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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[2.27.0] - 2026-03-06

Added

  • nmem_edit — Edit memory type, content, or priority by fiber ID. Preserves all neural connections
  • nmem_forget — Soft delete (expires) or hard delete (permanent removal with cascade)
  • Enhanced MCP instructions — Richer behavioral directives for brain growth, all 38 tools listed
  • Enhanced plugin instructions — Comprehensive agent guidance in .claude-plugin/plugin.json

Fixed

  • FK constraint errors — INSERT OR REPLACE INTO neuron_states and save_maturation catch IntegrityError for deleted neurons
  • Auto-type classifier bias — Insight checked before decision, tightened TODO/decision patterns

  • DECISION_PATTERNS greediness — Removed overly broad patterns from auto_capture.py

  • Cognitive Reasoning Layer — 8 new MCP tools for hypothesis-driven reasoning (38 tools total)

  • nmem_hypothesize — Create and manage hypotheses with Bayesian confidence tracking and auto-resolution
  • nmem_evidence — Submit evidence for/against hypotheses, auto-updates confidence via sigmoid-dampened shift
  • nmem_predict — Make falsifiable predictions with deadlines, linked to hypotheses via PREDICTED synapse
  • nmem_verify — Verify predictions as correct/wrong, propagates result to linked hypothesis
  • nmem_cognitive — Hot index: ranked summary of active hypotheses + pending predictions with calibration score
  • nmem_gaps — Knowledge gap metacognition: detect, track, prioritize, and resolve what the brain doesn't know
  • nmem_schema — Schema evolution: evolve hypotheses into new versions via SUPERSEDES synapse chain
  • nmem_explain — (moved to cognitive) Trace shortest path between concepts with evidence
  • Schema v21 — Three new tables: cognitive_state, hot_index, knowledge_gaps
  • Pure cognitive engine (engine/cognitive.py) — Stateless scoring and confidence functions
  • Bayesian confidence model — Auto-resolution at confidence thresholds
  • Prediction calibration — Tracks correct/wrong ratio
  • Schema version chain — SUPERSEDES synapse + parent_schema_id walk with cycle guard

[2.26.0] - 2026-03-05

Added

  • Brain Health Guide — comprehensive guide explaining all 7 health metrics with improvement roadmap
  • Connection Tracing docs (nmem_explain) — previously undocumented feature
  • Embedding auto-detection (provider = "auto") — auto-detects Ollama/sentence-transformers/Gemini/OpenAI
  • Consolidation post-run hints — warns about orphans and missing consolidation
  • Pre-ship verification script — automated quality gate before releases
  • MCP instructions update — health interpretation, priority scale, tagging, maintenance

[2.25.1] - 2026-03-05

Fixed

  • nmem flush stdin blocking — Process hangs forever when spawned as subprocess without piped input; sys.stdin.read() blocks because no EOF is sent. Added 5s timeout via ThreadPoolExecutor (fixes #27)
  • Consolidation prune — Protects fiber members from orphan pruning + invariant tests
  • Orphan rate — Counts fiber membership correctly, isolated E2E tests from production DB
  • Dashboard dist — Bundled for pip install compatibility

[2.25.0] - 2026-03-04

Added

  • Proactive Memory Auto-Save — 4-layer system ensures agents use NeuralMemory without explicit instructions
  • MCP instructions — Behavioral directives in InitializeResult, auto-injected into agent context
  • Post-tool passive capture — Server-side auto-analysis of recall/context/recap/explain results with rate limiting (3/min)
  • Plugin instructions field — Short nudge for all plugin users
  • Enhanced stop hook — Transcript capture 80→150 lines, session summary extraction, always saves at least one context memory
  • Ollama embedding provider — Local zero-cost inference via Ollama API (contributed by @xthanhn91)

Fixed

  • Scale performance bottlenecks — Consolidation prune, neuron dedup, cache improvements (PR #23)
  • OpenClaw plugin execute() signature — Missing id parameter broke all agent tool calls (issue #19)
  • Auto-consolidation crash — ValueError: 'none' is not a valid ConsolidationStrategy (issue #20)
  • nmem remember --stdin — CLI now supports piped input for safe shell usage (issue #21)
  • CI test compatibility — test_remember_sensitive_content mock fix for Python 3.11

[2.24.2] - 2026-03-03

Added

  • Dashboard Phase 2 — Complete visual dashboard overhaul
  • Sigma.js graph visualization — WebGL-rendered neural graph with ForceAtlas2 layout, node limit selector (100-1000), click-to-inspect detail panel, color-coded by neuron type
  • ReactFlow mindmap — Interactive fiber mindmap with dagre left-to-right tree layout, custom nodes (root/group/leaf), MiniMap, zoom/pan, click-to-select neuron details
  • Theme toggle — Light / Dark / System cycle button in TopBar, warm cream light mode (#faf8f3), class-based TailwindCSS 4 dark mode via @custom-variant
  • Delete brain — Trash icon on inactive brains in Overview table with confirmation dialog
  • Click-to-switch brain — Click inactive brain row to switch active brain
  • CLI update check fix — Editable/dev installs no longer show misleading "Update available" prompts

Removed

  • Legacy dashboard UI — Removed dashboard.html, index.html, legacy JS/CSS/locales (4,451 LOC), /static mount from FastAPI

Dependencies

  • Added @xyflow/react, @dagrejs/dagre (ReactFlow mindmap)
  • Added graphology-layout-forceatlas2 (Sigma.js graph layout)

[2.24.1] - 2026-03-03

Fixed

  • IntegrityError in consolidation — save_maturation FK constraint failed when orphaned maturation records referenced deleted fibers
  • Added cleanup_orphaned_maturations() to purge stale records before stage advancement
  • Defensive try/except for any remaining FK errors during _mature()

Tests

  • 2 new tests for orphaned maturation handling
  • Total: 3145 passing

[2.24.0] - 2026-03-03

Fixed

  • [CRITICAL] SQL Injection Prevention — get_synapses_for_neurons direction param validated against whitelist instead of raw f-string
  • [HIGH] BFS max_hops off-by-one — Nodes at depth=max_hops no longer uselessly enqueued then discarded
  • [HIGH] Bidirectional path search — memory_store.get_path() now respects bidirectional=True via to_undirected()
  • [HIGH] JSON-RPC parse errors — Returns proper {"code": -32700} error instead of silently dropping malformed messages
  • [HIGH] Encryption failure policy — Returns error instead of silently storing plaintext when encryption fails
  • [HIGH] disable_auto_save placement — Moved inside try block in tool_handlers and conflict_handler so finally always re-enables
  • [HIGH] Cross-brain depth validation — Added int coercion + 0-3 clamping for depth parameter
  • [HIGH] Factory sync exception handling — Narrowed bare except Exception to specific exception types
  • [HIGH] SSN pattern false positives — Excluded invalid prefixes (000, 666, 900-999); raised base64/hex minimums to 64 chars
  • [MEDIUM] MCP notification handling — Unknown notifications return None instead of error responses
  • [MEDIUM] Brain ID error propagation — New _get_brain_or_error() helper prevents uncaught ValueError in 6 handlers
  • [MEDIUM] Connection handler I/O — Removed unused brain fetch in _explain
  • [MEDIUM] Evidence fetch optimization — Removed wasted source neuron from evidence query
  • [MEDIUM] Narrative date validation — Added end_date < start_date guard
  • [MEDIUM] CORS port handling — Enumerate common dev ports instead of invalid :* wildcard
  • [MEDIUM] Embedding config — Graceful fallback instead of crash on invalid provider
  • [LOW] Type coercion — max_hops/max_fibers/max_depth safely coerced to int
  • [LOW] Immutability — Dict mutations replaced with spread patterns in review_handler and encoder
  • [LOW] Schema cleanup — Removed empty "required": [] from nmem_suggest

Tests

  • Fixed and added 5 tests (max_hops_capped, avg_weight, default_hops, tier assertions, embedding fallback)
  • Total: 3143 passing

[2.23.0] - 2026-03-03

Added

  • nmem_explain — Connection Explainer — New MCP tool to explain how two entities are related
  • Finds shortest path through synapse graph via bidirectional BFS
  • Hydrates path with fiber evidence (memory summaries)
  • Returns structured steps + human-readable markdown explanation
  • New engine module: connection_explainer.py with ConnectionStep and ConnectionExplanation dataclasses
  • New handler mixin: ConnectionHandler following established mixin pattern
  • Args: from_entity, to_entity (required), max_hops (optional, 1-10, default 6)

Fixed

  • OpenClaw Compatibility — Handle JSON string arguments in MCP tools/call handler
  • OpenClaw sends arguments as JSON string instead of dict — now auto-parsed
  • Prevents crash when receiving "arguments": "{\"content\": \"...\"}" format

Improved

  • Bidirectional BFS — get_path() in SQLite storage now supports bidirectional=True
  • Uses UNION ALL to traverse both outgoing and incoming synapse edges
  • Updated abstract base + all 5 storage implementations

Tests

  • 11 new tests for connection explainer (engine + MCP handler + integration)
  • Total: 3140+ passing

[2.22.0] - 2026-03-03

Fixed

  • #12 Version Mismatch — Detect editable installs in update hint, show version in nmem_stats
  • #14 Dedup on Remember — Enable SimHash dedup (Tier 1) by default, surface dedup_hint in remember response, skip content < 20 chars
  • #11 SEMANTIC Stage Blocked — Rehearse maturation records on retrieval so memories can reach SEMANTIC stage (requires 3+ distinct reinforcement days)
  • #15 Low Activation Efficiency — Fix Hebbian learning None activation floor (0.1 instead of None → delta > 0), add dormant neuron reactivation during consolidation

Added

  • #10 Semantic Linking — SemanticLinkingStep cross-links entity/concept neurons to existing similar neurons (reduces orphan rate)
  • #13 Neuron Diversity — ExtractActionNeuronsStep + ExtractIntentNeuronsStep extract ACTION/INTENT neurons from verb/goal phrases (improves type diversity from 4-5 to 6-7 of 8 types)
  • Dormant Reactivation — Consolidation ENRICH tier bumps up to 20 dormant neurons (access_frequency=0) with +0.05 activation

Tests

  • 55 new tests across 6 test files: version check (12), dedup default (9), maturation rehearsal (5), semantic linking (6), action/intent extraction (15), activation efficiency (8)
  • Total: 3127 passing

[2.21.0] - 2026-03-03

Added

  • Cross-Language Recall Hint — Smart detection when recall misses due to language mismatch
  • Detects query language vs brain majority language (Vietnamese ↔ English)
  • Shows actionable cross_language_hint in recall response when embedding is not enabled
  • Suggests pip install if sentence-transformers not installed, config-only if already installed

  • detect_language() extracted as reusable module-level function with Vietnamese-unique char detection

  • Embedding Setup Guide — Comprehensive docs for all embedding providers

  • New docs/guides/embedding-setup.md with provider comparison, config examples, troubleshooting
  • Free multilingual model recommendations: paraphrase-multilingual-MiniLM-L12-v2 (50+ languages, 384D, ~440MB)

  • Provider comparison table: sentence_transformer (free/local) vs Gemini vs OpenAI

  • Embedding Documentation & Onboarding

  • README: updated "None — pure algorithmic" → "Optional", added embedding quick-start section
  • .env.example: added GEMINI_API_KEY, OPENAI_API_KEY vars
  • Onboarding step 6: suggests cross-language recall setup for new users

Improved

  • Vietnamese Language Detection — More accurate short-text detection
  • Added _VI_UNIQUE_CHARS set (chars exclusive to Vietnamese, not shared with French/Spanish)
  • Short text like "lỗi xác thực" now correctly detected as Vietnamese

Tests

  • 18 new tests in test_cross_language_hint.py (8 detect_language + 10 hint logic)
  • All 3090+ tests pass

[2.20.0] - 2026-03-03

Added

  • Gemini Embedding Provider — Cross-language recall via Google Gemini embeddings (PR #9 by @xthanhn91)
  • GeminiEmbedding provider: gemini-embedding-001 (3072D), text-embedding-004 (768D)
  • Parallel anchor sources: embedding + FTS5 run concurrently (not fallback-only)
  • Config pipeline: config.toml[embedding] → EmbeddingSettings → BrainConfig → SQLite
  • Doc training embeds anchor neurons for cross-language retrieval
  • E2E validated: 100/100 Vietnamese queries on English KB (avg confidence 0.98)

  • Optional dependency: pip install 'neural-memory[embeddings-gemini]'

  • Sufficiency Enhancements — Smarter retrieval gating

  • EMA calibration: per-gate accuracy tracking, auto-downgrade unreliable gates
  • Per-query-type thresholds: strict (factual), lenient (exploratory), default profiles
  • Diminishing returns gate: early-exit when multi-pass retrieval plateaus

Fixed

  • Comprehensive Audit — 7 CRITICAL, 17 HIGH, 18 MEDIUM fixes
  • Security: auth guard on consolidation routes, CORS wildcard removal, path traversal fix
  • Performance: @lru_cache regex, cached QueryRouter/MemoryEncryptor, asyncio.gather embeddings
  • Infrastructure: .dockerignore, .env.example, bounded exports, async cursor managers
  • PR #9 Review Fixes — 3 HIGH, 6 MEDIUM, 3 LOW
  • Bare except → specific exceptions in doc_trainer
  • EmbeddingSettings frozen + validated (rejects invalid providers)
  • Probe-first early exit in embedding anchor scan (performance)
  • Correct task_type for semantic discovery consolidation
  • Hardcoded paths → env vars in E2E scripts

Tests

  • 33 new sufficiency tests (EMA calibration, query profiles, diminishing returns)
  • 6 new EmbeddingSettings validation tests
  • 13 new Gemini embedding provider tests
  • Full suite: 3054 passed, 0 failed

[2.19.0] - 2026-03-02

Added

  • React Dashboard — Modern dashboard replacing legacy Alpine.js/vis.js
  • Vite 7 + React 19 + TypeScript + TailwindCSS 4 + shadcn/ui
  • Warm cream light theme (#faf8f3) with dark mode support
  • 7 pages: Overview, Health (Recharts radar), Graph, Timeline, Evolution, Diagrams, Settings
  • TanStack Query 5 for data fetching, Zustand 5 for state
  • Lazy-loaded routes with skeleton loaders
  • /ui and /dashboard serve React SPA, legacy at /ui-legacy and /dashboard-legacy

  • Brain file info: paths, sizes, disk usage in Settings page

  • Telegram Backup Integration — Send brain .db files to Telegram

  • TelegramClient (aiohttp): send_message (auto-split >4096 chars), send_document, backup_brain
  • TelegramConfig frozen dataclass in unified_config.py ([telegram] TOML section)
  • CLI: nmem telegram status, nmem telegram test, nmem telegram backup [--brain NAME]
  • MCP tool: nmem_telegram_backup (28 total tools)
  • Dashboard API: GET /api/dashboard/telegram/status, POST .../test, POST .../backup
  • Dashboard Settings page: status indicator, test button, backup button
  • Bot token via NMEM_TELEGRAM_BOT_TOKEN env var only (never in config file)

  • Chat IDs in config.toml under [telegram] section

  • Brain Files API — GET /api/dashboard/brain-files

  • Returns brains directory path, per-brain file path + size, total disk usage

Tests

  • 15 new Telegram tests: config, token, client, status, MCP handler
  • MCP tool count updated (27→28)

[2.18.0] - 2026-03-02

Added

  • Export Markdown — nmem brain export --format markdown -o brain.md
  • Human-readable brain export grouped by memory type (facts, decisions, insights, etc.)
  • Tag index with occurrence counts
  • Statistics table with neuron/synapse/fiber breakdowns
  • Pinned memory indicators and sensitive content exclusion support

  • New module: cli/markdown_export.py (~180 LOC)

  • Original Timestamp — event_at parameter on nmem_remember

  • MCP: nmem_remember(content="Meeting at 8am", event_at="2026-03-02T08:00:00")
  • CLI: nmem remember "Meeting" --timestamp "2026-03-02T08:00:00"
  • Time neurons and fiber time_start/time_end use the original event time
  • Supports ISO format with optional timezone (auto-stripped for UTC storage)

Changed

  • Health Roadmap Enhancement — Concrete metrics in improvement actions
  • Actions now include specific numbers: "Store memories to build ~250 more connections (current: 0.5 synapses/neuron, target: 3.0+)"
  • Added timeframe field to roadmap: "~2 weeks with regular use"
  • Dynamic action strings computed from actual brain metrics (neuron counts, orphan rate, etc.)
  • Grade transition messages include estimated timeframe

Tests

  • 31 new tests: test_markdown_export.py (11), test_health_roadmap.py (13), test_event_timestamp.py (7)

[2.17.0] - 2026-03-02

Added

  • Knowledge Base Training — Multi-format document extraction with pinned memories
  • 12 supported formats: .md, .mdx, .txt, .rst (passthrough), .pdf, .docx, .pptx, .html/.htm (rich docs), .json, .xlsx, .csv (structured data)
  • doc_extractor.py — Format-specific extractors with 50MB file size limit
  • Optional dependencies via neural-memory[extract] for non-text formats (pymupdf4llm, python-docx, python-pptx, beautifulsoup4, markdownify, openpyxl)
  • Pinned Memories — Permanent knowledge that bypasses decay, pruning, and compression
  • Fiber.pinned: bool field — pinned fibers skip all lifecycle operations
  • 4 lifecycle bypass points: decay, pruning, compression, maturation
  • nmem_pin MCP tool for manual pin/unpin
  • Training File Dedup — SHA-256 hash tracking prevents re-ingesting same documents
  • training_files table with hash, status, progress tracking
  • Resume support for interrupted training sessions
  • Tool Memory System — Tracks MCP tool usage patterns and effectiveness
  • MemoryType.TOOL — New memory type (90-day expiry, 0.06 decay rate)
  • SynapseType.EFFECTIVE_FOR + USED_WITH — Tool effectiveness and co-occurrence synapses
  • PostToolUse hook — Fast JSONL buffer capture (<50ms, no SQLite on hot path)
  • engine/tool_memory.py — Batch processing during consolidation
  • PROCESS_TOOL_EVENTS consolidation strategy

Fixed (Comprehensive Audit — 4 CRITICAL, 8 HIGH, 12 MEDIUM)

  • CRITICAL: Auth guard on consolidation routes, CORS wildcard removal, path traversal fix, coverage threshold enforcement
  • HIGH: Reject null client IP, sanitize error messages, Windows ACL key protection, FalkorDB password warning
  • Performance: Module-level regex compilation with @lru_cache, cached QueryRouter + MemoryEncryptor (lazy singleton), asyncio.gather for parallel embeddings, batch neuron delete (chunked 500), SQL FILTER clause combining queries
  • Infrastructure: .dockerignore, .env.example, bounded export (LIMIT 50000), asyncio.Lock for storage cache, cursor context managers

Changed

  • Schema version 18 → 20 (tool_events table, pinned column on fibers, training_files table)
  • SynapseType enum: 22 → 24 types (EFFECTIVE_FOR, USED_WITH)
  • MemoryType enum: 10 → 11 types (TOOL)
  • MCP tools: 26 → 27 (added nmem_pin)
  • ROADMAP.md — Complete rewrite as forward-looking 5-phase vision
  • Agent instructions — 7 new sections covering all 28 MCP tools
  • MCP prompt — Added KB training, pin, health, review, import instructions

[2.16.0] - 2026-02-28

Added

  • Algorithmic Sufficiency Check — Post-stabilization gate that early-exits when activation signal is too weak
  • 8-gate evaluation (priority-ordered, first match wins): no_anchors, empty_landscape, unstable_noise, ambiguous_spread, intersection_convergence, high_coverage_strong_hit, focused_result, default_pass
  • Unified confidence formula from 7 weighted inputs (activation, focus_ratio, coverage, intersection_ratio, proximity, stability, path_diversity)
  • Conservative bias — false-INSUFFICIENT penalized 10× more than false-SUFFICIENT
  • engine/sufficiency.py (~302 LOC), storage/sqlite_calibration.py (~133 LOC)
  • Schema migration v17 → v18 (retrieval_calibration table)

[2.15.1] - 2026-02-28

Fixed

  • SharedStorage CRUD Endpoint Mismatch — Client called endpoints that didn't exist on server
  • Added 14 CRUD endpoints to server/routes/memory.py (neurons + synapses full lifecycle, state, neighbors, path)
  • 6 new Pydantic models in server/models.py
  • Brain Import Deduplication — Changed INSERT → INSERT OR REPLACE in sqlite_brain_ops.py for idempotent imports

[2.15.0] - 2026-02-28

Added

  • Trusted Networks for Docker/Container Deployments — Configurable non-localhost access via NEURAL_MEMORY_TRUSTED_NETWORKS env var (CIDR notation)
  • is_trusted_host() function with safe ipaddress module validation
  • Default remains localhost-only (secure by default)

Fixed

  • OpenClaw Plugin Zod Peer Dependency — Pinned zod to ^3.0.0

[2.14.0] - 2026-02-27

Added

  • MCP Tool Tiers — 3-tier system (minimal/standard/full) for controlling exposed tools
  • ToolTierConfig frozen dataclass with case-insensitive tier parsing
  • get_tool_schemas_for_tier() filters tools by tier level
  • Minimal: 4 core tools, Standard: 8 tools, Full: all 27 tools
  • Hidden tools still callable via dispatch (tier controls visibility, not access)
  • Consolidation Eligibility Hints — _eligibility_hints() explains why 0 changes happened
  • Habits Status — Progress bars for emerging patterns
  • Diagnostics Improvements — Actionable recommendations with specific numbers
  • Graph SVG Export — Pure Python SVG export with dark theme, zero external deps

[2.13.0] - 2026-02-27

Added

  • Error Resolution Learning — When a new FACT/INSIGHT contradicts an existing ERROR memory, the system creates a RESOLVED_BY synapse linking fix → error instead of just flagging a conflict
  • RESOLVED_BY synapse type added to SynapseType enum (22 types total)
  • Resolved errors get ≥50% activation demotion (2x stronger than normal conflicts)
  • Error neurons marked with _conflict_resolved and _resolved_by metadata
  • Auto-detection via neuron metadata {"type": "error"} — no caller changes needed
  • Zero-cost: pure graph manipulation, no LLM calls
  • 7 new tests in test_error_resolution.py

Changed

  • resolve_conflicts() accepts optional existing_memory_type parameter
  • conflict_detection.py now imports logging module for RESOLVED_BY synapse debug logging

[2.8.1] - 2026-02-23

Added

  • FalkorDB Graph Storage Backend — Optional graph-native storage replacing SQLite for high-performance traversal
  • FalkorDBStorage composite class implementing full NeuralStorage ABC via 5 specialized mixins
  • FalkorDBBaseMixin — connection pooling, query helpers (_query, _query_ro), index management
  • FalkorDBNeuronMixin — neuron CRUD with graph node operations
  • FalkorDBSynapseMixin — synapse CRUD with typed graph edges
  • FalkorDBFiberMixin — fiber CRUD with CONTAINS relationships, batch operations
  • FalkorDBGraphMixin — native Cypher spreading activation (1-4 hop BFS via variable-length paths)
  • FalkorDBBrainMixin — brain registry graph, import/export, graph-level clear
  • Brain-per-graph isolation (brain_{id}) for native multi-tenancy
  • Read-only query routing via ro_query for registry reads and fiber lookups
  • Per-neuron limit enforcement in find_fibers_batch via UNWIND+collect/slice Cypher pattern
  • Connection health verification via Redis PING with automatic reconnect
  • docker-compose.falkordb.yml — standalone FalkorDB service configuration
  • Migration CLI: nmem migrate falkordb to move SQLite brain data to FalkorDB
  • 69 tests across 6 test files (auto-skip when FalkorDB unavailable)
  • SQLite remains default — FalkorDB is opt-in via [storage] TOML config

Fixed

  • mypy: set_brain missing from ABC — Added set_brain(brain_id) to NeuralStorage base class, resolving 2 mypy errors in unified_config.py
  • Registry reads used write queries — Added _registry_query_ro() for read-only brain registry operations (get_brain, find_brain_by_name)
  • find_fibers_batch ignored limit_per_neuron — Rewrote with UNWIND+collect/slice Cypher for proper per-neuron limiting
  • FalkorDB health check was superficial — _get_falkordb_storage() now performs actual Redis PING instead of just _db is not None check
  • export_brain leaked brain_id in error — Sanitized to generic "Brain not found" message
  • Import sorting (I001) — Fixed falkordb.asyncio before redis.asyncio in falkordb_store.py
  • Unused import (F401) — Removed stale SQLiteStorage import from unified_config.py
  • Quoted annotation (UP037) — Unquoted _storage_cache and _falkordb_storage type annotations
  • Silent error logging — Upgraded index creation and connection close errors from debug to warning level

[2.8.0] - 2026-02-22

Added

  • Adaptive Recall (Bayesian Depth Prior) — System learns optimal retrieval depth per entity pattern
  • Beta distribution priors per (entity, depth) pair — picks depth with highest E[Beta(a,b)]
  • 5% epsilon exploration to discover better depths for known entities
  • Fallback to rule-based detection when < 5 queries or no priors exist
  • Outcome recording: updates alpha (success) or beta (failure) based on confidence + fibers_matched
  • 30-day decay (a = 0.9, b = 0.9) to forget stale patterns
  • DepthPrior, DepthDecision frozen dataclasses + AdaptiveDepthSelector engine
  • SQLiteDepthPriorMixin with batch fetch, upsert, stale decay, delete operations
  • Configurable: adaptive_depth_enabled (default True), adaptive_depth_epsilon (default 0.05)
  • Tiered Memory Compression — Age-based compression preserving entity graph structure (zero-LLM)
  • 5 tiers: Full (< 7d), Extractive (7-30d), Entity-only (30-90d), Template (90-180d), Graph-only (180d+)
  • Entity density scoring: count(neurons_referenced) / word_count per sentence
  • Reversible for tiers 1-2 (backup stored), irreversible for tiers 3-4
  • Integrated as COMPRESS strategy in ConsolidationEngine (Tier 2)
  • CompressionTier IntEnum, CompressionConfig, CompressionResult frozen dataclasses
  • SQLiteCompressionMixin for backup storage with stats
  • Configurable: compression_enabled (default True), compression_tier_thresholds (7, 30, 90, 180 days)
  • Multi-Device Sync — Hub-and-spoke incremental sync via change log + sequence numbers
  • Device Identity: UUID-based device_id generation, persisted in config, DeviceInfo frozen dataclass
  • Change Tracking: Append-only change_log table recording all neuron/synapse/fiber mutations
    • ChangeEntry frozen dataclass, SQLiteChangeLogMixin with 6 CRUD methods
    • record_change(), get_changes_since(sequence), mark_synced(), prune_synced_changes()
  • Incremental Sync Protocol: Delta-based merge using neural-aware conflict resolution
    • SyncRequest, SyncResponse, SyncChange, SyncConflict frozen dataclasses
    • ConflictStrategy enum: prefer_recent, prefer_local, prefer_remote, prefer_stronger
    • Neural merge rules: weight=max, access_frequency=sum, tags=union, conductivity=max, delete wins
  • Sync Engine: SyncEngine orchestrator with prepare_sync_request(), process_sync_response(), handle_hub_sync()
  • Hub Server Endpoints (localhost-only by default):
    • POST /hub/register — register device for brain
    • POST /hub/sync — push/pull incremental changes
    • GET /hub/status/{brain_id} — sync status + device count
    • GET /hub/devices/{brain_id} — list registered devices
  • 3 new MCP tools (full tier only):
    • nmem_sync — trigger manual sync (push/pull/full)
    • nmem_sync_status — show pending changes, devices, last sync
    • nmem_sync_config — configure hub URL, auto-sync, conflict strategy
  • SyncConfig frozen dataclass: enabled (default False), hub_url, auto_sync, sync_interval_seconds, conflict_strategy
  • Device tracking columns on neurons/synapses/fibers: device_id, device_origin, updated_at
  • Schema migrations v15 → v16 (depth_priors, compression_backups, fiber compression_tier) → v17 (change_log, devices, device columns)

Changed

  • SQLite schema — Version 15 → 17 (two migrations)
  • MCP tools — Expanded from 23 to 26 (nmem_sync, nmem_sync_status, nmem_sync_config)
  • MCPServer mixin chain — Added SyncToolHandler mixin
  • Fiber model — Added compression_tier: int = 0 field
  • BrainConfig — Added 4 new fields: adaptive_depth_enabled, adaptive_depth_epsilon, compression_enabled, compression_tier_thresholds
  • UnifiedConfig — Added device_id field and SyncConfig dataclass
  • ConsolidationEngine — Added COMPRESS strategy enum + Tier 2 registration + fibers_compressed/tokens_saved report fields
  • Hub endpoints — Pydantic request validation with regex-based brain_id/device_id format checks
  • Tests: 2687 passed (up from 2527), +160 new tests across 8 test files

[2.7.1] - 2026-02-21

Added

  • MCP Tool Tiers — Config-based filtering to reduce token overhead per API turn
  • 3 tiers: minimal (4 tools, ~84% savings), standard (8 tools, ~69% savings), full (all 23, default)
  • ToolTierConfig frozen dataclass in unified_config.py with from_dict()/to_dict()
  • get_tool_schemas_for_tier(tier) in tool_schemas.py — filters schemas by tier
  • [tool_tier] TOML section in config.toml for persistent configuration
  • Hidden tools remain callable via dispatch — only schema exposure changes
  • CLI command: nmem config tier [--show | minimal | standard | full]
  • Description Compression — All 23 tool descriptions compressed (~22% token reduction at full tier)

Changed

  • MCPServer.get_tools() now respects config.tool_tier.tier setting
  • tool_schemas.py refactored: _ALL_TOOL_SCHEMAS module-level list + TOOL_TIERS dict
  • Tests: added 28 new tests in test_tool_tiers.py

[2.7.0] - 2026-02-18

Added

  • Spaced Repetition Engine — Leitner box system (5 boxes: 1d, 3d, 7d, 14d, 30d) for memory reinforcement
  • ReviewSchedule frozen dataclass: fiber_id, brain_id, box (1–5), next_review, streak, review_count
  • SpacedRepetitionEngine: get_review_queue(), process_review() (calls ReinforcementManager), auto_schedule_fiber()
  • advance(success) returns new schedule instance — box increments on success (max 5), resets to 1 on failure
  • Auto-scheduling: fibers with priority >= 7 are automatically scheduled in _remember
  • SQLiteReviewsMixin: upsert, get_due, get_stats with min(limit, 100) cap
  • InMemoryReviewsMixin for testing
  • ReviewHandler MCP mixin: nmem_review tool (queue/mark/schedule/stats actions)
  • Schema migration v14 → v15 (review_schedules table + 2 indexes)
  • Memory Narratives — Template-based markdown narrative generation (no LLM)
  • 3 modes: timeline (date range), topic (spreading activation via ReflexPipeline), causal (CAUSED_BY chain traversal)
  • NarrativeItem + Narrative frozen dataclasses with to_markdown() rendering
  • Timeline mode: queries fibers by date range, sorts chronologically, groups by date headers
  • Topic mode: runs SA query, fetches matched fibers, sorts by relevance
  • Causal mode: uses trace_causal_chain() to follow CAUSED_BY synapses, builds cause→effect narrative
  • NarrativeHandler MCP mixin: nmem_narrative tool (timeline/topic/causal actions)
  • Configurable max_fibers with server-side cap of 50
  • Semantic Synapse Discovery — Offline consolidation using embeddings to find latent connections
  • Batch embeds CONCEPT + ENTITY neurons, evaluates cosine similarity pairs above threshold
  • Creates SIMILAR_TO synapses with weight = similarity * 0.6 and {"_semantic_discovery": True} metadata
  • Configurable: semantic_discovery_similarity_threshold (default 0.7), semantic_discovery_max_pairs (default 100)
  • Integrated as Tier 5 (SEMANTIC_LINK) in ConsolidationEngine strategy dispatch
  • 2× faster decay for unreinforced semantic synapses in _prune (reinforced_count < 2 → decay factor 0.5)
  • Optional — gracefully skipped if sentence-transformers not installed
  • SemanticDiscoveryResult dataclass: neurons_embedded, pairs_evaluated, synapses_created, skipped_existing
  • Cross-Brain Recall — Parallel spreading activation across multiple brains
  • Extends nmem_recall with optional brains array parameter (max 5 brains)
  • Resolves brain names → DB paths via UnifiedConfig, opens temporary SQLiteStorage per brain
  • Parallel query via asyncio.gather, each brain runs independent ReflexPipeline
  • SimHash-based deduplication across brain results (keeps higher confidence on collision)
  • Confidence-sorted merge with [brain_name] prefixed context sections
  • CrossBrainFiber + CrossBrainResult frozen dataclasses
  • Temporary storage instances closed in finally blocks

Changed

  • MCPServer mixin chain — Added ReviewHandler + NarrativeHandler mixins (16 → 18 handler mixins)
  • MCP tools — Expanded from 21 to 23 (nmem_review, nmem_narrative)
  • SQLite schema — Version 14 → 15 (review_schedules table)
  • nmem_recall schema — Added brains array property for cross-brain queries
  • BrainConfig — Added semantic_discovery_similarity_threshold and semantic_discovery_max_pairs fields
  • ConsolidationEngine — Added SEMANTIC_LINK strategy enum + Tier 5 + semantic_synapses_created report field
  • Consolidation prune — Unreinforced semantic synapses (_semantic_discovery metadata) decay at 2× rate
  • Tests: 2399 passed (up from 2314), +85 new tests across 4 features

[2.6.0] - 2026-02-18

Added

  • Smart Context Optimizer — Composite scoring replaces naive loop in nmem_context
  • 5-factor weighted score: activation (0.30) + priority (0.25) + frequency (0.20) + conductivity (0.15) + freshness (0.10)
  • SimHash-based deduplication removes near-duplicate content before token budgeting
  • Proportional token budget allocation: items get budget proportional to their composite score
  • Items below minimum budget (20 tokens) are dropped; oversized items are truncated
  • optimization_stats field in response shows items_dropped and top_score
  • Proactive Alerts Queue — Persistent brain health alerts with full lifecycle management
  • Alert frozen dataclass with AlertStatus (active → seen → acknowledged → resolved) and 7 AlertType enum values
  • SQLiteAlertsMixin with CRUD operations: record_alert (6h dedup cooldown), get_active_alerts, mark_alerts_seen, mark_alert_acknowledged, resolve_alerts_by_type
  • AlertHandler MCP mixin: nmem_alerts tool (list/acknowledge actions)
  • Auto-creation from health pulse hints; auto-resolution when conditions clear
  • Pending alert count surfaced in nmem_remember, nmem_recall, nmem_context responses
  • Schema migration v13 → v14 (alerts table + indexes)
  • Recall Pattern Learning — Discover and materialize query topic co-occurrence patterns
  • extract_topics() — keyword-based topic extraction from recall queries (min_length=3, cap 10)
  • mine_query_topic_pairs() — session-grouped, time-windowed (600s default) pair mining
  • extract_pattern_candidates() — frequency filtering + confidence scoring
  • learn_query_patterns() — materializes patterns as CONCEPT neurons + BEFORE synapses with {"_query_pattern": True} metadata
  • suggest_follow_up_queries() — follows BEFORE synapses for related topic suggestions
  • Integrated into LEARN_HABITS consolidation strategy
  • related_queries field added to nmem_recall response

Changed

  • MCPServer mixin chain — Added AlertHandler mixin (15 → 16 handler mixins)
  • MCP tools — Expanded from 20 to 21 (nmem_alerts)
  • SQLite schema — Version 13 → 14 (alerts table)
  • nmem_context response — Now includes optimization_stats when items are dropped
  • nmem_recall response — Now includes related_queries from learned patterns
  • Tests: 2314 passed (up from 2291)

[2.5.0] - 2026-02-18

Added

  • Onboarding flow — Detects fresh brain (0 neurons + 0 fibers) and surfaces a 4-step getting-started guide on the first tool call (_remember, _recall, _context, _stats). Shows once per server instance.
  • Background expiry cleanup — Fire-and-forget task auto-deletes expired TypedMemory + underlying fibers on a configurable interval (default 12h, max 100/run). Fires MEMORY_EXPIRED hooks. Piggybacks on _check_maintenance().
  • Scheduled consolidation — Background asyncio loop runs consolidation every 24h (configurable strategies: prune, merge, enrich). Shares _last_consolidation_at with MaintenanceHandler to prevent overlap. Initial delay of one full interval avoids triggering on restart.
  • Version check handler — Background task checks PyPI every 24h for newer versions of neural-memory. Caches result and surfaces update_hint in _remember, _recall, _stats responses when an update is available. Uses urllib (no extra deps), validates HTTPS scheme.
  • Expiry alerts — warn_expiry_days parameter on nmem_recall; expiring-soon count in health pulse thresholds
  • Evolution dashboard — /api/evolution REST endpoint + dashboard UI tab for brain maturation metrics (stage distribution, plasticity, proficiency)

Changed

  • MaintenanceConfig — Added 8 new config fields: expiry_cleanup_enabled, expiry_cleanup_interval_hours, expiry_cleanup_max_per_run, scheduled_consolidation_enabled, scheduled_consolidation_interval_hours, scheduled_consolidation_strategies, version_check_enabled, version_check_interval_hours
  • MCPServer mixin chain — Added OnboardingHandler, ExpiryCleanupHandler, ScheduledConsolidationHandler, VersionCheckHandler mixins
  • Server lifecycle — run_mcp_server() now starts scheduled consolidation + version check at startup, cancels all background tasks on shutdown

[2.4.0] - 2026-02-17

Security

  • 6-phase security audit — Comprehensive audit across 142K LOC / 190 files covering engine, storage, server, config, MCP/CLI, core, safety, utils, sync, integration, and extraction modules
  • Path traversal fixes — 3 CRITICAL path injection vulnerabilities in CLI commands (tools, brain import, shortcuts) patched with resolve() + is_relative_to()
  • CORS hardening — Replaced wildcard patterns with explicit localhost origins in FastAPI server
  • TOML injection prevention — Added _sanitize_toml_str() for user-provided dedup config fields
  • API key masking — BrainModeConfig.to_dict() now serializes api_key as "***" instead of plaintext
  • Info leak prevention — Removed internal IDs, adapter names, and filesystem paths from 5 error messages across MCP, integration, and sync modules
  • WebSocket validation — Brain ID format + length validation on subscribe action
  • Path normalization — SQLiteStorage and NEURALMEMORY_DIR env var paths now resolved with Path.resolve()

Fixed

  • Frozen core models — Synapse, Fiber, NeuronState, BrainSnapshot, FreshnessResult, MemoryFreshnessReport, Entity, WeightedKeyword, TimeHint dataclasses are now frozen=True per immutability contract
  • merge_brain() atomicity — Restore from backup on import failure instead of leaving empty brain
  • import_brain() orphan — Brain record INSERT moved inside transaction to prevent orphan on failure
  • Division-by-zero guards — _predicates_conflict() and homeostatic normalization protected against empty inputs
  • Datetime hardening — 4 datetime.fromisoformat() call sites wrapped with try/except + naive UTC enforcement
  • Lateral inhibition — Ceiling division for fair slot allocation across clusters
  • suggest_memory_type — Word boundary matching prevents false positives (e.g. "add" no longer matches "address")
  • Git update command — Detects current branch instead of hardcoded 'main'
  • Dead code removal — Removed unused updated_at field, duplicate index, stale imports

Performance

  • N+1 query elimination — consolidation._prune() pre-fetches neighbor synapses in batch (was 500+ serial queries); activation.activate() caches neighbors + batch state pre-fetch (was ~1000 queries); conflict_detection uses asyncio.gather() for parallel searches
  • Export safety caps — export_brain() limited to 50K neurons, 100K synapses, 50K fibers
  • Bounds enforcement — 15+ storage methods capped with min(limit, MAX), schema tool limits enforced
  • Regex pre-compilation — sensitive.py and trigger_engine.py patterns compiled at module level with cache
  • Enrichment optimization — Early exit on empty tags + zero intersection in O(n^2) Jaccard loop
  • ReDoS prevention — Content length cap (100K chars) before regex matching in sensitive content detection

Changed

  • BrainConfig.with_updates() — Replaced 80-line manual field copy with dataclasses.replace()
  • DriftReport.variants — Changed from mutable list to tuple on frozen dataclass
  • Mutable constants — VI_PERSON_PREFIXES and LOCATION_INDICATORS converted to frozenset
  • Error handling — 8 bare except Exception blocks narrowed to specific exception types with logging

[2.2.0] - 2026-02-13

Added

  • Config presets — Three built-in profiles: safe-cost (token-efficient), balanced (defaults), max-recall (maximum retention). CLI: nmem config preset <name> [--list] [--dry-run]
  • Consolidation delta report — run_with_delta() wrapper computes before/after health snapshots around consolidation, showing purity, connectivity, and orphan rate changes. CLI consolidate now shows health delta.

Fixed

  • CI lint parity — CI now passes: fixed 14 lint errors in test files (unused imports, sorting, Yoda conditions)
  • Release workflow idempotency — gh release create no longer fails when release already exists; uploads assets to existing release instead
  • CI test timeouts — Added pytest-timeout (60s default) and timeout-minutes: 15 to prevent stuck CI jobs

Changed

  • Makefile — Added verify target matching CI exactly (lint + format-check + typecheck + test-cov + security)
  • Auto-consolidation observability — Background auto-consolidation now logs purity delta for monitoring

[2.1.0] - 2026-02-13

Fixed

  • Brain reset on config migration — When upgrading to unified config (config.toml), current_brain is now migrated from legacy config.json so users don't lose their active brain selection
  • EternalHandler stale brain cache — Eternal context now detects brain switches and re-creates the context instead of caching the initial brain ID indefinitely
  • Ruff lint errors — Fixed 7 pre-existing lint violations (unused imports, naming convention, import ordering)
  • Mypy type errors — Fixed 2 pre-existing type errors (Any import, set() arg-type)

Added

  • CLI --version flag — nmem --version / nmem -V now prints version and exits (standard CLI convention)
  • Actionable health scoring — nmem_health now returns top_penalties: top 3 ranked penalty factors with estimated gain and suggested action
  • Semantic stage progress — nmem_evolution now returns stage_distribution (fiber counts per maturation stage) and closest_to_semantic (top 3 EPISODIC fibers with progress % and next step)
  • Composable encoding pipeline — Refactored monolithic encode() into 14 composable async pipeline steps (PipelineContext / PipelineStep / Pipeline)

Changed

  • Dependency warning suppression — pyvi/NumPy DeprecationWarnings are now suppressed at import time with targeted filterwarnings

[2.3.1] - 2026-02-17

Refactored

  • Engine cleanup — Removed 176 lines of dead code across 6 engine modules
  • Deduplicated stop-word sets into shared _STOP_WORDS frozenset in conflict_detection.py
  • Replaced manual Fiber() constructor with dc_replace() in consolidation.py
  • Removed unused reconstitute_answer() from retrieval_context.py
  • Hoisted expansion suffix/prefix constants to module level in retrieval.py
  • Used heapq.nlargest instead of sorted+slice in retrieval reinforcement
  • Typed consolidation dispatch dict with Callable[[], Awaitable[None]] instead of Any

Fixed

  • Unreachable break in dream — Outer loop guard added to prevent quadratic blowup when activated neuron list is large (max 50K pairs)
  • JSON snapshot validation — brain_versioning.py now validates parsed JSON is a dict before field access

[2.3.0] - 2026-02-16

Added

  • PreCompact + Stop auto-flush hooks — Pre-compaction hook fires before context compression, parallel CI tests support
  • Emergency flush (nmem_auto action="flush") — Pre-compaction emergency capture that skips dedup, lowers confidence threshold to 0.5, enables all memory types regardless of config, and boosts priority +2. Tag emergency_flush applied to all captured memories. Inspired by OpenClaw Memory's Layer 3 (memoryFlush)
  • Session gap detection — nmem_session(action="get") now returns gap_detected: true when content may have been lost between sessions (e.g. user ran /new without saving). Uses MD5 fingerprint stored on session_set/session_end to detect gaps from older code paths missing fingerprints
  • Auto-capture preference patterns — Detects explicit preferences ("I prefer...", "always use..."), corrections ("that's wrong...", "actually, it should be..."), and Vietnamese equivalents. New memory type preference with 0.85 confidence
  • Windows surrogate crash fix — MCP server now strips lone surrogate characters (U+D800-U+DFFF) from tool arguments before processing, preventing UnicodeEncodeError on Windows stdio pipes

Fixed

  • CI lint failure — Fixed ruff RUF002 (ambiguous EN DASH – in docstring) in mcp/server.py
  • CI stress test timeouts — Skipped stress tests on GitHub runners to prevent CI timeout failures

Changed

  • Release workflow hardened — release.yml now validates tag version matches pyproject.toml + __init__.py before publishing, and runs full CI (lint + typecheck + test) as a gate before PyPI upload

[Unreleased]

Fixed

  • Agent forgets tools after /new — before_agent_start hook now always injects systemPrompt with tool instructions, ensuring the agent knows about NeuralMemory tools even after session reset. Previously only prependContext (data) was injected, leaving the agent unaware of available tools
  • Agent confuses CLI vs MCP tool calls — systemPrompt injection explicitly states "call as tool, NOT CLI command", preventing agents from running nmem remember in terminal instead of calling the nmem_remember tool
  • openclaw plugins list not recognizing plugin on Windows — Changed main and openclaw.extensions from TypeScript source (src/index.ts) to compiled output (dist/index.js). Added prepublishOnly and postinstall build scripts. Fixed tsconfig.json module resolution from bundler to Node16 for broader compatibility
  • OpenClaw plugin ID mismatch — Added explicit "id": "neuralmemory" to openclaw section in package.json, fixing the plugin id mismatch (manifest uses "neuralmemory", entry hints "openclaw-plugin") warning
  • Content-Length framing bug — Switched from string-based buffer to raw Buffer for byte-accurate MCP message parsing. Fixes silent data corruption with non-ASCII content (Vietnamese, emoji, CJK)
  • Null dereference after close() — writeMessage() and notify() now guard against null process reference
  • Unhandled tool call errors — callTool() exceptions in tools.ts now caught and returned as structured error responses instead of crashing OpenClaw

Added

  • Configurable MCP timeout — New timeout plugin config option (default: 30s, max: 120s) for users on slow machines or first-time init
  • Actionable MCP error messages — Initialize failures now include Python stderr output and specific hints:
  • ENOENT → tells user to check pythonPath in plugin config
  • Exit code 1 → suggests pip install neural-memory
  • Timeout → prints captured stderr + verify command (python -m neural_memory.mcp)

Security

  • Least-privilege child env — MCP subprocess now receives only whitelisted env vars (PATH, HOME, PYTHONPATH, NEURALMEMORY_*) instead of full process.env. Prevents leaking API keys and secrets to child process
  • Config validation — resolveConfig() now validates types, ranges, and brain name pattern (^[a-zA-Z0-9_\-.]{1,64}$). Invalid values fall back to defaults instead of passing through
  • Input bounds on all tools — Zod schemas now enforce max lengths: content (100K chars), query (10K), tags (50 items × 100 chars), expires_days (1–3650), context limit (1–200)
  • Buffer overflow protection — 10 MB cap on stdio buffer; process killed if exceeded
  • Stderr cap — Max 50 lines collected during init to prevent unbounded memory growth
  • Auto-capture truncation — Agent messages truncated to 50K chars before sending to MCP
  • Graceful shutdown — close() now removes listeners, waits up to 3s for exit, then escalates to SIGKILL
  • Config schema hardened — Added additionalProperties: false and brain name pattern constraint

[1.7.4] - 2026-02-11

Fixed

  • Full mypy compliance: Resolved all 341 mypy errors across 79 files (0 errors in 170 source files)
  • Added TYPE_CHECKING protocol stubs to all mixin classes (storage, MCP handlers)
  • Added generic type parameters to all bare dict/list annotations
  • Narrowed str | None → str before passing to typed parameters
  • Removed 14 stale # type: ignore comments
  • Added proper type annotations to HybridStorage factory delegate methods
  • Fixed variable name reuse across different types in same scope
  • Fixed missing await on coroutine calls in CLI commands

Added

  • CLAUDE.md — Type Safety Rules: New section documenting mixin protocol stubs, generic type params, Optional narrowing, and # type: ignore discipline to prevent future mypy regressions

[1.7.3] - 2026-02-11

Added

  • Bundled skills — 3 Claude Code agent skills (memory-intake, memory-audit, memory-evolution) now ship inside the pip package under src/neural_memory/skills/
  • nmem install-skills — new CLI command to install skills to ~/.claude/skills/
  • --list shows available skills with descriptions
  • --force overwrites existing with latest version
  • Detects unchanged files (skip), changed files (report "update available"), missing ~/.claude/ (graceful error)
  • nmem init --skip-skills — skills are now installed as part of nmem init; use --skip-skills to opt out
  • Tests: 25 new unit tests for setup_skills, _discover_bundled_skills, _classify_status, _extract_skill_description

Changed

  • _classify_status() now recognizes "installed" and "updated" as success states
  • skills/README.md updated: manual copy instructions replaced with nmem install-skills

[1.7.2] - 2026-02-11

Security

  • CORS hardening: Default CORS origins changed from ["*"] to ["http://localhost:*", "http://127.0.0.1:*"] (C2)
  • Bind address: Default server bind changed from 0.0.0.0 to 127.0.0.1 (C4)
  • Migration safety: Non-benign migration errors now halt and raise instead of silently advancing schema version (C8)
  • Info leakage: Removed available brain names from 404 error responses (H21)
  • URI validation: Graphiti adapter validates bolt:///bolt+s:// URI scheme before connecting (H23)
  • Error masking: Exception type names no longer leaked in MCP training error responses (H27)
  • Import screening: RecordMapper.map_record() now runs check_sensitive_content() before importing external records (H33)

Fixed

  • Fix RuntimeError: Event loop is closed from aiosqlite worker thread on CLI exit (Python 3.12+)
  • Root cause: 4 CLI commands (decay, consolidate, export, import) called get_shared_storage() directly, bypassing _active_storages tracking — aiosqlite connections were never closed before event loop teardown
  • Route all CLI storage creation through get_storage() in _helpers.py so connections are properly tracked and cleaned up
  • Add await asyncio.sleep(0) after storage cleanup to drain pending aiosqlite worker thread callbacks before asyncio.run() tears down the loop
  • Bounds hardening: MCP _habits fiber fetch reduced 10K→1K; _context limit capped at 200; REST list_neurons capped at 1000; EncodeRequest.content max 100K chars (H11-H13, H32)
  • Data integrity: import_brain wrapped in BEGIN IMMEDIATE with rollback on failure (H14)
  • Code quality: AWF adapter gets ImportError guard; redundant enable_auto_save() removed from train handler (C7, H26)
  • Public API: Added current_brain_id property to NeuralStorage, SQLiteStorage, InMemoryStorage — replaces private _current_brain_id access (H25)

Added

  • CLAUDE.md: Project-level AI coding standards (architecture, immutability, datetime, security, bounds, testing, error handling, naming conventions)
  • Quality gates: Automated enforcement via ruff, mypy, pytest, and CI
  • 8 new ruff rule sets: S (bandit), A (builtins), DTZ (datetimez), T20 (print), PT (pytest), PERF (perflint), PIE, ERA (eradicate)
  • Per-file-ignores for intentional patterns (CLI print, simhash MD5, SQL column names, etc.)
  • Coverage threshold: 67% enforced in CI and Makefile
  • CI: typecheck job now fails build (removed continue-on-error and || true); build requires [lint, typecheck, test]; added security scan job
  • Pre-commit: updated hooks (ruff v0.9.6, mypy v1.15.0); added no-commit-to-branch and bandit
  • Makefile: added security, audit targets; check now includes security

Changed

  • Tests: 1759 passed (up from 1696)

[1.7.1] - 2026-02-11

Fixed

  • Fix __version__ reporting "1.6.1" instead of "1.7.0" in PyPI package (runtime version mismatch)

[1.7.0] - 2026-02-11

Added

  • Proactive Brain Intelligence — 3 features that make the brain self-aware during normal usage
  • Related Memories on Write — nmem_remember now discovers and returns up to 3 related existing memories via 2-hop SpreadingActivation from the new anchor neuron. Always-on (~5-10ms overhead), non-intrusive. Response includes related_memories list with fiber_id, preview, and similarity score.
  • Expired Memory Hint — Health pulse detects expired memories via cheap COUNT query on typed_memories table. Surfaces hint when count exceeds threshold (default: 10): "N expired memories found. Consider cleanup via nmem list --expired."
  • Stale Fiber Detection — Health pulse detects fibers with decayed conductivity (last conducted >90 days ago or never). Surfaces hint when stale ratio exceeds threshold (default: 30%): "N% of fibers are stale. Consider running nmem_health for review."
  • MaintenanceConfig extensions — 3 new configuration fields:
  • expired_memory_warn_threshold (default: 10)
  • stale_fiber_ratio_threshold (default: 0.3)
  • stale_fiber_days (default: 90)
  • Storage layer — 2 new optional methods on NeuralStorage:
  • get_expired_memory_count() — COUNT of expired typed memories (SQLite + InMemory)
  • get_stale_fiber_count(brain_id, stale_days) — COUNT of stale fibers (SQLite + InMemory)
  • HealthPulse extensions — expired_memory_count and stale_fiber_ratio fields
  • HEALTH_DEGRADATION trigger — TriggerType.HEALTH_DEGRADATION for maintenance events

Changed

  • Tests: 1696 passed (up from 1695)

[1.6.1] - 2026-02-10

Fixed

  • CLI brain commands (export, import, create, delete, health, transplant) now work correctly in SQLite mode
  • brain export no longer produces empty files when brain was created with brain create
  • brain delete correctly removes .db files in unified config mode
  • brain health uses storage-agnostic find_neurons() instead of JSON-internal _neurons dict
  • All version subcommands (create, list, rollback, diff) now find brains in SQLite mode
  • shared sync uses correct storage backend

[1.6.0] - 2026-02-10

Added

  • DB-to-Brain Schema Training (nmem_train_db) — Teach brains to understand database structure
  • 3-layer pipeline: SchemaIntrospector → KnowledgeExtractor → DBTrainer
  • Extracts schema knowledge (table structures, relationships, patterns) — NOT raw data rows
  • SQLite dialect (v1) via aiosqlite read-only connections
  • Schema fingerprint (SHA256) for re-training detection
  • Schema Introspection — engine/db_introspector.py
  • SchemaDialect protocol with SQLiteDialect implementation
  • Frozen dataclasses: ColumnInfo, ForeignKeyInfo, IndexInfo, TableInfo, SchemaSnapshot
  • PRAGMA-based metadata extraction (table_info, foreign_key_list, index_list)
  • Knowledge Extraction — engine/db_knowledge.py
  • FK-to-SynapseType mapping with confidence scoring (IS_A, INVOLVES, AT_LOCATION, RELATED_TO)
  • Structure-based join table detection (2+ FKs, ≤1 business column → CO_OCCURS synapse)
  • 5 schema pattern detectors: audit_trail, soft_delete, tree_hierarchy, polymorphic, enum_table
  • Training Orchestrator — engine/db_trainer.py
  • Mirrors DocTrainer architecture: batch save, per-table error isolation, shared domain neuron
  • Configurable: max_tables (1-500), salience_ceiling, consolidate, domain_tag
  • MCP Tool: nmem_train_db — train and status actions

Fixed

  • Security: read-only SQLite connections, absolute path rejection, SQL identifier sanitization, info leakage prevention

Changed

  • MCP tools expanded from 17 to 18
  • Tests: 1648 passed (up from 1596)

Skills

  • 3 composable AI agent skills — ship-faster SKILL.md pattern, installable to ~/.claude/skills/
  • memory-intake — structured memory creation from messy notes, 1-question-at-a-time clarification, batch store with preview
  • memory-audit — 6-dimension quality review (purity, freshness, coverage, clarity, relevance, structure), A-F grading
  • memory-evolution — evidence-based optimization from usage patterns, consolidation, enrichment, pruning, checkpoint Q&A

[1.5.0] - 2026-02-10

Added

  • Conflict Management MCP Tool (nmem_conflicts) — List, resolve, and pre-check memory conflicts
  • list, resolve (keep_existing/keep_new/keep_both), check actions
  • ConflictHandler mixin with full input validation
  • Recall Conflict Surfacing — has_conflicts flag and conflict_count in default recall response
  • Provenance Source Enrichment — NEURALMEMORY_SOURCE env var → mcp:{source} provenance
  • Purity Score Conflict Penalty — Unresolved CONTRADICTS reduce health score (max -10 points)

Fixed

  • 20+ performance bottlenecks — storage index optimization, encoder batch operations
  • 25+ bugs across engine/storage/MCP — deep audit fixes including deprecated datetime.utcnow() replacement

Changed

  • MCP tools expanded from 16 to 17
  • Tests: 1372 passed (up from 1352)

[1.4.0] - 2026-02-09

Added

  • OpenClaw Memory Plugin — @neuralmemory/openclaw-plugin npm package
  • MCP stdio client: JSON-RPC 2.0 with Content-Length framing
  • 6 core tools, 2 hooks (before_agent_start, agent_end), 1 service
  • Plugin manifest with configSchema + uiHints

Changed

  • Dashboard Integrations tab simplified to status-only with deep links (Option B)

[1.3.0] - 2026-02-09

Added

  • Deep Integration Status — Enhanced status cards, activity log, setup wizards, import sources
  • Source Attribution — NEURALMEMORY_SOURCE env var for integration tracking
  • 25 new i18n keys in EN + VI (87 total)

Changed

  • Tests: 1352 passed (up from 1340)

[1.2.0] - 2026-02-09

Added

  • Dashboard — Full-featured SPA at /dashboard (Alpine.js + Tailwind CDN, zero-build)
  • 5 tabs: Overview, Neural Graph (Cytoscape.js), Integrations, Health (radar chart), Settings
  • Graph toolbar, toast notifications, skeleton loading, brain management, EN/VI i18n
  • ARIA accessibility, 44px mobile touch targets, design system

Fixed

  • ModuleNotFoundError: typing_extensions on fresh Python 3.12 — added dependency

Changed

  • Tests: 1340 passed (up from 1264)

[1.1.0] - 2026-02-09

Added

  • ClawHub SKILL.md — Published neural-memory@1.0.0 to ClawHub
  • Nanobot Integration — 4 tools adapted for Nanobot's action interface
  • Architecture Doc — docs/ARCHITECTURE_V1_EXTENDED.md

Changed

  • OpenClaw PR #12596 submitted

[1.0.2] - 2026-02-09

Fixed

  • Empty recall for broad queries — format_context() truncates long fiber content to fit token budget
  • Diversity metric normalization — Shannon entropy normalized against 8 expected synapse types
  • Temporal synapse diversity — _link_temporal_neighbors() creates BEFORE/AFTER instead of always RELATED_TO
  • Consolidation prune crash — Fixed Fiber(tags=...) TypeError, uses dataclasses.replace()

[1.0.0] - 2026-02-09

Added

  • Brain Versioning — Snapshot, rollback, diff (schema v11, brain_versions table)
  • Partial Brain Transplant — Topic-filtered merge between brains with conflict resolution
  • Brain Quality Badge — Grade A-F from BrainHealthReport, marketplace eligibility
  • Optional Embedding Layer — SentenceTransformer + OpenAI providers (OFF by default)
  • Optional LLM Extraction — Enhanced relation extraction beyond regex (OFF by default)

Changed

  • Version 1.0.0 — Production/Stable, schema v10 → v11
  • MCP tools expanded from 14 to 16 (nmem_version, nmem_transplant)

[0.20.0] - 2026-02-09

Added

  • Habitual Recall — ENRICH, DREAM, LEARN_HABITS consolidation strategies
  • Action event log (hippocampal buffer), sequence mining, workflow suggestions
  • nmem_habits MCP tool, nmem habits CLI, nmem update CLI
  • Prune enhancements: dream synapse 10x decay, high-salience resistance
  • Schema v10: action_events table
  • 6 new BrainConfig fields for habit/dream configuration

Changed

  • ConsolidationStrategy extended with ENRICH, DREAM, LEARN_HABITS
  • Schema version 9 → 10

[0.19.0] - 2026-02-08

Added

  • Temporal Reasoning — Causal chain traversal, temporal range queries, event sequence tracing
  • trace_causal_chain(), query_temporal_range(), trace_event_sequence()
  • CAUSAL_CHAIN and TEMPORAL_SEQUENCE synthesis methods
  • Pipeline integration: "Why?" → causal, "When?" → temporal, "What happened after?" → event sequence
  • Router enhancement with traversal metadata in RouteDecision

Changed

  • Tests: 1019 passed (up from 987)

[0.17.0] - 2026-02-08

Added

  • Brain Diagnostics — BrainHealthReport with 7 component scores and composite purity (0-100)
  • Grade A/B/C/D/F, 7 warning codes, automatic recommendations
  • Tag drift detection via TagNormalizer.detect_drift()
  • MCP tool: nmem_health — Brain health diagnostics
  • CLI command: nmem health — Terminal health report with ASCII progress bars

[0.16.0] - 2026-02-08

Added

  • Emotional Valence — Lexicon-based sentiment extraction (EN + VI, zero LLM)
  • SentimentExtractor, Valence enum, 7 emotion tag categories
  • Negation handling, intensifier detection
  • FELT synapses from anchor → emotion STATE neurons
  • Emotional Resonance Scoring — Up to +0.1 retrieval boost for matching-valence memories
  • Emotional Decay Modulation — High-intensity emotions decay slower (trauma persistence)

Changed

  • Tests: 950 passed (up from 908)

[0.15.0] - 2026-02-08

Added

  • Associative Inference Engine — Co-activation patterns → persistent CO_OCCURS synapses
  • compute_inferred_weight(), identify_candidates(), create_inferred_synapse()
  • generate_associative_tags() from BFS clustering
  • Co-Activation Persistence — co_activation_events table (schema v8 → v9)
  • record_co_activation(), get_co_activation_counts(), prune_co_activations()
  • INFER Consolidation Strategy — Create synapses from co-activation patterns
  • Tag Normalizer — ~25 synonym groups + SimHash fuzzy matching + drift detection
  • 6 new BrainConfig fields for co-activation configuration

Changed

  • Schema version 8 → 9
  • Tests: 908 passed (up from 838)

[0.14.0] - 2026-02-08

Added

  • Relation extraction engine: Regex-based causal, comparative, and sequential pattern detection from content — auto-creates CAUSED_BY, LEADS_TO, BEFORE, SIMILAR_TO, CONTRADICTS synapses during encoding
  • Tag origin tracking: Separate auto_tags (content-derived) from agent_tags (user-provided) with backward-compatible fiber.tags union property
  • Auto memory type inference: suggest_memory_type() fallback when no explicit type provided at encode time
  • Confirmatory weight boost: Hebbian +0.1 boost on anchor synapses when agent tags confirm auto tags; RELATED_TO synapses (weight 0.3) for divergent agent tags
  • Bilingual pattern support: English + Vietnamese regex patterns for causal ("because"/"vì"), comparative ("similar to"), and sequential ("then"/"sau khi") relations
  • RelationType, RelationCandidate, RelationExtractor in new extraction/relations.py
  • Fiber.auto_tags, Fiber.agent_tags fields with Fiber.add_auto_tags() method
  • SQLite schema migration v7→v8 with backward-compatible column additions and backfill
  • 62 new tests: relation extraction (25), tag origin (10), confirmatory boost (5), relation encoding (7), auto-tags update (15)
  • ROADMAP.md with versioned plan from v0.14.0 → v1.0.0

Fixed

  • "Event loop is closed" noise on CLI exit: aiosqlite connections now properly closed before event loop teardown via centralized run_async() helper
  • MCP server shutdown now closes storage connection in finally block

Changed

  • All 32 CLI asyncio.run() calls replaced with run_async() for proper cleanup
  • Encoder pipeline extended with relation extraction (step 6b) and confirmatory boost (step 6c)
  • Fiber.create(tags=...) preserved for backward compat — maps to agent_tags
  • 838 tests passing

[0.13.0] - 2026-02-07

Added

  • Ground truth evaluation dataset: 30 curated memories across 5 sessions (Day 1→Day 30) covering project setup, development, integration, sprint review, and production launch
  • Standard IR metrics: Precision@K, Recall@K, MRR (Mean Reciprocal Rank), NDCG@K with per-query and per-category aggregation
  • 25 evaluation queries: 8 factual, 6 temporal, 4 causal, 4 pattern, 3 multi-session coherence queries with expected relevant results
  • Naive keyword-overlap baseline: Tokenize-and-rank strawman that NeuralMemory's activation-based recall must beat
  • Long-horizon coherence test framework: 5-session simulation across 30 days with recall tracking per session (target: >= 60% at day 30)
  • benchmarks/ground_truth.py — ground truth memories, queries, session schedule
  • benchmarks/metrics.py — IR metrics: precision_at_k, recall_at_k, reciprocal_rank, ndcg_at_k, evaluate_query, BenchmarkReport
  • benchmarks/naive_baseline.py — keyword overlap ranking and baseline evaluation
  • benchmarks/coherence_test.py — multi-session coherence test with CoherenceReport
  • Ground-truth evaluation section in run_benchmarks.py comparing NeuralMemory vs baseline
  • 27 new unit tests: precision (6), recall (4), MRR (5), NDCG (4), query evaluation (1), report aggregation (2), baseline (5)

Changed

  • run_benchmarks.py now includes ground-truth evaluation with NeuralMemory vs naive baseline comparison in generated markdown output

[0.12.0] - 2026-02-07

Added

  • Real-time conflict detection: Detects factual contradictions and decision reversals at encode time using predicate extraction — no LLM required
  • Factual contradiction detection: Regex-based extraction of "X uses/chose/decided Y" patterns, compares predicates across memories with matching subjects
  • Decision reversal detection: Identifies when a new DECISION contradicts an existing one via tag overlap analysis
  • Dispute resolution pipeline: Anti-Hebbian confidence reduction, _disputed and _superseded metadata markers, and CONTRADICTS synapse creation
  • Disputed neuron deprioritization: Retrieval pipeline reduces activation of disputed neurons by 50% and superseded neurons by 75%
  • CONTRADICTS synapse type for linking contradictory memories
  • ConflictType, Conflict, ConflictResolution, ConflictReport in new engine/conflict_detection.py
  • detect_conflicts(), resolve_conflicts() for encode-time conflict handling
  • 32 new unit tests: predicate extraction (5), predicate conflict (4), subject matching (4), tag overlap (4), helpers (4), detection integration (6), resolution (5)

Changed

  • Encoder pipeline runs conflict detection after anchor neuron creation, before fiber assembly
  • Retrieval pipeline adds _deprioritize_disputed() step after stabilization to suppress disputed neurons
  • SynapseType enum extended with CONTRADICTS = "contradicts"

[0.11.0] - 2026-02-07

Added

  • Activation stabilization: Iterative dampening algorithm settles neural activations into stable patterns after spreading activation — noise floor removal, dampening (0.85x), homeostatic normalization, convergence detection (typically 2-4 iterations)
  • Multi-neuron answer reconstruction: Strategy-based answer synthesis replacing single-neuron reconstitute_answer() — SINGLE mode (high-confidence top neuron), FIBER_SUMMARY mode (best fiber summary), MULTI_NEURON mode (top-5 neurons ordered by fiber pathway position)
  • Memory maturation lifecycle: Four-stage memory model STM → Working (30min) → Episodic (4h) → Semantic (7d + spacing effect). Stage-aware decay multipliers: STM 5x, Working 2x, Episodic 1x, Semantic 0.3x
  • Spacing effect requirement: EPISODIC → SEMANTIC promotion requires reinforcement across 3+ distinct calendar days, modeling biological spaced repetition
  • Pattern extraction: Episodic → semantic concept formation via tag Jaccard clustering (Union-Find). Clusters of 3+ similar fibers generate CONCEPT neurons with IS_A synapses to common entities
  • MATURE consolidation strategy: New consolidation strategy that advances maturation stages and extracts semantic patterns from mature episodic memories
  • StabilizationConfig, StabilizationReport, stabilize() in new engine/stabilization.py
  • SynthesisMethod, ReconstructionResult, reconstruct_answer() in new engine/reconstruction.py
  • MemoryStage, MaturationRecord, compute_stage_transition(), get_decay_multiplier() in new engine/memory_stages.py
  • ExtractedPattern, ExtractionReport, extract_patterns() in new engine/pattern_extraction.py
  • SQLiteMaturationMixin in new storage/sqlite_maturation.py — maturation CRUD for SQLite backend
  • Schema migration v6→v7: memory_maturations table with composite key (brain_id, fiber_id)
  • contributing_neurons and synthesis_method fields on RetrievalResult
  • stages_advanced and patterns_extracted fields on ConsolidationReport
  • Maturation abstract methods on NeuralStorage base: save_maturation(), get_maturation(), find_maturations()
  • 49 new unit tests: stabilization (12), reconstruction (11), memory stages (16), pattern extraction (8), plus 2 consolidation tests

Changed

  • Retrieval pipeline inserts stabilization phase after lateral inhibition and before answer reconstruction
  • Answer reconstruction uses multi-strategy reconstruct_answer() instead of reconstitute_answer()
  • Encoder initializes maturation record (STM stage) when creating new fibers
  • Consolidation engine supports MATURE strategy for stage advancement and pattern extraction

[0.10.0] - 2026-02-07

Added

  • Formal Hebbian learning rule: Principled weight update Δw = η_eff * pre * post * (w_max - w) replacing ad-hoc weight += delta + dormancy_bonus
  • Novelty-adaptive learning rate: New synapses learn ~4x faster, frequently reinforced synapses stabilize toward base rate via exponential decay
  • Natural weight saturation: (w_max - w) term prevents runaway weight growth — weights near ceiling barely change
  • Competitive normalization: normalize_outgoing_weights() caps total outgoing weight per neuron at budget (default 5.0), implementing winner-take-most competition
  • Anti-Hebbian update: anti_hebbian_update() for conflict resolution weight reduction (used in Phase 3)
  • learning_rate, weight_normalization_budget, novelty_boost_max, novelty_decay_rate on BrainConfig
  • LearningConfig, WeightUpdate, hebbian_update, compute_effective_rate, normalize_outgoing_weights in new engine/learning_rule.py
  • 33 new unit tests covering learning rule, normalization, and backward compatibility

Changed

  • Synapse.reinforce() accepts optional pre_activation, post_activation, now parameters — uses formal Hebbian rule when activations provided, falls back to direct delta for backward compatibility
  • ReflexPipeline._defer_co_activated() passes neuron activation levels to Hebbian strengthening
  • ReflexPipeline._defer_reinforce_or_create() forwards activation levels to reinforce()
  • Removed dormancy bonus from Synapse.reinforce() (novelty adaptation in learning rule replaces it)

[0.9.6] - 2026-02-07

Added

  • Sigmoid activation function: Neurons now use sigmoid gating (1/(1+e^(-6(x-0.5)))) instead of raw clamping, producing bio-realistic nonlinear activation curves
  • Firing threshold: Neurons only propagate signals when activation meets threshold (default 0.3), filtering borderline noise
  • Refractory period: Cooldown prevents same neuron firing twice within a query pipeline (default 500ms), checked during spreading activation
  • Lateral inhibition: Top-K winner-take-most competition in retrieval pipeline — top 10 neurons survive unchanged, rest suppressed by 0.7x factor
  • Homeostatic target field: Reserved homeostatic_target field on NeuronState for v2 adaptive regulation
  • fired and in_refractory properties on NeuronState
  • sigmoid_steepness, default_firing_threshold, default_refractory_ms, lateral_inhibition_k, lateral_inhibition_factor on BrainConfig
  • Schema migration v5→v6: four new columns on neuron_states table

Changed

  • NeuronState.activate() applies sigmoid function and accepts now and sigmoid_steepness parameters
  • NeuronState.decay() preserves all new fields (firing_threshold, refractory_until, refractory_period_ms, homeostatic_target)
  • DecayManager.apply_decay() uses state.decay() instead of manual NeuronState construction
  • ReinforcementManager.reinforce() directly sets activation level (bypasses sigmoid for reinforcement)
  • Spreading activation skips neurons in refractory cooldown
  • Storage layer (SQLite + SharedStore) serializes/deserializes all new NeuronState fields

[0.9.5] - 2026-02-07

Added

  • Type-aware decay rates: Different memory types now decay at biologically-inspired rates (facts: 0.02/day, todos: 0.15/day). DEFAULT_DECAY_RATES dict and get_decay_rate() helper in memory_types.py
  • Retrieval score breakdown: ScoreBreakdown dataclass exposes confidence components (base_activation, intersection_boost, freshness_boost, frequency_boost) in RetrievalResult and MCP nmem_recall response
  • SimHash near-duplicate detection: 64-bit locality-sensitive hashing via utils/simhash.py. New content_hash field on Neuron model. Encoder and auto-capture use SimHash to catch paraphrased duplicates
  • Point-in-time temporal queries: valid_at parameter on nmem_recall filters fibers by temporal validity window (time_start <= valid_at <= time_end)
  • Schema migration v4→v5: content_hash INTEGER column on neurons table

Changed

  • DecayManager.apply_decay() now uses per-neuron state.decay_rate instead of global rate
  • reconstitute_answer() returns ScoreBreakdown as third tuple element
  • _remember() MCP handler sets type-specific decay rates on neuron states after encoding

[0.9.4] - 2026-02-07

Performance

  • SQLite WAL mode + synchronous=NORMAL + 8MB cache for concurrent reads and reduced I/O
  • Batch storage methods: get_synapses_for_neurons(), find_fibers_batch(), get_neuron_states_batch() — single IN() queries replacing N sequential calls
  • Deferred write queue: Fiber conductivity, Hebbian strengthening, and synapse writes batched after response assembly
  • Parallel anchor finding: Entity + keyword lookups via asyncio.gather() instead of sequential loops
  • Batch fiber discovery: Single junction-table query replaces 5-15 sequential find_fibers() calls
  • Batch subgraph extraction: Single query replaces 20-50 sequential get_synapses() calls
  • BFS state prefetch: Batch get_neuron_states_batch() per hop instead of individual lookups
  • Target: 3-5x faster retrieval (800-4500ms → 200-800ms)

[0.9.0] - 2026-02-06

Added

  • Codebase indexing (nmem_index): Index Python files into neural graph for code-aware recall
  • Python AST extractor: Parse functions, classes, methods, imports, constants via stdlib ast
  • Codebase encoder: Map code symbols to neurons (SPATIAL/ACTION/CONCEPT/ENTITY) and synapses (CONTAINS/IS_A/RELATED_TO/CO_OCCURS)
  • Branch-aware sessions: nmem_session auto-detects git branch/commit/repo and stores in metadata + tags
  • Git context utility: Detect branch, commit SHA, repo root via subprocess (zero deps)
  • CLI nmem index command: Index codebase from command line with --ext, --status, --json options
  • 16 new tests for extraction, encoding, and git context

[0.8.0]

Added

  • Initial project structure
  • Core data models: Neuron, Synapse, Fiber, Brain
  • In-memory storage backend using NetworkX
  • Temporal extraction for Vietnamese and English
  • Query parser with stimulus decomposition
  • Spreading activation algorithm
  • Reflex retrieval pipeline
  • Memory encoder
  • FastAPI server with memory and brain endpoints
  • Unit and integration tests
  • Docker support

[0.1.0] - TBD

Added

  • First public release
  • Core memory encoding and retrieval
  • Multi-language support (English, Vietnamese)
  • REST API server
  • Brain export/import functionality