oh-my-pi/packages/mnemopi/CHANGELOG.md

Changelog

[Unreleased]

[16.3.9] - 2026-07-06

Fixed

• Fixed extractor JSON parsing to correctly unwrap object-shaped facts, instructions, preferences, and timeline items from known text fields instead of persisting literal [object Object] rows.

[16.3.7] - 2026-07-05

Added

• Added RecallOptions.contentPreviewChars to allow customizing or disabling the content preview cap (default is 500, set to 0 for full content).
• Added RecallResult.truncated and RecallResult.full_length properties to easily identify clipped previews without parsing trailing markers.

Fixed

• Fixed background LLM fact extraction to preserve specific extractor categories (instructions, preferences, timelines, and kg triples) in MEMORIA tables and graph triples instead of flattening them into generic fact/entity rows.
• Improved recall previews and factLine context to append a trailing ellipsis (…) when content is clipped, preventing mid-word truncation without a marker.

[16.3.5] - 2026-07-04

Fixed

• Fixed remember(..., { embedText }) so hosts can store full transcripts while embedding, FTS-indexing, and rebuild-reembedding a marker-free projection. (#4395)

[16.2.2] - 2026-06-27

Fixed

• Improved resilience during API extraction calls by enhancing the handling of rate limits and transient errors.

[16.1.17] - 2026-06-24

Fixed

• Fixed remember(..., { extract: true }) fact/entity extraction accepting an extractText override so hosts can store full transcripts while mining facts from a safer projection; also tightened deterministic Instruction: extraction to require an explicit I/you subject instead of treating every always/never clause as a user instruction. (#3372)

[16.1.8] - 2026-06-20

Fixed

• Capped per-input length in embed() at MNEMOPI_EMBEDDING_MAX_INPUT_CHARS (default 8192 chars, override via the env var or embeddings.maxInputChars runtime option; 0 disables) so a long retention transcript can no longer overflow the embedding model's context window. Oversized inputs are clipped with a head/tail split so chronological transcripts keep both the opening setup and the most recent turns instead of losing the latest content under a naive prefix slice. llama.cpp's /embeddings server used to reject the request with request (N tokens) exceeds the available context size, silently dropping vector recall for that memory (#3126).
• Fixed the proactive-linking write path ignoring host configuration: proactiveLinkIfEnabled read MNEMOPI_PROACTIVE_LINKING directly, so a host that enabled proactive linking through configureRecallFeatures() had no effect unless the environment variable was also set. proactiveLinking is now a RecallFeatureFlags option resolved through a proactiveLinkingEnabled() fallback, matching the existing polyphonic and enhanced recall flags, with the MNEMOPI_PROACTIVE_LINKING environment variable still taking precedence whenever it is set. (#2440)

[16.1.3] - 2026-06-19

Added

• Exposed setLocalModelInitializer (and the LocalEmbeddingModel, LocalModelInitializer, LocalModelInitOptions, StandardEmbeddingModel types) so hosts can route fastembed loads through a dedicated subprocess and keep onnxruntime-node's NAPI constructor + finalizer out of their own address space. Same wipe semantics as the existing setLocalModelInitializerForTests seam; the agent CLI uses it to crash-proof Windows when memory.backend: mnemopi is enabled (#3031).

Fixed

• Fixed background fact extraction skipping runtime-configured remote LLM endpoints when MNEMOPI_LLM_BASE_URL was unset, so remember(..., { extract: true }) now stores remote-distilled facts from mnemopi.llm config instead of falling back to regex heuristics. (#3041)
• Fixed local fastembed startup on macOS ARM64 by letting fastembed@2.1.0 install its matching onnxruntime-node@1.21.0 native runtime instead of forcing 1.26.0, and by repairing missing tokenizer sidecars from the upstream Hugging Face model cache when a stale fastembed archive lacks them. (#3054)

[16.0.6] - 2026-06-18

Fixed

• Forced the on-demand fastembed runtime install to override fastembed's archived onnxruntime-node@1.21.0 transitive pin with Mnemopi's onnxruntime-node@1.26.0 pin, fixing local embedding startup on macOS ARM64. (#2920)

Changed

• Updated OpenRouter request headers to use standard shared headers from the pi-ai package

[16.0.5] - 2026-06-17

Fixed

• Capped sleep_consolidation episodic rows at maxEpisodeChars (default 100KB, MNEMOPI_MAX_EPISODE_CHARS) so raw session transcripts cannot be stored and extracted as multi-megabyte episodes. (#2869)
• Skipped regex-only entity and pattern fact extraction for oversized raw transcripts so progress/log noise cannot flood MEMORIA with junk facts. (#2868)

[15.13.1] - 2026-06-15

Added

• Added a wipe-and-rebuild reconcile (reconcileEmbeddingModel) that runs when the configured embedding model changes. At store open, if the model stamped on stored memory_embeddings rows differs from the active currentEmbeddingModel(), the stale embeddings and their binary vectors are dropped and every existing memory is enqueued for background re-embedding (in bounded batches) at the new model/dimension. The destructive wipe is skipped whenever it could not be rebuilt — embeddings disabled via the runtime option or the MNEMOPI_NO_EMBEDDINGS env, an unresolved (empty) active model, or a read-only open (reconcile: false, used by ephemeral stats readers that would exit before the async rebuild finished) — so a stale-but-valid corpus is never destroyed without a replacement. Recall degrades gracefully (FTS-only) for memories whose vectors are not yet rebuilt (#2476)

Fixed

• Normalized enhanced recall fact scoring against lexical coverage so high-confidence facts that only match generic query tokens no longer outrank exact working-memory hits. (#2441)

[15.12.4] - 2026-06-13

Fixed

• Fixed consolidateToEpisodic (the function backing sleep / sleepAllSessions) never populating the episodic graph: the gists and graph_edges tables stayed at 0 rows across every bank even after multiple consolidation cycles, so Polyphonic Recall's graph voice (BFS over findGistsByParticipant / findRelatedMemories) always returned nothing. Consolidation now best-effort ingests the new episodic memory into EpisodicGraph so the gist row, gist→memory ctx edge, fact edges, and cross-memory similarity/entity/temporal edges land alongside the episodic row. Independent of the existing MNEMOPI_PROACTIVE_LINKING flag, which still gates the same enrichment on the remember() write path. (#2435)

[15.12.0] - 2026-06-12

Changed

• Moved fastembed and onnxruntime-node from dependencies to optional peerDependencies pinned to exact versions. When the peers are absent (bundled CLI, compiled binary, or installs that skip optional peers), the local embedding path bun installs the pinned pair into ~/.omp/cache/fastembed-runtime/<version-key> on first use and loads fastembed from there — restoring local embeddings in bundled distributions and removing ~270MB of eager native downloads from default installs (#2389)

[15.11.4] - 2026-06-12

Added

• Added configureRecallFeatures() (exported from the package root, core, and config) so hosts can enable the polyphonic recall engine and the enhanced recall query cache programmatically. polyphonicRecallEnabled(), enhancedRecallEnabled(), and isEnhancedRecallEnabled() now fall back to these configured defaults, with the MNEMOPI_POLYPHONIC_RECALL / MNEMOPI_ENHANCED_RECALL environment variables still taking precedence whenever they are set. (#2323)

Fixed

• Fixed the embedding pipeline's silent catch {} blocks (runEmbedding(), getLocalModel(), and the local-model path of embed()) swallowing failures with zero diagnostics. These best-effort paths still degrade gracefully (return null / skip the write), but now emit structured logger.debug entries with the error and per-site context (item count, model name). The mnemopi.debug config flag now propagates into the core library via runtime options (MnemopiOptions.debug → ResolvedMnemopiRuntimeOptions.debug) and escalates these logs to warn so they surface at the default log level. (#2322)

Changed

• Extraction, embedding, and remote-LLM clients now accept an ApiKey (static string or resolver) and resolve it per request through withAuth, so 401s force-refresh and rotate credentials via the central auth-retry policy instead of failing with a stale key. Empty-key setups (local/proxy endpoints without Authorization) and pinned literal keys behave exactly as before.
• Embedding and remote-LLM 401 errors now throw pi-ai's typed ProviderHttpError instead of Object.assign-patched Errors, keeping the same structural .status contract for the auth-retry classifier.
• SHMR consolidation clustering (core/shmr) now uses the real embedding provider when one is configured instead of always hashing: embed(), the new embedBatch(), clusterBySimilarity(), computeHarmonyScore(), harmonize(), and recallBeliefs() are now async, batch-embed candidate texts in a single provider call, and reuse precomputed vectors from memory_embeddings for episodic candidates. The SHA1 bag-of-words hash remains as the deterministic fallback when no provider is available or embedding fails. (#2324)

[15.10.12] - 2026-06-10

Changed

• Reworked the in-memory fallback vector search to build a normalized exact vector index per query, matching the shape needed for future quantized or TurboVec-style backends without adding a new dependency yet.

[15.10.11] - 2026-06-10

Fixed

• Fixed embedding provider detection to match openrouter by URL host, so custom embedding endpoints are now recognized correctly instead of being misclassified by substring matching
• Fixed the check for OpenRouter base URLs so only true openrouter hosts are treated as non-custom

[15.10.8] - 2026-06-09

Added

• Added a fetch option to ExtractionClient to inject a custom fetch implementation for remote LLM requests
• Added an optional fetch option to extractFacts to control the transport used for remote extraction calls
• Added support for passing a custom fetch implementation through complete and summarizeMemories via remote LLM options

[15.9.1] - 2026-06-04

Breaking Changes

• Changed Mnemopi.recall(), Mnemopi.recallEnhanced(), Mnemopi.search(), Mnemopi.query(), the module-level recall/recallEnhanced/search/query exports, the BeamMemory.recall/recallEnhanced methods, the free recall/recallEnhanced functions in core/beam/recall, and orchestrateRecall to return Promise<RecallResult[]> so the recall pipeline can auto-derive queryEmbedding from the query text via embedQuery. Callers must await recall calls; pass queryEmbedding: null to opt out of auto-embedding and stay on FTS-only.
• Changed the MCP entrypoints handleToolCall, callToolJson, and handleJsonRpc in mcp-server/mcp-tools to async so the recall/shared-recall handlers can await the new Promise<ToolResult[]> shape; external MCP transports must await these.

Fixed

• Fixed memory_embeddings never being populated by the production remember/rememberBatch/updateWorking/consolidateToEpisodic paths; embedding generation is now scheduled as a background task on beam.pendingExtractions (mirroring scheduleFactExtraction), so configured providers (fastembed, OpenAI-compatible API, custom) actually run and rows land in memory_embeddings(memory_id, embedding_json, model). (#1832)
• Fixed recall()/recallEnhanced() never deriving a query embedding from the query text, which silently degraded every deployment to FTS-only regardless of provider configuration. The recall pipeline now auto-calls embedQuery(query) when options.queryEmbedding is undefined; pass null to keep the old FTS-only behaviour. (#1832)
• Fixed toRecallOptions dropping queryEmbedding between the Mnemopi facade and the beam layer, so callers can now explicitly pin or disable the query vector through the public API.
• Fixed withMemory (CLI) and withBeam/withSharedBeam (MCP) closing the SQLite handle before background fact-extraction and embedding tasks finished, so short-lived mnemopi store/mnemopi sleep and MCP remember/update paths now drain flushExtractions before close instead of silently dropping memory_embeddings rows. CLI handlers and MCP handleRemember/handleUpdate/handleSleep/etc. are async as a result. (#1832, follow-up to #1833 review)
• Fixed the process-wide embedQuery() cache in core/embeddings.ts keying by query text alone, which let two Mnemopi instances in the same process with different providers/models cross-contaminate their dense_score rankings. The cache key now includes a WeakMap-assigned provider identity, the resolved model name, and the configured apiUrl, so disjoint runtimes never read each other's cached vectors. (#1832, follow-up to #1833 review)

[15.7.4] - 2026-05-31

Fixed

• Fixed the darwin-x64 release build failing in bun build --compile because the Windows ORT 1.24 preload pulled onnxruntime-node into the static graph and there is no darwin/x64 prebuilt for that line. The preload is now guarded behind a process.platform === "win32" literal that Bun dead-code-eliminates on non-Windows targets; macOS/Linux load fastembed's bundled ORT 1.21 binding as before.

[15.7.3] - 2026-05-31

Changed

• Changed embedding result normalization to return Float32Array vectors so embed and embedQuery now cache and emit float32 rows
• Changed the embedding provider contract to a single typed EmbeddingOutput (AsyncIterable<number[][]>) instead of unknown, matching fastembed's embed(), so EmbeddingProvider.embed and the provider runtime option stream the embedding matrix as async batches (async *embed(texts) { yield texts.map(embedOne); })
• Changed local model cache directory resolution for fastembed to use getFastembedCacheDir instead of the hard-coded ~/.hermes/cache/fastembed path

Fixed

• Fixed cosine similarity behavior across retrieval, clustering, and caching to consistently handle mismatched vector lengths as zero-padded and ignore non-finite values
• Fixed embedding API requests to retry transient failures with backoff via shared retry logic before returning null
• Fixed compiled omp binaries losing local Mnemopi embeddings by keeping fastembed and onnxruntime-node reachable to Bun's static compiler while preserving lazy runtime loading.

[15.7.2] - 2026-05-31

Fixed

• Fixed Windows startup crashes by keeping fastembed's older ONNX Runtime binding lazy until local embeddings are used.
• Fixed a segfault at startup from eagerly loading fastembed: importing the embeddings module pulled in fastembed, which eagerly loads the onnxruntime-node native addon. The import is now deferred until a local fastembed model is actually initialized, so API-model, disabled-embeddings, and test runtimes never load the native addon.

[15.6.0] - 2026-05-30

Added

• Added llm.extractionPrompt runtime option to override the fact-extraction prompt template using {text} and {lang} placeholders
• Added llm.consolidationPrompt runtime option to override the consolidation sleep prompt template using {memories}, {source}, and {memory_count} placeholders
• Published @oh-my-pi/pi-mnemopi to npm: the local SQLite memory engine is now built, checked, tested, and released through the monorepo CI pipeline alongside the other workspace packages.
• Exported the diagnostic inspector as the @oh-my-pi/pi-mnemopi/diagnose subpath for coding-agent memory maintenance commands.
• Added flushExtractions() (on Mnemopi, BeamMemory, and as a module-level export) to drain in-flight background fact extraction; used by tests and graceful shutdown so facts are persisted before the database closes.

Changed

• Changed fact extraction to prefer a configured runtime LLM completion path before host extraction, with automatic fallback when the configured completion returns no output or fails

Fixed

• Fixed rememberBatch(..., { extract: true }) to run background fact extraction for batch uploads (including per-item extract flags) so extracted facts are generated and recallable after extraction
• Fixed extract: true fact extraction to continue safely when no LLM is configured by turning extraction failures into no-op background tasks
• Fixed configured LLM fact extraction by using temperature 0 so re-ingesting the same text is deterministic and avoids near-duplicate extractions
• Fixed remember(..., { extract: true }) silently dropping the flag: it now schedules the LLM fact extractor (extractFactsSafe) over the stored content and persists the extracted facts so they become recallable. Previously the LLM extractor had no production callers and extract was dead.