Canonical language

Every memory in zero-memory is stored in canonical English, regardless of the language the fact was originally expressed in. The original phrasing is never lost: a translated memory keeps its pre-translation text and language alongside the canonical content, and a writer can additionally record the exact original wording in the memory's provenance.

Why one canonical language

Memory is written once and read many times — every session briefing and every recall re-reads the store. Keeping content in the language of origin has compounding costs:

• Cross-language duplicates. Deduplication is language-sensitive: the same fact stored once in English and once in another language produces different embeddings and never collapses into one memory. Each duplicate pair becomes work for the hygiene pipeline.
• More expensive reads. Non-English text often tokenizes into significantly more tokens than the equivalent English (roughly 1.5–2.5x for some languages) — a surcharge paid on every briefing and every recall, not once.
• Uneven retrieval. The embedding model is English-specialized — the stronger choice precisely because stored content is canonical English — so text left in another language is matched weakly, and the full-text leg of hybrid search cannot match across languages at all.
• Shared scopes. Teams are often mixed-language; a shared scope has to be readable by everyone in it.

The economics point one way: translation on the write path is nearly free, because extraction already makes an LLM call per chunk — emitting canonical English is just an instruction. Translating on the read path would invert the write-once/read-many economics and add latency to session start. It would also cost the property that makes a search auditable: once the server may rewrite a query, what a caller asked for and what actually ran are two different strings, and every surface that reports a search has to keep both in step.

How it works

Write path

• Watcher and ingest. The extractor emits memory content in English, keeping code, identifiers, file paths, and quoted domain terms verbatim. Any non-English content that still reaches the store goes through the same canonicalization as any other write (below), so the original text is preserved next to the English rendering.
• Explicit remember. The tool contract asks the calling LLM to author content in English. This costs nothing extra — the caller is already an LLM with the full conversation in view, so its own English rendering usually preserves intent better than a post-hoc server translation. Non-English content is still accepted, never rejected: the write is stored immediately, and a background pass canonicalizes the content to English right after it — the original text and its detected language stay on the memory (content_original), and an async worker retries any failed pass. When the memory captures someone's exact words — a preference or decision phrased just so — the caller can also pass verbatim: the operative phrase as originally said, in any language, preserved in provenance next to the canonical English content.

Read path

The read path does not translate at all. A query is embedded and matched exactly as it arrived, so the string a caller sent, the string the search ran on, and the string the activity log shows are the same string — they cannot drift apart, because there is only one of them.

• Agent recall and build_context. The tool contract asks for English queries, and the calling LLM renders the user's intent for free: it holds the whole conversation, so it translates better than the server could, and it costs nothing extra. This is a discipline the contract states, not a mechanism the server enforces — an agent that sends its user's language verbatim will match poorly against an English corpus, and that is visible in the activity log rather than silently patched.
• Human search in the dashboard. A person typing in their own language gets what they typed. Dashboard search is the same recall call with no translation gate: the query runs through the usual hybrid vector + full-text search as written. Searching in a language the corpus is not written in returns little — the remedy is to rephrase, which the searcher can see the need for, rather than to have the server guess at the intent behind the words.

Dashboard display

The dashboard follows the browser's language for its own labels. Independently of that, every memory card can reveal its original-language source: the pre-translation text with its language for a memory that was canonicalized, or the writer-supplied verbatim phrase when one was recorded. The canonical English content stays the primary rendering; the original is one disclosure away, so nothing is flattened irreversibly.

Design notes

• Deterministic server, probabilistic contract. The "author in English" instruction lives in the tool contract, which an LLM follows most but not all of the time. The server therefore keeps a cheap deterministic fallback: language detection on input, canonicalization by a write-triggered background pass. This server-plus-contract pattern recurs across zero-memory — the contract makes the common path free, the server makes the invariant hold.
• Rejected: dual storage (one record per language) — doubles embeddings and storage, and complicates dedup and hygiene for no read-side gain.
• Rejected: translate-on-read — an LLM call on every recall and every briefing, in the critical path of session start.
• Nuance loss is bounded. A translation can flatten domain phrasing, so the original is always preserved and auditable; code, identifiers, and quoted domain terms are kept verbatim.