Maximem Synap vs Supermemory
Maximem Synap is the memory layer to pick when the same customer has to resolve to one entity across channels, when a tenant rung above the user must be enforced at write time, or when the memory read has to fit inside a voice turn. On the same open LongMemEval harness, Synap scored 92% and Supermemory 71.3%.
Also comparing Zep? See Mem0 vs Zep, and where Synap differs.
| Feature | Maximem Synap | Supermemory |
|---|---|---|
| LongMemEval, reproduced on our open harness | 92% (Best) | 71.3% (Partial) |
| LongMemEval (self-reported) | 92% (Best) | 85.2% (Partial) |
| Published retrieval latency | Under 15ms P75 in-conversation, from a pre-fetched in-process cache (Best) | 220ms (No) |
| Open-source eval harness | Full config published (Best) | No (No) |
| Context management paradigm | Active: captures, compacts, recalls per agent (Best) | Semi-active: auto-extracts facts, explicit retrieval (Partial) |
| Ingestion approach | Extract-first: multi-stage pipeline (Best) | Extract-then-store: fact extraction (Partial) |
| Pipeline customization per agent | Custom architecture per agent via YAML (Best) | Universal pipeline (No) |
| Data connectors | Connectors for structured data sources (Best) | Text ingestion only (No) |
| Memory types | 5 structured: facts, preferences, episodes, emotions, temporal (Best) | Facts + user profiles (Partial) |
| Context compaction | 4 strategies + quality validation score (Best) | Automatic, no validation score (Partial) |
| Contradiction handling | Explicit detection & resolution (HITL when needed) (Best) | Explicit detection and resolution (Best) |
| Temporal awareness | Full bitemporal awareness (Best) | Handles temporal changes (Partial) |
| Entity resolution | Automatic, multi-strategy (Best) | Fact extraction with entity tracking (Partial) |
| Memory scoping mechanism | Intelligent & automated (Best) | Manual (No) |
| Scoping levels | Org hierarchy: User → Customer → Client (Best) | User-level (Partial) |
| Organizational memory | Customer + Client scopes (Best) | User-scoped only (No) |
| Context sharing in agent-swarm systems | Native: shared context + agent-specific memories (Best) | Single-agent focused (No) |
| SDK languages | Python + JavaScript (Best) | TypeScript + Python (Best) |
| Framework integrations | 23 frameworks (Best) | 7+ frameworks (Best) |
Last updated: . Benchmarks sourced from Synap's open-source LongMemEval and LoCoMo harness and vendor documentation. Feature availability may change.
01Why Synap
Why Teams Choose Maximem Synap Over Supermemory
The accuracy gap is measurable and reproducible.
On the same LongMemEval harness, Synap scored 92% and Supermemory scored 71.3% — a 20.7-point difference, measured with the same hardware, same conversations, same scoring. The eval harness is open-source. Anyone can verify these numbers.
Latency is a different order of magnitude.
Supermemory runs at 220ms P50. Synap serves in-conversation reads from a cache pre-fetched into your own process, at an asserted P75 under 15 ms, which is a different measurement rather than a like-for-like win. For voice agents inside a 300ms total budget, Supermemory consumes the entire budget before the LLM generates a single token. Synap leaves 285ms for the rest of the pipeline.
Per-agent customization versus universal pipeline.
Supermemory applies the same pipeline to every agent and every domain. Synap builds a custom context architecture per agent via YAML configuration. A customer support agent extracts different knowledge than a sales assistant, and their pipelines reflect that.
Context sharing across agent swarms.
Supermemory is single-agent focused with user-level scoping. If you are building a system where multiple agents serve the same customer, or where organizational knowledge needs to be accessible across agents while keeping individual user memories private, Supermemory does not have a native path for that. Synap's nested scope chain and native context sharing handle these patterns architecturally.
Where Supermemory has the edge.
Supermemory is SOC 2 and HIPAA certified, which matters for regulated industries. It offers TypeScript and Python SDKs, a self-hostable open-source version, and explicit contradiction detection and resolution. Supermemory's combined RAG-plus-memory approach means you do not need a separate retrieval pipeline for document search and memory search. If compliance certifications are a hard requirement today, Supermemory is further along on that front.
Benchmark note: LongMemEval scores were measured on Synap's standardized harness. Same hardware, same prompts, same conversations, same scoring. The eval harness is open-source.
Compare Synap against other alternatives
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Frequently Asked Questions
Yes, for agentic workloads. On the same LongMemEval harness Synap scored 92% versus Supermemory's 71.3%, at an asserted in-conversation P75 under 15 ms against Supermemory's published 220ms P50. Synap is purpose-built for multi-agent context management with nested scope isolation; Supermemory is single-agent focused with user-level scoping.
Synap focuses on agentic context management (conversations, entity resolution, temporal awareness, scope isolation). If you need combined RAG-plus-memory over mixed document and conversation content, Supermemory bundles both. If you need verified-accuracy (92% LongMemEval, 93.2% LoCoMo), low-latency context for multi-agent systems, Synap is the better fit.
Synap encrypts data at rest, keeps tenant data strictly isolated, and is architected for compliance review and offers enterprise deployment options. For current certification status, contact [email protected].