Omnigraph

Versioned graph database on object storage. Git-style workflows and multimodal retrieval.

[Andrew Altshuler @1eo]

My personal expertise graph
runs on @omnigraphdb
operated by @bot

Tracks:
Ideas
Events
Claims
People
Products
Signals
Companies
Concepts
Highlights
Explanations
Case studies

Organizes them into Domains & Threads
with full provenance:
Span → Artifact → Source

Has a thin custom UI↓

[Tech with Mak @techNmak]

Someone built Git for AI agent memory.

ModernRelay has open-sourced OmniGraph, a graph database that gives AI agents something most shared-memory systems lack: branches, commits, diffs, and three-way merges.

The idea is simple. If multiple agents are working on the same knowledge base, they should be able to make changes independently without corrupting or overwriting each others work.

Imagine three agents researching the same subject. One extracts information from documents, another resolves duplicate entities, and a third connects claims to supporting evidence.

With OmniGraph, each agent works on its own branch of the knowledge graph. Changes to entities, relationships, and evidence remain isolated until they're reviewed and merged.

And this is where the engineering gets interesting.

The merge engine compares changes against a common ancestor, much like Git. It can detect conflicting entity updates, delete-versus-modify conflicts, orphaned relationships, and schema violations.

Conflicting changes are reported explicitly. Successful merges are committed atomically, and previous graph states remain available through version history.

OmniGraph also combines several retrieval methods in one engine:
→ Graph traversal for following relationships
→ Vector search for semantic similarity
→ BM25 for keyword-based retrieval
→ Reciprocal Rank Fusion for combining results

Under the hood, its written in Rust and uses versioned Lance files for storage, with support for local and S3-compatible object stores.

Typed schemas enforce valid relationships, Cedar policies control mutations, and an MCP server lets coding agents interact with the graph.

The important distinction is that semantic retrieval alone doesn't solve the problem of multiple agents changing shared knowledge.

Agents also need to know what changed, who changed it, which version they're reading, and whether two sets of updates can safely coexist.

OmniGraph brings those familiar source-control ideas into persistent agent memory.

Open source. MIT licensed.

Here's the GitHub Repo: https://t.co/Zg1LF2ZYq8

[Omnigraph @omnigraphdb]

A new cookbook for Omnigraph: company brain

Feed it meeting transcripts or other sources and it keeps track of what was claimed and what got decided (those can be quite different). Most importantly, where things contradict each other. All in one graph.

https://t.co/p2Cq89N4Vd

[Omnigraph @omnigraphdb]

@Steve_Yegge It's our sixth cookbook. The others cover industry intel, pharma intel, a second brain, a VC operating system and a dev graph.

Each one is a schema, seed data, stored queries and access policies you run locally
https://t.co/Od9MCJGgFb

[Omnigraph @omnigraphdb]

Beads by @Steve_Yegge is a graph issue tracker for coding agents. Omnigraph is a graph database.

So we rebuilt it as an Omnigraph cookbook:
- issues are nodes, blockers are edges
- "ready" is computed on every read
- atomic claims: one task, one agent

https://t.co/VuZp99G7PN

[Omnigraph @omnigraphdb]

Omnigraph v0.13 is out

Change schema, queries and access rules on a live server:
- no restart
- untouched graphs keep serving
- preview every change

From v0.12: shorter queries, less memory, safer writes.

On 0.11 or earlier? Read the upgrade notes.

https://t.co/Ai7lzNmypM

[Roman Pronskiy @pronskiy]

Okay, @ClaudeDevs mods are wild.
Made one for @Omnigraphdb and now I watch every query on the graph as Claude works.
Plus a full graph browser right inside Claude Code

[Omnigraph @omnigraphdb]

In this video @pronskiy shows how to set up Omnigraph in @bot for industry intel with just a few messages

[Roman Pronskiy @pronskiy]

An open 4B open model + a graph world model beats frontier models without one.

The paper shows that, if we give LLM planner a graph world model, multi-task robot success goes like this:
Qwen3-4B: 2% → 75%
Qwen3-8B: 20% → 93%
GPT-5.6: 25% → 99%
Sonnet 5: 39% → 99%

[Andrew Altshuler @1eo]

it all converges:
shared world model for agents to stays current, coherent & deconflicted

great paper on graph world models:
https://t.co/17xQchVhcA

[Christoph Janz 🕊 @chrija]

@maxjendrall We feed him mostly electricity, but when he asks for it he sometimes gets little cooling water.

Seriously ... :) ... Claude, ModernRelay, Attio, Zendesk, a lot of other tools, skills, and a custom MCP gateway. @qui_rins & I will share more soon!

https://t.co/EtKUzeBjeG

[Andrew @dremnik]

every saas product is converging around capturing and holding your company's context hostage.

they want to own your 'company brain'. clearly, it isn't your company's brain if they own and control it.

seeing an open source solution to this is incredibly refreshing. every company will eventually need this in some form. you need to represent your 'company brain' specific to your needs, and not be beholden to a rent-seeking intermediary.

i think the reality is, we are moving into an era of data management that resembles the transition to data lakehouses - a consolidation of the entire company's context into a single control plane where relationship integrity can be enforced.

this could potentially be the single most important infrastructure decision of the coming years.

[Roman Pronskiy @pronskiy]

Markdown+Git is great for agents at a small scale. It stopped working for me when my knowledge base grew and I wanted several agents running at the same time, which is a big part of why I joined @omnigraphdb as a founding member

[Santiago @svpino]

As soon as you try to run 2 agents on the same data, everything breaks:

• Agent 1 reads some data and starts working.
• Two seconds later, Agent 2 changes that same data.

This is a problem. Agents will waste tokens on stale data, overwrite each other's work, and redo tasks the other has already finished.

You can't prompt your way out of this mess.

Everything old in software engineering is new again. We need proper coordination when running multiple agents.

Here is a good open-source solution for this:

Omnigraph is a graph database for context assembly and multi-agent coordination.

Here is how it works:

• It sets one shared place where multiple agents will work from
• Each agent gets their own copy of the data
• Agents write their changes to their copy
• When an agent is done, it merges its changes back

Three huge benefits from this model:

1. We know it works. We've been doing this for decades already.
2. No more lost information or agents stepping on each other's toes.
3. You get a complete trail of which agent did what and when.

I recorded a video to show you a bit more about this.

Here is the link to the Omnigraph GitHub repository:

https://t.co/v1zqecvujn

[Omnigraph @omnigraphdb]

https://t.co/dEFCl9uuYN

[Omnigraph @omnigraphdb]

We're opening the waitlist for Omnigraph Cloud
Managed Omnigraph on the same open-source engine. Private preview later this year.

[Simon Späti 🏔️ @sspaeti]

With such a solution, we get:

- Hybrid retrieval: Vector, full-text, and graph traversal
- Time travel: Query the graph as of any point in history
- Auditable: Every change traceable to whoever or whatever made it

[Simon Späti 🏔️ @sspaeti]

I show a use case that uses OmniGraph with Lance as the open columnar storage format, DataFusion as the query engine, and builds the typed graph with git-style branches.

I mimicked my Obsidian CRM and rebuilt it as a Company Brain. There's no database server; only a typed graph in plain-text files

[Omnigraph @omnigraphdb]

A personal brain and a company brain are like Single-player vs Multiplayer: things get a lot trickier with lots of people and agents writing at once.

Simon rebuilt the Obsidian CRM he runs his business on with Omnigraph to show what that changes.

Check out his writeup

[Simon Späti 🏔️ @sspaeti]

Ever wondered how to expand an Obsidian-style second brain to a company-wide brain? Wonder no more 🙂.

I explore how to make a second brain multi-user-able, keep it consistent, and keep it up to date with agents securely. As agents consume more context and get smarter, we need a way for them to write and update stale knowledge, right? But how?

[Omnigraph @omnigraphdb]

Omnigraph v0.11 is out

Branching got more reliable: we fixed stale fast-forwards, false merge conflicts, and reads racing branch cleanup.

Plus groundwork for a cloud version, more soon.

v0.9/v0.10 graphs need a one-off format migration.

https://t.co/aVlVpcUbyd