---
title: "Mammoth"
description: "DOT-based pipeline runner that turns directed graphs into multi-stage AI coding workflows with checkpointing, retry, and human-in-the-loop gates."
canonical_url: "https://2389.ai/products/mammoth/"
last_updated: "2026-03-17T11:10:28-05:00"
doc_version: "1.0"
date: 2026-02-26
product_type: "Tool"
status: "Alpha"
tags: ["pipeline", "agents", "dot", "go", "cli", "orchestration", "multi-agent", "checkpointing"]
---

# Mammoth

> DOT-based pipeline runner that turns directed graphs into multi-stage AI coding workflows with checkpointing, retry, and human-in-the-loop gates.


Mammoth reads a DOT file describing a pipeline graph and executes it node by node, dispatching each stage to an LLM coding agent. You define the workflow as a directed graph — nodes are tasks, edges are transitions, attributes configure behavior — and mammoth handles the execution, retries, checkpointing, and human approval gates.

## Install

```bash
# From source
go install github.com/2389-research/mammoth/cmd/mammoth@latest

# Or grab a binary from GitHub releases
# macOS (arm64/amd64), Linux (arm64/amd64)
```

## What it does

**Pipeline execution from DOT files.** Write a `.dot` file with nodes for each stage of your build — setup, code generation, testing, verification, integration. Mammoth parses the graph, validates it against 21 lint rules, and walks the DAG from start to exit.

**Multi-provider LLM agents.** Each codergen node spins up an agent session backed by OpenAI, Anthropic, or Gemini. Set an API key and mammoth picks the provider, or pin a specific model per node via attributes.

**Checkpoint and auto-resume.** Every successful node saves a checkpoint. If a run fails or gets interrupted, `mammoth pipeline.dot` picks up where it left off. Use `--fresh` to force a clean start.

**Parallel branches with fan-in.** Fork work across multiple branches that execute concurrently, then merge results at a fan-in node with configurable join policies (all-success, majority, first-success).

**Human gates.** Nodes with `shape=house` pause execution and prompt for human input — approve, reject, or provide guidance. Configurable timeouts with default choices for unattended runs.

**Verification nodes.** Octagon-shaped nodes run shell commands (like `swift test` or `go vet`) and gate progression on exit code. No LLM, no token cost, deterministic pass/fail.

**Terminal UI.** Run with `--tui` for a Bubble Tea dashboard showing the live DAG, event log, and node details. Or use the web UI via `mammoth serve`.

## How it works

Three layers, bottom up:

1. **llm/** — Unified client that normalizes OpenAI Responses API, Anthropic Messages API, and Gemini into a single interface with streaming, retry, and backoff.
2. **agent/** — Agentic loop that drives an LLM through tool calls (file read/write, shell exec, patch apply), with steering, loop detection, and subagent spawning.
3. **attractor/** — The pipeline engine. Parses DOT, validates the graph, walks nodes through a 5-phase lifecycle, manages checkpoints, and dispatches to the agent layer.

Pipeline files use standard DOT syntax with node attributes for configuration:

```dot
digraph build_app {
    setup [shape=box, prompt="Set up the project structure"]
    implement [shape=box, prompt="Implement the core logic"]
    verify [shape=octagon, command="go test ./..."]
    done [shape=doublecircle]

    setup -> implement -> verify -> done
    verify -> implement [condition="outcome=fail"]
}
```

## Requirements

- Go 1.25+ (for building from source)
- At least one LLM API key: `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, or `GEMINI_API_KEY`
- Graphviz (optional, for SVG/PNG graph rendering)


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