OmniRoute/docs/frameworks/MEMORY.md

title, version, lastUpdated

title	version	lastUpdated
Memory System	3.8.6	2026-05-28

Memory System

Source of truth: src/lib/memory/ and src/app/api/memory/ Last updated: 2026-05-28 — v3.8.6 (plan 21 — Memory Engine Redesign)

OmniRoute provides persistent conversational memory keyed by API key (and optionally session id). Memories are extracted automatically from LLM responses via lightweight regex pattern matching and injected back into subsequent requests as a leading system message (or first user message for providers that reject the system role).

Memory is scoped per API key, not per user — every request authenticated with the same API key shares the same memory pool, with optional further scoping by sessionId.

Architecture

Client → /v1/chat/completions (apiKeyInfo resolved upstream)
  → handleChatCore() [open-sse/handlers/chatCore.ts]
    → resolveMemoryOwnerId(apiKeyInfo)        # extracts id
    → getMemorySettings()                     # cached settings
    → shouldInjectMemory(body, {enabled})     # gate
    → retrieveMemories(apiKeyId, config)      # SQL + FTS5 + optional vector
    → injectMemory(body, memories, provider)  # system or user message
  → upstream provider call
  → on response: extractFacts(text, apiKeyId, sessionId)  # non-blocking
    → setImmediate → createMemory(fact) per match
                   → embed(content) + upsertVector(id, vec)

The injection and extraction call-sites are wired in open-sse/handlers/chatCore.ts (look for retrieveMemories, injectMemory, and extractFacts).

Engine architecture (3-tier resolution)

The Memory Engine resolves the retrieval path at runtime based on available infrastructure and settings. Three tiers exist, applied in priority order:

  ┌─────────────────────────────────────────────────────────────┐
  │  TIER 0 — Keyword (FTS5)                                     │
  │  Always available. SQLite FTS5 full-text search over         │
  │  content + key. Used when strategy = "exact" or as fallback. │
  └──────────────────────────────────┬──────────────────────────┘
                                     │ strategy = semantic|hybrid?
                                     ▼
  ┌─────────────────────────────────────────────────────────────┐
  │  TIER 1 — Embedded Vector (sqlite-vec)                       │
  │  sqlite-vec v0.1.9 loaded via db.loadExtension().            │
  │  KNN brute-force over Float32 vectors. Active when:          │
  │   • sqlite-vec loadExtension succeeds                        │
  │   • An embedding source is available (remote | static |      │
  │     transformers) that can produce a Float32Array            │
  │   • vec_memories table exists (created on first ready())     │
  └──────────────────────────────────┬──────────────────────────┘
                                     │ qdrant.enabled?
                                     ▼
  ┌─────────────────────────────────────────────────────────────┐
  │  TIER 2 — Qdrant (opt-in external vector database)           │
  │  When enabled, replaces sqlite-vec for semantic/hybrid.      │
  │  Requires running Qdrant instance + configured host/port.    │
  └─────────────────────────────────────────────────────────────┘

Degradation is automatic and transparent:

• If sqlite-vec fails to load, tier 1 is unavailable → falls back to tier 0.
• If embedding source returns an error, tier 1 falls back to tier 0.
• If Qdrant is unhealthy, tier 2 falls back to tier 1 (or tier 0 if tier 1 is also unavailable).

Embedding sources

The embedding layer (src/lib/memory/embedding/) resolves which source to use based on MemorySettingsExtended.embeddingSource:

Source	Description	Key required	Cold start
remote	Uses a configured provider's embedding API (OpenAI, Cohere, etc.)	Yes	None
static	Local lookup-table embedding via potion-base-8M (WordPiece + mean pooling)	No	~200ms
transformers	Local ONNX inference via @huggingface/transformers v4, all-MiniLM-L6-v2	No	~3s + ~400MB RAM
auto	Runtime resolution: remote (if key exists) → static → transformers → null	Depends	Depends

Resolution order for auto:

1. Find first provider in listEmbeddingProviders() with hasKey === true → remote.
2. If settings.staticEnabled === true → static.
3. If settings.transformersEnabled === true → transformers.
4. Otherwise → null (degrades to FTS5 keyword search).

The embedding cache (src/lib/memory/embedding/cache.ts) uses an in-memory LRU map keyed by ${source}:${model}:${dim}:${sha256(text)}, capped at MEMORY_EMBEDDING_CACHE_MAX entries (default 1000) with a TTL of MEMORY_EMBEDDING_CACHE_TTL_MS (default 5 min). Shared across all callers per process lifecycle.

Hybrid RRF (k=60)

When strategy = "hybrid" and the vector store is available, retrieval uses Reciprocal Rank Fusion to merge FTS5 and vector results:

RRF(d) = Σ  1 / (k + rank_i(d))      where k = 60 (configurable via MEMORY_RRF_K)
          i

Concretely:

1. Run FTS5 search → ranked list R_fts (position 1..N).
2. Run KNN vector search → ranked list R_vec (position 1..M).
3. For each unique memoryId:
   rrf_score = 1/(60 + fts_rank) + 1/(60 + vec_rank) (0 if not in list).
4. Sort by rrf_score DESC, apply token budget walk.

RRF is well-known to be effective without needing score normalization across heterogeneous retrieval systems. The default k=60 is from the original Cormack et al. paper and works well for small corpora (<10k memories).

Backfill (lazy + reindex)

When the embedding model changes (detected via embedding_signature), the vector store is rebuilt and all existing memories are marked needs_reindex = 1 in the memories table.

Lazy backfill: On the next retrieval, any memory missing a vector entry is embedded and inserted into vec_memories before the search runs. This amortizes the backfill cost across real requests without blocking startup.

Explicit reindex: The Engine tab in /dashboard/memory provides a "Reindex Now" button that calls POST /api/memory/reindex. The handler calls runReindexBatch() from src/lib/memory/reindex.ts, which processes up to limit pending entries per request. Progress can be polled via GET /api/memory/engine-status (vectorStore.needsReindex).

The memory_vec_meta table (migration 073_memory_vec.sql) stores:

• active_dim — current vector dimension (null = not yet calibrated).
• embedding_signature — ${source}:${model}:${dim} used to detect changes.
• last_reset_at — timestamp of last full reset.
• vec_loaded — 0/1 flag whether sqlite-vec loaded successfully.

Settings extension

Seven new fields were added to MemorySettingsExtended (plan 21, D9) in src/shared/schemas/memory.ts, persisted via src/lib/db/settings.ts:

Field	Type	Default	Description
embeddingSource	"remote" | "static" | "transformers" | "auto"	"auto"	Which embedding source to use
embeddingProviderModel	string | null	null	Provider/model in provider/model format
transformersEnabled	boolean	false	Opt-in for Transformers.js (MiniLM, ~400MB)
staticEnabled	boolean	false	Opt-in for static potion-base-8M local model
rerankEnabled	boolean	false	Enable reranking step (adds +200-500ms/req)
rerankProviderModel	string | null	null	Rerank provider/model in provider/model format
vectorStore	"sqlite-vec" | "qdrant" | "auto"	"auto"	Which vector backend to use

These are exposed via GET /PUT /api/settings/memory (schema MemorySettingsExtendedSchema).

TODO (D20): Scope global (sharing memories across all API keys) is not implemented in this release. It requires schema changes and a global retrieval path. Track separately.

Storage Layers

Primary: SQLite (memories table)

Created by migration 015_create_memories.sql:

Column	Type	Notes
id	TEXT PRIMARY KEY	UUID generated via crypto.randomUUID()
api_key_id	TEXT NOT NULL	Owning API key
session_id	TEXT	Optional per-conversation scope
type	TEXT NOT NULL	One of factual, episodic, procedural, semantic
key	TEXT	Stable upsert key, e.g. preference:i_prefer_python
content	TEXT NOT NULL	The actual fact text
metadata	TEXT	JSON blob (category, extractedAt, source, ...)
created_at / updated_at	TEXT	ISO 8601 strings
expires_at	TEXT	Optional expiry; NULL means permanent
memory_id	INTEGER UNIQUE	Added by 023_fix_memory_fts_uuid.sql to bridge UUIDs ↔ FTS5 rowids

Indexes: api_key_id, session_id, type, expires_at, plus the unique memory_id index.

Upsert semantics: createMemory() looks for an existing row with the same (api_key_id, key) and updates it in place when found (merging metadata via shallow spread). This keeps the table from growing unbounded for repeated preference statements.

Full-text Search (memory_fts virtual table)

022_add_memory_fts5.sql creates an FTS5 virtual table over content and key. 023_fix_memory_fts_uuid.sql fixes a real-world bug where the UUID primary key did not join to FTS5's integer rowid — the migration adds the memory_id column, recreates the FTS table, and wires triggers (memory_fts_ai, memory_fts_ad, memory_fts_au) that keep FTS in sync on INSERT, DELETE, and UPDATE.

Used by retrieval.ts for the semantic and hybrid strategies (see below). The retrieval code guards with hasTable("memory_fts") and falls back to chronological order if the FTS table is missing or the FTS query throws.

Optional: Qdrant (vector store tier 2)

src/lib/memory/qdrant.ts implements an optional Qdrant integration as tier 2 vector store. Enabled via qdrantEnabled in settings / toggle in Engine tab.

• upsertSemanticMemoryPoint() — embed key + content with the configured embedding model, ensure the collection exists (creates cosine-distance vectors on first use), and upsert a point with payload {memoryId, apiKeyId, sessionId, key, content, metadata, createdAtUnix, expiresAtUnix}.
• searchSemanticMemory(query, topK, scope) — embed the query, search the collection filtered by kind = "omniroute_memory" and optionally by apiKeyId / sessionId. Caps topK to [1, 20].
• deleteSemanticMemoryPoint(id) — single point delete. Called by deleteMemory() after the SQLite row is removed (D15).
• cleanupSemanticMemoryPoints({retentionDays}) — bulk delete points whose expiresAtUnix is in the past or whose createdAtUnix is older than the retention cutoff. Counts first so the dashboard can show actual numbers.
• checkQdrantHealth() — GET /readyz health probe with latency.

The settings UI exposes Qdrant config, health check, semantic search test, and cleanup in the Engine tab of /dashboard/memory. The corresponding routes under src/app/api/settings/qdrant/ are all wired as of v3.8.6:

Route	Method	Description
/api/settings/qdrant	GET / PUT	Read / update Qdrant settings
/api/settings/qdrant/health	GET	Liveness probe + latency
/api/settings/qdrant/search	POST	Semantic search test
/api/settings/qdrant/cleanup	POST	Remove expired / old points
/api/settings/qdrant/embedding-models	GET	List available embedding models

Memory Types

MemoryType (src/lib/memory/types.ts):

Type	Used for
factual	Preferences, stable user facts, behavioral patterns
episodic	Decisions tied to a specific moment ("I chose Postgres")
procedural	Workflow / how-to memory (reserved; no auto-extractor today)
semantic	Reserved for vector-store entries

MemoryConfig retrieval strategy is one of exact, semantic, or hybrid, and scope is one of session, apiKey, or global. The default scope from getMemorySettings() is apiKey.

Fact Extraction (extraction.ts)

Extraction is regex-based, not LLM-based — it runs in-process with setImmediate() so it never blocks the response stream:

• Preference patterns → MemoryType.FACTUAL (e.g. I prefer …, I really like …, my favorite is …, I hate …)
• Decision patterns → MemoryType.EPISODIC (e.g. I'll use …, I chose …, I went with …, I'm going to adopt …)
• Pattern patterns → MemoryType.FACTUAL (e.g. I usually …, I always …, I tend to …)

Each match is sanitised (trim, whitespace-collapse, capped at 500 chars), deduplicated within the batch via a stable factKey(category, content), and stored via createMemory() with metadata {category, extractedAt, source: "llm_response"}. Input text is capped at 64 KiB (MAX_EXTRACTION_TEXT_LENGTH) — when longer, the tail of the text is used so the most recent assistant content always participates.

extractFactsFromText(text) is exported for tests and returns the structured facts without storing them.

Retrieval (retrieval.ts)

retrieveMemories(apiKeyId, config) is the main entry point. It:

1. Normalises and validates the config through MemoryConfigSchema.
2. Returns [] immediately when enabled is false or maxTokens <= 0.
3. Clamps maxTokens to [1, 8000].
4. Detects whether the modern memories table exists (vs the legacy memory table) so older databases keep working.
5. Builds the base query with expiry guard (expires_at IS NULL OR datetime(expires_at) > datetime('now')), optional session scope, and optional retentionDays cutoff.
6. Branches on strategy:
   • exact (default): chronological ORDER BY created_at DESC LIMIT 100.
   • semantic: if config.query and memory_fts exists, JOIN memory_fts MATCH ? and order by FTS rank; fall back to chronological when FTS returns 0 rows.
   • hybrid: union of FTS results (higher relevance) and the chronological set, deduplicated by id.
7. Computes a keyword relevance score (getRelevanceScore) over content, key, and metadata JSON when a query is provided. Rows with zero score are filtered out.
8. Sorts by score desc, then createdAt desc.
9. Walks the ranked list and accepts entries while a running estimateTokens(content) (≈ length / 4) stays under the budget. Always returns at least one entry when any matched.

estimateTokens is exported and used by retrieval, summarisation, and the MCP omniroute_memory_search tool.

Injection (injection.ts)

injectMemory(request, memories, provider):

1. Joins all memory contents into a single Memory context: … string.
2. Picks a strategy by provider name:
   • System message (default for OpenAI, Anthropic, Gemini, …) — prepends a {role: "system", content: memoryText} ahead of any existing system messages so user system prompts still take precedence.
   • User message (fallback) — for providers in PROVIDERS_WITHOUT_SYSTEM_MESSAGE: o1, o1-mini, o1-preview, glm, glmt, glm-cn, zai, qianfan. These reject the system role and would 400 otherwise (cf. issue #1701 for GLM/Zhipu).
3. Logs the count, strategy, and model under memory.injection.injected.

providerSupportsSystemMessage(provider) is exported for callers that need to make routing decisions of their own. Unknown providers default to true (system role allowed) for safety.

Settings (settings.ts)

Memory configuration is stored in the DB settings table, not in env vars. getMemorySettings() reads from getSettings() and caches the result in-process; invalidateMemorySettingsCache() is called by the settings PUT route after writes.

Legacy fields (all versions)

DB key	Type	Default	UI control
memoryEnabled	boolean	true	Memory on/off
memoryMaxTokens	integer	2000 (range 0–16000)	Token budget for injection
memoryRetentionDays	integer	30 (range 1–365)	Retention window
memoryStrategy	enum	"hybrid" (one of recent, semantic, hybrid)	Retrieval strategy
skillsEnabled	boolean	false	Toggles per-key skill injection (see SKILLS.md)

Note: the UI strategy "recent" maps to the internal "exact" retrieval strategy via toMemoryRetrievalConfig() (chronological order).

New fields (v3.8.6, plan 21 D9)

See also the "Settings extension" section above for field descriptions.

DB key	API field	Default
memoryEmbeddingSource	embeddingSource	"auto"
memoryEmbeddingModel	embeddingProviderModel	null
memoryTransformersEnabled	transformersEnabled	false
memoryStaticEnabled	staticEnabled	false
memoryRerankEnabled	rerankEnabled	false
memoryRerankModel	rerankProviderModel	null
memoryVectorStore	vectorStore	"auto"

Qdrant-related DB keys (qdrantEnabled, qdrantHost, qdrantPort, qdrantApiKey, qdrantCollection default "omniroute_memory", qdrantEmbeddingModel default "openai/text-embedding-3-small") are read by normalizeQdrantConfig() in qdrant.ts.

Environment variables (v3.8.6)

Six optional env vars tune the engine's runtime behaviour (documented in .env.example):

Variable	Default	Description
MEMORY_EMBEDDING_CACHE_TTL_MS	300000	Embedding cache TTL (5 min)
MEMORY_EMBEDDING_CACHE_MAX	1000	Max entries in embedding LRU cache
MEMORY_TRANSFORMERS_MODEL	Xenova/all-MiniLM-L6-v2	HF repo for Transformers.js model
MEMORY_STATIC_MODEL	minishlab/potion-base-8M	HF repo for static potion model
MEMORY_STATIC_CACHE_DIR	<DATA_DIR>/embeddings	Where to store downloaded models
MEMORY_VEC_TOP_K	20	Default top-K for vector search
MEMORY_RRF_K	60	RRF k constant for hybrid search

Summarisation (summarization.ts)

summarizeMemories(apiKeyId, sessionId?, maxTokens = 4000) compacts older content when the running token total over a key's memories exceeds the budget. It iterates rows DESC by created_at, keeps rows that fit, and for the rest replaces content in place with the first three sentences of the original. tokensSaved is the difference in estimateTokens between old and new content.

This routine is available but not called automatically in the current chat pipeline — call it from a cron, an admin action, or MemoryConfig.autoSummarize glue if you need ongoing compaction. The data loss is one-way: original text is overwritten.

REST API

All endpoints require management auth (requireManagementAuth).

Core memory endpoints (existing + updated)

Method	Path	Description
GET	/api/memory	Paginated list with filters: apiKeyId, type, sessionId, q, limit, page, offset. Response includes stats.total, stats.tokensUsed, stats.hitRate, cacheStats
POST	/api/memory	Create entry (Zod-validated: content, key, optional type, sessionId, apiKeyId, metadata, expiresAt). Calls createMemory() which upserts on (apiKeyId, key)
GET	/api/memory/[id]	Fetch a single entry by UUID
PUT	/api/memory/[id]	Update entry fields (type, key, content, metadata). Body: MemoryUpdatePutSchema. Also syncs vector if embedding source available.
DELETE	/api/memory/[id]	Delete an entry; also deletes from vec_memories (D15) and Qdrant best-effort. Returns 404 when missing.
GET	/api/memory/health	Runs verifyExtractionPipeline("health-check") — round-trip create→list→delete. Returns {working, latencyMs, error?}

New memory engine endpoints (plan 21)

Method	Path	Description
POST	/api/memory/retrieve-preview	Dry-run of retrieveMemories — returns ranked results with score, tier, tokens. Body: RetrievePreviewSchema. Does NOT inject or modify memories.
GET	/api/memory/embedding-providers	Lists providers with embedding models, indicating which have a configured API key.
GET	/api/memory/engine-status	Returns full engine status: keyword tier, embedding resolution, vector store stats, Qdrant health, rerank config. Shape: MemoryEngineStatusSchema.
POST	/api/memory/summarize	Manually trigger memory compaction. Body: MemorySummarizeSchema (olderThanDays, apiKeyId?, dryRun). Returns {candidates, tokensSaved}.
POST	/api/memory/reindex	Trigger vector reindex for memories with needs_reindex=1. Body: MemoryReindexSchema (force). Returns {started, pending}.

Settings endpoints

Method	Path	Description
GET	/api/settings/memory	Current normalised MemorySettingsExtended (7 new fields + legacy)
PUT	/api/settings/memory	Update any field from MemorySettingsExtendedSchema (12 total fields)
GET	/api/settings/qdrant	Current Qdrant settings (QdrantSettingsSchema)
PUT	/api/settings/qdrant	Update Qdrant settings. Body: QdrantSettingsUpdateSchema. apiKey = empty string removes key.
GET	/api/settings/qdrant/health	Liveness probe against configured Qdrant instance. Returns QdrantHealthResultSchema.
POST	/api/settings/qdrant/search	Semantic search test against Qdrant. Body: QdrantSearchSchema (query, topK).
POST	/api/settings/qdrant/cleanup	Remove Qdrant points for expired / old memories.
GET	/api/settings/qdrant/embedding-models	List embedding models available for Qdrant.

The /api/memory list query supports either page-based pagination (parsePaginationParams) or raw offset — when offset is present it takes precedence and a derived page is computed for the response shape.

MCP Tools (open-sse/mcp-server/tools/memoryTools.ts)

When the MCP server is enabled, three memory tools are registered:

• omniroute_memory_search — {apiKeyId, query?, type?, maxTokens?, limit?} → wraps retrieveMemories(). As of v3.8.6 (D16), the strategy is read from getMemorySettings() instead of being hardcoded to "exact". If query is provided and strategy is semantic or hybrid, the vector store is used when available.
• omniroute_memory_add — {apiKeyId, sessionId?, type, key, content, metadata?} → wraps createMemory(). Accepts only the 4 canonical types: factual, episodic, procedural, semantic (D17).
• omniroute_memory_clear — {apiKeyId, type?, olderThan?} → lists matching entries, optionally filters by created-before timestamp, then deletes each via deleteMemory() (which also removes vectors from sqlite-vec + Qdrant).

See MCP-SERVER.md for transport and scope details.

Dashboard (Memory Studio)

src/app/(dashboard)/dashboard/memory/page.tsx is now a 3-tab Studio:

Tab: Memórias / Memories

• Concept card (collapsible "How it works" explainer).
• Real-time list, search, and pagination (debounced 300 ms).
• Type filter (factual / episodic / procedural / semantic / all).
• Add-memory modal (key, content, type).
• Inline edit (pencil button → PUT /api/memory/[id]).
• Delete per row (with confirmation dialog).
• JSON export of the current page; JSON import via file picker.
• Stat cards: totalEntries, tokensUsed, hitRate.
• "Compact old" button → POST /api/memory/summarize (dry-run first shows candidate count, then confirms).
• A green/red health dot driven by GET /api/memory/health.

Tab: Playground

• Query input + strategy selector (Exact / Semantic / Hybrid) + token budget.
• "Simulate" → POST /api/memory/retrieve-preview — shows ranked results with score, tier, tokens, vecScore, ftsScore.
• Resolution panel showing which embedding source / vector store was used and whether a fallback occurred.

Tab: Engine

• Engine status panel (keyword FTS5 chip, embedding chip, vector store chip, Qdrant health chip, rerank chip).
• "Reindex Now" button → POST /api/memory/reindex.
• Embedding source selector (auto / remote / static / transformers + toggles).
• Qdrant config card (enable toggle, host/port/collection/key, test connection, semantic search test, cleanup).
• Rerank config card (enable toggle, provider/model selector).

Memory and Qdrant settings also live under /dashboard/settings → Memory & Skills (MemorySkillsTab.tsx) for the legacy/global settings surface.

Caching

src/lib/memory/store.ts keeps an in-process LRU-ish cache (MEMORY_CACHE_TTL = 5 min, MEMORY_MAX_CACHE_SIZE = 10 000, with 20 % oldest eviction) for getMemory(id) reads, plus a generic key/value memoryCache layer (src/lib/memory/cache.ts) with get/set/invalidate methods used by callers that want their own scoped cache (1 000-entry LRU, default TTL 5 min).

Privacy & Lifecycle

• Memory ownership is the API key id (resolveMemoryOwnerId in chatCore.ts). Without an apiKeyInfo.id neither retrieval nor injection nor extraction runs.
• Entries with a future expires_at are filtered out of retrieval; old entries beyond retentionDays are excluded by the created_at >= cutoff clause in retrieveMemories.
• For hard deletion, use DELETE /api/memory/[id] or omniroute_memory_clear.
• Extraction is fire-and-forget via setImmediate; failures are logged under memory.extraction.background.failed and never surface to the caller.
• Verification round-trips (verifyExtractionPipeline) clean up their own test entries in a finally block.

See Also

• SKILLS.md — the skillsEnabled setting injects tool definitions alongside memory.
• MCP-SERVER.md — MCP transport / scopes.
• API_REFERENCE.md — broader API surface.
• Source modules:
  • src/lib/memory/types.ts, schemas.ts
  • src/lib/memory/store.ts, retrieval.ts, injection.ts, reindex.ts
  • src/lib/memory/extraction.ts, summarization.ts, verify.ts
  • src/lib/memory/settings.ts, qdrant.ts, cache.ts
  • src/lib/memory/vectorStore.ts — sqlite-vec + hybrid RRF
  • src/lib/memory/embedding/index.ts — multi-source embedding layer
  • src/lib/memory/embedding/types.ts, remote.ts, staticPotion.ts, transformersLocal.ts, cache.ts
  • src/shared/schemas/memory.ts — Zod schemas for all memory API bodies
  • src/shared/schemas/qdrant.ts — Zod schemas for Qdrant settings/ops
  • src/lib/db/memoryVec.ts — CRUD for memory_vec_meta
  • src/lib/db/migrations/015_create_memories.sql, 022_add_memory_fts5.sql, 023_fix_memory_fts_uuid.sql, 073_memory_vec.sql
  • src/app/api/memory/route.ts, [id]/route.ts, health/route.ts
  • src/app/api/memory/retrieve-preview/route.ts
  • src/app/api/memory/engine-status/route.ts
  • src/app/api/memory/embedding-providers/route.ts
  • src/app/api/memory/summarize/route.ts
  • src/app/api/memory/reindex/route.ts
  • src/app/api/settings/memory/route.ts
  • src/app/api/settings/qdrant/route.ts + sub-routes
  • src/app/(dashboard)/dashboard/memory/ — Studio UI (page + components + tabs + hooks)
  • open-sse/handlers/chatCore.ts (injection / extraction wiring)
  • open-sse/mcp-server/tools/memoryTools.ts