Launching today
Omnigraph
Open source graph database for your AI agents
70 followers
Open source graph database for your AI agents
70 followers
Omnigraph is an open-source object-storage native graph database with branching and typed ontology. It allows AI agents safely collaborate on shared knowledge. Give agents one typed graph with Git-like branching, review, and merge for every write, so changes can be validated before becoming shared context. Run it on your own S3-compatible storage, with semantic, keyword, and hybrid search plus graph traversal in one query. No hosted memory API or proprietary storage required.
Free
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Promoted
Hi Product Hunt 👋 Omnigraph is an open source graph database where agents can safely collaborate and write to the same graph.
It has a typed ontology, branches and time travel, and hybrid search with graph traversal in the same query. Your graph lives in your own object storage in an open format.
⭐ GitHub (MIT): github.com/ModernRelay/omnigraph
What can you use it for
More and more work inside a company is done by agents. They read the same data, write to it, make decisions on it, act on it. Without one shared world model they can trust, every agent ends up with its own version of the truth.
Omnigraph is a foundation for that shared world model. You can use it to build:
a company brain: people, projects, decisions, how they connect, all kept current by agents
context graphs for agents that work together on the same job
research graphs: papers, claims and the evidence behind them
You can check template ready-to-run graphs with real seed data (company brain, VC operating system, industry intel, beads): https://github.com/ModernRelay/omnigraph-cookbooks
What you get
Agents don't overwrite each other. Each one writes on its own branch, and you can review the changes before they reach main.
You always know who changed what and when, and you can see the graph as it was last week.
You define granular permissions to agents and humans in code.
Your schema says what can exist, and writes that don't fit are refused.
Agents find things by meaning or by keyword, then follow the connections over graph, in one query.
The graph lives in your own S3 bucket as open files you can take anywhere.
Every company needs a context layer, and we built Omnigraph to be the foundation for it.
Try it
The fastest way is to let your agent do it. Paste this into Claude Code, Codex or any coding agent:
Thanks @benln for hunting us! The whole team is here all day: @ragnor, @altshuler, @pronskiy, @azim_afroozeh. Ask us anything!
@benln @ragnor @pronskiy @azim_afroozeh 🚀🚀🚀
The visuals look super crisp, great job on that too! Also curious - how does it compare to the competitors?
@uddipta Thanks, Uddipta!
Short version: we built Omnigraph for multiple long-running agents writing to the same graph. Most graph databases assume a single application in control of the writes.
Typed schema: most graph databases are schema-optional, so agents drift. One writes Jack → spouse of → Jill, the next writes Jack → husband of → Jill. With Omnigraph you declare the schema up front and it's enforced on every write.
Branches: elsewhere agents write straight into live data. With us each agent can work on its own branch, then you or another agent review the diff and merge only what's good.
Open storage: data sits in an open format (Lance) on any S3-compatible storage, separate from compute. Most graph databases use a proprietary format and tie storage to compute.
It's also MIT licensed, and graph, vector and full-text search run in one engine.
What are you building? Happy to tell you honestly whether Omnigraph fits.
Congrats on the launch, @pronskiy. I like that it’s basically Git for agents. Good luck today!
@pronskiy @codingwisely Thank you, Vladimir!
Congrats on the launch! How do agents usually write to the graph: do they get the schema up front, or does Omnigraph infer the entities and relationships from what they store? I'm also curious how it handles conflicting facts from different agent runs.