pi-llamacpp-provider

A pi extension that registers your llama.cpp server as a provider and discovers its models automatically. Add a preset to the server, restart pi, and it is in /model — no models.json entry, no code change.

$ pi --list-models llamacpp
provider  model            context  max-out  thinking  images
llamacpp  fastcontext      80K      16.4K    no        no
llamacpp  gemma4-12b       131.1K   16.4K    yes       yes
llamacpp  gemma4-31b       100K     16.4K    yes       no
llamacpp  ornith-35b       262.1K   16.4K    yes       yes
llamacpp  qwen36a3b-35b    262.1K   16.4K    yes       yes

thinking is read from each model's chat template — the same thing llama.cpp itself uses to decide whether a model reasons — not guessed from its name.

Install

pi install npm:pi-llamacpp-provider

Or straight from git, or pinned, or just for one run:

pi install git:github.com/T0mSIlver/pi-llamacpp-provider
pi install npm:pi-llamacpp-provider@0.2.0   # pinned, skipped by pi update
pi -e npm:pi-llamacpp-provider              # try without installing

If your server is not on localhost:8080, point the extension at it:

export LLAMACPP_BASE_URL=http://gpu-box.lan:8080

Then select a model:

pi --model 'llamacpp/gemma4-31b'
pi --models 'llamacpp/*'    # cycle every llama.cpp model with Ctrl+P
/model                      # or pick one inside the TUI

What it reads from your server

At startup the extension calls GET /v1/models, then probes each model once at GET /props, and maps every entry to a pi model. Two server shapes are supported, because their payloads differ:

Server	What the payload gives us
Router (llama-server with no model, multi-preset)	status.args — the full llama-server argv per preset — plus status.preset and architecture.input_modalities. A meta block appears only for a preset that is currently loaded.
Plain llama-server (single model)	a meta block with n_ctx / n_ctx_train, and everything else from /props.

From that, per model:

pi field	Derived from
contextWindow	What one conversation actually gets: the per-slot n_ctx from /props or meta when the model is loaded (already divided by --parallel), else --ctx-size ÷ --parallel, else meta.n_ctx_train ÷ --parallel, else llama.cpp's own 4096 default. Reported honestly, even when it is small.
maxTokens	Max output tokens for one turn — not the context window. The preset's --predict / -n / --n-predict if it pins one, else LLAMACPP_MAX_OUTPUT_TOKENS (16384 by default), clamped to the context window. llama.cpp's own default is -1, "generate until the context is full", which is not a number pi can budget against.
reasoning	The model's chat template — see below.
input	["text", "image"] when the model reports an image modality (a router says so in architecture, a plain server in /props modalities), else ["text"]. Audio is dropped — pi models declare text and image only.
cost	Zero — local inference is free, and pi's session cost readout should say so.
compat	supportsDeveloperRole: false (llama.cpp chat templates expect a system role) and maxTokensField: "max_tokens" (it rejects max_completion_tokens).

How thinking support is detected

llama.cpp decides whether a model reasons by rendering its chat template and looking at what comes out (common_chat_templates_support_enable_thinking). That template is the only honest answer, and it is published at GET /props. So that is what this extension reads, in this order:

1. LLAMACPP_THINKING_MODELS / LLAMACPP_NON_THINKING_MODELS — comma-separated globs (gemma4-*,qwen3?-*). Your word is final.
2. Flags that make thinking impossible: --reasoning off and --no-jinja. Those two are the only ones llama.cpp consults — enable_thinking = enable_reasoning != 0 && template_supports_thinking — so either settles it. --reasoning-budget 0 is not one of them: it cuts the thinking block short at the sampler, it does not remove the model's ability to think.
3. The chat template, fetched live from GET /props?model=<id>&autoload=0. The autoload=0 matters: without it a router would load the model — spawning a llama-server and filling VRAM — just to answer us. With it, a router answers for what it already has in memory and replies "not loaded" for the rest, which we take as "no answer", never as a failure. The template is matched against the markers llama.cpp itself keys on: enable_thinking, reasoning_content, <think> and its variants, [THINK], <|channel|>analysis (gpt-oss), <|channel>thought (Gemma 4). This is validated against all 59 chat templates shipped in llama.cpp's models/templates, and it is why Gemma 4 is a thinking model here and Qwen3-Coder is not.
4. A cached verdict. A router only answers /props for what it has loaded, so a template we read once is remembered under $XDG_CACHE_HOME/pi-llamacpp-provider/, keyed by the preset's argv — edit the preset and the entry is dropped. A model the router has since unloaded keeps the answer its own template gave.
5. The preset, finally: a --chat-template-kwargs carrying a thinking switch (enable_thinking, preserve_thinking, …), a positive --reasoning-budget, or --reasoning on. A preset only configures a thinking switch for a model that has one.
6. Otherwise: no. No evidence, no claim — the model id is never consulted, because a name is not a capability.

Thinking models get compat.thinkingFormat = "chat-template" with chat_template_kwargs: { enable_thinking, preserve_reasoning, preserve_thinking }, so pi toggles thinking the way llama.cpp expects rather than through OpenAI's reasoning_effort, which llama.cpp does not implement.

Set LLAMACPP_PROBE=0 to skip probing altogether; detection then falls back to steps 1, 2, 5 and 6.

When the server is down

Discovery never blocks pi from starting. On a connection refusal, timeout, non-2xx response, malformed body, or empty model list, the extension registers the provider anyway with a single default model and warns once at session start:

[pi-llamacpp-provider] Model discovery from http://127.0.0.1:8080/v1 failed
(fetch failed). Registered provider "llamacpp" with a single "default" model.
Start the server and run /reload, or set LLAMACPP_BASE_URL.

Start the server and /reload — no restart needed.

Environment overrides

Variable	Default	Effect
LLAMACPP_BASE_URL	http://127.0.0.1:8080	Server address. /v1 is appended if you leave it off.
LLAMACPP_API_KEY	sk-llamacpp-local	Sent as Authorization: Bearer …. llama.cpp ignores it unless started with --api-key, but pi needs a non-empty key to treat the provider as authenticated.
LLAMACPP_TIMEOUT_MS	4000	How long discovery may take before falling back.
LLAMACPP_PROVIDER	llamacpp	Provider name, i.e. the …/model-id prefix. Change it to avoid clashing with a llamacpp provider defined in models.json.
LLAMACPP_MAX_OUTPUT_TOKENS	16384	maxTokens for models whose preset does not pin a --predict.
LLAMACPP_THINKING_MODELS	—	Comma-separated globs forced to reasoning: true, e.g. gemma4-*,my-model.
LLAMACPP_NON_THINKING_MODELS	—	The same, forced to reasoning: false.
LLAMACPP_PROBE	1	Set to 0 to skip the /props probe entirely.

All of them are read on every load, so /reload picks up a change without a reinstall.

Test evidence

Verified against a live llama.cpp router serving 9 presets: all 9 appear in pi --list-models with correct context windows, vision flags and thinking flags; text and thinking models both complete a real prompt end-to-end; killing the server degrades to the default model instead of breaking startup. Full transcripts: docs/test-evidence.md.

Development

npm install
npm run verify        # typecheck + test suite (needs Node ≥ 22.19)
pi install .          # smoke-test the package manifest against a local pi

test/provider.mjs runs the extension against throwaway HTTP servers — a router payload captured from a real llama.cpp router, a plain llama-server payload, and every failure mode (500, timeout, refused connection, malformed JSON, empty list) — asserting the fallback and the once-per-process warning. Thinking detection is checked against real chat templates lifted from llama.cpp's models/templates (test/fixtures/templates/): Gemma 4, Qwen3, gpt-oss and Ministral-Reasoning must all reason; Qwen3-Coder and Llama 3.3 must not.

License

MIT