---
title: "Tracker"
description: "Pipeline orchestration engine that runs DAG workflows from DOT files with human gates, LLM nodes, and automatic checkpointing."
canonical_url: "https://2389.ai/products/tracker/"
last_updated: "2026-03-17T11:10:28-05:00"
doc_version: "1.0"
date: 2026-03-09
product_type: "Tool"
status: "Alpha"
tags: ["pipeline", "orchestration", "tui", "go", "cli", "checkpointing", "dot", "agents", "multi-agent"]
---

# Tracker

> Pipeline orchestration engine that runs DAG workflows from DOT files with human gates, LLM nodes, and automatic checkpointing.


Tracker is a Go implementation of strongDM's [Attractor](https://github.com/strongdm/attractor) framework — a three-layer system for building AI-powered software factories. You define workflows as DOT files — directed acyclic graphs where each node is a step — and Tracker executes them in dependency order. It handles human-in-the-loop gates, LLM-powered nodes, automatic checkpointing, and retry logic. A bubbletea TUI dashboard shows pipeline progress in real time.

## Install

```bash
go install github.com/2389-research/tracker/cmd/tracker@latest
```

Run a pipeline:

```bash
tracker pipeline.dot
```

Resume from a checkpoint:

```bash
tracker -c .tracker/runs/<runID>/checkpoint.json pipeline.dot
```

## What it does

**DAG execution from DOT files.** Define your pipeline as a standard Graphviz DOT file. Nodes represent steps, edges represent dependencies. Tracker topologically sorts the graph and executes nodes in the correct order, running independent branches concurrently where possible.

**Human gate nodes.** Mark any node as a human gate and Tracker pauses execution to present a choice or freeform prompt. In TUI mode, these appear as scrollable modal dialogs with keyboard navigation. In console mode, they fall back to stdin/stdout.

**LLM-powered nodes.** Nodes can invoke language models with pipeline context automatically injected into prompts. Trace introspection surfaces token counts, latency, and prompt/completion text in both the TUI dashboard and console output.

**Automatic checkpointing.** Tracker saves pipeline state to `.tracker/runs/<runID>/checkpoint.json` after every node transition. If a run fails or is interrupted, resume from the last checkpoint. No manual configuration required — checkpointing is on by default when the artifact directory exists.

**Retry with backoff.** Failed nodes retry automatically with configurable backoff. The checkpoint tracks retry counts so resumed runs pick up where they left off.

**TUI dashboard.** A bubbletea-based terminal UI shows a live node list with status indicators, LLM trace details, and scrollable modal prompts for human gates. Disable with `--no-tui` for headless or CI environments.

## How it works

Tracker parses the DOT file into a DAG, validates it for cycles, and builds an execution plan. The engine walks the graph node by node, dispatching each to a handler based on its type. Pipeline context flows between nodes — outputs from earlier steps are available as inputs to later ones.

Artifacts land in `.tracker/runs/<runID>/`, one directory per run. Checkpoints, LLM traces, and any node-produced files all go here. The run ID is generated at startup and stays consistent across checkpoint resumes.

The handler registry maps node types to implementations. Built-in handlers cover shell commands, LLM calls, and human gates. Custom handlers plug in through the Go API.

## Requirements

- Go 1.24+ (for building from source)
- A DOT file defining your pipeline
- For LLM nodes: API credentials for your model provider (configured via environment variables)


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