Maximem Synap vs Cognee
Cognee is the knowledge graph-first memory system with 14 retrieval modes and self-improving memory. Maximem Synap is the agentic context management system that scored 92% on LongMemEval and 93.2% on LoCoMo with in-conversation reads served from a pre-fetched in-process cache at an asserted P75 under 15 ms and per-agent pipeline customization.
| Feature | Maximem Synap | Cognee |
|---|---|---|
| LongMemEval, reproduced on our open harness | 92% (Best) | Not verified (Not applicable) |
| LongMemEval (self-reported) | 92% (Best) | Not published (Not applicable) |
| Published retrieval latency | Under 15ms P75 in-conversation, from a pre-fetched in-process cache (Best) | Not published (Not applicable) |
| Open-source eval harness | Full config published (Best) | No (No) |
| Context management paradigm | Active: captures, compacts, recalls per agent (Best) | Semi-active: auto-extraction, explicit retrieval (Partial) |
| Ingestion approach | Extract-first: multi-stage pipeline (Best) | 6-stage pipeline (Best) |
| Pipeline customization per agent | Custom architecture per agent via YAML (Best) | Configurable per dataset, not per-agent (Partial) |
| Data connectors | Connectors for structured data sources (Best) | Multiple DB backends supported (Partial) |
| Memory types | 5 structured: facts, preferences, episodes, emotions, temporal (Best) | Entities + relationships + summaries (Partial) |
| Context compaction | 4 strategies + quality validation score (Best) | Graph summarization via memify (Partial) |
| Contradiction handling | Explicit detection & resolution (HITL when needed) (Best) | Edge reweighting via memify (Partial) |
| Temporal awareness | Full bitemporal awareness (Best) | Timestamps on graph edges (Partial) |
| Entity resolution | Automatic, multi-strategy (Best) | LLM-based, knowledge graph triplets (Best) |
| Memory scoping mechanism | Intelligent & automated (Best) | Semi-automated (Partial) |
| Scoping levels | Org hierarchy: User → Customer → Client (Best) | Per-user, per-group, shared graphs (Partial) |
| Context sharing in agent-swarm systems | Native: shared context + agent-specific memories (Best) | Multiple agents query same graph (Partial) |
| SDK languages | Python + JavaScript (Best) | Python (Partial) |
| Framework integrations | 23 frameworks (Best) | 3+ frameworks (Partial) |
| Observability | Dashboard: pipelines, memories, entity queue (Best) | Graph explorer + notebooks (Best) |
Last updated: April 2026. 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 Cognee
Both systems take extraction seriously.
Cognee and Synap are closer architecturally than most competitors in this space. Both run multi-stage ingestion pipelines, both build knowledge graphs, and both produce structured output before storage. The differences are in how that structure gets applied at retrieval time and at scale.
Per-agent customization versus per-dataset configuration.
Cognee allows configuration per dataset or per graph, but the pipeline itself does not change based on agent type. Synap builds a custom context architecture per agent via YAML. A customer support agent and a voice concierge get different extraction, retrieval, and retention settings because they operate in different domains.
Compaction with quality guarantees.
Cognee's memify process prunes stale nodes, strengthens frequent connections, and derives new facts. It is a graph optimization process. Synap's compaction provides four explicit strategies (conservative, balanced, aggressive, adaptive) with quality validation scores and preserved facts counts on every compaction result. You know when compression preserved the information that matters and when it did not.
Latency is published.
Synap serves in-conversation reads from a cache pre-fetched into your own process, at an asserted P75 under 15 ms. We could find no latency figure published by Cognee as of September 2026, so there is nothing here to compare it against. For latency-sensitive use cases (voice agents, real-time chat), ask whichever systems you shortlist for a number measured on your own traffic.
Where Cognee has the edge.
Cognee's self-improving memory (memify) is genuinely distinctive. The system prunes stale graph nodes, strengthens frequently used connections, reweights edges based on usage, and derives new facts from existing relationships. Cognee also offers 14 retrieval modes, the most mature graph traversal in this space, full open-source availability under Apache 2.0, and self-hosting.
Benchmark note: Cognee has not published a LongMemEval score. We will update this page when scores become available for independent comparison.
Compare Synap against other alternatives
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Frequently Asked Questions
Yes. Both systems are extract-first and build structured representations before storage. Synap adds per-agent pipeline customization, compaction with quality validation scores, an asserted in-conversation P75 under 15 ms, and a 92% LongMemEval result reproduced on our open harness (93.2% on LoCoMo). Cognee has not published a LongMemEval score.
Synap uses a multi-stage extract-first pipeline with entity resolution and structured memory types. It is not knowledge-graph-native in the way Cognee is, which means fewer graph traversal modes but better suited to conversational agentic workloads where low latency is critical.
Pick Cognee if you need graph-native retrieval with 14 traversal modes, self-improving graph memory (memify), or fully open-source self-hosting for graph-centric workloads. Pick Synap for low-latency conversational agent context with 92% LongMemEval and 93.2% LoCoMo accuracy.