Skip to main content

AI Orchestration Tool for Claude Code - Multi-agent orchestration for complex development tasks

Project description

Ensemble

"One task. Many minds. One result."

AI Orchestration Tool for Claude Code.

Overview

Ensemble is an AI orchestration system that combines the best practices from:

  • shogun - Autonomous AI collaboration with tmux parallel execution
  • takt - Workflow enforcement with quality gates
  • Boris's practices - Effective use of skills, subagents, CLAUDE.md, and hooks

Features

  • Autonomous AI Coordination: One instruction triggers multiple AI agents working together
  • Flexible Execution Patterns:
    • Pattern A: Simple tasks via subagent
    • Pattern B: Medium tasks via tmux parallel panes
    • Pattern C: Large tasks via git worktree separation
  • Parallel Review: Architecture + Security reviews run in parallel
  • Self-Improvement: Automatic learning and CLAUDE.md updates
  • Compaction Recovery: Built-in protocol to prevent role amnesia

Installation

Using uv (recommended)

# Install globally
uv tool install ensemble-claude

# Or add to your project
uv add ensemble-claude

Using pip

pip install ensemble-claude

From source

git clone https://github.com/ChikaKakazu/ensemble.git
cd ensemble

# Using uv
uv pip install -e .

# Or using pip
pip install -e .

Quick Start

# 1. Initialize Ensemble in your project
ensemble init

# 2. Launch the tmux session with Conductor + Dispatch
ensemble launch

# 3. Run a task (in the Conductor pane)
/go implement user authentication

# Light workflow (minimal cost)
/go-light fix typo in README

CLI Commands

Command Description
ensemble init Initialize Ensemble in current project
ensemble init --full Also copy agent/command definitions locally
ensemble launch Start tmux session with Conductor + Dispatch
ensemble launch --no-attach Start session without attaching
ensemble --version Show version

Requirements

  • Python 3.10+
  • Claude Code CLI (claude command available)
  • tmux
  • git 2.20+ (for worktree support)
  • Claude Max plan recommended (for parallel execution)

Agent Architecture

┌─────────────┐
│  Conductor  │ ← Orchestrator (planning, judgment, delegation)
└──────┬──────┘
       │
  ┌────┴────┐
  ▼         ▼
┌────────┐ ┌──────────┐
│Dispatch│ │ Learner  │
└───┬────┘ └──────────┘
    │        ↑ Learning records
    ▼
┌─────────────────────────────┐
│  Reviewer / Security-Reviewer│ ← Parallel reviews
└─────────────────────────────┘
    │
    ▼ (worktree mode)
┌──────────┐
│Integrator│ ← Merge & integrate
└──────────┘

License

MIT License - see LICENSE for details.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ensemble_claude-0.3.1.tar.gz (58.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ensemble_claude-0.3.1-py3-none-any.whl (69.9 kB view details)

Uploaded Python 3

File details

Details for the file ensemble_claude-0.3.1.tar.gz.

File metadata

  • Download URL: ensemble_claude-0.3.1.tar.gz
  • Upload date:
  • Size: 58.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ensemble_claude-0.3.1.tar.gz
Algorithm Hash digest
SHA256 90786c755b924edf7a5bee909f3dc0785d46b82dab89a4e34617e554492adf00
MD5 ac656d8cedd6401590bcacf772c93f2e
BLAKE2b-256 4fd21abdd34d2af9458a1e93c11c4448da0f8e4d78eb962bdf0fa413b2798580

See more details on using hashes here.

Provenance

The following attestation bundles were made for ensemble_claude-0.3.1.tar.gz:

Publisher: publish.yml on ChikaKakazu/ensemble

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file ensemble_claude-0.3.1-py3-none-any.whl.

File metadata

File hashes

Hashes for ensemble_claude-0.3.1-py3-none-any.whl
Algorithm Hash digest
SHA256 9972cc6126ea403a1a0e04eac38d3bac26e57cb9ff243ce8de904827ce81a0c9
MD5 ca3d27baab22eeb812448bc28d7e14e4
BLAKE2b-256 cb2f99e360e892e96dfb6ba2710cfebb77faab61a1c5593407c6cfa92dfc61a3

See more details on using hashes here.

Provenance

The following attestation bundles were made for ensemble_claude-0.3.1-py3-none-any.whl:

Publisher: publish.yml on ChikaKakazu/ensemble

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page