openocta/openocta

OpenOcta is an open-source AIOps Agent installed on Windows & macOS.

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Summary Information

Updated 2 hours ago
Added to GitGenius on September 21st, 2026
Created on February 26th, 2026
Open Issues & Pull Requests: 35 (+0)
GitHub issues: Enabled
Number of forks: 402
Total Stargazers: 3,236 (+0)
Total Subscribers: 75 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 26.2 hours
Mean response time: 6.7 days
90th percentile: 19.5 days
Tracked items: 68

How this project is maintained

Roughly one issue in five opened in the past year never receives a reply. 62% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. 70% of issues opened in the past year have been closed, leaving a working backlog. Three people close 95% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 26
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 108 days
Stale 30+ days: 24
Stale 90+ days: 15

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (44)
  • enhancement (20)

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Detailed Description

OpenOcta is an AIOps agent that runs on Windows and macOS desktops to automate IT operations tasks across the full stack.

The tool addresses the gap between general-purpose AI assistants and the specific needs of operations teams. Rather than building another chatbot, OpenOcta integrates directly with monitoring systems, logs, cloud platforms, databases, and script toolchains to execute real operational work. Operations engineers install it locally and assign tasks in natural language, which the agent then executes across application, server, network, desktop, database, and cloud infrastructure. The workflow moves beyond conversation to implement a complete ops loop: automatic inspection, alert analysis, intelligent data querying, and remediation advice or execution.

Teams should adopt OpenOcta if they need an agent that understands operational context and can take action within their existing tooling rather than simply discuss problems. It suits organizations running Kubernetes, Prometheus, Zabbix, or similar monitoring stacks where engineers want to delegate routine inspection and troubleshooting to an AI agent. The desktop installation model means no additional server infrastructure is required—engineers run it directly on their machines.

The project shows active development with regular releases and ongoing refinement of core functionality. Recent work has focused on fixing tool-call argument handling, hardening file operations on Windows, and redesigning the Knowledge Vault interface. The codebase is written in TypeScript and includes a single Go binary with an embedded control UI, indicating a shift toward streamlined deployment and reducing external dependencies.