
The AI workspace where teams and agents get work done together
HydraDB OSS: Delivers robust open-source graph storage on object stores, but is a database rather than a turnkey agent memory API. HydraDB OSS records 29,635 monthly visits with +5.8% month-over-month growth and Freemium, Contact for Pricing pricing. supermemory records 156,579 monthly visits with -7.7% growth and Contact for Pricing pricing.

Graph database built on object storage for agent memory and context
| Dimension / Metric | ![]() | ![]() |
|---|---|---|
| Official Domain | hydradb.com | supermemory.ai |
| Pricing Model | Freemium, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 29.64K (29,635) | 156.58K (156,579) |
| MoM Traffic Delta | +1.61K (+5.8%) | -12.98K (-7.7%) |
| Primary Market & Share | 🇮🇳 India (65%) | 🇺🇸 United States (46.2%) |
| Direct Visits / mo | 17.15K | 71.16K |
| User Rating | 4.4 | 4.5 |
| Supported Platforms | Website | Website |

Graph database built on object storage for agent memory and context
Graph, WAL, value, and index data all live in S3-compatible storage with a hot/warm/cold tiering strategy, cutting infrastructure cost dramatically versus memory-resident graph databases.
One query can traverse graph relationships and run vector, BM25, and reranked search together, so agents do not have to orchestrate four separate systems.
Every change is tracked, letting agents recall what happened, when, and in what sequence, which supports auditability and long-running agent workflows.
OpenCypher over Bolt plus an HTTP API and SDKs make it approachable for teams already familiar with property graphs.

A memory and continual-learning engine that gives any AI agent persistent context
A dedicated model extracts, merges, infers and forgets across stored context so the memory graph keeps improving instead of simply accumulating.
Learnings link to the entities, sources and moments around them, storing not just content but a time-aware map of how facts relate.
Memory lives outside model providers and is used through APIs, SDKs, plugins and MCP, so context carries across models and tools.
Relevant memories are injected as tokens into the prompt at request time, giving agents relevant context without unbounded transcripts.

The AI workspace where teams and agents get work done together

SpecialistFocuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context.
SpecialistFocuses on tool integrations and authentication for agents, complementing memory engines rather than storing cross-session context.

Free / Open-sourceOne open-source AI gateway for 100+ LLM providers

SpecialistThe universal context engine connecting agents to data, tools, and models
SpecialistOffers enterprise-grade scalable vector search and RAG storage, but lacks supermemory's automated agent memory graph abstractions.
SpecialistAI gateway, observability and guardrails for production LLM apps
Free / Open-sourceProvides open-source persistent user modelling for stateful agents, but focuses more on conversational psychology than universal vector graphs.

Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus
Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus
Free / Open-sourceProvides high-performance open-source vector similarity search, requiring custom schema design for agent memory management.
Free / Open-sourceProvides high-performance open-source vector similarity search, requiring custom schema design for agent memory management.
Free / Open-sourceBuild, run and manage AI agents — visually or in code

Free / Open-sourceOffers an open-source memory OS for AI agents, but requires more self-hosting and setup than supermemory's managed API.
Free / Open-sourceOffers an open-source memory OS for AI agents, but requires more self-hosting and setup than supermemory's managed API.

SpecialistThe relevance engine with a built-in feedback loop