Quickstart: TypeScript
In ten minutes: a pack with two tools, an agent that calls them, a guardrail on its answers and a flow, running on your machine.
Before you start, set up what the Install page describes:
Node 22.12, Docker, and access to the runtime image. The image is in private
preview: request access at contact@kindgi.com, then log in once with
kindgi auth registry.
1. Create the pack
Section titled “1. Create the pack”npx @kindgi/cli init my-pack --template=samplecd my-packpnpm installmy-pack is the pack's id: every tool, agent and flow in it is named
my-pack.<name>. The sample template gives you:
my-pack/├── kindgi.config.ts # the pack's id, version and folders├── tools/echo/index.ts # a tool: echoes a message├── tools/greet/index.ts # a tool: greets a name├── tools/fetch-httpbin/index.ts # a tool that calls an HTTP API├── agents/echo-agent/index.ts # an agent that calls the tools├── guardrails/response-not-empty/ # a check on the agent's answers├── flows/echo-flow/index.ts # a flow: a tool step, then the agent└── .claude/skills/ # skills for your coding agent(The default template, minimal, gives you the folders and none of the
examples.)
2. Run it
Section titled “2. Run it”pnpm exec kindgi devkindgi dev starts the Kindgi runtime in Docker, indexes the pack,
registers every tool, agent, guardrail and flow, and does it again on every
save. It prints the API's URL and a token, and writes them to
.kindgirc.json in the pack, so the commands below find the runtime by
themselves. Leave it running.
3. Run the agent
Section titled “3. Run the agent”In a second terminal, in my-pack:
pnpm exec kindgi runs start --agent=my-pack.echo-agent --input='{"userMessage":"hi"}' "status": "completed",…⚠ Answered by "dev-echo", a fallback provider: no other registered provider satisfies agent "my-pack.echo-agent".There's no model yet, so the answer comes from dev-echo, a stand-in a new
pack gets: it calls the agent's first tool with {"message": <your userMessage>} and replies with what the tool returned, and the run carries a
fallback-provider warning. That's enough to see the whole path: the agent's
turn, the tool call into your code, the guardrail's check.
4. Run the flow
Section titled “4. Run the flow”pnpm exec kindgi runs start --flow=my-pack.echo-flow --input='{"name":"Ada"}' "status": "completed",… "greeting": "Hello, Ada!"The flow greets the name with the greet tool, then hands the greeting to
the agent and returns both. Add --dry-run to see which steps would run
without running the tools that change anything.
5. Look at the code
Section titled “5. Look at the code”A tool is a typed function. Its input and output are schemas, checked on every call:
import { defineTool } from '@kindgi/sdk/define';import type { ToolId } from '@kindgi/sdk/types';import { z } from 'zod';
const GreetInput = z.object({ name: z.string().min(1).max(100), greeting: z.string().min(1).max(50).default('Hello'),});
const GreetOutput = z.object({ message: z.string(),});
const defined = defineTool({ id: 'my-pack.greet' as ToolId, description: 'Formats a greeting for the named recipient.', version: '0.1.0', input: GreetInput, output: GreetOutput, effects: [], mutating: false, handler: async (input) => ({ message: `${input.greeting}, ${input.name}!`, }),});
if (defined.kind === 'err') { throw new Error(`my-pack.greet failed to compile: ${defined.error.message}`);}
export default defined.value;mutating: false says the tool changes nothing, so a dry run calls it and
approval gates don't stop it by default. Leave it out for a tool that writes,
sends or charges anything: a tool is treated as changing something unless it
says otherwise. effects names what a tool does outside your code (writes,
network calls), for policies and the audit trail.
The agent is data: its instructions, the tools it may call, the guardrails
on its answers, its budget. Change a file and save; kindgi dev picks it
up.
6. Connect a real model
Section titled “6. Connect a real model”Put an Anthropic key in the pack's .env, then register the provider:
echo 'ANTHROPIC_API_KEY=sk-ant-…' >> .envpnpm exec kindgi providers register --preset=anthropicIt takes over from dev-echo at the next turn. Run the agent again and it
answers with a real model, still calling your tools. Gemini on Vertex AI
has a preset too (--preset=gemini --project=<gcp-project>); any
OpenAI-compatible endpoint (vLLM, llama.cpp, Ollama, OpenRouter) registers
from a short spec file.
- Build a support desk: tools over your own code, a typed answer, a flow that acts on it.
- Guides: one task at a time: tools, agents, models, flows, runs, webhooks, approvals, secrets.
- Add Kindgi to an existing app: your app's own code as tools, and your app starting runs.
- Concepts: packs, runs and the journal, security.
- Set up your coding agent: it already has Kindgi's skills.