavy, costly, and overkill for Docker-first small teams | Starts lighter and targets operator decision support |
| Auto-remediation tools | Fast automatic response | Risky when rules or context are wrong | Keeps humans in control in MVP |
| Tool | Primary focus | Strengths | Limits compared to OpsAuto |
|---|---|---|---|
| Prometheus + Alertmanager | Metrics and alerts | Strong ecosystem, flexible alerting | Does not natively explain incidents or suggest next actions |
| Grafana | Visualization and dashboards | Great charts and integrations | Excellent visibility, limited operational guidance by itself |
| ELK / OpenSearch | Centralized logging and search | Powerful search and analysis | Raw log shipping, storage cost, and triage complexity can grow fast |
| Portainer | Docker management UI | Easy container visibility and management | More management-focused than incident interpretation |
| Netdata | Real-time infrastructure visibility | Fast setup, rich real-time metrics | Focuses on visibility more than guided response |
In short: OpsAuto is strongest when a team needs a product that says:
“Here is the likely issue, why it matters, and what you should check next.”
- Agent detects an abnormal pattern in Docker runtime state
- OpsAuto groups repeated signals into a deduplicated issue
- Recent log patterns are summarized locally
- The Control Plane receives metadata only
- Dashboard and notifications show:
- issue summary
- severity
- likely cause
- recommended actions
- The operator responds with much less guesswork
A core product decision in OpsAuto is that raw operational data should stay as close to the workload as possible.
Benefits:
- Better privacy posture
- Lower central storage burden
- Reduced risk around sensitive log contents
- Easier adoption in environments that resist inbound access
This is especially important for teams that want operational guidance without immediately sending large volumes of raw logs to a SaaS backend.
This repository is intended to serve as the public landing repository for the OpsAuto product.
Recommended contents over time:
- product overview
- architecture overview
- screenshots and diagrams
- demo assets
- roadmap and vision
- links to implementation repositories
opsauto-agentopsauto-control-planeopsauto-dashboard
- Docker state collection
- Basic dashboard and issue list
- Rule-based anomaly detection
- Slack and email notifications
- Issue detail experience
- Log summarization
- Action suggestion quality improvements
- Lightweight structure visualization
- UX refinement
- Approval-based actions
- More advanced explanation layers
- Team collaboration workflows
- Kubernetes and broader environment support
Replace the placeholder assets below with real product visuals when available.
OpsAuto is currently in MVP design and implementation stage.
The current product direction is centered on:
- Docker-first incident detection
- deterministic issue analysis
- local-first data handling
- actionable guidance for operators
Contributions, feedback, and design discussion are welcome. If you are interested in Docker operations UX, incident triage, or local-first ops tooling, feel free to open an issue or start a discussion.
OpsAuto is not trying to flood operators with more dashboards. It aims to make operational problems easier to understand and safer to act on.
OpsAuto helps teams move from raw signals to confident action.