mcpctl/docs/virtual-llms.md

# Virtual LLMs

A **virtual LLM** is an `Llm` row in mcpd that's *registered by an mcplocal
client* rather than created by hand with `mcpctl create llm`. Inference for
a virtual LLM is relayed back through the publishing mcplocal's SSE control
channel — **mcpd never needs to know the local URL or hold its API key**.

When the publishing mcplocal goes away (or the user shuts down their
laptop) the row decays: `active → inactive` after 90 s without a
heartbeat, then deleted after 4 h of inactivity. A reconnecting mcplocal
adopts the same row using a sticky `providerSessionId` it persisted at
first publish.

## When to use this

- **Local model on a developer laptop** that you want everyone on the
  team to be able to chat with via `mcpctl chat-llm <name>`. The model
  doesn't need to be reachable from mcpd's k8s pods — only the user's
  mcplocal does (which is already the case because mcplocal pulls
  projects from mcpd over HTTPS).
- **Hibernating models** that wake on demand (v2 — see "Roadmap").
- **Pool of identical models** distributed across user laptops, eligible
  for load balancing (v4).

If your model is reachable from mcpd's k8s pods over LAN/VPN, you don't
need a virtual LLM — just `mcpctl create llm <name> --type openai --url …`
and you're done.

## Publishing a local provider

mcplocal's local config (`~/.mcpctl/config.json`) gains a `publish: true`
opt-in per provider:

```json
{
  "llm": {
    "providers": [
      {
        "name": "vllm-local",
        "type": "openai",
        "model": "Qwen/Qwen2.5-7B-Instruct-AWQ",
        "url": "http://127.0.0.1:8000/v1",
        "tier": "fast",
        "publish": true
      }
    ]
  }
}
```

Restart mcplocal:

```fish
systemctl --user restart mcplocal
```

The registrar:
1. Reads `~/.mcpctl/credentials` for `mcpdUrl` + bearer token.
2. POSTs to `/api/v1/llms/_provider-register` with the publishable set.
3. Persists the returned `providerSessionId` to
   `~/.mcpctl/provider-session` so the next restart adopts the same
   mcpd row.
4. Opens the SSE channel at `/api/v1/llms/_provider-stream`.
5. Heartbeats every 30 s.
6. Listens for `event: task` frames and runs them against the local
   `LlmProvider`.

If `~/.mcpctl/credentials` doesn't exist (e.g. you haven't run
`mcpctl auth login`), the registrar logs a warning and skips —
publishing is a best-effort feature, not a boot blocker.

## Verifying

```fish
$ mcpctl get llm
NAME             KIND     STATUS    TYPE     MODEL                          TIER  KEY                                 ID
qwen3-thinking   public   active    openai   qwen3-thinking                 fast  secret://litellm-key/API_KEY        cmofx8y7u…
vllm-local       virtual  active    openai   Qwen/Qwen2.5-7B-Instruct-AWQ   fast  -                                   cmoxz12ab…

$ mcpctl chat-llm vllm-local
─────────────────────────────────────────────────────────
LLM: vllm-local  openai → Qwen/Qwen2.5-7B-Instruct-AWQ
Kind: virtual    Status: active
─────────────────────────────────────────────────────────
> hello?
Hi! …
```

You can also chat with public LLMs the same way:

```fish
$ mcpctl chat-llm qwen3-thinking
```

The CLI doesn't care about `kind` — mcpd's `/api/v1/llms/<name>/infer`
route branches on it server-side.

## Lifecycle in detail

| State          | What it means                                                                   |
|----------------|---------------------------------------------------------------------------------|
| `active`       | Heartbeat received within the last 90 s and the SSE channel is open.            |
| `inactive`     | Either the SSE closed or the heartbeat watchdog tripped. Inference returns 503. |
| `hibernating`  | Publisher is online but the backend is asleep; the next inference triggers a `wake` task before relaying. |

Two timers on mcpd run the GC sweep:

- **90 s** without a heartbeat → flip `active` → `inactive`.
- **4 h** in `inactive` → delete the row entirely.

A reconnecting mcplocal with the same `providerSessionId` revives every
inactive row it owns; it only orphans rows that fell past the 4-h cutoff.

## Inference relay

When mcpd receives `POST /api/v1/llms/<virtual>/infer`:

1. Look up the row, see `kind=virtual` + `status=active`.
2. Find the open SSE session for that `providerSessionId`. Missing
   session → 503.
3. Push a `{ kind: "infer", taskId, llmName, request, streaming }`
   task frame onto the SSE.
4. mcplocal pulls, calls `LlmProvider.complete(...)`, and POSTs the
   result back to `/api/v1/llms/_provider-task/<taskId>/result`:
   - non-streaming: `{ status: 200, body: <chat.completion> }`
   - streaming: per-chunk `{ chunk: { data, done? } }`
   - failure: `{ error: "..." }`
5. mcpd forwards the result/chunks out to the original caller.

**v1 caveat — streaming granularity**: `LlmProvider.complete()` returns
a finalized `CompletionResult`, not a token stream. Streaming requests
therefore arrive at the caller as a single delta + `[DONE]`. Real
per-token streaming is a v2 concern.

## Wake-on-demand (v2)

A provider whose backend hibernates (a vLLM instance that suspends
when idle, an Ollama daemon that exits when nothing's connected, …)
can declare a **wake recipe** in mcplocal config. When that provider's
`isAvailable()` returns false at registrar startup, the row is
published as `status=hibernating`. The next inference request that
hits the row triggers the recipe and waits for the backend to come up
before relaying.

Two recipe types:

```jsonc
// HTTP — POST to a "wake controller" that starts the backend out of band.
{
  "name": "vllm-local",
  "type": "openai",
  "model": "...",
  "publish": true,
  "wake": {
    "type": "http",
    "url": "http://10.0.0.50:9090/wake/vllm",
    "method": "POST",
    "headers": { "Authorization": "Bearer ..." },
    "maxWaitSeconds": 60
  }
}
```

```jsonc
// command — spawn a local process (systemd, wakeonlan, custom script).
{
  "name": "vllm-local",
  "type": "openai",
  "model": "...",
  "publish": true,
  "wake": {
    "type": "command",
    "command": "/usr/local/bin/start-vllm",
    "args": ["--profile", "qwen3"],
    "maxWaitSeconds": 120
  }
}
```

How a request flows when the row is `hibernating`:

```
client → mcpd POST /api/v1/llms/<name>/infer
         mcpd: status === hibernating → push wake task on SSE
         mcplocal: receive wake task → run recipe → poll isAvailable()
                   → heartbeat each tick → POST { ok: true } back
         mcpd: flip row → active, push the original infer task
         mcplocal: run inference → POST result back
mcpd → client (forwards the inference result)
```

Concurrent infers for the same hibernating Llm share a single wake
task — only the first request triggers the recipe; later ones await
the same in-flight wake promise. After the wake settles, every queued
infer dispatches in order.

If the recipe fails (HTTP non-2xx, command exits non-zero, or the
provider doesn't come up within `maxWaitSeconds`), every queued infer
is rejected with a clear error and the row stays `hibernating` —
the next request gets a fresh wake attempt.

## Roadmap (later stages)

- **v3 — Virtual agents**: mcplocal publishes its local agent configs
  (model + system prompt + sampling defaults) into mcpd's `Agent` table.
- **v4 — LB pool by model**: agents can target a model name instead of
  a specific Llm; mcpd picks the healthiest pool member per request.
- **v5 — Task queue**: persisted requests for hibernating/saturated
  pools. Workers pull tasks of their model when they come online.

## API surface (v1)

```
POST  /api/v1/llms/_provider-register      → returns { providerSessionId, llms[] }
GET   /api/v1/llms/_provider-stream        → SSE channel; require x-mcpctl-provider-session header
POST  /api/v1/llms/_provider-heartbeat     → { providerSessionId }
POST  /api/v1/llms/_provider-task/:id/result
                                           → one of:
                                             { error: "msg" }
                                             { chunk: { data, done? } }
                                             { status, body }

GET   /api/v1/llms                         → list (now includes kind, status, lastHeartbeatAt, inactiveSince)
POST  /api/v1/llms/<virtual>/infer         → routes through the SSE relay
DELETE /api/v1/llms/<virtual>              → delete unconditionally (also runs GC's job)
```

RBAC piggybacks on `view/edit/create:llms` — no new resource. Publishing
a virtual LLM is morally a `create:llms` operation.

## See also

- [agents.md](./agents.md) — what an Agent is and how it pins to an LLM.
- [chat.md](./chat.md) — `mcpctl chat <agent>` (full agent flow).
- The CLI: `mcpctl chat-llm <name>` (this doc) is the stateless
  counterpart for raw LLM chat.