# 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 15ms retrieval and per-agent pipeline customization.

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Strong Partial / Limited No / Weak Not verified / Not published

### Head-to-Head Comparison

Feature

Maximem Synap

Cognee

LongMemEval (independently verified)

Best 92%

Not applicable Not verified

LongMemEval (self-reported)

Best 92%

Not applicable Not published

P50 retrieval latency

Best 15ms

Not applicable Not published

Open-source eval harness

Best Full config published

No No

Context management paradigm

Best Active: captures, compacts, recalls per agent

Partial Semi-active: auto-extraction, explicit retrieval

Ingestion approach

Best Extract-first: multi-stage pipeline

Best 6-stage pipeline

Pipeline customization per agent

Best Custom architecture per agent via YAML

Partial Configurable per dataset, not per-agent

Data connectors

Best Connectors for structured data sources

Partial Multiple DB backends supported

Memory types

Best 5 structured: facts, preferences, episodes, emotions, temporal

Partial Entities + relationships + summaries

Context compaction

Best 4 strategies + quality validation score

Partial Graph summarization via memify

Contradiction handling

Best Explicit detection & resolution (HITL when needed)

Partial Edge reweighting via memify

Temporal awareness

Best Full bitemporal awareness

Partial Timestamps on graph edges

Entity resolution

Best Automatic, multi-strategy

Best LLM-based, knowledge graph triplets

Memory scoping mechanism

Best Intelligent & automated

Partial Semi-automated

Scoping levels

Best Org hierarchy: User → Customer → Client

Partial Per-user, per-group, shared graphs

Context sharing in agent-swarm systems

Best Native: shared context + agent-specific memories

Partial Multiple agents query same graph

SDK languages

Best Python + JavaScript

Partial Python

Framework integrations

Best 22 frameworks

Partial 3+ frameworks

Observability

Best Dashboard: pipelines, memories, entity queue

Best Graph explorer + notebooks

Last updated: April 2026. Benchmarks sourced from Synap's open-source LongMemEval and LoCoMo harness and vendor documentation. Feature availability may change.

01/Why 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 runs at 15ms P50. Cognee does not publish latency numbers. For latency-sensitive use cases (voice agents, real-time chat), published and verifiable latency is a production requirement.

### 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

[Synap vs Mem0](https://www.maximem.ai/compare/maximem-synap-vs-mem0)[Synap vs Zep](https://www.maximem.ai/compare/maximem-synap-vs-zep)[Synap vs Letta](https://www.maximem.ai/compare/maximem-synap-vs-letta)[Synap vs Supermemory](https://www.maximem.ai/compare/maximem-synap-vs-supermemory)[Synap vs Evermind](https://www.maximem.ai/compare/maximem-synap-vs-evermind)[Best Cognee alternatives](https://www.maximem.ai/cognee-alternatives)[Synap vs all alternatives](https://www.maximem.ai/compare/maximem-synap-vs-mem0-vs-zep-vs-letta-vs-supermemory-vs-cognee-vs-evermind)

## Ready to evaluate Synap against Cognee?

Free tier, no credit card. Open-source SDK and a published eval harness.

[Start free — no credit card](https://synap.maximem.ai/?utm_source=site&utm_medium=compare_footer&utm_campaign=cognee)[Try it live — no signup](https://www.maximem.ai/playground)

## Frequently Asked Questions

### Is Maximem Synap a Cognee alternative?

Yes. Both systems are extract-first and build structured representations before storage. Synap adds per-agent pipeline customization, compaction with quality validation scores, published 15ms P50 latency, and a verified 92% LongMemEval result (93.2% on LoCoMo). Cognee has not published a LongMemEval score.

### Does Synap use a knowledge graph like Cognee?

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.

### When should I pick Cognee over Synap?

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.

[Learn more about Maximem Synap](https://www.maximem.ai/synap)·[Best Cognee alternatives](https://www.maximem.ai/cognee-alternatives)·[Compare Synap against all alternatives](https://www.maximem.ai/compare/maximem-synap-vs-mem0-vs-zep-vs-letta-vs-supermemory-vs-cognee-vs-evermind)·[How we measure memory quality](https://www.maximem.ai/measuring-agent-memory)·[What switching from Cognee actually costs](https://www.maximem.ai/tco?mode=switch&from=cognee_self)·[Or build it yourself?](https://www.maximem.ai/build-vs-buy-agent-memory)

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Source: [https://www.maximem.ai/compare/maximem-synap-vs-cognee](https://www.maximem.ai/compare/maximem-synap-vs-cognee)