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Monorepo contributors depend on this package via\n> the workspace protocol.\n\nOpenContext memory storage + search SDK with optional HTTP and MCP server\nentry points. The package is intentionally decoupled from the opencontext\nweb app's database layer — every consumer wires up its own backend via\nthe `db` contract on `MemoryStoreConfig`.\n\n- **SDK** (`memory-store`) — embed in a Node.js host\n- **HTTP** (`memory-store/http`) — REST daemon\n- **MCP** (`memory-store/mcp`) — stdio tools for Claude Desktop / Cursor\n\n## Install\n\n```bash\npnpm add @melandlabs/opencontext\n```\n\n## Quick start\n\n```ts\nimport { createMemoryStore } from \"@melandlabs/opencontext\";\n\nconst store = await createMemoryStore({\n\t// Required for the postgres backend (OPENCONTEXT_MEMORY_STORE_BACKEND=postgres).\n\t// For the default sqlite backend, omit db and the SDK writes to\n\t// ~/.opencontext/memory/store.db (override with MEMORY_STORE_DB_PATH).\n\tdb: { getDb: () => drizzleDb() },\n\tunified: {\n\t\tembedQuery: async ({ query }) => myEmbed(query),\n\t},\n});\n\nconst hits = await store.search({ userId, query });\n```\n\n## Capabilities\n\nThe store exposes the four-verb memory API plus two read/write reflection\nendpoints:\n\n| Capability | SDK method | HTTP | MCP tool |\n|---|---|---|---|\n| Read-only search across all sources | `store.search(input)` | `POST /v1/search` | `memory.search` |\n| Read a single `RawMessage` | `manager.getMessageById(id)` | `GET /v1/raw-messages/:id?userId=…` | `memory.getRawMessage` |\n| Persist a `RawMessage` | `manager.storeMessages([…])` | `POST /v1/raw-messages` | `memory.writeRawMessage` |\n| Read-only LLM synthesis over evidence | `store.search({ ...input, synthesize: true })` | `POST /v1/search` (set `synthesize: true`) | `memory.search` (set `synthesize: true`) |\n| LLM reasoning layer (query-rewrite / iterative planner) | `store.search({ ...input, reasoningStrategy: \"rewrite\" \\| \"iterative\" })` | `POST /v1/search` (set `reasoningStrategy`) | `memory.search` (set `reasoningStrategy`) |\n| **Agentic write-back** (gather → plan → vet → persist) | `store.consolidate(input)` | `POST /v1/consolidate:apply` | `memory.consolidate` |\n| Health probe | — | `GET /health` | `memory.health` |\n\n## Entry points\n\n| Import              | What you get                                    |\n| ------------------- | ----------------------------------------------- |\n| `memory-store`      | `createMemoryStore(config)` facade              |\n| `memory-store/http` | `startHttpServer(options)` — Hono app on a port |\n| `memory-store/mcp`  | `startMcpServer()` — stdio MCP server           |\n\nCLI bins ship with the package:\n\n```bash\nopencontext-memory-http --port 7421 --host 127.0.0.1\nopencontext-memory-mcp\n```\n\nBoth bins accept the same `--embedding-provider` / `--*-backend` flag surface\n(see `--help` for the full list). Reasoning is **off by default** — opt in\nwith `--reasoning`, which reads `OPENCONTEXT_LLM_API_KEY` /\n`OPENCONTEXT_LLM_BASE_URL` / `OPENCONTEXT_LLM_MODEL` from the environment:\n\nFor reproducible OpenRouter runs, set `OPENCONTEXT_LLM_PROVIDER` to one\nprovider name. The CLI disables provider fallback and rejects a response\nreported from a different provider; leaving it unset preserves automatic\nrouting.\n\n```bash\n# HTTP daemon with local embeddings + sqlite-vec memory + LLM reasoning\nopencontext-memory-http \\\n  --reasoning \\\n  --embedding-provider local \\\n  --memory-backend sqlite-vec\n\n# Same wiring, stdio MCP daemon (Claude Desktop / Cursor / Claude Code)\nopencontext-memory-mcp \\\n  --reasoning \\\n  --embedding-provider local \\\n  --memory-backend sqlite-vec\n```\n\nLocal cross-encoder reranking is also opt-in and runs after RRF/source\nfusion but before the public Top-K limit:\n\n```bash\nopencontext-memory-http \\\n  --embedding-provider local \\\n  --memory-backend sqlite-vec \\\n  --reranker-provider local \\\n  --reranker-model Xenova/ms-marco-MiniLM-L-6-v2\n```\n\nThe equivalent environment variables are `RERANKER_PROVIDER=local`,\n`LOCAL_RERANKER_MODEL`, `LOCAL_RERANKER_CACHE_DIR`,\n`LOCAL_RERANKER_BATCH_SIZE`, and `LOCAL_RERANKER_MAX_TOKENS`. Optional\n`LOCAL_RERANKER_DTYPE=q8` selects the quantized CPU model and\n`LOCAL_RERANKER_LOCAL_ONLY=true` requires all weights to exist in the cache.\nA configured reranker is warmed up at startup; model download or runtime\nerrors fail startup instead of silently falling back to RRF-only ordering.\n\nOnce the daemon is up, `POST /v1/search` (HTTP) and `memory.search` (MCP)\nhonor a `reasoningStrategy` field:\n\n| `reasoningStrategy` | What runs |\n|---|---|\n| `\"none\"` (default) | Plain hybrid search — no LLM call. |\n| `\"rewrite\"`         | One LLM call rephrases the query into a first-person memory-check question, then the normal hybrid search runs. |\n| `\"iterative\"`       | An LLM planner searches, notes evidence, searches again — multi-hop / temporal questions benefit most. |\n\nThe response carries a `reasoning` block (`{ strategy, iterations,\nevidenceCount, degraded }`) so callers can observe what ran. See\n[Recipe 9](#9-http-daemon) and [Recipe 10](#10-mcp-daemon) below for the\nprogrammatic equivalents.\n\n## Configuration matrix\n\n| Key                                                   | Required                        | Description                                               |\n| ----------------------------------------------------- | ------------------------------- | --------------------------------------------------------- |\n| `db.getDb()`                                          | yes for postgres backend        | Drizzle DB handle factory                                 |\n| `db.tables.rawMessages` / `db.tables.memorySummaries` | when using postgres backend     | Drizzle table references owned by the host                |\n| `dbPath`                                              | optional                        | SQLite path override (also reads `MEMORY_STORE_DB_PATH`)  |\n| `vector.backend`                                      | `sqlite-vec`, `chroma`, `lancedb`, or `milvus` | raw-message child vector backend                |\n| `vector.sqliteVec.dbPath`                             | when backend = sqlite-vec       | sqlite-vec DB path                                        |\n| `vector.chroma.url`                                   | when backend = chroma           | chroma server URL                                         |\n| `vector.lancedb.uri`                                  | when backend = lancedb          | LanceDB local directory or URI                            |\n| `vector.milvus.address`                               | when backend = milvus           | Milvus endpoint                                           |\n| `unified.embedQuery`                                  | yes for unified search          | query embedder                                            |\n| `unified.searchKnowledge`                             | optional                        | RAG over uploaded documents                               |\n| `unified.searchInsights`                              | optional                        | semantic search over extracted insights                   |\n| `unified.searchRawMessagesAnn`                        | optional                        | postgres-side ANN over `raw_messages` (database fallback) |\n| `unified.searchRawMessagesLexical`                    | optional                        | BM25/FTS5 fallback when lexical search is enabled         |\n| `unified.searchSummaries`                              | optional                        | L1/L2/L3 summary recall (used by `reflect`)               |\n| `unified.peerScopeCheck`                              | optional                        | host check that gates `peerFilter` narrowing              |\n| `unified.reranker`                                    | optional                        | cross-encoder / learned ranker applied after merge        |\n| `unified.reasoning.queryRewriter`                     | optional, enables `\"rewrite\"`   | turns third-person questions into first-person memory checks before embedding |\n| `unified.reasoning.iterativePlanner`                  | optional, enables `\"iterative\"` | LLM-driven multi-step recall; planner calls back into `search` and `note` actions |\n| `unified.reasoning.complete`                          | optional, enables `synthesize`  | single-turn LLM callback for evidence synthesis (`reflect`) |\n| `unified.reasoning.defaultStrategy`                   | optional                        | `\"none\" \\| \"rewrite\" \\| \"iterative\"` — applied when callers omit `reasoningStrategy` |\n| `graphStore`                                          | optional                        | `MemoryGraphStoreWithOperationHistory`; powers `consolidate` |\n| `storage`                                             | optional                        | `MemoryStorageAdapter` for `deprecateRecords` writes      |\n| `logger`                                              | optional                        | `console`-shaped logger; defaults to `console`            |\n\nBackend selection is **env-var driven, not host-injected**: the default is\nSQLite at `~/.opencontext/memory/store.db`; set\n`OPENCONTEXT_MEMORY_STORE_BACKEND=postgres` to opt into the host's\nregistered Postgres factory (call `registerPostgresFactory` at startup).\n\nIf any of `unified.embedQuery`, `unified.searchKnowledge`, `unified.searchInsights`,\nor the configured raw-message search provider are absent, the corresponding source in\n`searchUnifiedMemory` returns empty results with a warning. The SDK still\nworks and lexical child search remains available when the SQLite catalog is present.\n\nSee `src/config.ts` for the full type surface.\n\n## Recipes\n\n### 1. Postgres backend (server / agent daemon)\n\n```ts\nimport { drizzle } from \"drizzle-orm/postgres-js\";\nimport {\n\tcreateMemoryStore,\n\tregisterPostgresFactory,\n\ttype PostgresRawMessageManagerLike,\n} from \"@melandlabs/memory-store\";\nimport { myPostgresRawMessageManager } from \"./postgres-raw-message-store\";\nimport * as schema from \"./schema\";\n\nregisterPostgresFactory<typeof myPostgresRawMessageManager>(\n\tasync () =>\n\t\tmyPostgresRawMessageManager as unknown as PostgresRawMessageManagerLike,\n);\n\nconst db = drizzle(process.env.DATABASE_URL!, { schema });\n\nconst store = await createMemoryStore({\n\tdb: {\n\t\tgetDb: () => db,\n\t\ttables: {\n\t\t\trawMessages: schema.rawMessages,\n\t\t\tmemorySummaries: schema.memorySummaries,\n\t\t},\n\t},\n\tunified: {\n\t\tembedQuery: async ({ query }) => myEmbedder.embedQuery(query),\n\t\tsearchInsights: mySearchInsights,\n\t\tsearchKnowledge: mySearchKnowledge,\n\t\tsearchRawMessagesAnn: async ({\n\t\t\tuserId,\n\t\t\tqueryEmbedding,\n\t\t\tlimit,\n\t\t\tthreshold,\n\t\t\tbotId,\n\t\t}) => myAnnSearch({ userId, queryEmbedding, limit, threshold, botId }),\n\t},\n});\n```\n\n### 2. SQLite-vec backend (desktop / local)\n\n```ts\nconst store = await createMemoryStore({\n\tdbPath: `${appDataDir()}/memory.sqlite`,\n\tvector: {\n\t\tbackend: \"sqlite-vec\",\n\t\tsqliteVec: { dbPath: `${appDataDir()}/vectors.sqlite` },\n\t},\n\tunified: {\n\t\tembedQuery: async ({ query }) => localOnnxEmbedder.embed(query),\n\t},\n});\n```\n\n### 3. Chroma backend (managed vector store)\n\n```ts\nconst store = await createMemoryStore({\n\tvector: {\n\t\tbackend: \"chroma\",\n\t\tchroma: {\n\t\t\turl: process.env.CHROMA_URL ?? \"http://127.0.0.1:8000\",\n\t\t\trawMessagesCollection: \"opencontext_raw_messages\",\n\t\t\tinsightsCollection: \"opencontext_insights\",\n\t\t},\n\t},\n\tunified: { embedQuery: myEmbedder },\n});\n```\n\nChroma stores only child chunks for dense retrieval. SQLite child FTS5 provides\nthe lexical channel, and the full parent text remains in the raw-message store.\n\n### 4. Cross-source search wiring\n\n`createUnifiedSearch(deps)` accepts the per-source searchers independently.\nYou don't need to wire all of them; the ones you omit just emit a warning:\n\n```ts\nimport { createUnifiedSearch } from \"@melandlabs/memory-store/unified-search\";\n\nconst search = createUnifiedSearch({\n\tembedQuery: async ({ userId, query, authToken }) =>\n\t\tembedder.embed(query, { userId, authToken }),\n\n\tsearchRawMessagesAnn: async ({\n\t\tuserId,\n\t\tqueryEmbedding,\n\t\tlimit,\n\t\tthreshold,\n\t\tbotId,\n\t}) =>\n\t\tdb.rawMessages.searchAnn({\n\t\t\tuserId,\n\t\t\tembedding: queryEmbedding,\n\t\t\tlimit,\n\t\t\tthreshold,\n\t\t\tbotId,\n\t\t}),\n\n\tsearchInsights: async ({ userId, query, limit, threshold, botIds }) =>\n\t\tinsightIndex.search({ userId, query, limit, threshold, botIds }),\n\n\tsearchKnowledge: async ({ userId, query, options, authToken }) =>\n\t\tragIndex.search({ userId, query, ...options, authToken }),\n});\n\nconst result = await search.searchUnifiedMemory({\n\tuserId: \"u-1\",\n\tquery: \"what changed since yesterday?\",\n\tsources: [\"memory\", \"insights\", \"knowledge\"],\n\tlimit: 10,\n\tthreshold: 0.7,\n\tbotIds: [\"bot-42\"],\n});\n\n// result.results: UnifiedMemorySearchResult[]\n// result.warnings: UnifiedMemorySearchWarning[]\n//   e.g. { source: \"memory\", code: \"raw_message_storage_unavailable\", ... }\n```\n\n### 5. FactType filter\n\nAtomic facts are classified as `world` (facts about the world), `experience`\n(first-person events), or `mental_model` (generalised patterns). The\n`FactType` union lives in `@melandlabs/ai/memory/contracts`; the\n`rawMessageToMemoryRecord` adapter (in both `indexeddb` and `memory-store`)\ncarries it through.\n\n```ts\n// Write side — set factType on each RawMessage.\nawait manager.storeMessages([\n\t{\n\t\tmessageId: \"f-1\",\n\t\tuserId: \"u-42\",\n\t\tcontent: \"water boils at 100°C at sea level\",\n\t\tplatform: \"tutorial\",\n\t\tbotId: \"tutorial-bot\",\n\t\ttimestamp: Date.now(),\n\t\tfactType: \"world\",\n\t},\n\t{\n\t\tmessageId: \"f-2\",\n\t\tuserId: \"u-42\",\n\t\tcontent: \"I went hiking last weekend\",\n\t\tplatform: \"tutorial\",\n\t\tbotId: \"tutorial-bot\",\n\t\ttimestamp: Date.now(),\n\t\tfactType: \"experience\",\n\t},\n]);\n\n// Read side — filter by factType.\nconst hits = await store.search({\n\tuserId: \"u-42\",\n\tquery: \"what did I do recently?\",\n\tfactTypes: [\"experience\"],\n\tlimit: 5,\n});\n```\n\nSchema migration: IndexedDB `DB_VERSION` 3 → 4 (additive `factType` index on\n`raw_messages`), SQLite `RAW_MESSAGES_SCHEMA_VERSION` 3 → 4 (additive\n`fact_type` column + partial index). Both migrations are idempotent and\ntolerate v3 rows whose `factType` is undefined.\n\n### 6. Read-only `search({ synthesize: true })` — LLM synthesis\n\n`store.search({ ...input, synthesize: true })` is the read-only sibling of\nthe write-back loop. It fans out across raw messages, summaries,\ninsights, and knowledge chunks, then asks the LLM to produce a single\nsynthesised answer. No writes.\n\n```ts\nconst out = await store.search({\n\tuserId: \"u-42\",\n\tquery: \"what does the user like to do on weekends?\",\n\ttiers: [\"summary\", \"raw\", \"insight\", \"knowledge\"],\n\tlimit: 20,\n\tthreshold: 0.7,\n\tsynthesize: true,\n});\n\nconsole.log(out.answer);          // LLM synthesis\nconsole.log(out.evidence);        // the bracket-cited evidence items\nconsole.log(out.warnings);        // structured warnings, never throws\n```\n\nThe LLM is wired via `unified.reasoning.complete` — the same hook used by\nthe search-rewrite and iterative-recall strategies. When no LLM is wired,\n`reflect()` returns the gathered evidence with a\n`reflect_llm_not_configured` warning instead of throwing.\n\n### 7. Agentic write-back `consolidate()` — gather → plan → vet → persist\n\n`store.consolidate()` is the agentic counterpart. It runs the same\nevidence pipeline as `reflect()`, then:\n\n1. Builds a `MemoryConsolidationPlan` from the evidence (rule-based; the\n   LLM is not free to invent `MemoryGraphOperation`s).\n2. **Optionally** asks the LLM (`reasoning.complete`) to approve or veto\n   plan entries. The LLM never adds new entries — it only marks existing\n   ones as `approve` or `veto` with a reason.\n3. Persists via `MemoryGraphStore.persistPlan` (when `graphStore` is wired)\n   and always runs `storage.deprecateRecords` for `deprecate` entries.\n\n```ts\nimport { createMemoryStore, attachMemoryGraphStore } from \"@melandlabs/memory-store\";\n\n// 1. Wire the graph store (opt-in).\nattachMemoryGraphStore(store, {\n\tstorage: myIndexedDbStorage,\n\townerScope: { userId: \"u-42\" },\n});\n\n// 2. Inspect the plan first (dry-run).\nconst dry = await store.consolidate({\n\tuserId: \"u-42\",\n\tquery: \"summarise the last week\",\n\townerScope: { userId: \"u-42\" },\n\ttiers: [\"raw\", \"summary\"],\n\tdryRun: true,\n});\nconsole.log(dry.plan);             // MemoryConsolidationPlan\nconsole.log(dry.applied);          // false\n\n// 3. Apply for real.\nconst result = await store.consolidate({\n\tuserId: \"u-42\",\n\tquery: \"summarise the last week\",\n\townerScope: { userId: \"u-42\" },\n\ttiers: [\"raw\", \"summary\"],\n\tdryRun: false,\n});\nconsole.log(result.applied);               // true\nconsole.log(result.persistenceResult);     // { applied, skipped, conflicts }\nconsole.log(result.deprecationCounts);     // [{ supersededBySummaryId, count }]\n```\n\nFailure modes are typed:\n- `reflect_apply_llm_skipped` — no LLM configured; the rule-based plan runs.\n- `reflect_apply_llm_vet_failed` — LLM threw; approve-all fallback.\n- `reflect_apply_graph_store_not_configured` — `deprecateRecords` still runs.\n- `reflect_apply_dry_run` — `dryRun: true`; nothing was written.\n- `reflect_apply_no_writes` — plan contains no actionable entries.\n\n### 8. Postgres manager factory (lazy registration)\n\nIf the host application owns the postgres raw-message manager (e.g. an\nexisting Drizzle-based repo), register it via the factory pattern. The\nmemory-store package will resolve it on first use:\n\n```ts\nimport { registerPostgresFactory } from \"@melandlabs/memory-store/postgres-raw-message-factory\";\nimport { myPostgresManager } from \"./managers/postgres-raw-message\";\n\nregisterPostgresFactory(async () => myPostgresManager);\n// Later — anywhere in the codebase:\nimport { getRawMessageManager } from \"@melandlabs/memory-store\";\nconst manager = await getRawMessageManager();\n```\n\nIf you don't register a factory, the default SQLite backend (`dbPath` or\n`~/.opencontext/memory/store.db`) is used. For server-side hosts that\nwant Postgres, registering the factory is mandatory.\n\n### 9. HTTP daemon\n\n```ts\nimport { startHttpServer } from \"@melandlabs/memory-store/http\";\n\nconst { url, port, stop } = await startHttpServer({\n\tport: 7421,\n\thost: \"127.0.0.1\",\n\t// MemoryStoreConfig passthrough:\n\tvector: { backend: \"chroma\", chroma: { url: process.env.CHROMA_URL! } },\n\tunified: { embedQuery: myEmbedder },\n});\n\nconsole.log(`listening at ${url}`);\n// later: await stop();\n```\n\nOr run the CLI:\n\n```bash\nMEMORY_HTTP_HOST=0.0.0.0 MEMORY_HTTP_PORT=7421 opencontext-memory-http\n```\n\nTo opt into LLM reasoning from the CLI, add `--reasoning`. The bin reads\n`OPENCONTEXT_LLM_API_KEY` / `OPENCONTEXT_LLM_BASE_URL` / `OPENCONTEXT_LLM_MODEL`\nfrom the environment — your existing `.env` works as-is:\n\n```bash\nOPENCONTEXT_LLM_API_KEY=sk-... \\\n  opencontext-memory-http \\\n    --reasoning \\\n    --embedding-provider local \\\n    --memory-backend sqlite-vec\n```\n\nProgrammatically, wire the reasoning layer via `unified.reasoning`:\n\n```ts\nimport { startHttpServer } from \"@melandlabs/memory-store/http\";\nimport {\n\tcreateUserVoiceRewriter,\n\tcreateIterativeRecallPlanner,\n} from \"@melandlabs/memory-store\";\n\nconst complete = async (prompt: string) => callMyLlm(prompt); // any OpenAI-compatible chat completions endpoint\n\nconst { url, stop } = await startHttpServer({\n\tport: 7421,\n\thost: \"127.0.0.1\",\n\tunified: {\n\t\tembedQuery: myEmbedder,\n\t\treasoning: {\n\t\t\tqueryRewriter: createUserVoiceRewriter({ complete }),\n\t\t\titerativePlanner: createIterativeRecallPlanner({ complete }),\n\t\t\t// defaultStrategy: \"iterative\",  // uncomment to enable by default\n\t\t},\n\t},\n});\n```\n\nEndpoints:\n\n| Method + path                          | Body                                                                                                |\n| -------------------------------------- | --------------------------------------------------------------------------------------------------- |\n| `GET  /health`                         | — (includes safe retrieval backend/readiness details when configured)                               |\n| `POST /v1/search`                      | `{ userId, query, limit?, threshold?, mergeStrategy?, includeRetrievalDiagnostics?, sources?, botIds?, documentIds?, factTypes?, synthesize?, reasoningStrategy? }` |\n| `POST /v1/consolidate:apply`           | `{ userId, query, ownerScope, tiers?, limit?, threshold?, dryRun?, expectedVersion?, llmPlanReview? }` |\n| `POST /v1/raw-messages`                | `{ userId, messages: RawMessage[] }`                                                                |\n| `GET  /v1/raw-messages/:id?userId=...` | —                                                                                                   |\n\n`reasoningStrategy` accepts `\"none\"` (default — no LLM call),\n`\"rewrite\"` (single LLM rephrase), or `\"iterative\"` (multi-step planner).\nWhen set and the server is not wired with the corresponding provider, the\nresponse includes a `*_not_configured` warning and the search falls back to\nthe default path.\n\nFor container / LAN deployment, put it behind a reverse proxy:\n\n```nginx\nlocation /memory/ {\n  proxy_pass         http://127.0.0.1:7421/;\n  proxy_set_header   X-Forwarded-User $remote_user;\n  proxy_read_timeout 60s;\n}\n```\n\n### 10. MCP daemon\n\n```bash\nopencontext-memory-mcp\n```\n\n**Wire format on stdio: NDJSON.** Each line is a single JSON-RPC object —\nthe request, the response, or a notification. The server writes\n`JSON.stringify(message) + '\\n'` and reads by `indexOf('\\n')`. This is the\ndefault set by `@modelcontextprotocol/sdk@^1.25.3`'s `StdioServerTransport`.\nMost official MCP clients (Claude Desktop, Cursor, Claude Code) handle this\ntransparently; if you fork a client, serialize outgoing requests with\n`JSON.stringify(obj) + '\\n'` and parse incoming lines with `JSON.parse`.\n\nTo expose LLM reasoning to MCP clients (Claude Desktop, Cursor, Claude Code):\n\n```bash\nOPENCONTEXT_LLM_API_KEY=sk-... \\\n  opencontext-memory-mcp \\\n    --reasoning \\\n    --embedding-provider local \\\n    --memory-backend sqlite-vec\n```\n\nTools exposed over stdio:\n\n| Tool                     | Required args                                                                                                                                                                                  |\n| ------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| `memory.health`          | — (returns safe backend, embedding identity/dimensions, child counts, and readiness)                                                                                                            |\n| `memory.search`          | `userId`, `query`, optional `limit`, `threshold`, `mergeStrategy`, `includeRetrievalDiagnostics`, `sources`, `botIds`, `documentIds`, `factTypes`, `synthesize`, `tiers`, **`reasoningStrategy`** (`\"none\" \\| \"rewrite\" \\| \"iterative\"`) |\n| `memory.writeRawMessage` | `userId`, `message: { role, content, platform?, botId?, factType?, peer?, ... }`                                                                                                               |\n| `memory.getRawMessage`   | `userId`, `messageId`                                                                                                                                                                          |\n| `memory.consolidate`     | `userId`, `query`, `ownerScope`, optional `tiers`, `dryRun`, `expectedVersion`, `llmPlanReview`, `plan`                                                                                        |\n\n#### Claude Desktop (`claude_desktop_config.json`)\n\n```json\n{\n\t\"mcpServers\": {\n\t\t\"opencontext-memory\": {\n\t\t\t\"command\": \"npx\",\n\t\t\t\"args\": [\"-y\", \"memory-store\", \"memory-mcp\"],\n\t\t\t\"env\": {\n\t\t\t\t\"DATABASE_URL\": \"postgres://user:pass@host:5432/opencontext\"\n\t\t\t}\n\t\t}\n\t}\n}\n```\n\nFor local dev against this monorepo, point at the built CLI directly:\n\n```json\n{\n\t\"mcpServers\": {\n\t\t\"opencontext-memory\": {\n\t\t\t\"command\": \"node\",\n\t\t\t\"args\": [\n\t\t\t\t\"/path/to/opencontext/packages/memory-store/dist/server/cli-mcp.js\"\n\t\t\t]\n\t\t}\n\t}\n}\n```\n\n#### Cursor\n\nSame shape — Cursor reads `mcpServers` from its MCP settings\n(Cursor → Settings → MCP → Add new global MCP server):\n\n```json\n{\n\t\"mcpServers\": {\n\t\t\"opencontext-memory\": {\n\t\t\t\"command\": \"opencontext-memory-mcp\"\n\t\t}\n\t}\n}\n```\n\nAfter registering, the six `memory.*` tools become available inside the\neditor. Tool calls appear in the chat like any other MCP tool.\n\n## Subpath exports\n\n| Subpath                                       | Contents                                                                                          |\n| --------------------------------------------- | ------------------------------------------------------------------------------------------------- |\n| `memory-store`                                | `createMemoryStore`, `attachMemoryGraphStore`, top-level types                                     |\n| `memory-store/http`                           | `startHttpServer`, `StartedHttpServer`                                                            |\n| `memory-store/mcp`                            | `startMcpServer`                                                                                  |\n| `memory-store/unified-search`                 | `createUnifiedSearch(deps)` factory + result types                                                |\n| `memory-store/raw-message-store`              | `createRawMessageStore`, `getRawMessageManager`, `isRawMessageStorageAvailable`                   |\n| `memory-store/sqlite-raw-message-store`       | SQLite-vec raw-message manager (Tauri / desktop)                                                  |\n| `memory-store/postgres-raw-message-factory`   | `registerPostgresFactory`, `resolvePostgresFactory`                                               |\n| `memory-store/sqlite-vector-index`            | Direct sqlite-vec insight index helpers                                                           |\n| `memory-store/chroma-memory-index`            | Direct chroma upsert / search helpers                                                             |\n| `memory-store/memory-graph-write-policy`      | `resolveMemoryGraphWritePolicy`, allowlist gating                                                 |\n| `memory-store/memory-graph-correction-policy` | `resolveMemoryGraphCorrectionPolicy`                                                              |\n\n## Behaviour notes\n\n- An empty / whitespace `query` short-circuits and returns\n  `{ query, sources, results: [], count: 0, warnings: [] }` without\n  touching any backend.\n- If `isRawMessageStorageAvailable()` returns `false`, the memory source\n  emits a `raw_message_storage_unavailable` warning instead of searching.\n- When `botIds` is provided, the memory source fans out across each\n  `botId` filter in parallel (`Promise.all(filters.map(...))`) and\n  flattens the results. If `botIds` is empty, a single unfiltered query\n  is sent.\n- `limit` caps final results. Internally, each channel gathers\n  `min(50, max(limit, 4 × limit))` candidates so RRF and a configured reranker\n  operate on a useful window. RRF is the default merge strategy; when its\n  threshold is omitted, the full ranked candidate window is fused. Explicit\n  `threshold` values are clamped to `[-1, 1]`.\n- `retrievalDiagnostics.reranker` reports whether reranking was enabled, its\n  non-secret provider/model identity, input/output counts, latency, and whether\n  it changed candidate order. Health reports the same configured model and\n  readiness state.\n- `RawMessage.content` remains the complete parent text. Search indexing uses\n  400-estimated-token child chunks with 80-token overlap. Short-message hits\n  return the complete parent; long-message hits return the matched child plus\n  its same-parent predecessor and successor as one continuous source slice.\n- SQLite uses child sqlite-vec plus child FTS5; Chroma uses dense child search\n  plus SQLite FTS5; LanceDB and Milvus use their native dense/BM25 hybrid path.\n  All four return the parent `messageId` and the same bounded content semantics.\n- `reflect()` and `consolidate()` reuse the same evidence pipeline;\n  `reflect()` is read-only, `consolidate()` additionally persists\n  via the graph store + storage adapter.\n- `applyReflectedConsolidationPlan` is idempotent — `persistPlan`\n  deduplicates on `operationId` and `deprecateRecords` no-ops on\n  already-deprecated rows.\n\n## See also\n\n- [`docs/architecture.md`](../../docs/architecture.md) — the overall data\n  model, lifecycle, and transport surfaces that the memory primitives\n  plug into.\n- [`docs/tutorials/03-advanced-usage.md`](../../docs/tutorials/03-advanced-usage.md#reflection-and-write-back)\n  — end-to-end walkthrough of `reflect()` + `consolidate()`.\n","readmeFilename":"README.md"}