Introduction

What kenpachi is and why teams use it to ship agent features faster.

kenpachi is a small TypeScript SDK for building tool-using LLM agents — without pulling in a heavy vendor framework. You get a typed agent loop, pluggable providers (Anthropic & OpenAI over raw fetch), and a handful of production features that most minimal agent loops skip.

Start here if you’re evaluating: read this page, then follow Quickstart. You’ll have a working agent with tools in under 5 minutes.

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What you get out of the box

Feature	What it solves
Typed tools	Define tools once with Zod — the model gets a JSON schema, you get runtime validation.
Argument pre-coercion	Primitive argument types (like stringified numbers and booleans) are automatically coerced before schema validation keeps your agent loop resilient.
Time-travel context	Snapshot every turn; branch or rewind without re-calling the model for history you already paid for.
Saga rollback	Register undo handlers per tool — if a later step in the same batch fails, earlier steps are reversed.
Streaming	Token-by-token output via agent.stream() or a one-line onText callback on run().
Handoffs	Wrap specialist agents as tools — a triage agent delegates to billing or support automatically.
Dynamic tool synthesis	Let the model author small sandboxed helpers; secrets stay in your connector registry, never in model output.

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Minimal example

This is the whole mental model: provider → tools → agent → run.

import { z } from "zod";
import { Agent, defineTool, createAnthropicProvider } from "kenpachi";

const getWeather = defineTool({
  name: "get_weather",
  description: "Get current weather for a city",
  schema: z.object({ city: z.string() }),
  async execute({ city }) {
    return { city, tempC: 24, condition: "sunny" };
  },
});

const provider = createAnthropicProvider({
  apiKey: process.env.ANTHROPIC_API_KEY!,
  model: "claude-sonnet-4-6",
});

const agent = new Agent(provider, [getWeather]);
const result = await agent.run("What's the weather in Nashik?");

console.log(result.text);
// → "It's sunny and 24°C in Nashik."

No boilerplate loop, no manual tool-call parsing — agent.run() handles the back-and-forth until the model finishes.

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How it compares

Most teams building agents today either:

1. Roll their own loop — fast to start, painful when you need undo, argument coercion, streaming, or multi-agent routing.
2. Adopt a large framework — powerful, but heavy and opinionated.

kenpachi sits in the middle: small surface area, production-shaped defaults. You keep control of your stack (plain TypeScript, zero vendor SDK deps) while getting the guardrails that matter once you ship to real users.

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When kenpachi is a good fit

• You’re building a chat product and need undo / edit / regenerate without duplicate API costs.
• Your agents call external APIs via tools and you want validation + rollback when things go wrong.
• You want specialist sub-agents (billing, support, research) behind a single front-door agent.
• You need streaming text in your UI without rewriting your agent loop.

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Next steps

Install

Add kenpachi to your project and set up API keys.

Quickstart

Build your first tool-using agent in a few lines.

Streaming

Stream tokens to your UI as the model responds.

Handoffs

Route work to specialist agents automatically.