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kaizen

Builds signature-based AI agents with multi-agent coordination, multimodal processing, and observability using the Kaizen framework.

Updated Apr 2, 2026
Search Tags:#rag#ai agents#multi-agent#orchestration#observability#kaizen#signatures
One-click install
npx skills add https://github.com/Hamza-Haadi/disease-risk-classifier-hamza --skill kaizen-hamza-haadi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kaizen
Source: https://github.com/Hamza-Haadi/disease-risk-classifier-hamza/tree/main/.claude/skills/04-kaizen
Command: npx skills add https://github.com/Hamza-Haadi/disease-risk-classifier-hamza --skill kaizen-hamza-haadi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Building production AI agents requires juggling type-safe interfaces, multi-agent coordination, memory, checkpointing, cost tracking, and observability, and ad-hoc implementations drift, break, and lack auditability. This Skill encodes the Kailash Kaizen framework's patterns so agents are built with validated signatures, governed budgets, and enterprise trust controls from the start. ## Core Features & Use Cases - Signature-Based Agents: Define type-safe agent interfaces with InputField/OutputField signatures and BaseAgent, with automatic validation, config auto-conversion, and defensive result extraction helpers. - Multi-Agent Orchestration: Coordinate agents with 9 pipeline patterns (Ensemble, Router, Blackboard, Supervisor-Worker, Consensus, Debate, and more), Google A2A semantic capability matching, SharedMemoryPool, and AgentRegistry for 100+ agent systems. - Enterprise Autonomy & Governance: Use checkpoint/resume, 3-tier memory, observability hooks (tracing, metrics, logging, audit), EATP cryptographic trust chains, budget tracking with posture transitions, and L3 autonomy primitives (envelope enforcement, scoped context, plan DAGs). - Use Case: Build a supervisor-worker system where a supervisor agent semantically routes research tasks to specialized worker agents, shares findings through a memory pool, tracks token costs against a budget, and emits audit events for compliance. ## Quick Start Ask the AI to create a Kaizen BaseAgent with a signature for your task, following the kaizen-baseagent-quick and kaizen-quickstart-template references.

Frequently Asked Questions about kaizen

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a Kaizen BaseAgent in Python?▼

Define a Signature subclass with InputField and OutputField, create a dataclass config with llm_provider and model, then subclass BaseAgent passing config and signature to super().__init__(). Call self.run() with your inputs to execute the agent.

How do I build a multi-agent system with Kaizen?▼

Create a SharedMemoryPool and pass it to each agent with a unique agent_id, then coordinate them using patterns like SupervisorWorkerPattern or Pipeline.router with A2A semantic matching. Agents write findings with write_to_memory() and read with read_relevant().

What is the difference between Kaizen agent types like ReAct and chain-of-thought?▼

Chain-of-thought agents produce step-by-step reasoning in a single inference cycle, while ReAct agents loop through reasoning and tool-calling cycles until convergence. RAG agents add retrieval over a knowledge base before generation.

Why does Kaizen VisionAgent fail with base64 images or 'prompt' parameter?▼

VisionAgent expects a file path for the image parameter, not base64 strings, and uses the 'question' parameter rather than 'prompt'. Results are returned under the 'answer' key, not 'response', as documented in the multimodal pitfalls reference.

Does Kaizen support agent checkpointing and resume?▼

Yes, the checkpoint system saves agent state to filesystem, Redis, PostgreSQL, or S3 backends with compression and incremental checkpoints. Long-running agents can resume from failures, and the interrupt mechanism supports graceful shutdown on Ctrl+C.

When should I not use Kaizen for an AI agent project?▼

Avoid Kaizen for simple one-off LLM calls where signature and config overhead adds no value, or when you cannot run real infrastructure for integration testing. It is designed for production agentic systems needing coordination, observability, and governance.

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