ailog-cli 2.0.0

AI-powered Android/AOSP log interpreter

AILog — AI-Powered Android/AOSP Log Interpreter

Stop drowning in 50,000 lines. Let AI find what matters.

AILog is a CLI tool that interprets Android build logs, logcat output, and AOSP compilation errors using AI. It filters noise first (free, instant), then sends only important lines to an AI model for root cause analysis and fix suggestions.

Features

• Two-stage filtering: Rule-based noise filter removes ~70% of lines before AI, saving tokens and time
• Multi-provider AI: Ollama (local, free), OpenAI-compatible APIs, Anthropic Claude
• Build wrapper: Wraps m/make with real-time error interpretation
• Logcat wrapper: Wraps adb logcat with noise filtering and batch AI analysis
• File analyzer: Batch analyze saved log files with chunked processing
• Automotive-aware: Special patterns for VHAL, CarService, CarAudio, EVS

Requirements

• Python 3.8+ (stdlib only — no pip packages needed)
• adb (for ailog cat) — included with Android SDK Platform Tools
• For local AI: Ollama with a model pulled
• For cloud AI: API key from OpenAI, Anthropic, Groq, Together, etc.

Installation

Platform	analyze	cat	build	Install method
Linux	Yes	Yes	Yes	install.sh
macOS	Yes	Yes	Yes	install.sh
Windows	Yes	Yes	No (AOSP builds are Linux/macOS only)	Manual

Linux / macOS

git clone <repo-url> && cd AILog
bash install.sh

If ~/.local/bin is not in your PATH:

echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc && source ~/.bashrc   # Bash
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.zshrc && source ~/.zshrc     # Zsh

Windows

git clone <repo-url> && cd AILog
python ailog.py --help

Quick Start

Ollama (Local, Free — Default)

# 1. Install Ollama from https://ollama.com, or:
brew install ollama              # macOS
curl -fsSL https://ollama.ai/install.sh | sh   # Linux

# 2. Start the server and pull a model
ollama serve                     # keep running in background
ollama pull qwen2.5-coder:3b    # in another terminal (~2 GB)

# 3. Verify and select a model
ailog config --list-models       # list all pulled models
ailog config --model qwen2.5-coder:3b   # select the model to use

# 4. Test it
ailog analyze examples/build_error.log

Model tips: qwen2.5-coder:3b is the default — fast and lightweight (~2 GB). For better results, try qwen2.5-coder:7b or codellama:13b. Pull any model with ollama pull <name>, then select it with ailog config --model <name>.

Cloud Providers

# OpenAI / Groq / Together / etc.
ailog config --provider openai
ailog config --api-key sk-...
ailog config --model gpt-4o-mini
ailog config --base-url https://api.groq.com/openai/v1   # for non-OpenAI providers

# Anthropic Claude
ailog config --provider anthropic
ailog config --api-key sk-ant-...

Usage

# Analyze a saved log file
ailog analyze build.log
ailog analyze logcat.txt --focus CarService
ailog analyze build.log --output report.md

# Live logcat with AI
ailog cat --explain                              # AI explains each error inline
ailog cat -p com.example.myapp --explain         # Filter to your app only
ailog cat -s DEVICE_SERIAL --explain             # When multiple devices connected
ailog cat --focus VHAL --noise-level high        # Focus + aggressive filtering

# Wrap an AOSP build
ailog build
ailog build -- -j16 framework

Commands

ailog analyze <file>

Flag	Description
--type build|logcat|auto	Log type (default: auto-detect)
--full	Disable noise filtering
--output <path>	Save report to file
--focus <keyword>	Focus AI on specific component

ailog build [-- make args]

Flag	Description
--no-filter	Show all logs
--summary-only	Hide raw logs, show AI summary only
--module <name>	Module hint for better AI context

ailog cat [adb logcat args]

Flag	Description
-s, --device <serial>	Target device when multiple are connected
-p, --package <pkg>	Filter by app package name (resolves PID automatically)
--noise-level low|medium|high	Filtering aggressiveness
--focus <tag/keyword>	Focus AI attention
--explain	Inline AI explanations for each error
--batch-interval <seconds>	AI summary interval (default: 5)

ailog config

ailog config --show              # Show current config
ailog config --provider ollama   # Switch provider
ailog config --api-key sk-xxx    # Set API key
ailog config --model <name>      # Set model
ailog config --base-url <url>    # Custom base URL
ailog config --list-models       # List available Ollama models
ailog config --reset             # Reset to defaults

Configuration

Config file: ~/.config/ailog/config.json

Key	Default	Description
provider	ollama	AI provider: ollama, openai, anthropic
ollama_url	http://localhost:11434	Ollama server URL
ollama_model	qwen2.5-coder:3b	Ollama model
openai_url	https://api.openai.com/v1	OpenAI-compatible base URL
openai_model	gpt-4o-mini	OpenAI model
anthropic_model	claude-sonnet-4-20250514	Anthropic model
noise_level	medium	Default noise filter level
batch_interval	5	Seconds between AI batches
max_ai_calls	5	Max AI calls per session

API keys can also be set via environment variables: OPENAI_API_KEY, ANTHROPIC_API_KEY.

How It Works

Input (log file, adb logcat, or make output)
         |
Stage 1: Rule-Based Noise Filter (instant, free)
         |
Stage 2: AI Analysis (only if errors detected, max 5 calls/session)
         |
Terminal Display (color-coded lines, boxed AI analysis, stats bar)

Development

Testing: AILog has 136 unit tests covering all major modules. Tests run in CI across Python 3.8, 3.9, 3.10, 3.11, and 3.12 via GitHub Actions, with ruff linting.

python3 -m unittest discover -s tests -v

Further Reading

• TESTING.md — Step-by-step guide to test AILog with a real Android app
• UNINSTALL.md — How to completely remove AILog
• ARCHITECTURE.md — Technical architecture and design decisions
• CHANGELOG.md — Version history

License

MIT