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 (2-4 workers, auto-scaled)
    • Pattern C: Large tasks via git worktree separation
  • Parallel Execution Enhancements:
    • Dynamic worker count (auto-scales based on task count)
    • Worker-level subagent parallelization (for 3+ files)
  • Parallel Review: Architecture + Security reviews run in parallel
  • Self-Improvement: Automatic learning and CLAUDE.md updates
    • Categorized learning (communication, workflow, code quality, tools)
    • Duplicate detection and consolidation
    • Subagent execution result collection
  • Compaction Recovery: Built-in protocol to prevent role amnesia
  • Extensibility:
    • /create-skill - Generate project-specific skill templates
    • /create-agent - Auto-generate specialized agents from tech stack
  • RPI Workflow: Research → Plan → Implement staged workflow for large features
  • Hooks Notification: Terminal bell on agent completion (Stop) and errors (PostToolUseFailure)
  • Status Line: Real-time display of git branch, session state, worker count
  • CLAUDE.md 150-line Limit Check: Pre-commit hook to prevent instruction bloat

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 sessions (2 separate sessions)
ensemble launch

# 3. Open another terminal to view workers session
tmux attach -t ensemble-workers

# 4. Run a task in the Conductor session
/go implement user authentication

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

# Create project-specific tools
/create-skill my-feature "Description of the skill"
/create-agent  # Interactive tech stack analysis

CLI Commands

Command Description
ensemble init Initialize Ensemble in current project
ensemble init --full Also copy agent/command definitions locally
ensemble launch Start 2 tmux sessions (conductor + workers)
ensemble launch --no-attach Start sessions without attaching
ensemble upgrade Sync template updates (agents, commands, scripts)
ensemble --version Show version

In-Session Commands (Conductor)

Command Description
/go <task> Full workflow with auto-pattern detection
/go-light <task> Lightweight workflow for simple changes
/go-issue [number] Start implementation from GitHub Issue
/rpi-research <task> Research phase: requirement analysis, technical investigation, feasibility assessment
/rpi-plan Plan phase: detailed planning, architecture design, task breakdown
/rpi-implement Implement phase: execute implementation based on plan (delegates to /go)
/create-skill <name> <desc> Generate project-specific skill template
/create-agent Auto-generate specialized agent from tech stack
/review Run architecture + security review
/improve Manual self-improvement analysis
/status View current progress
/deploy Version bump, merge, and publish to PyPI

Requirements

  • Python 3.11+
  • 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-0.4.11.tar.gz (87.5 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-0.4.11-py3-none-any.whl (96.2 kB view details)

Uploaded Python 3

File details

Details for the file ensemble_claude-0.4.11.tar.gz.

File metadata

  • Download URL: ensemble_claude-0.4.11.tar.gz
  • Upload date:
  • Size: 87.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for ensemble_claude-0.4.11.tar.gz
Algorithm Hash digest
SHA256 e4d4d4364609910a48be27687b7db1bae511052da607cf0225f1f97033ad513f
MD5 b4ec7efd29329009ef4184f2adc5c62a
BLAKE2b-256 2fe98c9aa4f00ff928c827bac79eccd80b651f281419118ed3259b19c1934a83

See more details on using hashes here.

Provenance

The following attestation bundles were made for ensemble_claude-0.4.11.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-0.4.11-py3-none-any.whl.

File metadata

File hashes

Hashes for ensemble_claude-0.4.11-py3-none-any.whl
Algorithm Hash digest
SHA256 54a59f90c80dcb249aec02f9692df6ed086c196da171fcc8ab3b53876ad24bac
MD5 6aa27eaef545a5e9aa214a9cfecfb331
BLAKE2b-256 da43afa67c50444e127150b8ba11dd12ee9a943120fe2fabab01d17d1bc246d4

See more details on using hashes here.

Provenance

The following attestation bundles were made for ensemble_claude-0.4.11-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