HydraDB: The Graph AI Runs On

Agents fail in production when they cannot access the right context at the right time. LLMs are stateless by design. They don't remember users, preferences, interactions, or outcomes across sessions, and enterprise context is scattered across workplace files and apps. Most systems patch this gap with vector databases or “memory layers” that orchestrate existing graphDBs with vectorDBs. Both approaches break down:

1. Vector databases were never designed for relationally aware context. A flat index creates a soup of embeddings where developers hope for relevant chunks and rarely get them. Structure, time, and causality are flattened away. As context grows more nuanced, retrieval becomes noisy, outdated, or incorrect. Graphs fix this: they respect ontologies, capture decision traces that record why a piece of context matters (not just the chunk itself), and retain user preferences. Agents get a structured, navigable context map rather than a haystack to search.
2. Context and memory apps are abstractions over graph databases largely designed before modern AI workloads, without agent primitives at their core. Tools such as Mem0 add those capabilities at the application layer but remain constrained by the engines underneath. Databases like Neo4j rely on JVM-based execution and provisioned disk and memory, so their cost, latency, and scaling constraints carry through to the apps built on them.

Larger context windows are not the solution either. The effective context window of any model is far smaller than the theoretical maximum, and nothing persists across interactions. No learning compounds. No outcomes feed back into future decisions.

No company has built the graph database that AI actually needs. That is the wedge for HydraDB. We are not a memory app. We are the context infrastructure underneath every memory app.

Part I - Enter through the use cases agents demand today.

Three use cases define AI context infrastructure right now: agent memory, company brain, and ontologies. Each is high-volume, structurally graph-shaped, and unserved by incumbents. Winning them puts HydraDB in the critical path of every production agent, and every workload we serve deepens the structured context enterprises accumulate on our platform.

Part II - Build the graph that runs the enterprise.

The database is just our entry point. IT context, customer context, and operational context all live in the same graph. As enterprises consolidate their fragmented context (Gmail, Slack, Notion, internal databases, proprietary systems) into HydraDB, the graph becomes the operating layer through which employees, agents, and tools interact.

Part III - HydraDB owns the outcome for sovereign AI.

We believe eventually: every company owns their weights, their models, their AI. That future is built on structured, verifiable, internal enterprise context. When companies use HydraDB, they are not just getting world-class graph storage. They are getting the gateway to train their own SLMs, generate RL environments, and fine-tune their own models. The data is the moat. The graph is the enabler. Selling the use cases agents demand today is how we get there.

The definition of context is expanding. Context is evolving from a set of documents to every bit of information in an organization: decisions, preferences, outcomes, and the relationships between them. Flat retrieval cannot represent that. Graphs can.

Agent reliability has crossed a threshold. Long-horizon and computer-use agents are now reliable enough that the next billion users in the workplace won't be humans. Agents need complete, structured context to operate, and they need infrastructure they can operate independently.

The incumbent technology is ripe for disruption. Graph databases were architected for a pre-AI era: JVM-based engines, SSD-bound scaling, exponential cost curves. Object storage plus GraphBLAS makes a 10x cheaper, dramatically faster architecture possible today that was not possible when Neo4j was designed.

Here are a series of conscious decisions we've taken from day one to be different. Our upcoming OSS launch will validate this.

1. Built on object storage with intelligent tiering of data: hot cache lives in memory to ensure real-time latencies; data not accessed in a long time lives in S3/object storage, reducing the bulk of the costs. 10x cheaper than FalkorDB and Neo4j.
2. Lightning-fast query engine: HydraDB has a structural advantage over incumbents like Neo4j that were built on Java binaries (JVM). HydraDB is built on object storage with GraphBLAS (C-based, native) and avoids JVM overhead entirely, which contributes to the latency wins we're seeing in our benchmarks.
3. Temporal versioning: one of the best features HydraDB offers is automatically versioning nodes in the graph when two similar nodes are detected. We were inspired by git and git-style versioning of code. Instead of performing destructive operations on previous nodes, we version nodes as if you were accessing a Google Document with its history. This ensures agents have complete context on “how” we got here in addition to what's true today.
4. Architecture that's agent-native by design: we're designing the database as if we were building the version that's going to be valuable in 2050. In addition to being fast and 10x cheaper, HydraDB is the only graph database that can be independently operated by agents without human intervention. Try it out: agents.hydradb.com.

SOTA on both graph performance and application performance. All benchmarks are public and reproducible: research.hydradb.com.

Graph performance: up to 90% cost reduction over SSD-bound incumbents. P50 = 100ms (most users see ~100ms), P90 = 400ms (1 in 10 queries sees 400ms+).

Application performance:

• LongMemEval-S: 90.79%, state of the art, vs 71.2% for Zep, the nearest alternative. Neo4j does not even do this yet.
• BEAM 1M: 82% overall across ten memory dimensions at the million-token scale, vs 74% for Hindsight, the prior state of the art, including a +31 point lead on temporal reasoning.
• FinanceBench: 91.4% Recall@10 over dense 10-K, 10-Q, and 8-K filings, delivered in a compact ~8K-token context package per query.

We launched two months ago and are already at $400K ARR with 2,000 developers on the platform. We are in production across more than 12,000+ agents today. We serve some of the fastest-growing AI companies including smallest.ai, clinsight.ai, calsoft.ai, and Composio for their agentic workloads.

We are backed by Jeff Dean, Gokul Rajaram (backed NeonDB), Wade Foster (CEO, Zapier), Sky9 Capital (backers of LanceDB, Kimi etc) and other operators. We are in talks with F500 companies to deploy enterprise solutions; the President and CPO of two F500s came onboard as angels.

Act 1. Win the three core use cases (agent memory, company brain, ontologies) for AI-native companies. Build the developer base, the benchmark credibility, and the data flywheel.

Act 2. Launch open source. Validate our efficiency claims publicly and accelerate adoption into enterprise AI.

Act 3. Become the graph that runs the enterprise: the unified context layer connecting every system of record to every agent in production.

Act 4. Enterprises train their own models on the structured context HydraDB holds. Sovereign AI, built on HydraDB.

Our Mission: Build the best graph database.

Our Vision: Build the enterprise layer that makes AI sovereign and stateful.

The graphDB is the wedge, not the endgame. It is the best-in-class database for the use cases agents demand today, and for us the graph is the enabler: it lets enterprises structure their context so agents can operate as first-class members of the workspace.

The future we are building toward is one where every company owns their weights, their models, their AI. That future runs on structured, verifiable, internal enterprise context. When companies adopt HydraDB, they are not just getting world-class graph storage for a company brain. They are getting the gateway to train their own SLMs and LLMs, generate RL environments, and fine-tune their own agents. The data is the moat. The graph is the enabler. This is the Databricks playbook: enter through the workload, own the data layer, and become the platform the enterprise cannot run AI without.

We have discussed this trajectory at length with our investors, who have backed infrastructure companies like NeonDB and organizations like Google DeepMind and OpenAI. They see two likely outcomes for HydraDB: a NeonDB-style strategic acquisition, or the long path to a category-defining public company. We are building for the second while staying positioned for the first.