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Bidworthy"},"license":"MIT","homepage":"https://github.com/aparry3/agntz#readme","keywords":["ai","agents","memory","agntz","sqlite","postgres"],"repository":{"type":"git","url":"git+https://github.com/aparry3/agntz.git","directory":"packages/memrez"},"description":"Durable tagged memory layer for agntz agents","maintainers":[{"name":"aparry3","email":"aaron.parry.18@gmail.com"}],"readme":"# @agntz/memrez\n\nDurable tagged memory for agntz agents.\n\nmemrez stores facts, preferences, events, and summaries under namespace scopes. It plugs into agntz through the generic resource provider system, so agents get memory tools such as `memory_read` and `memory_write` while application code keeps control of runtime namespace grants.\n\nmemrez owns memory organization, not the calling agent. The agent's `memory_write` takes **content only** — choosing the scope, topics, and entry type, normalizing the text, and detecting duplicates are all the reasoner's job. By default that reasoner is a built-in LLM (`llmReasoner()`), so memory handling is genuinely offloaded: the agent says *what* to remember, memrez decides *how* it's filed.\n\n## Install\n\n```bash\npnpm add @agntz/memrez\n```\n\nThe default reasoner makes direct model calls and reads its API key from the provider env var (e.g. `OPENAI_API_KEY`). A missing key throws on the first write; set one, or pass your own `reasoner`.\n\n## Use with the embedded SDK\n\nDeclare memory in an LLM agent:\n\n```yaml\nid: support-with-memory\nkind: llm\nmodel:\n  provider: openai\n  name: gpt-5.6-sol\ninstruction: |\n  Help the user. Use memory when it is relevant.\nresources:\n  memory:\n    mode: read-write\n    autoScan: true\n```\n\nWire the provider and pass trusted runtime grants:\n\n```ts\nimport { agntz } from \"@agntz/sdk\";\nimport { createMemrez, SqliteMemoryStore } from \"@agntz/memrez\";\n\nconst memrez = createMemrez({\n  store: new SqliteMemoryStore(\"./memory.db\"),\n});\n\nconst client = await agntz({\n  agents: \"./agents\",\n  resources: { memory: memrez.provider() },\n});\n\nawait client.agents.run({\n  agentId: \"support-with-memory\",\n  input: \"Remember that I prefer metric units.\",\n  context: [\"app/user/\" + userId],\n});\n```\n\n`context` is a namespace grant array for resources. It is different from legacy `contextIds` scratchpad buckets.\n\n## Direct API\n\n```ts\nconst grants = [\"app/user/\" + userId];\n\nawait memrez.write(grants, \"Prefers metric units.\", {\n  topicsHint: [\"preferences\"],\n});\n\nconst entries = await memrez.read(grants, \"preferences\", { limit: 10 });\nconst multi = await memrez.read(grants, [\"preferences\", \"goals\"]); // one call, deduped\nconst topics = await memrez.scan(grants);\n\n// Deterministic full read — the viewer/audit primitive. Pass\n// includeSuperseded to see supersession chains.\nconst everything = await memrez.list(grants);\nconst auditTrail = await memrez.list(grants, { includeSuperseded: true });\n\n// Correct an entry: the replacement inherits scope/topics/type and the\n// original is superseded (never edited in place), preserving the audit trail.\nconst { entry } = await memrez.correct(grants, staleEntryId, \"Has dumbbells and a bench.\");\n```\n\nDirect calls use the same grant validation as resource tool calls.\n\n## Stores\n\n```ts\nimport {\n  createMemrez,\n  InMemoryMemoryStore,\n  PostgresMemoryStore,\n  SqliteMemoryStore,\n} from \"@agntz/memrez\";\n\ncreateMemrez({ store: new InMemoryMemoryStore() });\ncreateMemrez({ store: new SqliteMemoryStore(\"./memory.db\") });\ncreateMemrez({ store: new PostgresMemoryStore(process.env.DATABASE_URL!) });\n```\n\nUse in-memory storage for tests, SQLite for local or single-node deployments, and Postgres for multi-process deployments.\n\n## Resource behavior\n\n- `autoScan` injects visible memory topic summaries into the model context before tool calls.\n- Topic taxonomy and reasoner policy belong to Memrez-level configuration, not the agent resource declaration.\n- `preload` inlines full entries at invoke time so agents don't burn a turn recalling obvious context. Omit it for no full-entry preload; use `preload: true` for core-only preload, or the object form below for precise control.\n- `preload.limit` caps the preloaded entries (default 50), `preload.maxChars` caps the rendered context (default 12000), and `preload.types` filters entry types. Overflow is noted to the model.\n- `preload: all`, `preload: [goals, equipment]`, and legacy `preloadLimit` still work as shorthands, but the object form is preferred.\n- `memory_read` accepts one topic or a list of topics in a single call.\n- `mode: read` exposes read tools only.\n- `mode: read-write` exposes read and write tools.\n- The memory provider validates writes against runtime grants and `writePolicy`.\n- The model never receives a namespace parameter.\n\n```yaml\nresources:\n  memory:\n    kind: memory\n    mode: read-write\n    autoScan: true\n    preload:\n      core: true\n      topics: [goals, equipment]\n      limit: 30\n      maxChars: 10000\n      types: [fact, preference, summary]\n```\n\n## The reasoner\n\nThe reasoner is how memrez organizes memory. It has two jobs:\n\n- **tag** — runs on every `write`: pick the namespace from the grants, assign topics (reusing existing ones), classify the entry type, normalize the content, flag duplicates.\n- **curate** — runs on every `curate`: merge duplicates, reconcile contradictions, compact stale events into summaries, maintain topic blurbs.\n\n`createMemrez({ store })` uses `llmReasoner()` for both — direct model calls, no agntz client or runner involved, so memrez stays strictly below the agent layer (an agent's `memory_write` can never re-enter the agent platform). Override it only when you need different reasoner models or the explicit deterministic kill switch:\n\n```ts\nimport {\n  createMemrez,\n  llmReasoner,\n  DeterministicReasoner,\n} from \"@agntz/memrez\";\n\n// Default — built-in LLM reasoner, keyed from env. Customize the models:\ncreateMemrez({ store, reasoner: llmReasoner({ taggerModel: { provider: \"anthropic\", name: \"claude-haiku-4-5\" } }) });\n\n// Kill-switch / tests — no LLM. Files content under `general`; curate is a no-op:\ncreateMemrez({ store, reasoner: new DeterministicReasoner() });\n```\n\nThe agntz-agent-loop reasoner is deliberately not supported yet. Tagging and\ncuration are bounded structured model calls owned by memrez; keeping them out\nof the agent loop avoids circular setups where an agent writes memory, memrez\nruns an agent, and that agent can see memory tools again.\n\n## The `core` topic\n\nImportance is a topic convention the reasoner maintains, not schema. The\nbuilt-in tagger files durable profile facts (equipment, schedule, goals, hard\nconstraints) under the Memrez core topic alongside their subject topic — one\nentry, two topic rows. Today that topic is `core`; future Memrez-level config\nwill own renaming it for applications that already use a different label such\nas `profile`.\n\n`preload.core: true` loads that always-load set into the agent context. The\ncurator promotes and demotes entries by rewriting replacement topics during\ncuration, and the per-scope core blurb serves as a one-line profile in\n`autoScan` output. No schema change, no agent involvement — the writing agent\njust passes content.\n\n## Curation\n\n`memrez.curate(grants)` reconciles a scope through the reasoner's `curate`\nstep (the `DeterministicReasoner` has none, so it's a no-op there). The store\ntracks which `(scope, topic)` pairs have writes newer than their last curation\npass: `store.listDirtyTopics()` enumerates them globally (the work-discovery\nprimitive for curation crons), and `scan()` surfaces the per-topic\n`hasUncuratedWrites` flag. The hosted worker wires this up as\n`POST /memory/curate` plus an optional `MEMREZ_CURATE_INTERVAL` sweep.\n\nSee the public docs for the full guide: `/docs/tools/memory-memrez`.\n","readmeFilename":"README.md"}