Next-generation AI memory system inspired by hippocampal memory encoding and neocortical knowledge consolidation. Spans the full knowledge lifecycle: perception → extraction → association → forgetting. LLM-driven extraction converts conversations into structured entity-relationship triples stored in a Neo4j knowledge graph, while a parallel vector store enables hybrid semantic+keyword retrieval. A biologically-motivated forgetting engine (dormancy → decay → clearance) prunes low-value knowledge automatically.
- Storage
- Dual-store architecture: Neo4j knowledge graph stores extracted entity-relationship triples with temporal anchors and provenance; a vector store (BERT embeddings) holds parallel semantic representations for similarity search. LLM extraction at write time converts raw text into structured triples and syncs them to both stores. Each knowledge item carries a dynamic memory-strength score updated by usage frequency and association activity.
- Retrieval
- Dual-engine hybrid retrieval: BERT-based semantic vector search (synonym and implicit-intent aware) expands the candidate space; BM25 keyword search then performs precise filtering over candidates. The two signals are fused via RRF. Interactive knowledge-graph visualisation available for human inspection. Self-reported internal benchmark: 92% retrieval accuracy vs 57% single-mode baseline (vendor measurement, untracked benchmark — config not published).
- Self-host
- Self-host: heavy
- License
- Apache-2.0
- Pricing
- Open-source Apache-2.0. Self-hosting requires Neo4j (non-trivial to operate). The commercial cloud at memorybear.ai now publishes tiers (page is Chinese-language, prices quoted in USD): Free Experience $0/mo (1 space, 5 skills, 2 apps, 1 knowledge base at 300MB, 1 memory engine, 10 end users, community support); Personal Productivity $19/mo (20 skills, 10 apps, 3 KBs at 1GB each, 3 standard memory engines, 50 end users); Professional $299/mo (3 spaces, unlimited skills, 30 apps, 10 KBs at 10GB each, 10 advanced memory engines, 500 end users, ticket support); Business OEM is on-premises with pricing on request (20,000 memory records, 10,000 smart searches/mo, 99.9% SLA). · Free + paid
- GitHub stars
- 5,034
- Last release
- 2026-07-29
- Last commit
- 2026-08-07
- First catalogued
- 2026-06-28
Strengths
- Full knowledge lifecycle with biologically-motivated forgetting: dormancy → decay → clearance keeps memory collections pruned rather than accumulating stale data
- Dual Neo4j + vector store architecture supports rich relational queries (graph traversal) alongside semantic similarity — stronger than pure vector-only memory
- Multimodal affective intelligence path (arXiv:2603.22306) extends to audio, image, and emotion-aware memory for assistants
- v0.3.9 (Jun 2026, codename PoJun) added event-timeline editing on entity descriptions (add/update/delete individual events, not just whole-fact overwrite), a latency-optimized Express Retrieval mode, and multi-relationship entity types
- Memory MCP service listed on the ModelScope MCP marketplace as of v0.3.9 — a distribution channel distinct from a first-party MCP server entry in this catalog until independently verified
- v0.3.12 (Jul 2026, codename MingJing) made Express Retrieval and Persona Memory first-class Agent-callable tools and added activity-aware forgetting (memories are pruned by real creation time + usage frequency, not just static rules)
- 4860 stars, actively maintained with frequent releases (roughly weekly since v0.3.9); Apache-2.0
Watch out
- Neo4j dependency makes self-hosting heavier than most catalog entries — production deployments need a managed or self-hosted Neo4j instance
- Organisation is a Chinese entity (Suanmo Suanyang Technology / RedBear AI); evaluate long-term maintainership and supply-chain considerations
- Self-reported 92% retrieval accuracy is on an untracked internal benchmark with no published config — treat as directional, not comparable to LoCoMo/LongMemEval results
- Cloud commercial tier (memorybear.ai) pricing not publicly listed — the marketing site is a JS SPA with no reachable pricing page as of 2026-07-23
- v0.3.9 announced removal of the sync-write/async-read endpoints and deprecated the async status-check endpoints; v0.3.10 (2026-07-01) confirmed both removed outright — integrations still tracking the old API surface must migrate
- 'Memory MCP' is referenced repeatedly in release notes (persistence in v0.3.10, native Agent tool-calling in v0.3.12) but no first-party MCP server path is discoverable in the open-source repo — treat as a hosted/cloud-product capability, not a self-hostable MCP integration, until a repo location is confirmed
Best for
- AI assistants and agents that need rich relational knowledge management — especially where entity relationships, temporal tracing, and automated forgetting matter more than pure vector recall speed
Benchmark results
No sourced results yet.
Sources
- MemoryBear pricing page (Chinese-language) — tiers now published where the July card recorded 'pricing was not captured': Free Experience $0/mo, Personal Productivity $19/mo, Professional $299/mo, Business OEM on-prem custom, with per-tier space/skill/app/knowledge-base/engine/end-user quotas (vendor)
- Monthly card re-verification — GitHub API repo metadata: 4,948 stars, license Apache-2.0 confirmed, archived=false, last commit on the default branch 2026-07-31 (third-party)
- MemoryBear README (vendor)
- Memory Bear AI Memory Science Engine for Multimodal Affective Intelligence (arXiv) (paper)
- GitHub API repo metadata — re-verified 2026-06-30: 4645 stars (was 4661), Apache-2.0, pushed 2026-06-30, v0.3.9 latest release (third-party)
- MemoryBear v0.3.9 'PoJun' release notes — event-timeline editing on entity descriptions, Express Retrieval mode, multi-relationship entity types, memory MCP service listed on ModelScope MCP marketplace, and removal of the sync-write/async-read endpoints (breaking change) (vendor)
- MemoryBear v0.3.10 'DongWei' release notes — confirms removal of /v1/memory/write/status and /v1/memory/read/status endpoints (fulfilling the v0.3.9 deprecation) (vendor)
- MemoryBear v0.3.12 'MingJing' release notes — Express Retrieval + Persona Memory as first-class Agent tools, activity-aware forgetting engine, storage ceiling with auto-forget (vendor)
- GitHub API re-check — 4860 stars, Apache-2.0, not archived, pushed 2026-07-23, latest release v0.3.12 (third-party)
- memorybear.ai re-check — JS SPA, marketing copy only, no pricing/plans content reachable via curl or web-to-md (vendor)
Last verified 2026-08-01 · updated by refresh-framework-cards