Source-linked capability map comparing SLM V4 with six agent-memory systems. Product boundary data is taken from each project's primary source. Protocol-scoped LoCoMo evidence is disclosed. No pricing comparisons.
This is not a pretend winner table. It maps the documented product boundary of each system to the job it is designed to do, using the linked primary source for every entry.
Multi-level memory, hybrid retrieval, entity linking, and temporal reasoning.
Choose it when: Teams choosing an SDK or managed memory platform.
Read primary source →Temporal knowledge graphs, governed context assembly, and enterprise context infrastructure.
Choose it when: Teams building graph-centric, service-operated agent context.
Read primary source →In-context memory blocks, archival memory, files, and agent-managed retrieval.
Choose it when: Builders who want to operate agents inside a dedicated runtime.
Read primary source →Hot-path tools, background extraction, prompt refinement, and LangGraph storage integration.
Choose it when: Teams already standardized on LangGraph.
Read primary source →Memory, profiles, RAG, connectors, and personal-assistant workflows.
Choose it when: Teams seeking an API-led context stack or personal-memory app.
Read primary source →Profile construction, buffered processing, and per-user event memory.
Choose it when: Product teams optimizing personalized user experiences.
Read primary source →Choose SLM when your agents need a local-first operating control plane: persistent dated memory, multi-scope boundaries (personal / shared / global), explicit three-mode configuration, nine framework adapters, bounded loops with independent-gate verification, per-workspace RBAC, GDPR controls, and four integration surfaces — all from one runtime.
Mode A keeps the core memory path local after required assets are present. Mode B adds a configured Ollama endpoint. Mode C adds a configured cloud provider. Optional connectors, proxies, backups, and client applications have separate network paths that require independent assessment.
Attributes are configuration-dependent. Network behavior varies by mode and optional feature. Review each item independently for your deployment.
| Capability | SLM V4 | Typical Alternatives |
|---|---|---|
| Storage layer | SQLite-backed, local-first; configurable data root | Provider-hosted or runtime-managed |
| Operating modes | A (local sentence-transformer) / B (Ollama) / C (configured cloud provider) | Not documented or service-only |
| Framework adapters | 9 adapter packages (npm) | API-only or framework-specific |
| Teams & workspace isolation | Admin / member / viewer RBAC; per-workspace isolation | Provider-dependent |
| Multi-scope memory | Personal / shared / global boundaries | Service-dependent |
| EU AI Act self-assessment | Per-mode (self-assessment, not certification) | Not documented |
| Bounded loops | Independent-gate verified (v4) | Varies |
| Integration surfaces | MCP / CLI / hooks / dashboard | API-only or partial |
| GDPR controls | Art. 15/17/20; hash-chained audit trail; opt-in PII redaction | Provider-dependent |
| Published preprints | 3 arXiv preprints (2603.14588, 2603.02240, 2604.04514) | Varies |
| License | AGPL v3 | Varies by project |
Published V3 results carried into V4 with original protocol scope. These are not a fresh V4 rerun and are not an ordinal ranking against competitor claims.
10 conversations / 1,276 questions. Local embeddings, local retrieval. No LLM answer construction.
10 conversations / 1,276 questions. Local retrieval with GPT-4.1-mini answer synthesis disclosed.
Conv-30 only / 81 questions. text-embedding-3-large plus GPT-4.1-mini generation and judge.
Published V3 architecture evidence carried into V4. Mode A covers 10 conversations and 1,276 questions; the 74.8% retrieval result discloses GPT-4.1-mini answer synthesis. Mode C covers Conv-30 only (81 questions) with cloud embeddings and GPT-4.1-mini. Mode B has no separately published LoCoMo run. Scope differences between Mode A and Mode C make direct ordinal comparison unreliable.
Documented in the V4 release. Absent from or not documented in most agent memory SDKs — verify each for your deployment.
The canonical memory source is SQLite-backed at a configurable data root. No cloud persistence is required for the core memory path in Mode A or B.
Three documented operating modes with distinct embedding paths and network behaviors. Mode can be switched at runtime — memories persist across the transition.
Nine adapter packages published on npm in V4. Adapters provide framework-specific bindings without requiring a rewrite of existing tool configuration.
V4 adds bounded loop orchestration with an independent verification gate at each iteration boundary. Gate results are inspectable via CLI and dashboard.
Admin / member / viewer role-based access with per-workspace isolation. Single-user setups are unaffected — no login required for solo use.
Personal, shared, and global memory boundaries within the same runtime. Namespaces can have independent mode configurations for different projects.
GDPR Art. 15/17/20 controls (access, erasure, portability), a hash-chained audit trail, and opt-in PII redaction. A per-mode EU AI Act self-assessment is shipped with the tool — a technical-control map, not a legal certification.
The same local memory system is accessible through four surfaces — Model Context Protocol, structured CLI, event hooks, and the multi-agent dashboard — without reconfiguring existing workflows.
Four public preprints with protocol-scoped LoCoMo evidence and architecture documentation. Mechanism is inspectable. See Research →
SuperLocalMemory ships a per-mode EU AI Act self-assessment. It is a technical-control map, not a legal certification — applicability depends on your deployment, data, and operator role.
Regardless of mode: GDPR access / erasure / portability (Art. 15, 17, 20), a hash-chained audit trail, per-workspace isolation, opt-in PII redaction, and admin / member / viewer role-based access. See Governance & EU AI Act controls →
Open source, AGPL v3. A Qualixar Research Initiative.