Skip to content
/

Aperant

aperant · AndyMik90/Aperant · ★ 14k · last commit 2026-03-23

Cross-platform Electron desktop for autonomous multi-agent coding with kanban UI, parallel git worktrees, self-validating QA loop, and GitHub/Linear integration.

Best whenAutonomous coding requires visual task management and up to 12 parallel agents in isolated worktrees — not a CLI methodology.
Skip ifSingle sequential agent, Manual conflict resolution
vs seeds
claude-flowboth manage parallel Claude Code agents in git worktrees but Aperant wraps this in an Electron desktop GUI with 12-agent…
Primitive shape 12 total
Subagents 12
00

Summary

Aperant — Summary

Aperant (formerly "Auto Claude") is a cross-platform Electron desktop application that wraps Claude Code to provide a visual kanban board, parallel agent terminals, and a self-validating QA loop for autonomous software development. Users describe a task on the kanban board; Aperant spawns up to 12 parallel agent terminals, each running Claude Code in an isolated git worktree; a built-in QA loop validates results before presenting them for human review. Native features include GitHub/GitLab issue import, Linear sync, AI-powered merge conflict resolution, and a cross-platform installer for Windows/macOS/Linux.

Aperant is the most feature-complete autonomous coding desktop in this batch. Unlike Eigent (CAMEL-AI Python backend) or Agent Deck (Go TUI), Aperant stays firmly in the Claude Code + Electron ecosystem and adds a thin orchestration layer on top of vanilla Claude Code sessions. Compared to claude-flow (closest seed), Aperant provides the kanban/terminal UI claude-flow lacks while reusing standard Claude Code sessions rather than building an MCP toolserver.

01

Overview

Aperant — Overview

Origin

Started as "Auto Claude" by Andre Mikalsen (AndyMik90). Renamed to Aperant; AGPL-3.0 licensed with commercial licensing available. 14,304 stars, 24 contributors (develop branch). Version 2.7.6 stable, 2.8.0-beta in progress as of 2026-05-26.

Philosophy

From the README: "Autonomous multi-agent coding framework that plans, builds, and validates software for you."

The core thesis is that autonomous coding at scale requires three things together: parallelism (up to 12 concurrent agents), workspace isolation (git worktrees per task), and a self-validating QA loop (agents catch issues before human review). The desktop GUI makes this accessible without terminal expertise.

Key Design Decisions

  1. Claude Code as agent: Aperant does not build its own LLM agent; it spawns Claude Code instances. Requires "Claude Pro/Max subscription."
  2. git worktrees for isolation: Each task gets its own worktree — main branch never touched during agent work.
  3. AI-powered merge: Automatic conflict resolution when integrating results back to main.
  4. QA loop: "Self-Validating QA" catches issues before human review — agents check their own work.

Security Model

Three-layer: OS Sandbox (bash in isolation), Filesystem Restrictions (operations limited to project directory), Dynamic Command Allowlist (approved commands based on detected project stack).

02

Architecture

Aperant — Architecture

Distribution

  • Type: Electron desktop application (TypeScript)
  • Platforms: Windows (.exe), macOS (arm64 .dmg + x64 .dmg), Linux (.AppImage, .deb, .flatpak)
  • Install: Download from GitHub Releases; SHA256 checksums + VirusTotal scan provided

Directory Tree

Aperant/
├── apps/
│   └── desktop/         # Electron desktop application
│       ├── src/         # TypeScript agent layer + React UI
│       └── package.json
├── guides/              # Additional documentation
│   └── linux.md
├── scripts/             # Build utilities
├── .claude/             # Claude Code settings
└── package.json

Required Runtime

  • Claude Pro/Max subscription (Claude Code OAuth)
  • Claude Code CLI (npm install -g @anthropic-ai/claude-code)
  • Git repository (project must be a git repo)
  • No Node.js required for end users (bundled in Electron)

Config Files

  • .claude/ (reuses standard Claude Code settings)
  • Memory.md (agent memory across sessions)
  • CLAUDE.md (custom instructions)

Target AI Tools

Claude Code (Claude Pro/Max required). Not model-agnostic.

Tech Stack

  • Electron (TypeScript)
  • React + TypeScript (UI layer)
  • Claude Code SDK (agent execution)
  • Git worktrees (isolation layer)
  • GitHub API + Linear API (integrations)
03

Components

Aperant — Components

Desktop UI Views

View Purpose
Kanban Board Visual task management — create tasks, monitor agent progress from planning through completion
Agent Terminals AI-powered terminals with one-click task context injection; spawn multiple parallel agents
Roadmap AI-assisted feature planning with competitor analysis and audience targeting
Insights Chat interface for exploring the codebase
Ideation Discover improvements, performance issues, and vulnerabilities
Changelog Generate release notes from completed tasks

Core Features

Feature Purpose
Autonomous Tasks Describe goal; agents handle planning, implementation, validation
Parallel Execution Up to 12 agent terminals concurrently
Isolated Workspaces All changes in git worktrees — main branch protected
Self-Validating QA Built-in quality assurance loop before human review
AI-Powered Merge Automatic conflict resolution when integrating to main
Memory Layer Agents retain insights across sessions (Memory.md)
GitHub/GitLab Integration Import issues, investigate with AI, create merge requests
Linear Integration Sync tasks with Linear for team progress tracking
Auto-Updates App updates automatically

Security Components

Layer Mechanism
OS Sandbox Bash commands in isolation
Filesystem Restrictions Operations limited to project directory
Dynamic Command Allowlist Approved commands based on detected project stack
05

Prompts

Aperant — Prompts

Aperant's prompt content lives in the TypeScript desktop application layer. The .claude/ directory and CLAUDE.md file provide agent instructions. The Memory.md file carries cross-session context.

Verbatim excerpt 1 — Feature description (from README)

Autonomous Tasks — Describe your goal; agents handle planning, implementation, and validation

Prompting technique: Goal-oriented prompt injection. The user's task description becomes the agent's primary prompt. Planning, implementation, and validation are phases triggered from a single natural-language input — no structured spec format required.

Verbatim excerpt 2 — QA loop framing

Self-Validating QA — Built-in quality assurance loop catches issues before you review

Prompting technique: Agent self-review. The QA step is a second agent pass that reviews the implementation output before surfacing to the human. This is the "adversarial subagent review" pattern — a separate validation prompt run against the implementation output.

Verbatim excerpt 3 — Memory layer description

Memory Layer — Agents retain insights across sessions for smarter builds

Prompting technique: Persistent context injection via Memory.md. At session start, the agent is instructed to read Memory.md before proceeding. This is the same pattern as claude-conductor's TASKS.md/ERRORS.md markdown context files, but framed as "memory" rather than task state.

09

Uniqueness

Aperant — Uniqueness & Positioning

differs_from_seeds

Aperant is the most direct visual counterpart to claude-flow: both manage parallel Claude Code agents in git worktrees with hierarchical coordination. The architectural delta is that Aperant wraps this in an Electron kanban UI with self-validating QA, GitHub/Linear integrations, and AI-powered merge conflict resolution — all without adding a single MCP tool or custom subagent definition. Claude-flow has 305 MCP tools, SPARC phases, and a vector database; Aperant has zero of those but has a polished desktop application. Among the 11 seeds, Aperant is also closer to kiro's "desktop-first" posture than any other, but kiro is a VS Code fork IDE while Aperant is purely a task/agent management surface built on top of standard Claude Code. The three-layer security model (OS sandbox + filesystem restriction + dynamic command allowlist) is not found in any seed.

Positioning

  • Only framework in batch with hard-coded 12-agent concurrency limit (explicitly documented).
  • Only framework with native GitHub/GitLab MR creation + Linear sync in a desktop GUI.
  • AGPL-3.0 license with commercial licensing for closed-source use cases.
  • Requires Claude Pro/Max — not usable with free tier or alternative LLMs.

Observable Failure Modes

  1. Claude Pro/Max lock-in: Requires paid subscription; free users cannot use Aperant at all.
  2. Worktree proliferation: 12 parallel worktrees on a large repo can consume significant disk space and slow git operations.
  3. QA loop quality depends on model: Self-validating QA is a second Claude Code pass — it can miss issues a real test suite would catch.
  4. No unit test enforcement: No TDD gate; QA loop is LLM-based review, not automated test runner.
  5. Single-model: Cannot use cheaper models for non-critical tasks, raising API costs.
04

Workflow

Aperant — Workflow

Phase Flow

Phase Description Artifact
1. Task Create User describes goal on kanban board Task card
2. Planning Agent plans implementation approach Plan
3. Implementation Up to 12 parallel agents execute in git worktrees Code in worktrees
4. Self-Validation QA Agents validate their own work; QA loop catches issues QA report
5. Human Review User reviews results presented for approval Approval/rejection
6. Merge AI-powered merge conflict resolution; integration to main Merged PR/commit

Approval Gates

  • Human Review: After QA loop completes, user reviews before merge
  • GitHub/GitLab MR gate: Can create merge requests for standard PR review flow

Worktree Lifecycle

Each task gets its own git worktree. Main branch is never modified during agent work. Merge step handles integration. Orphaned worktrees are cleaned up after merge.

Memory Continuity

Memory.md file stores agent insights across sessions. Agents read this file at session start to retain context from previous work.

06

Memory Context

Aperant — Memory & Context

Memory Type

File-based. Memory.md is the primary cross-session memory artifact.

Memory Persistence

Project-level. Memory.md lives in the project repository.

State Files

  • Memory.md — accumulated agent insights
  • CLAUDE.md — project-specific Claude Code instructions
  • Git worktrees (per-task isolation)

Cross-Session Handoff

Agents read Memory.md at session start, providing context from previous builds. The changelog feature also compiles completed task history.

Context Compaction

Not explicitly addressed. Claude Code's default compaction applies; Memory.md is the persistence layer.

Streaming

The desktop UI shows live agent terminal output (spawned Claude Code sessions visible in the Agent Terminals view).

07

Orchestration

Aperant — Orchestration

Multi-Agent Pattern

Parallel fan-out. Up to 12 Claude Code instances run concurrently, each in a separate git worktree. No explicit hierarchy — the kanban board is the coordination surface; the human moves tasks and reviews results.

Isolation Mechanism

git-worktree (per-task). Each task gets its own git worktree. Main branch never touched. AI-powered merge handles integration.

Execution Mode

Interactive-loop. User drives task creation; agents execute autonomously; QA loop self-validates; human reviews before merge.

Multi-Model

No. Aperant is locked to Claude Pro/Max (Claude Code). No model routing.

Subagent Definition

Not applicable. Aperant spawns plain Claude Code sessions — no custom subagent definitions, persona files, or code classes. The task description becomes the agent's instruction.

Self-Validation QA

The QA loop is an additional Claude Code pass that reviews implementation before surfacing to human. This is the "adversarial-subagent" pattern but implemented as a serial second pass, not a concurrent reviewer.

Max Concurrent Agents

12 (explicitly documented).

Consensus

None. Human is the final arbitrator.

08

Ui Cli Surface

Aperant — UI / CLI Surface

CLI Binary

  • Exists: No user-facing CLI. Build scripts only (npm run package, etc.).
  • Binary name: null

Local UI Surface

Desktop Application (Primary)

  • Type: Electron desktop application
  • Platforms: Windows, macOS (arm64 + x64), Linux (AppImage, .deb, Flatpak)
  • Tech stack: Electron + React + TypeScript (TypeScript agent layer + UI)
  • Distribution: GitHub Releases with SHA256 checksums + VirusTotal scan; code-signed on macOS

Features

Feature Description
Kanban Board Create tasks, drag across stages (To Do / In Progress / Done), monitor in real-time
Agent Terminals 12 parallel terminals; one-click task context injection; live Claude Code output
Roadmap AI-assisted feature planning; competitor analysis; audience targeting
Insights Chat with codebase for understanding
Ideation Discover improvements, vulnerabilities, performance issues
Changelog Auto-generate release notes from completed tasks

IDE Integration

None. Aperant is a standalone Electron desktop application separate from any IDE.

Observability

  • Live terminal output: All Claude Code sessions stream directly to the Agent Terminals view
  • Kanban status: Task status updates in real-time as agents progress
  • GitHub/GitLab integration: Import issues → track as kanban items → create MRs
  • Linear sync: Reflect task progress in Linear for team visibility
  • No dedicated audit log: The kanban board and terminal history serve as the implicit audit surface

Comparison with CLAUDE-FLOW UI

Claude-flow has no local UI; all interaction is via slash-commands in Claude Code terminals. Aperant is the exact inverse: the desktop GUI is the primary interaction surface. Both manage parallel Claude Code sessions and git worktrees, but Aperant presents this as a visual kanban workflow while claude-flow presents it as a programmatic MCP + command surface. Aperant's max 12 concurrent agents matches claude-flow's multi-agent architecture, but Aperant provides no SPARC methodology, no shared vector memory, and no consensus protocols — it is the UI layer claude-flow is missing.

Related frameworks

same archetype · same primary tool · same memory type

Claude-Flow / Ruflo ★ 55k

Eliminates single-agent context limits and sequential bottlenecks by orchestrating fault-tolerant swarms of specialized AI agents…

Hermes Agent (NousResearch) ★ 168k

Self-improving personal AI agent with closed learning loop, 7 terminal backends, and messaging gateway — not tied to any AI…

OpenCode ★ 165k

Terminal-first AI coding agent with multi-model routing, native desktop app, and a typed .opencode/ configuration system for…

OpenHands ★ 75k

Open-source AI software development platform (open-source Devin alternative) with Docker sandbox isolation, 77.6% SWE-bench…

DeerFlow ★ 70k

Long-horizon superagent that researches, codes, and creates by orchestrating parallel sub-agents with isolated contexts in Docker…

oh-my-openagent (omo) ★ 60k

Multi-provider AI agent orchestration for OpenCode: escape vendor lock-in by routing Sisyphus (Claude/Kimi/GLM) and Hephaestus…