A HydraDB alternative for agent memory

• Zep and HydraDB share an approach: a temporal context graph (graph + vector + time) as the memory layer for agents, rather than a flat vector index.
• They differ on maturity and operations: Zep is an established, managed Context Lake with published benchmarks, enterprise governance, proven scale, and analyst validation; HydraDB is a newer entrant with a smaller public track record.
• If you want a managed, governed agent-memory platform you can deploy in production today — with SOC 2 Type II, HIPAA, and BYOK/BYOC — Zep is the lower-risk choice.

What HydraDB is. HydraDB (hydradb.com) provides infrastructure to build and scale an agent's context and memory layer. It describes a composite data layer that fuses a “Git-style temporal graph” for relational integrity with a high-dimensional vector data layer for semantic breadth, and frames recall as closer to a “personalized PageRank for memory” than vector similarity. It targets persistent, cross-session context for customer-support bots, research copilots, and internal knowledge assistants.

What Zep is. Zep is the Context Lake for AI agents — a managed platform that builds bi-temporal context graphs (via the open-source Graphiti), where every fact carries a validity window and provenance. It serves millions of graphs at sub-200ms p95, governs memory in the data layer (ABAC, retention, audit), and deploys managed, BYOK, or BYOC. It reports 94.7% on LoCoMo and 90.2% on LongMemEval (results), with the architecture documented in the Zep paper and external validation from S&P Global Market Intelligence.

HydraDB's published table (its cortex.pdf research note) reports 90.79% on LongMemEval-S and lists Zep at 71.2%. Several caveats — most of them visible in HydraDB's own paper — matter before treating that as a head-to-head:

• The backbones differ, by their own labeling. HydraDB's 90.79% is on Gemini 3.0 Pro; the table marks the Zep 71.2% figure as GPT-4o. A Gemini-3 result and a GPT-4o result aren't comparable. And 71.2% is Zep's original 2025-paper number — not Zep's current published results (90.2% LongMemEval, 94.7% LoCoMo).
• No disclosed context-token budget. The paper describes retrieval as a “dynamically determined” budget bounded by “context window constraints,” but publishes no token figure — so there's nothing to compare against the ~4,408-token context Zep reports.
• No reproducible benchmark code. HydraDB is proprietary and publishes no runnable benchmark code — only a research PDF and a client SDK. Its numbers can't be independently reproduced or code-inspected.

Compare current, matched-backbone numbers, and weigh accuracy alongside latency and context-token cost, before drawing conclusions.