Open-Source Observability · c0m4r

kula

Kula is a lightweight, single-binary Linux monitoring tool written in Go that collects system metrics every second from /proc and /sys, stores them in a custom ring-buffer engine, and serves real-time dashboards via web UI and terminal TUI. It requires no external dependencies, databases, or external services for core functionality.

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Source: GitHub — github.com/c0m4r/kula

1.2k

GitHub stars

Forks

Primary language

AGPL-3.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	c0m4r/kula
Owner	c0m4r
Primary language	Go
License	AGPL-3.0 — OSI-approved
Stars	1.2k
Forks	58
Open issues	1
Latest release	0.18.4 (2026-06-16)
Last updated	2026-06-18
Source	https://github.com/c0m4r/kula

What kula is

Built in Go with embedded JavaScript/Chart.js frontend, Kula reads kernel metrics directly, aggregates them across three storage tiers (1s, 1m, 5m samples), and exposes data via HTTP REST API and WebSocket. Optional Ollama integration provides local LLM-based analysis; authentication uses Argon2id hashing with session tokens.

Quickstart

Get the kula source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/c0m4r/kula.git2cd kula3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Rapid Deployment Monitoring

Deploy a single binary to VPS, bare-metal, or container environments with zero dependency installation. Useful for quick observability setup where traditional stacks (Prometheus, Grafana) are overkill.

Edge & Resource-Constrained Servers

Self-contained binary with minimal footprint; custom ring-buffer storage avoids external databases. Suitable for monitoring small fleets or IoT-class systems with limited compute.

Real-Time Visual System Analysis

Interactive dashboard with drag-select zoom, per-device filtering, and optional AI-assisted chart analysis via local Ollama. Good for on-call troubleshooting and capacity planning discussions.

Implementation considerations

Single binary requires only /proc and /sys read access; consider SELinux/AppArmor rules if enforced. For GPU monitoring, NVIDIA setup varies per driver version.
Storage tiers (250 MB / 150 MB / 50 MB default) are fixed at startup; no dynamic resizing. Verify disk I/O and RAM budget before production deployment.
Optional authentication uses sliding-window session tokens; review crypto primitives (Argon2id, secure cookies) for your threat model before exposed to untrusted networks.
Ollama AI assistant requires separate Ollama server; local inference latency and model size affect dashboard responsiveness.
Docker deployment requires --pid host and --network host for accurate metrics; evaluate security implications for your container orchestration policy.

When to avoid it — and what to weigh

Multi-Host Observability Required — Kula monitors single machines only. For fleet-wide metrics, federated scraping, or cross-datacenter correlation, use Prometheus + Grafana or equivalent.
Enterprise Compliance & Data Retention — AGPL-3.0 license triggers source-code disclosure requirements if used in SaaS/hosted context. Ring-buffer storage is time-bounded; long-term archival requires external export.
Complex Alerting & Automation Pipelines — Kula includes basic alerts (clock sync, low entropy, overload) but lacks webhook, PagerDuty, or runbook integration. Consider Alertmanager or dedicated incident-response tools for complex on-call workflows.
Heterogeneous Infrastructure — Linux-only. No native support for Windows, macOS, Kubernetes, or cloud-managed databases beyond basic exporters for PostgreSQL, MySQL, nginx, Apache.

License & commercial use

AGPL-3.0 (GNU Affero General Public License v3.0). Requires source-code disclosure of modifications and any SaaS/hosted deployments that provide the tool as a service. For internal use (single-host self-hosted), obligations are minimal.

AGPL-3.0 is NOT a permissive OSI license for closed-source commercial use. If you deploy Kula as a hosted/SaaS offering or substantially modify and redistribute it, you must disclose source. For standalone enterprise self-hosted deployments, review your legal team's AGPL interpretation. Requires careful review before embedding in proprietary products.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

Kula reads /proc and /sys directly; requires appropriate file permissions or elevated privileges. Authentication is optional (Argon2id hashing, session tokens, secure cookies when enabled). No independent security audit data provided. AGPL-3.0 license means source is available for review. WebSocket and HTTP endpoints are unauthenticated by default; enable optional auth before exposing to untrusted networks. Evaluate ring-buffer file permissions and tempfile handling in your threat model.

Alternatives to consider

Prometheus + Grafana

Industry-standard time-series stack with multi-host support, mature alerting, and ecosystem. Higher operational complexity; requires separate scrape targets and storage backend.

Netdata

Single-host real-time monitoring with zero-config and rich web UI. Apache 2.0 licensed; no AGPL restrictions. Active community; cloud features available.

Telegraf + InfluxDB + Grafana

Modular collector, time-series DB, and visualization. Supports multi-host and heterogeneous infrastructure. More moving parts; suitable for larger deployments.

Software development agency

Build on kula with DEV.co software developers

Download Kula now and get real-time system insights in minutes. No external dependencies, no databases—just a single binary. Review the AGPL-3.0 license terms for your use case.

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kula FAQ

Can I use Kula in a commercial SaaS product?

Not without source disclosure. AGPL-3.0 requires you to provide source to end-users if Kula is part of a networked service. For internal self-hosted use, obligations are minimal; consult legal counsel for your specific use case.

What if I want to modify Kula and keep my changes private?

AGPL-3.0 does not allow private modifications without disclosing source if you distribute or host the service. For internal use only, you may modify freely but must retain the license. Consider contributing changes upstream or licensing under a permissive alternative.

How long does Kula retain metrics?

Fixed ring-buffer tiers: Tier 1 (1-second raw, ~250 MB) = ~3 days; Tier 2 (1-minute aggregates, ~150 MB) = ~100 days; Tier 3 (5-minute aggregates, ~50 MB) = ~500 days. Oldest data is overwritten automatically. Export to Prometheus or external storage for longer retention.

Do I need Ollama to use Kula?

No. Ollama (local LLM inference) is optional for the AI assistant feature. Core monitoring, dashboards, and APIs work without it.

Custom software development services

From first prototype to production, DEV.co delivers software development services around tools like kula. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source observability and beyond.

Ready to Deploy Lightweight Monitoring?

Download Kula now and get real-time system insights in minutes. No external dependencies, no databases—just a single binary. Review the AGPL-3.0 license terms for your use case.

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