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memory plugin stores durable facts, recalls relevant facts into prompts, and learns candidates from completed sessions. SQL schemas, exported types, tools, and tests are authoritative.","maintainers":[{"name":"sentry-bot","email":"accounts@sentry.io"}],"readme":"# @sentry/junior-memory\n\nThe memory plugin stores durable facts, recalls relevant facts\ninto prompts, and learns candidates from completed sessions. SQL schemas,\nexported types, tools, and tests are authoritative.\n\n## Surfaces\n\n- `createMemory`, `archiveMemory`, `listMemories`, and `searchMemories` are\n  model-visible tools registered by `src/plugin.ts`. `archiveMemory` can forget\n  public memory or private memory owned by the current User.\n- `userPrompt` recall contributes bounded memory context before a run.\n- `processSession` reviews completed sessions asynchronously for passive\n  learning.\n- The `memory` CLI namespace provides explicit administrative search and\n  inspection.\n- The dashboard exposes a searchable, paginated **Memories** user page. It\n  includes public memory and private memory owned by the authenticated user.\n  The overview charts global passive-extraction cost from the durable\n  `memory/memories_captured` events. The System plugin report uses the same\n  event-cost feed.\n- Authenticated REST clients can list and search authorized memories through\n  `GET /api/plugins/memory/memories`, read one through\n  `GET /api/plugins/memory/memories/:id`, and forget an authorized private\n  memory through `DELETE /api/plugins/memory/memories/:id`. Public memory is\n  read-only in the dashboard and REST API.\n\n## Scope And Visibility\n\n- The Source sets memory visibility. Model output cannot set it.\n- Public memory is visible everywhere.\n- Private memory belongs to one User. Every Identity linked to that User can\n  access it.\n- Junior records the optional Location where it learned a memory. Location is\n  a record of where Junior learned it. It does not grant access.\n- The subject says what a memory is about. It does not set access. A user\n  preference can be public or private based on its Source.\n- Recall filters candidates by visibility, status, and relevance before content\n  reaches the model.\n- Administrative reads require explicit selectors and safe output defaults.\n- Memory content, embeddings, source excerpts, and review prompts must not be\n  logged or traced.\n\n## Storage\n\n- The Drizzle schema in `src/db/schema.ts` and generated migrations define the\n  database contract.\n- Records retain provenance, the optional origin `conversation_id`, lifecycle\n  status, supersession relationships, and timestamps needed for review and deletion.\n- Embeddings are derived indexes, not independent memory authority.\n- Embedding distance never decides that two memories are duplicates. Exact\n  content and preference review own duplicate and supersession decisions.\n- Writes are idempotent where a completed session or tool retry can repeat.\n- Removal and supersession preserve enough lifecycle information to prevent\n  deleted facts from being recalled or silently recreated.\n\n## Learning And Recall\n\n- Explicit user requests to remember or forget take priority over passive\n  learning.\n- Passive extraction creates only durable, reusable facts—not transient tasks,\n  conversation summaries, secrets, or speculative interpretation.\n- Every completed passive extraction emits the namespaced\n  `memory/memories_captured` conversation event with its best-effort model cost.\n  Empty extraction outcomes remain durable for reporting but do not produce a\n  transcript row.\n- Candidate review resolves duplicates and supersession before activation.\n- Search combines independently ranked vector and PostgreSQL full-text matches\n  with reciprocal rank fusion; provider-specific raw scores are never added\n  together.\n- Both retrieval legs always run in parallel as bounded top-k probes. Each leg\n  fetches at least the caller's requested limit (and never more than the store\n  limit ceiling). Recall keeps a smaller overfetch window than explicit search\n  and slightly prefers lexical ranks so exact tokens survive soft semantic\n  neighbors. Vector recall also applies the cosine distance cutoff in SQL, and\n  embeddings use an HNSW cosine index (`vector_cosine_ops`).\n- Automatic recall also searches private memory by itself. This keeps newer\n  public memory with common words from hiding older private memory. On equal\n  RRF scores, private memory ranks first.\n- Automatic recall retrieves a bounded candidate window, then uses the\n  memory relevance model and prompt limit to select useful memories. An empty\n  result adds no prompt text.\n- Every completed automatic recall attempt emits an invisible, namespaced\n  `memory/memories_recalled` conversation event with the admitted memory IDs\n  and best-effort embedding and relevance-model cost, including retrievals that\n  find no candidates and decisions that admit no memories.\n- Automatic recall degrades to no prompt contribution when relevance selection\n  fails. Review, extraction, and write failures still fail their owning\n  hook/task without corrupting existing memory state.\n\n## Configuration\n\n- `AI_MEMORY_MODEL` or `memoryPlugin({ modelId })` selects the structured\n  review model.\n- `memoryPlugin({ disableRecall: true })` disables automatic prompt recall.\n- `memoryPlugin({ disableExtraction: true })` disables passive session\n  extraction. The two flags are independent and do not disable explicit memory\n  tools.\n- Automatic recall uses a fixed cosine distance cutoff of `0.45` (for\n  `text-embedding-3-small`). Explicit search does not apply that cutoff.\n- Generate schema changes with `pnpm --filter @sentry/junior-memory db:generate`.\n\nFollow `../../policies/data-redaction.md`, `../../policies/security.md`, and the\nplugin contract in `../junior-plugin-api/README.md`.\n","readmeFilename":"README.md"}