kdcube-cli 2026.3.16.107

KDCube Apps bootstrap CLI

KDCube CLI

Bootstrap installer for the KDCube platform stack. This package clones the repository (if needed) and launches the guided setup wizard.

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Prerequisites

macOS

• Python 3.9+ (Homebrew recommended)
• Git (Xcode Command Line Tools or Homebrew)
• Docker Desktop (includes Docker Compose)

Windows

• Python 3.9+
• Git for Windows
• Docker Desktop (enable WSL2 backend)

Linux

• Python 3.9+
• Git
• Docker Engine + Docker Compose plugin

Install

pipx install kdcube-cli

Alternative (pip):

python -m pip install --user kdcube-cli

Run

kdcube-setup

Quick start (new users)

1. Run kdcube-setup
2. Choose release-latest (pull prebuilt images)
3. Answer yes to “Run docker compose now?”

That brings up the stack with no local build required.

We aim for a setup that is simple to try, and easy to explore further using the installed admin assistant and bundled tools.

What it installs (default)

• Repo clone: ~/.kdcube/kdcube-ai-app
• Workdir: ~/.kdcube/kdcube-runtime
• Docker images: pulled (release-latest/release-tag) or built (upstream/workspace/local)

CLI options (common)

Option	Purpose
--repo <url>	Git repo URL (default: official kdcube repo).
--path <repo>	Use a local repo checkout for templates and builds (skips install-source menu).
--workdir <path>	Use a specific workdir instead of ~/.kdcube/kdcube-runtime.
--reset-config	Re‑prompt for config values without deleting files.
--reset	Alias for --reset-config.
--clean	Clean local Docker cache and unused KDCube images.
--secrets-prompt	Prompt for LLM keys (OpenAI/Anthropic/Brave/OpenRouter) and inject them at runtime (sidecar).
--secrets-set KEY=VALUE	Inject a secret value without prompting (repeatable).
--proxy-ssl	Force SSL proxy config (overrides assembly descriptor).
--no-proxy-ssl	Force non‑SSL proxy config (overrides assembly descriptor).
--dry-run	Generate env files and print their paths without running Docker.
--dry-run-print-env	With --dry-run, also print the full env file contents.

Use a local checkout (dev)

kdcube-setup --path /Users/you/src/kdcube/kdcube-ai-app

When --path is provided, the wizard uses that repo for templates and local builds and does not show the Install source menu.

Re-run prompts (edit existing values):

kdcube-setup --reset

Clean local Docker images/cache:

kdcube-setup --clean

Tip: if kdcube-setup is not on your PATH, run python -m pipx ensurepath or re-open your shell after installation.

What the wizard does (today)

When you run kdcube-setup, the wizard performs the steps below:

1. Creates a workdir with config/, data/, and logs/ folders.
2. Writes compose env files into config/ (only if missing; it won’t overwrite existing files).
3. Copies nginx configs into config/ for runtime overrides:
   • nginx_ui.conf
   • runtime proxy config (based on selected auth mode)
4. Selects auth mode (simple or cognito) and writes:
   • AUTH_PROVIDER in .env.ingress + .env.proc
   • Cognito fields when applicable (see below)
5. Generates frontend runtime config (hardcoded or cognito).
6. Creates local data folders for Postgres/Redis/exec workspace/bundle storage.
7. Optionally builds images and starts docker compose up -d.

Authentication modes

The wizard prompts for an auth mode and updates both backend and frontend config.

Simple (hardcoded)

• AUTH_PROVIDER=simple
• Frontend config: frontend.config.hardcoded.json
• Uses a hardcoded admin token in config (local dev only)

Cognito

• AUTH_PROVIDER=cognito
• Frontend config: frontend.config.cognito.json
• Required fields:
  • COGNITO_REGION
  • COGNITO_USER_POOL_ID
  • COGNITO_APP_CLIENT_ID
  • COGNITO_SERVICE_CLIENT_ID
• The frontend authority is composed as:
  • https://cognito-idp.<COGNITO_REGION>.amazonaws.com/<COGNITO_USER_POOL_ID>

Delegated

• AUTH_PROVIDER=cognito
• Uses the delegated proxy template (proxylogin) while still validating tokens via Cognito.

Routes prefix & nginx proxy

The frontend config includes routesPrefix (default: /chatbot). The wizard patches the runtime proxy config in config/ so nginx uses the same prefix. This keeps /chatbot (or any custom prefix) consistent between UI and proxy.

Secrets (third services tokens)

The wizard does not write OpenAI/Anthropic/Brave keys to .env files. If you provide them during setup, they are injected at runtime into the kdcube-secrets sidecar (in‑memory only) when assembly.yaml uses secrets.provider: secrets-service. If you restart the stack, you’ll be prompted again to re‑inject keys.

Order (automatic):

1. Start kdcube-secrets
2. Wait for it to be ready
3. Inject keys
4. Start/restart ingress + proc (they fetch secrets)

Manual re‑inject:

kdcube-setup --secrets-prompt --workdir ~/.kdcube/kdcube-runtime

Or pass explicit values:

kdcube-setup --secrets-set OPENAI_API_KEY=... --secrets-set ANTHROPIC_API_KEY=...

You can also override the git HTTPS token this way:

kdcube-setup --secrets-set GIT_HTTP_TOKEN=...

Note: re‑inject will restart kdcube-secrets, chat-ingress, and chat-proc to refresh per‑run tokens (and the web proxy to keep upstreams in sync). Per‑run tokens are generated by the CLI and are not stored in config/.

If you set LLM keys in env files (managed infra / custom setups), those env values still work and take precedence. The secrets sidecar is only used when env keys are missing.

Git HTTPS token (private bundles)

If you choose https-token auth for git bundles, the token is treated as a secret and is not stored in .env.proc. It is injected at runtime (same flow as LLM keys). You will be prompted again on the next run unless you pass it via --secrets-set GIT_HTTP_TOKEN=....

More details: https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/ops/local/local-setup-README.md

Current scope: the wizard is optimized for docker‑compose (all‑in‑one). It creates a workdir (default: ~/.kdcube/kdcube-runtime) and lets you:

• generate config/data/log folders
• choose where all artifacts come from (templates + images)
• start docker compose (optional)

Install source menu (shown only when --path is not provided):

• upstream: clone/pull the repo into the workspace and build images locally
• release-latest: pull prebuilt images for the latest release
• release-tag: pull prebuilt images for a specific version (platform.ref)
• local: use a local repo path you provide (build locally)
• workspace: use the repo already cloned in the workspace (build locally)

Defaults:

• If the workspace repo exists → default is workspace
• If it does not exist → default is upstream

The workspace option only appears when a repo is already cloned there.

Only release-latest and release-tag pull images. All other choices build locally.

Tip: you can select the install source using the ↑/↓ arrow keys and Enter.

Example workdir layout:

~/.kdcube/kdcube-runtime
├─ config/
│  ├─ .env
│  ├─ .env.ingress
│  ├─ .env.proc
│  ├─ .env.metrics
│  ├─ .env.postgres.setup
│  ├─ .env.proxylogin
│  ├─ frontend.config.hardcoded.json
│  ├─ nginx_ui.conf
│  └─ nginx_proxy.conf
├─ data/
│  ├─ postgres/
│  ├─ redis/
│  ├─ kdcube-storage/
│  │  ├─ cb/
│  │  │  └─ tenants/<tenant>/projects/<project>/
│  │  │     ├─ conversation/<user>/<conversation>/<turn>/
│  │  │     └─ executions/<user>/<conversation>/<turn>/<exec_id>/
│  │  ├─ accounting/<tenant>/project/<YYYY.MM.DD>/<service>/<bundle_id>/
│  │  └─ analytics/<tenant>/project/accounting/{daily,weekly,monthly}/
│  ├─ exec-workspace/
│  └─ bundle-storage/
└─ logs/
   ├─ chat-ingress/
   └─ chat-proc/

Advanced usage

Assembly descriptor (platform / frontend / infra)

You can point the CLI to an assembly descriptor YAML (assembly.yaml) that defines platform metadata, frontend settings, auth, infra, and proxy defaults.

The wizard prompts for this as Assembly descriptor path. Wizard flow (descriptor usage):

1. Provide the assembly.yaml path (defaults to workdir/config/assembly.yaml). If you provide another path, the CLI copies it into workdir/config/assembly.yaml and uses the copied file as the source of truth.
2. Choose whether to apply the Frontend section (build or image).

When an assembly descriptor is provided, the wizard writes non‑secret values back into assembly.yaml (tenant/project, auth, infra, paths) and then renders .env* from it. This makes assembly.yaml the source of truth for install‑time config.

Template:

• app/ai-app/deployment/assembly.yaml (copied into the workdir if no path is provided)

References:

• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/sdk/bundle/bundle-ops-README.md
• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/assembly-descriptor-README.md

Bundles descriptor (optional)

You can provide a bundles descriptor (bundles.yaml) and an optional bundles secrets file (bundles.secrets.yaml). This is the preferred way to define bundles and their non‑secret config, with secrets kept separate.

If workdir/config/bundles.yaml already exists, the wizard pre-fills the prompt with that path so it is reused on subsequent runs.

Note: secrets descriptors are not prefilled or cached.

The CLI stages bundles.yaml into the workdir and, when enabled:

• mounts bundles.yaml as /config/bundles.yaml
• sets AGENTIC_BUNDLES_JSON=/config/bundles.yaml
• enables bundle git resolution and env sync on startup

bundles.secrets.yaml is not copied into the workdir. It is read from the path you provide and used only to inject secrets at runtime.

Example (bundles.yaml):

bundles:
  version: "1"
  default_bundle_id: "react@2026-02-10-02-44"
  items:
    - id: "react@2026-02-10-02-44"
      name: "ReAct (example)"
      repo: "[e-mail]:kdcube/kdcube-ai-app.git"
      ref: "v0.3.2"
      subdir: "app/ai-app/services/kdcube-ai-app/kdcube_ai_app/apps/chat/sdk/examples/bundles"
      module: "[e-mail]"
      config:
        embedding:
          provider: "openai"
          model: "text-embedding-3-small"
        role_models:
          solver.react.v2.decision.v2.strong:
            provider: "anthropic"
            model: "claude-sonnet-4-6"

Example (bundles.secrets.yaml):

bundles:
  version: "1"
  items:
    - id: "react@2026-02-10-02-44"
      secrets:
        openai:
          api_key: null

Templates:

• app/ai-app/deployment/bundles.yaml
• app/ai-app/deployment/bundles.secrets.yaml

References:

• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/configuration/bundle-configuration-README.md
• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/sdk/bundle/bundle-dev-README.md

Secrets descriptor (optional)

You can provide a secrets.yaml path in the wizard (or via KDCUBE_SECRETS_DESCRIPTOR_PATH). The CLI does not copy this file into the workdir; it is used only to prefill runtime secrets (OpenAI/Anthropic/Brave/Git HTTP token and delegated Cognito client secret). Values are injected into the secrets sidecar at runtime and not written to .env.proc.

Secrets are keyed by dot‑path (e.g. services.openai.api_key).

Template:

• app/ai-app/deployment/secrets.yaml

Gateway config descriptor (optional)

You can provide a gateway.yaml path in the wizard (or via KDCUBE_GATEWAY_DESCRIPTOR_PATH). The CLI loads it and replaces GATEWAY_CONFIG_JSON in .env.ingress, .env.proc, and .env.metrics, then patches tenant and project from your prompts.

If workdir/config/gateway.yaml already exists, the wizard pre-fills the prompt with that path so it is reused on subsequent runs.

Template:

• app/ai-app/deployment/gateway.yaml

References:

• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/gateway-config-README.md

Custom UI via assembly descriptor (build or image)

If your assembly.yaml includes a frontend section, the CLI will switch to custom‑ui‑managed‑infra compose mode.

Minimal example:

frontend:
  build:
    repo: "[e-mail]:org/private-ui.git"
    ref: "ui-v2026.02.22"
    dockerfile: "ops/docker/Dockerfile_UI"
    src: "ui/chat-web-app"
  image: "registry/private-ui:2026.02.22"  # optional; if set, CLI skips UI build
  frontend_config: "ops/docker/config.cognito.json"

How to activate:

1. Run kdcube-setup
2. Choose Use an assembly descriptor → provide assembly.yaml
3. Confirm Frontend usage when prompted.
4. The CLI selects deployment/docker/custom-ui-managed-infra/docker-compose.yaml.

Full details:

• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/assembly-descriptor-README.md
• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/custom-cicd-README.md
• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/cli-README.md

Manual compose (advanced)

If you want to run compose manually, use the workdir env file:

docker compose --env-file ~/.kdcube/kdcube-runtime/config/.env up -d --build

Note: --env-file is a Docker Compose option (not a CLI flag).

Where data is stored

• Config: workdir/config/ (env files, nginx config, UI config)
• Data: workdir/data/ (postgres/redis storage, bundle storage, exec workspace)
• Logs: workdir/logs/

Infra credentials (Postgres/Redis) are stored in config/.env* for local compose. LLM keys are not stored in files; they live only in the secrets sidecar.

Compose usage (recommended)

1. Run the wizard and choose a workdir (example: /srv/kdcube-local).
2. It will generate:
   • /srv/kdcube-local/config/.env
   • /srv/kdcube-local/config/.env.ingress
   • /srv/kdcube-local/config/.env.proc
   • /srv/kdcube-local/config/.env.metrics
   • /srv/kdcube-local/config/.env.postgres.setup
   • /srv/kdcube-local/config/.env.proxylogin
   • /srv/kdcube-local/config/frontend.config.hardcoded.json
   • /srv/kdcube-local/config/nginx_ui.conf
   • /srv/kdcube-local/config/nginx_proxy.conf
3. Start compose from deployment/docker/all_in_one_kdcube:

docker compose --env-file /srv/kdcube-local/config/.env up -d --build

Open the UI:

• http://localhost:${KDCUBE_UI_PORT}/chatbot/chat (via proxy, omit :${KDCUBE_UI_PORT} if it is 80)

Notes

• The wizard does not overwrite existing config files in your workdir. It only fills placeholders in newly created files.
• Use kdcube-setup --reset to re-enter values without deleting files.
• Config upgrades/migrations will be added later when configs are versioned.
• The wizard auto‑saves after major sections, so if you exit early (Ctrl+C) most values entered so far are preserved in config/ and will appear as defaults next run.

Tip: you can edit workdir/config/nginx_ui.conf and workdir/config/nginx_proxy.conf without rebuilding images (they are mounted into the containers at runtime).

UI config source of truth

The web UI loads its runtime config from /config.json inside the web-ui container. Docker compose mounts the host file defined by PATH_TO_FRONTEND_CONFIG_JSON to:

/usr/share/nginx/html/config.json

Clean / reset

Clean local Docker cache and unused KDCube images:

kdcube-setup --clean

Reset prompts without deleting files:

kdcube-setup --reset

Full reset (delete workdir):

rm -rf ~/.kdcube/kdcube-runtime

If the UI is calling the wrong tenant/project, check:

• PATH_TO_FRONTEND_CONFIG_JSON in the generated .env
• curl http://localhost:<ui_port>/config.json

See the full local setup flow on GitHub: https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/ops/local/local-setup-README.md

More documentation:

• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/assembly-descriptor-README.md
• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/custom-cicd-README.md
• https://github.com/kdcube/kdcube-ai-app/blob/main/app/ai-app/docs/service/cicd/cli-README.md

License

MIT License. See app/ai-app/services/kdcube-ai-app/LICENSE.