Installation

Using Go

go install github.com/control-theory/gonzo/cmd/gonzo@latest

Using Homebrew (macOS/Linux)

brew install gonzo

Download Binary

Download the latest release for your platform from the releases page.

Using Nix package manager (beta support)

nix run github:control-theory/gonzo

Build from Source

git clone https://github.com/control-theory/gonzo.git
cd gonzo
make build

Using with Claude Code (plugin and skill)

This repo includes a Claude Code plugin with a guided log-analysis skill. Inside Claude Code:

/plugin marketplace add control-theory/gonzo
/plugin install gonzo@gonzo

Then ask Claude to "tail my logs", "watch my Vercel logs", or "analyze my Kubernetes logs". The skill detects your deployment platform, installs Gonzo if needed, configures AI analysis, and generates the right pipe command with platform-specific normalizers. See skills/gonzo/ for the skill content.

Analyze Docker container logs

docker logs -f my-container 2>&1 | gonzo

Run Claude in a container (Podman/Docker)

export GONZO_CLAUDE_PATH="podman exec -it claude-container claude" gonzo --ai-provider=claude-code -f logs.json

export GONZO_CLAUDE_PATH="docker exec -it my-claude-container claude" gonzo --ai-provider=claude-code --ai-model=haiku -f /var/log/app.log --follow

**Available Models:**
- `sonnet` (default) - Claude Sonnet, balanced performance and capability
- `haiku` - Fastest and most efficient, best for high-volume logs
- `opus` - Most capable, best for complex analysis

**Notes:**
- Claude Code CLI manages authentication independently. The `OPENAI_API_KEY` environment variable is not needed.
- Use `GONZO_CLAUDE_PATH` to specify a custom path or command (useful for running Claude in containers).

#### LM Studio (Local AI)

```bash

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## 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.