AI Chatbot

Full-stack AI chatbot with agentic loop, HTTP service, authentication, evaluation harness, and a Platform tenant chart whose ModelGateway is the agent's model path.

Name	`ai-chatbot`
Version	`0.1.0`
Tags	ai, chatbot, fullstack, typescript
Source	`composites/ai-chatbot.yaml`

Render it

# CLI
npx @nanohype/sdk render-composite ai-chatbot --out ./my-app

# SDK
import { LocalSource, renderComposite } from "@nanohype/sdk";
const result = await renderComposite(source, "ai-chatbot", variables);

# MCP — from an agent
get_composite({ name: "ai-chatbot" })

What it draws together

Template	Rendered at	Notes
`monorepo`	root	the root project
`agentic-loop`	`packages/ai`
`ts-service`	`apps/api`
`module-auth-ts`	`packages/auth`
`eval-harness`	`packages/evals`	only when `IncludeEvals`
`k8s-app-tenant`	`infra`

Variables

Required

ProjectName string: Project name used across all templates Must be lowercase kebab-case.

Optional

LlmProvider string — defaults to gateway: LLM provider the packages deployed on the substrate call: gateway, the Platform ModelGateway, which holds the Bedrock role there, so none of them holds a model credential. The eval harness runs on a CI runner and keeps its own bedrock default
IncludeEvals bool — defaults to true: Include evaluation harness
Tenant string — defaults to ${ProjectName}: Owning team: the tenants-<team> namespace the Platform, BudgetPolicy and ModelGateway CRs are applied in, the Platform's spec.tenant, and the team label on the tenant chart's resources and telemetry
Image string — defaults to ${ProjectName}: Container image repository the tenant chart deploys, without a tag; each values-<env>.yaml pins one
AppMetric string — defaults to http: Prefix of the RED series the tenant chart's Grafana dashboard reads: <prefix>_requests_total, <prefix>_errors_total and <prefix>_request_duration_seconds. The default, http, is the prefix apps/api (a ts-service) emits them under