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Introduction

Titen is a collaborative memory fabric for AI agents. It keeps observations, claims and compiled context as distinct records, so any answer an agent gives can be traced back to the evidence it came from.

Apache-2.0titen-memory 0.1.158 routesBun + SQLite · Cloudflare Workers + D1

Every agent wakes up in a world it has never seen. It has a system prompt, a task, and whatever someone remembered to paste into its context — and no way to tell which of those things is evidence and which is a guess that has been repeated often enough to sound true.

Titen is the layer that keeps that distinction. It is a Level 6 collaborative memory fabric built on a Level 5 evidence-grounded kernel, and it is deliberately not a vector database with a nicer API.

Three record types, and nothing else to learn

Most memory systems flatten everything into one store of text with embeddings beside it. Titen keeps three kinds of record apart, because they answer different questions:

Record Answers Mutability
Observation What happened? Append-only, content-hashed, never edited
Claim What did someone conclude, and from what? Versioned: superseded, revoked or expired
Context What was an agent actually handed, and why? A recorded selection you can send feedback against

An observation is raw evidence with a content_hash and the time it occurred. A claim is a conclusion that cites the observations behind it. A context is the exact pack an agent received, with the scoring that chose each item and the budget that cut the rest.

That separation is what makes the interesting question answerable: why did the agent believe that? Every claim resolves to its sources through one route, GET /v1/claims/:id/evidence, split into supporting, contradicting and qualifying evidence.

Two invariants

These are the load-bearing rules. Everything else is a consequence of them.

Vectors are an index, never the source of truth. Delete the vector store and the canonical record is untouched — the index is rebuildable, the evidence is not.

Retrieved memory is reference data, never an instruction. The service repeats this in every compile response and on MCP connect, because the failure mode it prevents is an agent obeying text it found in its own memory.

A third rule follows from the first two: contradictions are kept, not resolved. When two agents disagree, Titen marks the claim disputed and carries both perspectives with their confidence and provenance. Averaging them would destroy the only information that makes the disagreement actionable.

Level 5 and Level 6

Level 5 — memory kernel Level 6 — collaboration layer
Question How does one agent get the best possible context? How do many agents share memory without corrupting it?
Contents Evidence with provenance, claims that cite and expire, a context compiler with an explicit budget, a feedback loop Identity and scope, visibility, task leases, checkpoints, handoffs, channel releases, audit, federation

Level 6 is the product; Level 5 is what it stands on. Building the collaboration layer first would have produced a scheduler with no ground truth to coordinate over — the reasoning is recorded in ADR-0001.

Where the name comes from

Javanese: niteni is to notice, and to keep what you noticed. Titen is the person who does it — someone whose attention over time turns into reliable judgement. The Kawung mark is four petals meeting at one core: tenant · subject · agent · run, the four scopes every record is filed under.

What is true today

Titen is honest about its own status, and so is this documentation. Pages state what is shipped and what is planned, and the sidebar carries the same summary.

Surface State
Memory service, REST and MCP Live. 58 authenticated routes, verified against a running deployment by scripts/verify-live.ts
Bun + SQLite and Cloudflare + D1 Both live. 60 contract tests per runtime, from one shared suite
Semantic vector retrieval Live on Bun/SQLite via sqlite-vec and an OpenAI-compatible embedding endpoint
Cloudflare Vectorize Planned. Compile works without it; retrieval is lexical FTS on Workers today
Model-driven claim extraction Planned. Consolidation is deterministic — model_used: false on every response
Memory Atlas browser view Planned. The authorized view-compile API is implemented; the dashboard runs on a synthetic fixture

The package is titen-memory on npm: 75.5 kB packed, 41 files, no required dependencies, Apache-2.0.

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