🐳 DockAI – AI-powered Docker Log Analysis Tool (CLI + Cloud)
DockAI is an intelligent CLI tool that analyzes Docker container logs using Large Language Models (LLMs). It helps developers, DevOps engineers, and system administrators quickly identify issues, summarize logs, and provide actionable insights.
🚀 Features
-
AI Log Analysis Understands and summarizes logs using LLMs, identifying possible root causes and suggesting solutions.
-
Performance Monitoring (CPU & Memory) Measure container performance in real-time or over a time window using
--perfor--instant-perf. -
Local & Cloud AI Modes (Ollama + OpenAI) Analyze with a local model (e.g.,
llama3) or cloud-based OpenAI API:dockai analyze my-container --mode local dockai analyze my-container --mode cloud
-
Live Container Status Even when no logs are generated, DockAI provides a live summary including container status, restart count, and health.
-
Simple CLI Usage
dockai analyze <container-name> --since 15m --tail 3000
📊 Example Output
🤖 AI Analysis:
**Summary:** Database connection failed.
**Root Cause:** TCP/IP connection refused.
**Solution:**
- Restart the database service inside the Docker container.
- Check port accessibility and network configuration.
⚙️ Performance
- CPU p95: 0.3% | max: 1.1%
- Mem p95: 12.7%
🧠 Supported Models
- Local:
Ollama(e.g.,llama3,mistral,gemma) - Cloud:
OpenAI GPT-4,GPT-4o-mini
🔧 Default Model
By default, DockAI uses:
DOCKAI_OLLAMA_MODEL = "qwen2.5:7b-instruct"
This model offers excellent multilingual support (including Turkish 🇹🇷) and strong technical reasoning for analyzing Docker logs.
To override the model, set an environment variable:
export DOCKAI_OLLAMA_MODEL="aya:23b"
⚙️ Installation
pip install dockai
🧩 Ollama Installation (All Platforms)
macOS
brew install ollama
ollama pull qwen2.5:7b-instruct
Linux
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5:7b-instruct
Windows (PowerShell)
winget install Ollama.Ollama
ollama pull qwen2.5:7b-instruct
💡 Tip: DockAI automatically uses the model defined in the environment variable
DOCKAI_OLLAMA_MODEL(default:qwen2.5:7b-instruct).
🧩 Developer Commands
make build # build the package
make publish # publish to PyPI
make testpublish # publish to TestPyPI
🧾 License
Apache License 2.0 Copyright (c) 2025 Ahmet Atakan
🧩 Plugin Architecture
DockAI supports a modular plugin system that allows developers to extend functionality without modifying the core codebase.
Each plugin can react to lifecycle hooks such as on_start, on_finish, or on_error.
🔌 How Plugins Work
- Plugins are loaded automatically from:
dockai/plugins/(built-in plugins)~/.dockai/plugins/(user-installed plugins)
- Each plugin defines a
plugin.jsonfile that describes:{ "enabled": true, "name": "telemetry", "version": "0.2.0", "config": { "sqlite_path": "~/.dockai/usage.db" } }
✨ Example Plugin Hooks
def on_start(self, ctx):
print("[plugin] analysis started")
def on_finish(self, ctx):
print("[plugin] analysis completed")
📈 Telemetry & Usage Tracking
DockAI includes a built-in Telemetry Plugin for tracking usage and performance statistics.
This plugin helps monitor how DockAI is used, improving future versions and providing analytics for paid plans.
📊 Data Model
- usage table — stores each analysis run (time, container, mode, latency, etc.)
- findings table — stores detected errors/warnings and AI insights per run
🔒 Privacy
All telemetry data is stored locally in SQLite (~/.dockai/usage.db) and never sent externally.
Users can disable or extend telemetry via plugin configuration.
💰 Licensing & Monetization Roadmap
DockAI is open source (Apache 2.0) but designed to support optional monetization:
- Free Plan: limited analysis history and findings
- Pro Plan (License Key): unlocks unlimited telemetry, detailed analytics, and advanced plugins
- Plugin Marketplace (future): third-party verified plugins with SHA-based signature validation
A JWT-based license verification system is planned to allow easy activation via:
dockai license activate --key <YOUR_LICENSE_KEY>
🗺️ Future Roadmap
- 🧠 Enhanced AI reasoning & multi-model ensemble
- 📊 Graphical performance reports (PDF or Web)
- 🔐 Secure license key & API-based billing
- 🧩 Plugin Store with auto-update mechanism
- 🌍 Cloud telemetry dashboard
Release files for dockai 0.5.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dockai-0.5.3.tar.gz | 29.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dockai-0.5.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 62.6 kB
Release files / dockai-0.5.3.tar.gz
| Download URL | dockai-0.5.3.tar.gz |
|---|---|
| Size | 29.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.0
|
Release files / dockai-0.5.3-py3-none-any.whl
| Download URL | dockai-0.5.3-py3-none-any.whl |
|---|---|
| Size | 32.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7f07fb135c812dbb9d8ea6437389b74e84f6bddd8f37f307b7288385781dd14d
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.14.0
|