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January 5, 2026 LightSprint Team

The AI Agent Orchestration Showdown: CLI Power vs Web Elegance

Two approaches to the same problem. Both brilliant. Which one fits your workflow? Comparing Gastown's CLI-first approach with LightSprint's modern web experience.

The AI coding revolution is here, and with it comes a fascinating challenge: how do you orchestrate multiple AI agents working on your codebase without chaos?

Two projects have emerged with distinctly different philosophies to solve this problem. Gastown takes the CLI-first, git-native approach beloved by infrastructure engineers. LightSprint offers a modern web experience with real-time collaboration and deep GitHub integration.

The Problem Both Solve

If you've tried using AI coding assistants at scale, you've hit these walls:

  1. Context Amnesia — AI agents restart and forget what they were doing
  2. Coordination Chaos — Multiple agents step on each other's toes
  3. Visibility Gaps — "What is that agent even working on?"
  4. Manual Overhead — Copying context, managing branches, tracking progress
Both Gastown and LightSprint tackle these head-on, but with radically different approaches.

Gastown: The CLI Purist's Dream

Gastown is built for engineers who live in the terminal. Written in Go with an npm wrapper for easy installation, it treats git as the source of truth for everything.

The Architecture

Town (workspace)
├── Mayor (primary AI coordinator)
├── Rigs (project containers)
│   └── Polecats (ephemeral worker agents)
└── Beads (git-backed issue tracking)

The genius of Gastown is the Polecat model—ephemeral agents that spawn, do work, and disappear. No stale sessions. No zombie processes. Names come from a pool (Mad Max-themed: Furiosa, Nux, Toast) and get recycled.

What Gastown Does Brilliantly

Git Worktrees for Isolation — Each agent gets its own worktree on a unique branch. True parallel work without conflicts.

The Mail System — Agents can message each other, escalate blockers, and coordinate through built-in communication primitives.

Attribution — Every commit, every record shows exactly which agent did what (gastown/polecats/toast). Enterprise audit requirements? Solved.

Formula System — YAML templates for multi-step workflows that can be composed and reused.

The Trade-off

Gastown requires comfort with CLI workflows. There's no web UI—you're managing everything through gt commands and tmux sessions. For infrastructure teams who already live in the terminal, this is a feature. For others, it's a learning curve.

LightSprint: AI-Native Project Management for the Modern Team

LightSprint takes the opposite approach: a beautiful web interface that makes AI-powered development accessible to entire teams, not just CLI power users.

The Experience

The moment you open LightSprint, you see your project as a real-time Kanban board. Tasks flow from Todo → In Progress → In Review → Done, with live updates across all connected users.

But this isn't just another project management tool. LightSprint is AI-native from the ground up.

What LightSprint Does Brilliantly

Natural Language Task Generation — Describe what you want to build in plain English. LightSprint's AI scans your actual codebase and generates implementation-ready tasks with full context.

You: "Add user authentication with OAuth"

LightSprint AI:
├── Analyzes your existing auth patterns
├── Identifies related files (routes, middleware, config)
├── Generates subtasks with specific file references
└── Suggests implementation approach based on your stack

Automatic Todo Completion — This is where it gets magical. Push a commit? LightSprint's AI analyzes the changes and automatically checks off completed todos. No more "updating Jira" after every commit. Your task board stays current because of your code, not despite it.

One-Click Agent Launch — See a task? Click "Launch Agent." LightSprint spins up a cloud agent (Cursor Cloud or Claude Code Web) with full context: task description, related files, your codebase patterns, custom rules. The agent works, creates a PR, and the task auto-closes when merged.

Deep GitHub Integration — Your board updates the instant things happen in GitHub. PR opened? Task moves to "In Review." PR merged? Task closes. Branch deleted? Cleanup happens automatically.

Multi-Model Flexibility — Switch between 20+ AI models. Claude Opus for complex reasoning, GPT-4o for speed, Gemini Flash for cost efficiency. Per-user model selection means everyone can optimize their own workflow.

Real-Time Collaboration — Multiple team members see the same board, same updates, same AI operations in progress. Disconnect and reconnect? You automatically catch up on everything you missed.

Head-to-Head Comparison

FeatureGastownLightSprint
InterfaceCLI + tmuxWeb UI (real-time)
Learning CurveSteep (terminal fluency required)Gentle (familiar Kanban UX)
Multi-RepoExcellent (built for it)Single project focus
Team CollaborationVia git + mail systemReal-time sync
AI Task GenerationManual (via beads)Natural language → tasks
Auto-TrackingGit-nativeAI-powered (automatic)
Agent Launchgt polecat spawnOne-click button
GitHub IntegrationGit-levelInstant sync
Model SelectionConfigurable20+ AI models
DeploymentSelf-hostedSaaS (lightsprint.ai)
Offline CapableYes (git-based)Yes (works offline)

When to Choose Gastown

Gastown shines when you need:

    1. Multi-repo orchestration — A "town" managing 10+ projects
    2. Maximum git control — Worktrees, bare repos, branch strategies
    3. Self-hosted everything — No external dependencies
    4. CLI-centric workflows — Your team already lives in tmux
    5. Enterprise attribution — Audit trails on every action

When to Choose LightSprint

LightSprint shines when you need:

    1. Team adoption — Non-terminal users need access
    2. Visual task management — Kanban beats CLI for planning
    3. Instant AI task generation — Describe → Generate → Execute
    4. Auto-tracking without effort — Code drives task status
    5. GitHub-native workflow — PR-centric development
    6. Model flexibility — Different models for different tasks
    7. Zero infrastructure — Sign up and start

The LightSprint Workflow in Action

Let me walk through what a typical session looks like:

1. Describe What You Want

Open LightSprint. Click "New Task." Type:

> "Add a dark mode toggle to the settings page that persists user preference"

2. AI Does the Research

LightSprint's agent scans your codebase:

    1. Finds your existing settings components
    2. Identifies your theme/styling approach
    3. Locates localStorage or preference storage patterns
    4. Notes your component library (shadcn, custom, etc.)

3. Get Implementation-Ready Tasks

You receive: 

Task: Add dark mode toggle to settings
├── Todo: Create ThemeToggle component in /src/components/ui/
├── Todo: Add theme context/store in /src/lib/stores/
├── Todo: Update CSS variables in /src/app.css
├── Todo: Add persistence to localStorage
├── Todo: Update Settings page to include toggle
└── Related Files: [5 files identified]

4. One-Click Implementation

Click "Launch Agent" on the task. A cloud agent spins up with:

    1. Full task context
    2. Your codebase loaded
    3. Custom rules you've defined
    4. The specific todos to complete
The agent works. You watch progress in real-time.

5. Automatic PR and Completion

Agent finishes → PR created automatically → You review and merge → Task closes itself.

Total manual work: Describe the feature. Review the PR.

The Secret Sauce: Codebase Awareness

What makes LightSprint's AI generation different from "just asking ChatGPT" is codebase awareness.

When you describe a feature, LightSprint doesn't hallucinate file paths or assume your architecture. It actually reads:

    1. Your file structure
    2. Your existing code patterns
    3. Your git history and recent changes
    4. Your project dependencies
The result: tasks that reference your actual files and match your actual patterns.

The Auto-Completion Magic

My favorite LightSprint feature is something you don't even see: automatic todo completion.

Traditional workflow:

  1. Write code
  2. Commit and push
  3. Open project management tool
  4. Find the task
  5. Check off what you did
  6. Repeat 100 times a day
LightSprint workflow:
  1. Write code
  2. Commit and push
  3. (LightSprint AI analyzes your commit)
  4. (Relevant todos auto-check)
  5. Done.
The AI looks at your commit message, the changed files, and the diff. It matches against your todos with confidence scoring. High confidence? Auto-complete. Low confidence? Leave for human review.

Your task board becomes a live reflection of your codebase, not a stale document you forgot to update.

Both Are Excellent. Here's How to Decide.

Choose Gastown if:

    1. You want maximum control and git-native everything
    2. You're orchestrating many repos/projects
    3. Your team is terminal-fluent
    4. Self-hosting is a requirement
Choose LightSprint if:
    1. You want the fastest path from idea to implementation
    2. Your team includes non-terminal users
    3. You value automatic tracking that just works
    4. You want instant GitHub integration
    5. Model flexibility matters for your use cases

The Bigger Picture

Both tools represent something important: the shift from AI as a single-shot assistant to AI as a coordinated workforce. Whether you prefer Gastown's terminal-native approach or LightSprint's visual interface, the underlying insight is the same—managing AI agents requires the same thoughtfulness we bring to managing human teams.

Context matters. Coordination matters. Visibility matters.

The best tool is the one that fits how you already work. If you live in the terminal, Gastown will feel like home. If you prefer visual workflows and want your whole team onboard quickly, LightSprint is worth exploring.

Both projects are actively developed and improving. The AI orchestration space is still young, and we're all figuring out the best patterns together.

Have thoughts on AI orchestration approaches? Working on something similar? I'd love to hear about it.