llmgw/docs/data.md

What llmgw stores, and what leaves the machine

Written for the review that happens before a gateway is allowed near anyone's documents. It describes what the software does. Mapping that onto an obligation — GDPR, a sector rule, an internal policy — is work for whoever holds the obligation; this page exists so that work starts from facts rather than from a vendor's summary.

Everything below is checkable in the source, and the places to look are named.

The short version

By default llmgw stores no prompt or completion text anywhere. It stores who made a request, when, how many tokens, and what it cost.

Two features change that, and both are off until somebody turns them on for a named project, and neither can be turned on without also saying for how long.

Nothing on the serving path leaves the cluster — unless the catalog has a model served outside it (external: in a model definition): a request for that model, or one that falls back to it, sends its prompt to that provider. A guardrail policy with no_store refuses both for the scopes it covers.

Guardrails read every prompt and answer they are configured for, in the gateway's own memory, to judge them. What they record — in the usage row and the log — is the action, the policy, the category and how long it took: never the text, the value that matched, or the placeholder map of a redaction. See guardrails.md.

What is stored, exactly

Usage records, one per request: the key, the project, the model asked for, the model that answered, prompt and completion token counts, cost, HTTP status and latency. No message content. The row is in internal/store/store.go as Usage and there is no field for text in it. What the client chose to say about the request is kept with it: OpenAI's user field and its metadata or x-llmgw-tags — short strings, set by the client, for attributing spend. A client that puts a person's name or address there puts it in the usage records; the field is the client's, and so is that choice. Tags are not sent on to the engine.

API keys, as SHA-256 hashes. The key itself is shown once, at creation, and never stored — so a leaked database hands over no working credential, and neither llmgw nor an administrator can recover a key somebody lost. A prefix is kept so a key can be identified in a listing without revealing it.

The store's own secrets, like the one engine keys are derived from, plain unless secrets.master_key seals them — with Vault's transit engine, so that neither the store nor its backups hold them.

People and tenancy: accounts, the organisation/team/project tree, and the limits attached to it. Passwords are hashed.

Sessions for the browser pages, as tokens.

Conversations and agent notes — only for a project that asked. See below.

Kept requests — only for a project that asked (llmgw keys requests): what each request sent the model, after the guardrails' redaction and with any stored conversation in it, and the answer the client got, for the hours it said, all of them or a share. Never under a no_store policy.

Batch files — when a client uses the Batch API: the file of requests it uploaded (prompts) and the files the batch wrote (answers), in the store, until deleted (DELETE /v1/files/{id}) or, for what a batch wrote, until router.batch.retain_days (30) pass. Only keys of the project that made them can read them, and a project under a no_store policy cannot make them.

Reused answers — only for a project that asked (llmgw keys cache): each answer that may be reused, as the client got it, and a hash of the request it answers, in the gateway process's memory — never in the store, never on disk — for the project's TTL or until pushed out by newer ones (router.cache.max_bytes), and gone with a restart. Never under a no_store policy. Forgetting a person does not reach into it; the answers age out by the TTL. The store keeps only the setting, and a usage record's mark that it was answered from the cache.

What is not stored

Prompts. Completions. Anything the model was shown or produced, unless the project turned on conversations, notes or kept requests, or sent a batch. (Reused answers are held in memory only, as above.)

The chat page in llmgw's own web interface keeps the conversation in a JavaScript variable in the browser. Refresh it and the conversation is gone, because it was never anywhere else.

Conversations and notes, when they are turned on

Two features keep text, and both work the same way:

• Conversations (x-llmgw-thread) let a client send only its new turn while llmgw replays the history — including to a different model than the one the conversation started on.
• Agent notes (/v1/memory, and MCP tools) let an agent keep something for itself across conversations.

Both are off for every project until an operator runs llmgw keys threads or llmgw keys memory for that project by name. A request carrying a thread id against a project that has not is refused with an explanation, not quietly served without its history.

Both require a retention window. There is no way to turn either on without saying how many hours, because a store with no expiry is a transcript of everything a team ever typed, kept forever because nobody chose a number. The window is measured from the last turn, so an active conversation does not expire underneath it, and a background sweep removes what has passed it.

Both are scoped to a project, and the scope is part of every lookup rather than a check afterwards. Reading a conversation that belongs to another project returns the same answer as reading one that does not exist, so a leaked id cannot be used to discover what exists elsewhere.

DELETE /v1/threads/{id} removes a conversation and its messages immediately. It is a client endpoint, not an administrative one: the person who said the thing can unsay it without asking somebody else to read it first.

Turning retention off stops new storage, and existing conversations go at the next hourly sweep.

What leaves the machine

Nothing on the serving path. A request arrives, is routed to an engine on your own hardware, and the answer comes back. The router talks to engines, the engines talk to nothing, the agents talk to the control plane. All of it is inside the cluster.

Around the serving path, a cluster set up the ordinary way does reach out. None of it carries a prompt, an answer, a key or a user — it is software and public model files coming in — but a review should know each one:

Installing engines. Each node's agent downloads the llama.cpp build for its hardware from GitHub, checked against the checksum GitHub publishes, and vLLM (SGLang when asked) from PyPI, with uv from GitHub. On every node, when it joins and when the engine version changes.

Releases. llmgw node add, llmgw ctl upgrade and llmgw upgrade fetch llmgw's own release binaries from the project's release server, checked against its signed checksums.

Fetching weights. When a model's source is a URL, the agent downloads it; the mirror fetches what it is asked to, from Hugging Face or another URL, and server init has it fetch the starter model and the guardrails' judge. Point sources at the mirror and the nodes fetch only from it — which is also the faster arrangement, because eight nodes pulling forty gigabytes from the internet is a day's work and from a local mirror it is minutes. A source that is a path on disk is never fetched.

The registry. With a mirror configured, the control plane asks Hugging Face every six hours whether the models served from it have newer files (mirror.update_check), and /admin/mirror searches it. llmgw ctl plan reads a model's architecture from huggingface.co to work out whether it fits your hardware.

There is no telemetry, no licence check, and no call home.

A cluster with no way out

A site with no route to the internet installs from an offline bundle instead, and then nothing at all leaves the cluster (docs/install.md, "Offline sites"). The bundle is made on a machine that has internet — llmgw's release binaries with their signed checksums, the llama.cpp builds with the checksums GitHub publishes, the model files with the checksums Hugging Face publishes, each checked as it is downloaded — carried in, and imported into the mirror. With offline.mirror set in server.yaml, nodes install engines from the mirror and never from GitHub, releases come from the mirror, and the registry is not asked about newer weights.

Verification is not weakened by any of this. A release is held to the project's signing key wherever it is fetched from. An engine file is held to the checksum recorded when the bundle was made, which the control plane keeps and hands each node over the agent's authenticated connection: the mirror serves plain HTTP, and nothing a node runs is taken on the mirror's word. The bundle itself is checked against the sha256 its maker printed, carried to the site apart from the bundle.

What does not come in a bundle: vLLM and SGLang, which are Python environments from PyPI — an offline node serves with llama.cpp unless one is installed on it by hand — and the operating system's own packages (the OpenMP runtime the engine needs, PostgreSQL when chosen), which come from the machines' package sources: at an offline site, the distribution mirror it already has. Searching the registry from /admin/mirror and llmgw ctl plan need the internet and do not work offline; mirror.registry can point the first at an internal copy.

What the software cannot do for you

The admin port serves pprof, and pprof can dump the process heap — where API keys sit in plaintext while they are being checked. Bind it to loopback and reach it over SSH. The default does this; a deployment that changes it should know why.

The store holds everything. With conversations turned on it holds text as well as accounting, so where it lives, who can read it and how it is backed up are decisions with the same weight as the ones above. The PostgreSQL DSN belongs in a file readable only by the user llmgw runs as, which is why the configuration takes @/path/to/file rather than the address itself.

Model weights are yours. llmgw reclaims only files it downloaded, and it identifies those by a name it generated. Anything you put on a node it will not touch, whatever the catalog says.

Where to look

Claim	Where
Usage rows carry no text	internal/store/store.go, type Usage
Keys are stored hashed	internal/store/keys.go
Conversations are off by default	internal/store/threads.go, ThreadRetention
Retention is required	llmgw keys threads --help
Scope is part of the lookup	internal/store/threads.go, Thread
Deletion is a client endpoint	internal/router/threads.go, handleDeleteThread
Weight fetching is the only serving-path egress	internal/agent/weights/fetch.go

Forgetting a person

llmgw users forget <email> --yes (or Forget them… in the console) is for a request to be forgotten:

• their usage — every request record and every sum — is kept, so totals and budgets stay true, but under a random pseudonym that links to nothing;
• their kept requests (keys requests), personal files and batches, and their own limits are deleted;
• their email and id are taken out of the audit log, which keeps what was done;
• then the account goes, as users delete removes it: sessions ended, keys revoked and left without an owner.

Conversations and agent notes belong to projects, not to the people in them, and are not touched: a project forgets them by its retention, and a thread by DELETE /v1/threads/{id}. Backups taken before still hold everything until they age out; that is what their retention is for.

Keeping a project's requests in the cluster

llmgw keys residency --project <path> --in-cluster refuses the project's keys any model served by an outside provider, and never uses one for them as a fallback or a canary: their prompts do not leave the machines the cluster runs on. --anywhere lifts it.