Architecture Benchmarks Pricing FAQs Blogs Documentation Log In Sign Up Data connectors now live: Slack, Notion, GitHub, Gmail, and more → The Graph AI Runs On. GraphDB built on object storage: 10x cheaper, ultra fast, and purpose-built for modern AI workloads. Build ontologies, agent memory, company brains, and context graphs. Talk to us Sign Up $6.5M Raised Jeff Dean Researchers from OpenAI and DeepMind Sky9 Capital and more 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 . 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 % LongMemEval-S Overall 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 % Single Session Recall > 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 % Accurate vs Full Context GPT-4 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 K Avg. Token / Stack // Use Cases // What Engineers Are Building With HydraDB 01 AGENT MEMORY Build in-house memory systems. With your ideas, for your AI. 02 ONTOLOGIES 03 COMPANY BRAIN 04 AGENTIC ACTIONS 05 CONTEXT ENGINEERING Own Your Memory Layer. No Third-Party Abstraction. No Data Leaving Your Stack. Graphs work better for storing user preferences, past interactions, and agent traces. Git-style temporal versioning recalls what was true at any point in time. Entity resolution and preference checks across sessions prevent duplicate memory records. Similarity Isn't Always Relevance. Similarity search often returns what’s close and not what’s related. HydraDB connects your context, builds a structured graph, and delivers the exact context agents need. Relational-first, preference-aware, temporally versioned, precision recall. Without Graphs Retrieve similar ≠ relevant data Missed relationships between concepts, entities, events Lost agent traces, interactions, user preferences across sessions Juggling with VectorDB, GraphDB, Postgres with Temporal & filesystems across pipelines With HydraDB Make AI stateful with relevant context. Built to compound intelligence. Get a complete structured view of your knowledge Personalize results powered by what your agents have learnt from your users One unified layer combining graphs with all primitives needed to deliver context to AI systems Everything You Need To Compound Intelligence High Recall Accuracy Learn how we lead on LongMemEval-S (90%+), BEAM, and FinanceBench. 90.79 % Accuracy Scales With Your Systems Designed for high throughput using tiered storage: a hot in-memory cache, NVMe SSD for warm storage, and object storage for cold archival. Context moves fluidly between tiers. In Memory -> SSD -> Object Storage Recall Everything Assemble context from business data, workplace apps, chat sessions, documents. Remember user preferences while retrieving. Data Chat Preference Built For Low Latency Apps Built for low-latency apps — so you can build real-time applications with HydraDB. < 200ms Recall Degradation As A Bottleneck Embeddings hit a hard geometric ceiling as context scales VectorDBs are stateless by design, cannot personalize results Current systems are stitched implementations between vectorDBs, graphs, relational data stores; difficult to maintain, hard to scale Accuracy vs Context length ACCURACY % 100 80 60 40 20 0 HydraDB 90.79% 71% 38% 8K 32K 64K 96L 115K CONVERSATION TOKENS HydraDB VectorDB Full Context GPT-4o Graph Native Context Infrastructure For Agents Purpose-Built To Deliver Precise Context & Observability Into Why Agents Act The Way They Do. Total documents ingested 1 Billion+ Recall accuracy 92% Retrievals per month ~1 Million Trusted by 2k devs Architecture Overview ORCHESTRATION AROUND THE GRAPH DATABASE User Request Understanding routing · entity extraction · query rewrite · safety flags Retrieval Orchestrator cypher reads / writes Vectorstore (Plugin) semantic + bm25 + rerank Connectors (Plugin) 100+ sources: workspace, email, crm DB Filters (Plugin) SQL / NoSQL THE GRAPH DATABASE CORE Extremely fast, multi tenant, and built on object storage NAMESPACE — multi-tenant isolation, auto-scalable Writer Node Cypher writes → WAL + value log (S3 + disk cache) Indexer Node reads WAL entries → builds index (GraphBLAS) Reader Node Cypher queries → index + pending WAL = strongly consistent Unified Storage S3 · object-storage native namespace/data/wal namespace/data/value namespace/index namespace/manifest.json TIERED · CONTEXT FLOWS ACROSS TIERS ON DEMAND Hot in-memory cache → Warm NVMe SSD → Cold object storage State Of The Art On Various Benchmarks HydraDB outperforms various context applications on five of six LongMemEval-S categories. Last updated: March 2026 Gemini 3.0 primary 90.79% GPT 5 Mini (Compact) 85.80% GPT 5.2 (Latest) 84.73% Category HydraDB Mem0-OSS ZEP Full Context Single Session (User) 100.00 38.71 92.90 81.40 Single Session (Assistant) 100.00 8.93 80.40 94.60 Preference Extraction 96.67 40.00 56.70 20.00 Knowledge updates 97.43 52.56 83.30 78.20 Temporal Reasoning 90.97 25.56 62.40 45.10 Multi-session reasoning 76.69 20.30 57.90 44.30 Overall Score 90.79 29.07 71.20 60.20 Pricing Storage-based pricing with minimum commitment. No per-seat, feature, API limits, or infra caps. Pay for how much context your agents consume. Ship For shipping your first team of agents Free Unlimited API calls & tenants Multi-tenancy Observability & traces dashboard Native connectors to apps (coming soon) Community Slack & Email Support Start building Surge For agents scaling fast in production month Everything in free, plus: Up to 2GB graph storage Overage at $0.50/GB/mo Private Slack Channel SOC2, GDPR reports, DPA Get started Scale Making your agents enterprise- ready month Everything in surge, plus: Up to 10GB graph storage Overage at $0.25/GB/mo Dedicated infrastructure for guaranteed throughput Option to self-host (license) Get started Enterprise For teams deploying HydraDB in their own VPC Custom BYOC and fully self-hosted Dedicated account manager Support & Uptime SLAs Book a call Frequently Asked Questions What Is HydraDB? How Is It Different From Other Graph Providers? We are building HydraDB to be the fastest graph layer that acts as the context substrate for your agents. The difference starts at storage. We maintain tiered storage: a hot in-memory cache for actively needed context, NVMe SSD for warm storage, and object storage for cold archival. Context moves fluidly between these tiers based on recency and importance rather than static rules. Frequently accessed context stays fast and immediately available. Older or lower-priority context gets archived cheaply without being discarded. This allows us to make HydraDB extremely fast, cheap, and high precision. Who Is HydraDB For? Our goal is to unlock the primitives and give you the infrastructure so that you can build your own context stores, company brains, agentic actions for workplace apps, and memory layers on top of HydraDB. How Long Does Integration Take? Under a day for most teams. Drop in our SDK, ingest your first records, and run your first query in under 10 minutes. We provide a forward-deployed engineer for enterprise pilots. Do I Need To Replace My Existing Vector Database? No — HydraDB is designed to complement or replace your vector database. We handle setup, deployment, and ongoing optimization, so no technical expertise is needed. What Data Sources Can I Connect? Agent logs, documents, user memories, knowledge from app sources. Anything that helps you create a complete context substrate for your AI. How Does Pricing Work As I Scale? You pay for knowledge stored and queries served. Start free, upgrade when your usage grows. Is HydraDB SOC 2 Compliant? Yes. We are SOC 2 and ISO 27001 certified. Self-hosting is available on the Scale plan for teams with strict data residency requirements. Build AI With Compounding Intelligence Why HydraDB Features Architecture Use Cases Pricing FAQ's Contact BEAM Finance Bench Blogs Privacy Policy Terms & Conditions X / Twitter LinkedIn © 2026 AGI Context, Inc