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Benchmark

Public, evidence-based comparison of AI agent memory infrastructure. Every claim is verified against official documentation.

Data verified: June 2026

Feature Comparison

Encryption at Rest

SL✅
M0✅
Zep✅
LM❌

Trust Quotient

SL✅
M0❌
Zep❌
LM❌

Cross-Agent Memory

SL✅
M0⚠️
Zep⚠️
LM❌

MCP Native

SL✅
M0✅
Zep✅
LM❌

Audit Trail

SL✅
M0⚠️
Zep✅
LM❌

Designed for LGPD/GDPR

SL✅
M0⚠️
Zep✅
LM❌

Secure Agent Handover

SL✅
M0❌
Zep❌
LM❌

Open Source

SL✅
M0✅
Zep✅
LM✅

TypeScript SDK

SL✅
M0✅
Zep✅
LM❌

Python SDK

SL✅
M0✅
Zep✅
LM✅

Recall Latency P95

SL✅
M0?
Zep?
LM?
✅ Verified in public docs⚠️ Partial / not clearly documented❌ Absent from public docs? Not publicly verifiable

Sources & Notes

  • Encryption at Rest: Synapse: AES-256-GCM per-memory. Mem0: documented in cloud service. Zep: BYOK with AWS KMS. LangMem: library-only, no built-in encryption.
  • Trust Quotient: Per-memory confidence score (0.0–1.0) for filtering noise. Unique to Synapse Layer.
  • Cross-Agent Memory: Synapse: native cross-agent recall with TQ scoring. Mem0/Zep: achievable via MCP but not a dedicated feature. LangMem: library, requires custom implementation.
  • MCP Native: Synapse: MCP server on Smithery. Mem0: official MCP server (mcp.mem0.ai). Zep: MCP server via Graphiti. LangMem: library, no MCP server.
  • Audit Trail: Synapse: handover + operation logging. Mem0: cloud-only, limited documentation. Zep: comprehensive audit + API logging with SOC 2 Type II. LangMem: library, no audit.
  • Designed for LGPD/GDPR: Synapse: designed for alignment. Mem0: mentions compliance frameworks but had 23 reported vulnerabilities (2026). Zep: SOC 2 Type II + HIPAA BAA. LangMem: library, N/A.
  • Secure Agent Handover: Handover V2: AES-256-GCM encrypted context transfer with single-use SHA-256 tokens. Unique to Synapse Layer.
  • Open Source: All four projects have open-source components. Synapse: Apache-2.0. Mem0: Apache-2.0. Zep: Graphiti is open-source. LangMem: MIT.
  • TypeScript SDK: LangMem is Python/LangGraph only as of June 2026.
  • Recall Latency P95: Synapse: <50ms target. Others: not publicly documented with P95 benchmarks.
  • Methodology: All claims verified against official documentation, public GitHub repositories, and published security audits as of June 2026. This comparison is maintained by the Synapse Layer team. If you believe any data point is inaccurate, contact us.
  • References: Mem0 Docs · Zep Docs · LangMem GitHub · Synapse Layer Docs

Why Trust Quotient Matters

The Problem

Long-running agents accumulate contradictory memories. A user says "I prefer Python" in January and "I'm switching to Rust" in March. Without a reliability signal, the agent treats both as equally valid — leading to incoherent responses, wasted tokens on stale context, and declining user trust.

The Solution

Trust Quotient (TQ) is a per-memory confidence score from 0.0 to 1.0. It measures content density, semantic coherence, and recency. When you recall with min_tq=0.7, the noise disappears. Your agent receives only high-confidence memories — fewer tokens in, better decisions out.

The Result

Agents that improve with time instead of degrading. Trust Quotient creates a natural selection pressure on memory quality: high-TQ memories surface first, low-TQ noise is filtered. The longer an agent runs, the more reliable its context becomes. This is the opposite of what happens with flat memory stores — and the core reason Synapse Layer exists.

Key Differentiators

Trust Quotient

The only memory layer with per-memory confidence scoring. Filter noise at query time, not at ingest time.

Native Cross-Agent Memory

Purpose-built for multi-agent systems. One agent stores, another recalls — with full attribution and TQ scoring.

Secure Handover V2

Encrypted context transfer with single-use SHA-256 tokens and full audit trail. No other memory layer offers this.

Try It Yourself

Run the cookbook examples and see the difference Trust Quotient makes.

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