Introducing HydraDB. The graph native context infrastructure for agents. Purpose built to deliver precise context & observability into why agents act the way they do. We've always believed graphs are the best way to manage AI context, but they've been too expensive to scale or impractical for storing full... context. Until now. HydraDB combines in memory, NVMe, and object storage into a single graph layer, making context delivery faster, cheaper, and more precise. We want context delivery to be extremely fast, 1000x cheap, and highly precise. Give your agents a brain.show more

One graph. Compounding intelligence. A connected brain for your agents. Sign up and start building for free.

Thankful for our investors' continued support since Day 1: @Sky9Capital, @JeffDean, @gokulr, @shyamalanadkat, @klyap_, @laura_yao, @caldbeckj, @anshulbhide, @vaibhavbetter, @SeanZCai, @azavery @missionstcap, @prateeks, @MartinGTobias, @PickensAllison, @wadefoster, @PuriSid, @mattsechrest, @CryogenicPlanet, @jaimalhotra8, @jpatel41, @gracegongGG, @jthandy

AI agents are finally getting longterm memory

Good times

Flat embeddings + 10M docs = confident hallucinations at scale. The fix isn't a better embedding model. It's a fundamentally different retrieval layer - one that tracks relationships, ownership, and temporal state. Super proud to be building this since Day 0 Big year ahead 🚀 and ofc, congrats boss - lesss goo

day 0 crew. this one's yours too.

RAG is dead, long live RAG

long live RAG, now with a memory that survives the session

Agents need fast context in the moment and verifiable memory over time. Graphs help with the first part. Verifiable storage is for the second: persistent storage that isn’t locked to one app, runtime, or vendor.

Finally a very solid solution for that problem :)

appreciate it!

Hydra is the best way to manage context!

Thanks @kamath_sutra!

The funniest part of “AI agents” is that most of them wake up every session like it’s their first day on the job. No memory. No continuity. Just vibes and injected context.

Congrats on the launch! Looks extremely useful

Thanks Vas, put it to work and tell me where it breaks.

for real!! agents miss out till they understand flat embeddings that compounds to making them dumber

Well said. Flat embeddings are fine until the agent actually has to think. Then the cracks show and everything downstream gets shakier.

absolutely only cracked people understand those cracks

Context is the whole game.

if an agent learns something incorrect once, how does it update or correct that later?

borrowed this correction principle from human cognition - principally we want agents to remember the mistake and then in the graph append the new information / learning as the most recent node! this was you have a snapshot of the current state as well as past mistakes.

This is awesome - congrats @contextkingceo and the @hydra_db team!!

@hydra_db Thank you!

The shift from bigger models to better context is real. This feels like a meaningful step in that direction.

highly bullish on graphs being used in this field! Congrats for the launch

ty!

Reddit is not kind to you bro. Gotta do something.

banger, lfgggggggg 🥳 excited to give hydra a spin

Awesome! Lmk your first thoughts

Agent memory is one of the most important problem statements. The pain to explain things back to a coding agent is real.

Congrats @contextkingceo and HydraDB team - excited to support your mission to deliver precise and scalable agent context and observability.

Thanks @gokulr! Plenty more coming on context and observability, excited to be building together!

genuinely the infrastructure we’ve been missing this whole time!!

🙌🏻

you're solving a great problem!

thanks!

Agent infrastructure is evolving fast. Love to see it 🔥

we're here for it

agent memory has been the biggest unsolved problem in AI, this is huge.

Biggest and least glamorous. Nobody wanted to build the plumbing. Here we are.

did you vibe create this?

Yessir. Gemini-3.5 flash did it.

LFG on the launch !!!

:))!!

the reason RAG struggles at enterprise scale is context precision, graphs naturally solve that. affordable graph infra with agent observability could be the game changer 🤌🏻

the expensive part with graph context was never storage, it was traversal latency when you need it mid-loop. did you solve the read path or the constant rewrites as the agent updates state?

> read path updates borrowed from some interesting research from stanford > however, i think storage was also an issue. graphs have always been clunky, super expensive to scale, and they couldn't store full working context

AI agents finally getting long-term memory

goldfish arc is over

1. Do you support procedural memory? 2. Memory seems to be limited to user scope. For a company brain, you likely want a user's agent to be able to access other user's memories and decide if its relevant for itself. Is there a way to do this without forcing to use knowledge?

my thoughts on this subject: 1. if by procedural you mean in steps / time series - then yes 2. company knowledge shouldn't be affected by personal (user) preferences. the tacit knowledge that lives in slack, gmail, notion is the original state of some information. 3. only the final state consumed by agents should be affected by user scoped memories

1. I mean mostly for the case of analyzing an agent's tool calls, coming up with a cookbook for future runs that anyone in the org's agent can use to do improve itself for speed and reliability when doing similar tasks 2. the issue im trying to solve for here is that its hard for llms to determine what is user preference vs what is actually useful to others in the org and is actually knowledge that should be extracted really cool product btw just trying to understand if we can use it

Long context was never memory. It was a very expensive way to pretend your agent remembered something it was just rereading badly.

i think working context = semantic knowledge + user preferences + episodic experiences

All the best Nishkarsh ♥️

thanks Ojasvika!

Nice work!

mentions frontier benchmark: longmemevals....

Impressive. Solving a real problem