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top of Graphorin.\n\n`@graphorin/memory` ships the `createMemory()` facade, the six tier\nsub-modules (working, session, episodic, semantic, procedural, shared),\nthe eleven memory tools that the agent runtime registers with\n`@graphorin/tools`, the built-in Reciprocal Rank Fusion reranker (k=60),\nthe embedder migration runner, and the interface stubs picked up later\nby the conflict-resolution pipeline, the consolidator, and the context\nengine.\n\nThe package depends on:\n\n- `@graphorin/core` - typed contracts (`MemoryStore`, `EmbedderProvider`,\n  `Tool`, `Tracer`, `Sensitivity`, …).\n- `@graphorin/observability` - the `Tracer` used to emit one `AISpan`\n  per memory operation.\n- `@graphorin/security` - the memory-modification guard (`MemoryGuardTier`)\n  every memory tool is wired against.\n- `@graphorin/tools` - the `tool({...})` builder used to declare the\n  eleven memory tools.\n- `zod` (peer) - schema typing for the memory tools.\n\nStorage is provided by any `MemoryStore` implementation; the default\nproduction adapter is `@graphorin/store-sqlite` (built on\n`better-sqlite3@^12.9.0` + `sqlite-vec@~0.1.9`). Embeddings come from\nany `EmbedderProvider`; the default is\n`@graphorin/embedder-transformersjs` (multilingual e5 base, 768-dim).\n\n## Highlights\n\n- **`createMemory({ store, embedder, ...})` facade.** Wires the six\n  tiers + the tools array + the `compile()` / `metadata()` /\n  `consolidator` interface stubs in one call.\n- **Six tier sub-modules.** Every tier exposes a typed CRUD surface and\n  emits one `AISpan` per operation. Writes are append-only (soft-delete\n  via `deletedAt` / `validTo`); supersede chains are first-class.\n- **Hybrid search.** Semantic memory composes vector + FTS5 results\n  through the built-in `RRFReranker` (k=60 default). The\n  `setReranker(custom)` hook accepts any `ReRanker` implementation\n  (cross-encoder, LLM judge, custom).\n- **Eleven memory tools.** `block_append`, `block_replace`,\n  `block_rethink`, `fact_remember`, `fact_search`, `fact_supersede`,\n  `fact_forget`, `recall_episodes`, `conversation_search`,\n  `fact_history`, `fact_validate` - every tool is a typed `Tool` with\n  `inputSchema` + `outputSchema`, the appropriate `memoryGuardTier`, and\n  the right `sideEffectClass`. `fact_search` accepts an `asOf` instant\n  for point-in-time reads; `fact_history` returns a fact's bi-temporal\n  supersede chain; `fact_validate` promotes a quarantined fact to active.\n- **Provenance + quarantine (memory-safety gate).** Every fact carries\n  a `provenance` tag (`user` / `tool` / `extraction` / `reflection` /\n  `imported`) and a retrieval-trust `status`. *Derived* writes\n  (consolidator extraction, future reflection) and candidates that trip\n  the offline injection heuristics (`ignore previous instructions`,\n  role-markup smuggling, secrecy / exfiltration directives) land\n  `status: 'quarantined'` and are **excluded from default recall** until\n  a human promotes them via `fact_validate`. Quarantine is a retrieval\n  gate, never a delete - quarantined rows stay fully auditable, and the\n  `includeQuarantined` search option surfaces them for the validation\n  UI. This is the precondition for safely shipping synthesized memory\n  (reflection / reconciliation / induction) against memory-poisoning\n  (MINJA, MemoryGraft).\n- **Multi-stage conflict resolution pipeline.** Every\n  `SemanticMemory.remember(...)` call now flows through a five-stage\n  pipeline (exact dedup → embedding three-zone → heuristic regex →\n  subject/predicate → defer-to-deep). Decisions are recorded into\n  `fact_conflicts`; ambiguous cases land in the `conflict_check_pending`\n  queue for the consolidator's deep phase. The English locale pack\n  ships by default; additional locales plug in via\n  `defineLocalePack({...})`. Operators can disable the pipeline with\n  `createMemory({ conflictPipeline: { mode: 'off' } })` (a one-shot\n  WARN surfaces so the regression risk is visible).\n- **Neighbour-aware write reconciliation** (consolidator standard phase).\n  Extracted facts are reconciled against their nearest neighbours via an\n  extract→reconcile loop: a cheap pre-filter (the pipeline's exact-dedup +\n  embedding zones over `SemanticMemory.neighbors(...)`) resolves clear\n  duplicates / independents with no LLM call, and only the ambiguous mid-zone\n  spends one reconcile pass choosing add / update / noop / conflict. Updates\n  and conflicts route through the bi-temporal supersede (never a delete);\n  every decision is audited in `fact_conflicts` and new facts inherit the\n  `extraction` provenance gate.\n- **Auto-importance + episode formation** (consolidator standard phase).\n  Each processed slice is summarized into one episode via a single budgeted\n  LLM call that also rates its importance `1-10` (normalized to `[0, 1]`),\n  so episodic triple-signal retrieval (recency × relevance × importance) finally\n  runs on all three signals. Auto-formed episodes carry `provenance: 'extraction'`\n  + `status: 'quarantined'` - surfaced for review via\n  `episodic.search(..., { includeQuarantined: true })`. Importance is a *soft*\n  signal (never a retention gate). Controlled by the per-tier `formEpisodes` /\n  `importanceScoring` flags (on at `standard` / `full`); budget-aware\n  (an exhausted budget degrades to fact-only).\n- **Reflection + insight synthesis** (consolidator deep phase). When the\n  accumulated importance of recent episodes crosses `importanceThreshold`, the\n  deep phase asks the model for the few most salient questions, retrieves\n  evidence for each, and synthesizes a higher-order **insight** (Generative\n  Agents). Insights are a distinct memory type (`memory.insights`:\n  `search` / `list`) that land `provenance: 'reflection'` +\n  `status: 'quarantined'`, carry **mandatory citations set from the retrieved\n  evidence** (never hallucinated), and are **rank-capped below the facts they\n  cite** (`capInsightsBelowFacts`). An ExpeL salience counter (start `2`,\n  pruned at `0`) manages the set. Off by default except at the `full` tier;\n  budget-aware and a no-op without an episodic tier + insight-capable store.\n- **Contextual retrieval** (write path). Before a fact is embedded + FTS-indexed\n  the framework prepends a short **situating context** (entities / timeframe /\n  topics, Anthropic's Contextual Retrieval) so a terse fact stays findable; the\n  canonical `text` is preserved. The offline default `'late-chunk'`\n  (`createMemory({ contextualRetrieval })`) derives the context deterministically\n  from the fact's own structured signals with **no extra LLM call** - a no-op for\n  plain-text writes. An opt-in, **consolidator-only** `'llm'` mode\n  (`consolidator: { contextualRetrieval: 'llm' }`) spends one budgeted cheap-model\n  call per write to author the prefix, degrading to late-chunk on any failure.\n- **Recall explainability** (\"why was this recalled?\"). `explainRecall(hits, {\n  query, rerankerId })` decomposes a `search(...)` result into the per-memory\n  signals that drove its score (`bm25` / `vector` / fused `rrf` / `decay`), in\n  final-rank order; `formatRecallExplanation(...)` renders it. `search` also\n  records the decay multiplier as a `decay` signal and attaches the breakdown\n  (ids + scores + signals, never the query text) to the `memory.search.semantic`\n  span. Operators inspect the rest via `graphorin memory inspect <factId>`\n  (supersede chain / quarantine / conflicts / citing insights) and\n  `graphorin memory activity` (consolidator / reflection activity).\n- **Multi-signal forgetting** (cost / staleness control, *not* an accuracy\n  lever). The light phase scores each fact with `salience(...)` - the Ebbinghaus\n  `retention` curve (recency + access frequency, including an opt-in\n  `accessReinforcement` term fed by per-fact recall counters) combined with the\n  `importance` hint and a security-risk negative term (a quarantined fact is\n  evicted first, a foreign-provenance one slightly sooner). With neutral importance + an active,\n  first-party fact, `salience === retention`. Setting `decayCapacity`\n  (`createMemory({ consolidator: { decayCapacity } })`, default unbounded)\n  bounds storage: the lowest-salience facts are **soft-archived** (recoverable -\n  `archived = 1`, never deleted) until the window fits.\n- **Query transformation** (read path; multi-query / RAG-Fusion + opt-in HyDE).\n  `search(scope, query, { multiQuery: N })` fans the query into up to `N - 1`\n  reworded variants via one cheap LLM call, retrieves each, and fuses every list\n  through the same RRF reranker - recovering memories whose stored wording\n  differs from the question. `{ hyde: true }` additionally embeds a hypothetical\n  answer (arXiv:2212.10496) and fuses its neighbours. Both are **opt-in**, wired\n  by `createMemory({ queryTransform: { provider } })`; with no transformer (the\n  default) search stays **offline + single-shot** and these options are silent\n  no-ops. Reserve it for retrieval-heavy recall - it adds provider latency.\n- **Weighted fusion + reranker guidance** (search quality, opt-in). RRF is a\n  good zero-tuning default, but once you have labels (the `@graphorin/evals`\n  harness) a calibrated weight can do better. `search(scope, query, { fusion: {\n  strategy: 'weighted', weights: { vector: 3, fts: 1 } } })` fuses through\n  `WeightedRRFReranker`, scaling each candidate list's reciprocal-rank\n  contribution by its retriever *kind* (FTS vs vector; the HyDE list counts as\n  vector). **RRF stays the default** - omit `fusion` (or pass `{ strategy:\n  'rrf' }`) and behaviour is unchanged; equal weights reproduce RRF exactly. The\n  pure `fuseWeighted(lists, weights, k)` is exported for offline weight tuning\n  against the eval harness. For a further precision lift, install a cross-encoder\n  reranker (`@graphorin/reranker-transformersjs` / `-llm`) via\n  `createMemory({ reranker })` or `setReranker(...)` and raise `candidateTopK` -\n  reranking pays off **only on an already-high-recall candidate set**, so fix\n  recall first (contextual retrieval / multi-query) before reaching for it.\n- **Relation graph + one-hop expansion** (new capability, opt-in). Activates the\n  dormant `(subject, predicate, object)` substrate: facts now carry s/p/o, and an\n  **entity resolver** folds the subject/object strings into canonical entities\n  (\"Anna\", \"Anna S.\", \"my sister\" → one entity) via lexical + embedding dedup -\n  with **auditable, reversible merges** (an append-only ledger; `merged_into` is\n  single-level). At read time `search(scope, query, { expandHops: 1 })` seeds on\n  the candidates and fuses in facts sharing an entity through a recursive CTE,\n  answering \"what did the person I met in Tbilisi recommend?\" without leaving\n  SQLite. Enable linking with `createMemory({ graph: { entityResolution: true } })`\n  (offline; lexical + embedding). The ambiguous-similarity band mints a *new*\n  entity by default - it never auto-merges on weak evidence; opt into LLM\n  adjudication (`graph: { llmAdjudication: true, provider }`) to resolve that band.\n  On top of hop expansion: `graphScoring: 'ppr'` weights neighbours by a\n  PPR-lite random-walk score instead of flat inclusion, `entityMatch: true`\n  adds exact entity-name hits as a candidate list, and `expandHops: 2` is\n  available for offline / analysis flows (expensive under dense hubs). Omit it\n  all and the default path is unchanged + fully offline (`expandHops`\n  defaults to `0`).\n- **Agentic / iterative retrieval** (search quality, gated + opt-in). A\n  CRAG/Self-RAG-style grade-then-reformulate loop for hard multi-hop / temporal\n  questions one pass can't answer. A cheap **local difficulty gate**\n  (`assessQueryDifficulty`) keeps simple lookups single-shot; only a query judged\n  hard *and* with a grader configured (`createMemory({ iterativeRetrieval: {\n  provider } })`) is graded for sufficiency and, when weak, reformulated +\n  retrieved again - **widening to one-hop graph expansion** on each reformulation -\n  up to a hard-capped `maxIterations` (≤ 5), then **abstaining**\n  (`result.abstained === true`) rather than confabulating. Exposed as\n  `SemanticMemory.searchIterative(...)` and the gated **`deep_recall`** tool (a\n  twelfth tool, registered only when `iterativeRetrieval` is configured). Omit it\n  and `searchIterative` degrades to one difficulty-gated pass with **no provider\n  call**; the tool surface stays at the canonical eleven.\n- **Procedural memory extraction** (new capability, gated + opt-in). AWM-style\n  workflow induction: from a **successful** agent trajectory,\n  `ProceduralMemory.induce(scope, trajectory)` distils a reusable procedure -\n  goal + value-abstracted steps (`\"search for {product}\"`) + the variable names\n  + Voyager-style success criteria for self-verification on reuse\n  (`checkSuccessCriteria`). Induction fires on **success only** (a failed run\n  never calls the inducer), and the result lands **quarantined** +\n  `provenance: 'induction'` - procedures drive *actions*, so this is the\n  highest-poisoning-risk write and is excluded from `activate()` until validated.\n  `trajectoryFromRunState(runState)` distils the agent's already-emitted run\n  state, so capture needs **no agent change**. Wire it with\n  `createMemory({ procedureInduction: { provider } })`; omit it and `induce(...)`\n  throws `ProcedureInductionNotConfiguredError` - the procedural tier stays pure\n  offline CRUD with no provider call.\n- **Memory evolution knobs** (all opt-in). `runbookSearch: true` registers the\n  gated `runbook_search` tool over the procedural tier; `consolidator:\n  { learnedContext: true, learnedContextMaxChars }` maintains a compact\n  learned-context digest block from consolidation output; facts, episodes, and\n  rules carry an optional `owner` principal so multi-principal deployments can\n  filter reads per owner; and per-fact access counters feed the salience\n  `accessReinforcement` term.\n- **Per-record `embedder_id` enforced.** Every embedded write registers\n  the embedder via the storage layer's `EmbeddingMetaRepository` and\n  records the canonical id (`'<provider>:<model>@<dim>'`); attempts to\n  silently swap embedders fail-fast under the default `lock-on-first`\n  policy.\n- **Embedder migration runner** (`migrateEmbedder`). Three strategies:\n  `lock-on-first` (default; refuses silent swap), `multi-active` (read\n  union, write to active), `auto-migrate` (re-embeds existing rows in\n  resumable batches; checkpointed via the `migration_state` table; can\n  be aborted with `AbortSignal`).\n- **Multi-agent attribution.** Every session message carries an\n  optional `agentId`; `Session.list({ agentId })` filters per-agent;\n  `Session.attributedFor(...)` exposes the registry; episodes can be\n  recorded `'merged'` / `'per-agent'` (the rendering mode is consumed\n  by the consolidator in Phase 10c).\n- **Default-on bi-temporal storage.** Fact writes set `validFrom = now`\n  and leave `validTo = null`; a supersede closes the old fact's `validTo`\n  (so `as_of` queries reflect the change) and keeps the chain intact for\n  later replay. The advanced `as_of_date` query API is opt-in\n  (post-MVP).\n- **`memory.compile(scope)` + `memory.metadata(scope)`.** The\n  interface used by the context engine (Phase 10d) to assemble the\n  six-layer memory-aware system prompt; `@graphorin/memory@0.6.0`\n  ships the deterministic minimum-viable rendering so the agent\n  runtime in Phase 12 can already exercise the surface.\n- **Memory-modification guard wiring.** Every tool's\n  `memoryGuardTier` is annotated per `DEC-153`; the executor in\n  `@graphorin/tools` honours the tier through the `createGuard(...)`\n  helper from `@graphorin/security/guard`.\n\n## Stable sub-paths\n\n```ts\nimport { createMemory } from '@graphorin/memory';\nimport { RRFReranker } from '@graphorin/memory/search';\nimport { migrateEmbedder } from '@graphorin/memory/migration';\nimport {\n  blockAppendTool,\n  factRememberTool,\n} from '@graphorin/memory/tools';\nimport {\n  EmbedderRegistrationError,\n  MemoryToolDeniedError,\n} from '@graphorin/memory/errors';\nimport {\n  createConflictPipeline,\n  defineLocalePack,\n  enLocalePack,\n  DEFAULT_CONFLICT_THRESHOLDS,\n} from '@graphorin/memory/conflict';\n```\n\n## Quick start\n\n```ts\nimport { createSqliteStore } from '@graphorin/store-sqlite';\nimport { createTransformersJsEmbedder } from '@graphorin/embedder-transformersjs';\nimport { createMemory } from '@graphorin/memory';\n\nconst sqlite = await createSqliteStore({ path: './assistant.db' });\nawait sqlite.init();\n\nconst memory = createMemory({\n  store: sqlite.memory,\n  embeddings: sqlite.embeddings,\n  embedder: createTransformersJsEmbedder(),\n});\n\nawait memory.semantic.remember(\n  { userId: 'alex' },\n  { text: 'Loves mountain hiking and fresh espresso.' },\n);\n\nconst hits = await memory.semantic.search(\n  { userId: 'alex' },\n  'mountain trip ideas',\n);\nconsole.log(hits[0]?.record.text);\n```\n\n## Tier overview\n\n| Tier        | Surface (read)                               | Surface (write)                                             |\n|-------------|-----------------------------------------------|-------------------------------------------------------------|\n| working     | `list`, `read`, `compile`                     | `define`, `write`, `append`, `replace`, `rethink`, `forget`, `attach`, `detach` |\n| session     | `list`, `search`, `attributedFor`             | `push`, `flushImportant`, `compact`                         |\n| episodic    | `recent`, `search`                            | `record`                                                    |\n| semantic    | `search`                                      | `remember`, `supersede`, `forget`                           |\n| procedural  | `list`, `activate`                            | `define`, `remove`                                          |\n| shared      | `listFor`                                     | `attach`, `detach`                                          |\n\n## Memory tools (registered with `@graphorin/tools`)\n\n`memory.tools` is a typed `Tool[]`. Pass it through to\n`createToolRegistry({ ... })` (Phase 12) so the agent loop sees every\ntool exactly once:\n\n| Tool name            | Tier       | Purpose                                                |\n|----------------------|------------|--------------------------------------------------------|\n| `block_append`       | working    | Append text to a working memory block.                 |\n| `block_replace`      | working    | Replace a unique substring inside a block.             |\n| `block_rethink`      | working    | Replace a block's value entirely.                      |\n| `fact_remember`      | semantic   | Persist a new fact through the multi-stage conflict pipeline. |\n| `fact_search`        | semantic   | Hybrid (vector + FTS5 + RRF) search over facts.        |\n| `fact_supersede`     | semantic   | Mark an old fact superseded by a new one.              |\n| `fact_forget`        | semantic   | Soft-delete a fact (kept for replay).                  |\n| `recall_episodes`    | episodic   | Triple-signal episode retrieval.                       |\n| `conversation_search`| session    | FTS5 search over the active session messages.          |\n| `fact_history`       | semantic   | Trace a fact's bi-temporal supersede chain.            |\n| `fact_validate`      | semantic   | Promote a quarantined fact to active (audited).        |\n\nTwo further tools are **gated** behind their configuration and absent by\ndefault: `deep_recall` (registered when `iterativeRetrieval` is configured)\nand `runbook_search` (procedural runbook lookup, registered when\n`createMemory({ runbookSearch: true })`).\n\n## Embedder migration\n\n```ts\nimport { migrateEmbedder } from '@graphorin/memory/migration';\nimport { createTransformersJsEmbedder } from '@graphorin/embedder-transformersjs';\n\nconst target = createTransformersJsEmbedder({ model: 'Xenova/multilingual-e5-large' });\n\nfor await (const progress of migrateEmbedder({\n  store: sqlite,\n  embeddings: sqlite.embeddings,\n  source: memory.embedder,\n  target,\n  strategy: 'auto-migrate',\n})) {\n  console.log(`${progress.processed}/${progress.total} (${progress.kind})`);\n}\n```\n\n`lock-on-first` (default) refuses any silent embedder swap with an\nactionable error pointing at the planned migration. `multi-active`\nkeeps both vec0 tables alive (reads union, writes to active).\n`auto-migrate` re-embeds rows in resumable batches (checkpointed\nvia the storage layer's `migration_state` table; can be aborted\nwith `AbortSignal`).\n\n## Conflict pipeline (Phase 10b)\n\n```ts\nimport { createMemory, defineLocalePack } from '@graphorin/memory';\n\nconst memory = createMemory({\n  store: sqlite.memory,\n  embeddings: sqlite.embeddings,\n  embedder: createTransformersJsEmbedder(),\n  conflictPipeline: {\n    mode: 'on', // default\n    thresholds: { hot: 0.95, nearDup: 0.85, cold: 0.4 },\n    localePack: defineLocalePack({ id: 'fr', /* ... */ }),\n  },\n});\n\nconst outcome = await memory.semantic.rememberWithDecision(scope, {\n  text: 'I just moved to Tbilisi for the new gig.',\n});\nconsole.log(outcome.decision.kind); // 'supersede' | 'dedup' | 'pending' | 'admit'\n```\n\nThe pipeline visits five stages in order:\n\n1. **Exact dedup** - MD5 hash on the canonical (lowercase + collapsed-\n   whitespace + trimmed) candidate body short-circuits on a hit.\n2. **Embedding three-zone** - top-K neighbours from `searchVector`\n   classify the candidate into HOT (`>= 0.95`), NEAR-DUP\n   (`>= 0.85`), CONFLICT-CHECK (`> 0.4`), or COLD. HOT zone always\n   dedups (semantic identity outranks every other signal).\n3. **Heuristic regex** - the active locale pack's supersede +\n   negation markers fire when the candidate has an explicit\n   change signal (`moved to`, `no longer`, `got promoted`, …) and\n   there is at least one existing candidate.\n4. **Subject / predicate** - naive `(subject, predicate, object)`\n   split using the locale pack's predicate normalisers; matching\n   subject + predicate with a different object is a strong supersede\n   signal.\n5. **Defer to deep LLM judge** - Stages 1-4 yielded no decision but\n   the candidate sits in CONFLICT-CHECK zone - the row is admitted\n   `pending` and queued in `conflict_check_pending` for the\n   consolidator's deep phase (Phase 10c).\n\nEvery decision lands one row in `fact_conflicts` with the producing\nstage, the detection zone, the cosine similarity (where applicable),\nand a reason string for replay/debug. A `memory.conflict` AISpan is\nemitted per call.\n\n`createMemory({ conflictPipeline: { mode: 'off' } })` falls back to the\n10a straight-through write path; a single per-process WARN surfaces so\noperators notice that bi-temporal supersede / dedup will not fire.\n\n## Forward links\n\n| Surface                             | Owned by   |\n|--------------------------------------|------------|\n| Background `Consolidator` (triggers + 3 phases + DLQ + cost budget)            | Phase 10c |\n| Six-layer memory-aware system prompt + locale-aware base templates             | Phase 10d |\n| `@graphorin/sessions` hybrid facade wrapping `memory.session.*`                 | Phase 11  |\n| `@graphorin/agent` wiring `memory.tools` into the tool registry                | Phase 12  |\n| `/v1/memory` REST + `graphorin memory migrate` CLI                              | Phases 14 / 15 |\n\n## License\n\nMIT © 2026 Oleksiy Stepurenko.\n\n---\n\n**Project Graphorin** · v0.15.1 · MIT License · © 2026 Oleksiy Stepurenko · <https://github.com/o-stepper/graphorin>\n","readmeFilename":"README.md"}