HydraDB

Founded by Nishkarsh Srivastava in 2024

Store and retrieve AI agent memory using a graph database on object storage

Pricing model

Free Tier

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use HydraDB to build a graph database layer for AI agents that stores memory, user preferences, past interactions, and knowledge relationships. It combines vector search, graph traversal, and temporal versioning in one system, so you don't need to stitch together separate vector and relational databases. It connects to Slack, Notion, GitHub, Gmail, and 100+ other sources. It returns context with under 200ms latency and achieves 90%+ recall accuracy on long-context benchmarks.

What HydraDB does

Write and read graph data using Cypher queriesConnect Slack, Notion, Gmail, and GitHub as data sourcesResolve duplicate entities across conversation sessions automaticallyQuery historical state of the graph at any past timestampRetrieve relevant context for an AI prompt with sub-200ms latencyMonitor agent memory traces and observability logsIngest and index over 1 billion documents into the graphDeploy a dedicated or bring-your-own-cloud graph database instanceGraph-native storage that links entities, events, and user preferences across sessionsGit-style temporal versioning so you can query what was true at any past point in timeTiered storage: hot in-memory cache, NVMe SSD warm tier, and cold S3 object storage90%+ recall accuracy on LongMemEval-S benchmark, outperforming full-context GPT-4oEntity resolution across sessions to prevent duplicate memory recordsUnder 200ms retrieval latency for real-time applications100+ data connectors including Slack, Notion, GitHub, and GmailObservability layer showing why agents retrieved specific context

Pricing breakdown

Pricing model: Free tier, Paid

PlanList price

FreeFree

Ship$25 / mo

Scale$799 / mo

Tutorials & Demos

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