AI runtimes and local models on DuDuClaw OS

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DuDuClaw OS ships the AI coding CLIs in the image. A factory-fresh box can be handed an API key and put to work without installing anything: claude, codex, gemini, qwen, kimi, copilot, grok, agent (Cursor), opencode and vibe are all on PATH at first boot, and llama-server is there for running a model locally with no network at all.

The payload is the bundle generated on 2026-09-05 (DUDUCLAW_AI_RUNTIMES_DATE = "20260905" in the recipe). v0.2.0, v0.3.0 and v0.4.0 ship the same bundle, so the CLI versions below did not change when v0.3.0 moved the embedded platform from 1.63.0 to 1.73.1, or when v0.4.0 moved it to 1.73.4. Every bundled CLI is a runtime in platform 1.73.4’s runtime catalog (crates/duduclaw-core/src/runtime_catalog.rs in the platform repo). The catalog also lists Kiro CLI and Google Antigravity, which the image does not bundle (see Not installed).

The platform deprecated its Gemini CLI runtime in v1.67.0 and removed it in v1.75.0. OS v0.4.0 still embeds platform 1.73.4 and bundles gemini; the Gemini API provider is not affected. This image does not include the platform v1.75.0 Codex remote-login fix or its account-removal UI. Those features need a separately built and verified OS image, not a platform release alone.

This guide covers where that payload comes from, how to refresh it, where credentials are stored, and what the image deliberately does not carry. For the product-side view of multi-runtime task delegation, see docs/features/13-multi-runtime.md in the platform repo.

What is installed

Command	Package	Version (2026-09-05 bundle)	Source	Licence
`claude`	`@anthropic-ai/claude-code`	2.1.261	npm	Proprietary (Anthropic Commercial ToS)
`codex`	`@openai/codex`	0.153.4	npm	Apache-2.0
`gemini`	`@google/gemini-cli`	0.58.0	npm	Apache-2.0
`qwen`	`@qwen-code/qwen-code`	0.23.0	npm	Apache-2.0 (by the LICENSE file in the package; its npm metadata publishes no `license` field)
`kimi`	`@moonshot-ai/kimi-code`	0.41.0	npm	MIT
`copilot`	`@github/copilot`	1.0.83	npm	Proprietary (GitHub Copilot terms)
`grok`	Grok Build	1.0.13	`x.ai/cli`	Proprietary (xAI ToS)
`agent` / `cursor-agent`	Cursor agent	build 2026.09.02-c22c1a3	`downloads.cursor.com`	Proprietary (Anysphere ToS)
`opencode`	OpenCode	1.18.29	GitHub releases	MIT
`vibe`	`mistral-vibe`	2.25.0	PyPI	Apache-2.0
`llama-server`, `llama-cli`	llama.cpp	recipe `llama-cpp_0.4.0.bb`	GitHub, built from source	MIT

The versions are read from the bundle’s own MANIFEST.txt (Grok resolves its stable channel and Cursor its current build at generation time, so neither has a pin in the generator).

Everything except llama.cpp lives under /opt/duduclaw/runtimes/, and every /usr/bin name above is a symlink to one dispatcher, /usr/libexec/duduclaw/duduclaw-runtime-exec, which sets NODE_PATH, PATH and PYTHONPATH into that tree before exec’ing the real entry point.

/opt/duduclaw/runtimes/MANIFEST.txt on the running image is the authoritative record: exact version, source URL and sha256 for every component, plus a skipped: line for anything that is not there. /opt/duduclaw/runtimes/LICENSES/ holds each upstream’s own licence text.

Not installed

Google Antigravity (agy). The platform’s catalog lists it (already in the 1.63.0 snapshot v0.2.0 embedded, and the named replacement for the deprecated Gemini CLI since 1.73.1), but the 2026-09-05 bundle does not contain it. The catalog installs it with the vendor script (https://antigravity.google/cli/install.sh), which drops agy in $HOME/.local/bin; on this image that is /data/duduclaw/.local/bin, a path the gateway probes. Installing it on a running v0.4.0 image has not been tested.

Kiro CLI. Not in the image, and there is deliberately no /usr/bin/ kiro-cli stub: duduclaw decides “installed” by whether the binary exists, so a stub would show Kiro as ready in the dashboard and OOBE when it is not. Kiro’s Linux artifact is 1039 MiB unpacked (615 MiB zip: kiro-cli-chat 839 MiB, kiro-cli 114 MiB, kiro-cli-term 87 MiB) — see the budget table at the end of this guide for what that costs. Shipping only the 114 MiB launcher is not an option either: every agent subcommand execs kiro-cli-chat. The dashboard’s runtime page shows the vendor install command from the platform’s runtime catalog (curl -fsSL https://cli.kiro.dev/install | bash, marked manual because Kiro’s FAQ disallows use through third-party automation harnesses); run as root it installs under /data/duduclaw/.local/bin (the gateway’s $HOME), a path the gateway already probes, on the partition systemd-repart grows to the whole disk.

Model weights. No GGUF ships in the image. A curated model is 1–5 GB and belongs on the durable partition, so the dashboard downloads one into /data/duduclaw/models on request.

Vulkan acceleration in llama.cpp. The shipped llama-server is a CPU build against the distro’s x86-64-v3 tune (AVX2/FMA/BMI2). The design calls for -DGGML_VULKAN=ON; the recipe wires it as PACKAGECONFIG[vulkan] but leaves it off, because ggml builds and runs vulkan-shaders-gen as a host tool during compilation and its cross-compilation path reaches for a build-host compiler that bitbake did not provide. Turning it on needs a GGML_VULKAN_SHADERS_GEN_TOOLCHAIN file pointing at OE’s own native toolchain plus a real on-hardware check — llama-server --list-devices, because a Vulkan build that silently falls back to CPU looks identical from the outside. Full reasoning is in the recipe header, meta-duduclaw/recipes-ai/llama-cpp/llama-cpp_0.4.0.bb.

Where credentials live

Every one of these CLIs resolves its credential directory from $HOME:

~/.claude/.credentials.json   ~/.codex/auth.json     ~/.gemini/oauth_creds.json
~/.qwen/                      ~/.kimi/               ~/.copilot/
~/.grok/auth.json             ~/.local/share/opencode/
~/.local/share/cursor-agent/  ~/.config/mistral/

On this image $HOME for the gateway is /data/duduclaw, set by /usr/lib/systemd/system/duduclaw-gateway.service.d/20-home.conf. That matters twice over: root is read-only on the shipping image, so a login writing to /root would fail outright; and root is what an A/B update overwrites wholesale, so anything that did land there would vanish on the next update. /data survives both.

The directory is mode 0700, owner duduclaw (uid 1000, the account the gateway runs as since v0.4.0; before that it was owner root). Ownership is set at boot by systemd-tmpfiles from duduclaw-home.conf (duduclaw-home-owner.service), and duduclaw-firstboot-provision.sh uses the same file. It holds ten vendors’ live API tokens; duduclaw-kiosk, the only other real local account, must not be able to read them.

The one file under that home another account used to read — the Windows RemoteApp registry the kiosk shell turns into Launcher tiles — no longer lives there: duduclaw compat windows-vm app-add writes it to /data/system/windows-vm/apps.toml (owned by duduclaw since v0.4.0, world-readable, created by the tmpfiles file in duduclaw-cli), because the gateway unit and root’s profile set DUDUCLAW_WINDOWS_VM_APPS_DIR=/data/system/windows-vm. Migration 1788717600 moves an existing registry on machines provisioned before this change.

The dispatcher applies the same default for interactive use: a human running claude in a root login shell gets HOME=/data/duduclaw too, rather than scattering tokens into /root. Since v0.4.0 the dispatcher then re-executes the CLI as duduclaw through setpriv --no-new-privs, so hooks and MCP servers the CLI starts never run as root. (SSH is off by default, and no account can log in over it yet.)

Subscription logins vs API keys

API keys are the supported path and the default recommendation. Signing a consumer subscription (Claude Pro/Max, ChatGPT, Google, Copilot, …) into a third-party product is a vendor-terms question: Anthropic’s policy does not let third-party products route requests through Claude subscription credentials and may be enforced without notice, and Google’s Gemini CLI terms and Antigravity terms (section 6) say third-party software accessing the service through a subscription sign-in may get the account suspended or terminated. OpenAI’s and xAI’s positions are unclear; their terms still apply. The OOBE shows that risk and requires an explicit acknowledgement before offering a subscription login.

Local models

llama-server runs under duduclaw-llama-server.service, which is inert until a model exists:

ConditionPathExists=/data/duduclaw/llama-server.env
ConditionPathIsDirectory=/data/duduclaw/models

A failed condition is not a failure — systemd marks the unit condition-failed and moves on. An appliance that only ever uses cloud runtimes never sees a restart loop. Configure it by writing the env file and restarting:

install -d -m 0755 /data/duduclaw/models
cat >/data/duduclaw/llama-server.env <<'EOF'
LLAMA_MODEL=/data/duduclaw/models/Qwen3-4B-Q4_K_M.gguf
LLAMA_CTX=8192
LLAMA_PORT=8080
LLAMA_EXTRA_ARGS=
EOF
systemctl restart duduclaw-llama-server
curl -s http://127.0.0.1:8080/v1/models

The dashboard’s local-models page drives exactly this through the gateway’s inference.local.* RPCs. The server binds 127.0.0.1 only: it is an unauthenticated HTTP API, and the firewall is a second layer, not the first.

Since v0.4.0 the service runs as duduclaw, the same account as the gateway, and no longer loads llama-server.env as an EnvironmentFile= (through v0.3.0 it ran as root and read that file, which the gateway’s account can write). /usr/libexec/duduclaw/llama-server-run reads the file as uid 1000 and accepts only the four LLAMA_* keys shown above: LLAMA_MODEL must be under /data/duduclaw/models, and LLAMA_EXTRA_ARGS may not contain --host or --port.

Fine-tuning

There is no on-device training path, and the image does not pretend otherwise. Every 2026-era post-training stack (LLaMA-Factory, Unsloth, Axolotl) assumes CUDA or ROCm, and both reference machines — Intel N305 and Ryzen 8845HS — have integrated graphics only.

What ships instead is a compat.d declaration, /usr/share/duduclaw/compat.d/llamafactory.toml, that gives duduclaw compat list a row explaining the requirement (discrete NVIDIA GPU + NVIDIA Container Toolkit) and a one-command Docker entry point for anyone who has one. Dataset export and remote/cloud training are the supported path for everyone else, from the dashboard’s fine-tuning page.

Refreshing the bundle

The payload is one pre-generated tarball, meta-duduclaw/recipes-duduclaw/duduclaw-ai-runtimes/files/duduclaw-ai-runtimes-<YYYYMMDD>.tar.zst (606 MB compressed, 1.9 GB unpacked). It is gitignored — reproducible from the generator plus the versions pinned inside it, and 600 MB of vendor binaries does not belong in git history.

To regenerate, from the repo root:

docker run --rm --platform linux/amd64 \
  -v "$PWD/meta-duduclaw/recipes-duduclaw/duduclaw-ai-runtimes:/gen" \
  -v "$PWD/meta-duduclaw/recipes-duduclaw/duduclaw-ai-runtimes/files:/out" \
  -w /gen node:22-bookworm \
  bash /gen/gen-ai-runtimes-bundle.sh 2>&1 | tee gen-ai-runtimes-bundle-$(date +%F).log

Then bump DUDUCLAW_AI_RUNTIMES_DATE in duduclaw-ai-runtimes.bb to the new date stamp.

--platform linux/amd64 is not optional. Half the payload is prebuilt native code — npm resolves optionalDependencies by process.platform, the vendor binaries come from explicit linux-x86_64 URLs, and the Python wheels are pinned to cp314 / manylinux_2_28_x86_64 to match the image’s own Python 3.14.5. Generating on an arm64 host without that flag produces a tree that installs cleanly and then fails at exec time on the appliance.

Version pins live at the top of the generator (V_CLAUDE, V_CODEX, …). Every one of these upstreams ships several releases a week, so a refresh means bumping them deliberately and reading the diff, not silently taking whatever latest resolved to on the day someone rebuilt.

Two things to re-check after a version bump:

Cursor’s launcher. The generator prunes cursor-agent-sea and cursor-agent-worker-sea (273 MB) because the shipped cursor-agent bash launcher execs its bundled node against index.js and never touches either blob. Re-read that launcher after a bump; if it changes, the prune becomes a breakage.
The root slot. See below.

Root slot budget

DUDUCLAW_AB_SLOT_SIZE_MB in duduclaw-image-appliance.bb is 8192 (raised from 7168 for this payload). That number is a ceiling, not a step on a ladder: it equals MAX_ROOT_BYTES in the platform repo’s crates/duduclaw-gateway/src/os_update.rs, which the update transfer enforces against the full declared partition length, not against real content. Root-A and root-B must stay identical (the update writes a slot-A-sized payload into slot B with no resize step), so one constant governs both.

Rough costs, measured on the 2026-09-05 generation run:

Item	Unpacked
`node_modules/` (six npm CLIs, hoisted once)	1.2 GB
`bin/` (grok 166 MB + opencode 185 MB)	335 MB
`cursor-agent/` (after pruning the two SEA blobs)	252 MB
`python/` (mistral-vibe, 97 packages)	159 MB
`nodejs` + `nodejs-npm`	~90 MB
`llama-cpp` (two statically linked binaries)	tens of MB

Anything added to duduclaw-image-runtimes.inc from here needs a measured before/after against that ceiling.