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Context memory is the heart of OneLamp: a durable store of the facts, events, instructions, and tasks worth remembering across every tool and session. A small tool surface sits on top of it — save_context, save_session, get_context, list_context, resume_session, and forget_context — and every client you connect shares the same store.

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Where it lives

Each user gets their own private store (a per-user Durable Object). Your context is the source of truth; it isn’t pooled with anyone else’s, and it isn’t buried inside a single AI provider’s chat history. Every tool call is scoped to the signed-in user — the user identity is derived server-side from the OAuth token, never from client input.

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Saving — durable facts, not transcripts

save_context is for durable knowledge: a convention, a decision, a preference, a fact that should outlive the current conversation. It is designed to stay clean over time:

Idempotent

Saving the same content twice returns the same id with deduped: true. A repeated save is harmless.

Topic supersession

Pass a stable topic and a newer entry replaces the older one — so a changed preference updates in place instead of piling up.

Typed

Each entry has a memory_type — fact, event, instruction, or task — that drives retrieval, plus a display kind, tags, and a source_surface.

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Memory types — and ephemeral tasks

Every entry carries a memory_type: a fact (stable knowledge), an event (something that happened), an instruction (a procedure or runbook), or a task (what you’re working on right now). Tasks are ephemeral — they expire after a short TTL and are swept automatically, so “what I’m doing now” never calcifies into stale knowledge. While a task is live it’s embedded and retrievable like anything else; get_context can also filter to specific types (e.g. just your instructions). You rarely set this by hand: save_session distills a transcript and classifies each item’s type for you.

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Retrieving — a ranked pack, never an answer

get_context is retrieval, not generation. It returns a ranked pack of context chunks — your own source material — for the tool to reason over. It never synthesizes an answer on the query path, so what comes back is always grounded in what you actually saved. Ranking fuses four signals with weighted Reciprocal Rank Fusion:
  • Semantic — embedding similarity to your query. At save time OneLamp also generates a few likely questions and folds them into what’s embedded, so a fact stored declaratively (“prefers dark mode”) still matches an interrogative query (“what theme?”).
  • Keyword — full-text search with stemming, so “deploying” matches a saved “deploy”.
  • Fact-key — when your query names a known topic, that exact entry is a high-precision direct hit.
  • Recency — newer entries break ties, so fresh context surfaces without burying older but relevant facts.
They’re combined so results are relevant even when wording differs. An empty store returns an empty pack — never an error — so a brand-new user’s first call just proceeds cleanly. Each chunk in the pack carries its kind, memory_type, source_surface, timestamp, and a relevance score, so the tool can weigh and cite what it uses.

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Exporting — your data, always portable

From your Account in the OneLamp web app you can export your entire store as portable JSON. There’s no lock-in and no export queue: export from the dashboard and you get everything back, any time.

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In practice

You (in Claude Code):  "Remember we use pnpm, never npm."
  → save_context(content: "Use pnpm, never npm", kind: "preference",
                 topic: "package-manager")

You (later, in Codex): "Add the dependency."
  → get_context(query: "package manager")
  → pack includes "Use pnpm, never npm" → Codex runs pnpm add
The fact saved from one tool is retrieved by another. That’s the whole point: one context layer, every tool — teach one, they all learn.
For exact parameters and return shapes, see the context tools reference.