Source-linked capability map. Mem0's product boundary is taken from its primary documentation. Protocol-scoped LoCoMo evidence is disclosed. No pricing comparisons.
Product boundary taken from Mem0's primary repository. Verify current behavior from the versioned release documentation before making deployment decisions.
Multi-level memory across user, session, and agent scopes. Hybrid retrieval combining semantic search with entity linking and temporal reasoning. Three deployment paths: Python/JS SDK, self-hosted mem0 server, and Mem0 Platform (managed cloud).
Choose Mem0 when: Team-shared memory via managed platform is preferred, multi-level user/session/agent memory is required, or a Python/JS SDK integration pattern fits your stack.
Read primary source →Choose SLM when your agents need a local-first operating control plane: SQLite-backed persistent memory, three explicit operating modes, nine npm framework adapters, bounded loops with independent-gate verification, per-workspace RBAC, GDPR Art. 15/17/20 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 clients have separate network paths that require independent assessment.
Attributes are configuration-dependent. Review each item independently for your deployment. Mem0 values are sourced from primary documentation; verify current behavior from the versioned release.
| Capability | SLM | Mem0 |
|---|---|---|
| Storage layer | SQLite-backed, local-first; configurable data root | Verify selected deployment (SDK / server / platform) |
| Operating modes | A (no language model; local embeddings) / B (a model on your machine, Ollama by default) / C (configured cloud) | Deployment-dependent; verify from release documentation |
| Memory scope levels | Personal / shared / global boundaries | User, session, and agent-level memory |
| Framework adapters | 9 Python adapter packages | Python SDK + JavaScript SDK |
| Teams & RBAC | Admin / member / viewer; per-workspace isolation | Mem0 Platform team features; verify selected product tier |
| Bounded loops | Independent-gate verified | Not separately documented |
| GDPR controls | Art. 15/17/20; hash-chained audit trail; opt-in PII redaction | Verify provider terms for selected deployment |
| EU AI Act self-assessment | Per-mode (self-assessment, not certification) | Not documented |
| Integration surfaces | MCP / CLI / hooks / dashboard | Python SDK / JS SDK / REST API |
| Published preprints | 4 arXiv preprints (2608.08253, 2604.04514, 2603.14588, 2603.02240) | Varies |
| License | AGPL v3 | Apache 2.0 (mem0 OSS) |
Documented in the current 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 Python adapter packages. Adapters provide framework-specific bindings without requiring a rewrite of existing tool configuration.
SLM 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 ships with the tool — a technical-control map, not a legal certification.
Four integration 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. See Research →
Published paper results, with their original protocol scope. Not a fresh rerun of the current package and not an ordinal ranking against Mem0 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 architecture-paper evidence, not a fresh run of the current package. 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. Scope differences between Mode A and Mode C make direct ordinal comparison unreliable. LoCoMo protocols are not comparable without matching dataset, answerer, judge, prompts, and context budget.
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.