Kaizen Agentic

AI agency framework: 18 specialized agents that arrive in your project informed, learn from experience, and improve over time.

kaizen-agentic provides two things: a library of agent instruction sets you deploy into projects, and an agency framework that gives those agents persistent memory and coordination. Agents accumulate project-scoped knowledge across sessions. A Coach meta-agent synthesises patterns across the entire fleet and briefs incoming agents on what to know first.

This project embraces the Japanese concept of "kaizen" (continuous improvement) applied to AI agent development. Every agent becomes part of an optimization loop where performance is measured, patterns are analyzed, and knowledge is carried forward.

Quick Start

Install the Package

From Source (Development):

git clone https://github.com/kaizen-agentic/kaizen-agentic.git
cd kaizen-agentic
make setup-complete
make agents-install-cli
source .venv/bin/activate  # Required for each session

Global Installation (Available from any directory):

git clone https://github.com/kaizen-agentic/kaizen-agentic.git
cd kaizen-agentic
make setup-complete
python3 -m build && make install-global
# No virtual environment activation needed

Local Package Testing:

git clone https://github.com/kaizen-agentic/kaizen-agentic.git
cd kaizen-agentic
make setup-complete
python3 -m build && make install-local
source .venv/bin/activate  # Required for each session

From Gitea PyPI (v1.1.0+):

export GITEA_PACKAGE_USER=<gitea-user>
export GITEA_PACKAGE_TOKEN=<package-token>

pip install kaizen-agentic \
  --extra-index-url "https://${GITEA_PACKAGE_USER}:${GITEA_PACKAGE_TOKEN}@gitea.coulomb.social/api/packages/coulomb/pypi/simple/"

# or global CLI via pipx
pipx install kaizen-agentic \
  --pip-args="--extra-index-url https://${GITEA_PACKAGE_USER}:${GITEA_PACKAGE_TOKEN}@gitea.coulomb.social/api/packages/coulomb/pypi/simple/"

See docs/PACKAGE_RELEASE.md for release and CI details.

Your First Project (New Users)

👋 New to Kaizen Agentic? Follow our Hello World Tutorial for a complete step-by-step guide.

Create a Project (Experienced Users)

# Create a new project with AI agents
kaizen-agentic init my-project --template python-web
cd my-project

# Set up development environment
make setup-complete

# Start coding with agent assistance!
make help  # See all available commands

Add Agents to Existing Project

# Navigate to your project
cd your-existing-project

# Install relevant agents
kaizen-agentic install keepaTodofile keepaChangelog tdd-workflow

# Check what was installed
kaizen-agentic status

Agency Framework

Agents deployed into a project can accumulate project-scoped memory — a structured file written at session close and read at session start. A Coach meta-agent reads across all agent memories and produces targeted orientation briefs for incoming agents.

# Scaffold memory for an agent
kaizen-agentic memory init sys-medic

# Brief an incoming agent using all existing project memories
kaizen-agentic memory brief tdd-workflow

# Review an agent's accumulated knowledge
kaizen-agentic memory show project-management

See docs/agency-framework.md for the full model.

Orientation

Read in this order for strategic context:

1. INTENT.md — purpose, boundaries, design principles
2. wiki/KaizenAgenticMission.md — product narrative
3. wiki/AboutKaizenAgents.md — agent concepts and metrics pilot
4. wiki/EcosystemIntegration.md — ecosystem composition
5. SCOPE.md — repository boundaries and current state
6. history/ — persisted assessments and gap analyses

Released v1.1.0 — see CHANGELOG.md. Workplans: WP-0001 through WP-0004 completed.

Feedback: kaizen-agentic feedback · docs/FEEDBACK.md

Features

• 20 Specialized Agents: Project management, testing, code quality, infrastructure, meta
• Agency Framework: Project-scoped agent memory + Coach meta-agent for cross-agent synthesis
• CLI Tool: Easy agent installation, management, and memory commands (kaizen-agentic)
• Project Templates: Pre-configured setups for different project types
• Claude Code Integration: Seamless integration with Claude Code workflows
• Comprehensive Testing: Full test coverage with multiple testing strategies

Available Agents

Project Management

• keepaTodofile: Manages TODO.md files following Keep a Todofile format
• keepaChangelog: Maintains CHANGELOG.md files following Keep a Changelog format
• keepaContributingfile: Creates and updates CONTRIBUTING.md files
• project-management: General project management and coordination

Development Process

• tdd-workflow: Test-driven development workflow guidance
• requirements-engineering: Requirements analysis and documentation
• test-maintenance: Test suite maintenance and optimization

Code Quality

• code-refactoring: Code improvement and refactoring guidance
• optimization: Agent definition optimization and improvement
• datamodel-optimization: Data model design and optimization

Infrastructure

• setupRepository: Repository initialization and standards compliance
• claude-documentation: Claude Code configuration and documentation
• tooling-optimization: Repository tooling usage optimization
• sys-medic: Infrastructure health monitoring and diagnostics

Meta

• coach: Coaching meta-agent — reads all project agent memories, synthesises cross-agent briefs, and orients incoming agents

View complete agent list

Project Templates

# Available templates
kaizen-agentic templates

# python-basic: Basic Python project setup
# python-web: Web application development
# python-cli: Command-line tool development
# python-data: Data science and analysis
# comprehensive: All available agents

Known Issues

Click Library Workaround

The CLI currently implements a workaround for spurious error messages in the Click library. This affects the install command but is transparent to users. See CLICK_WORKAROUND.md for technical details and removal timeline.

User Impact: None - the workaround provides clean CLI output Status: Monitoring Click library updates for resolution