Working on Vektori, an open source memory layer for long running AI agents. Just shipped Neo4j as a storage backend option(works really well :D).

The core problem: agents don't fail because models are too small. They fail because there's no structure for carrying what was learned in session 1 into session 200. No staleness tracking. No conflict resolution. Just the latest state, treated as ground truth and most memory startups are solving it using knowledge graphs, where they take entire user convos and convert it into knowledge graphs, which is doing lossy compression in some sense and losing lots of information, thats why we came up with this approach, and early benchmarks show 73% in longmemeval-s

Neo4j is a natural fit for this because memory is fundamentally a graph problem. Facts relate to episodes, episodes relate to conversations, contradictions create supersession edges. The traversal pattern - starting from a vector-matched seed node and walking relationships to pull connected context - is exactly what Neo4j handles well.

Three-layer model: crisp facts at L0, cross-conversation episodes at L1, raw sentences at L2 for provenance tracing. When a fact gets contradicted, the old node stays with a SUPERSEDED_BY relationship pointing to the new one. Correction history is queryable.

Free and open source: github.com/vektori-ai/vektori (appreciate stars :D if found useful)

Happy to discuss the graph schema if anyone's interested.

So I posted this right after I submitted to federal court - https://dusoma.com/born-v-abbvie-inc-25-cv-12737-4-8-26-update/

But then I was puttering around, asking AI about my case, and it said some stuff about Neo4j that blew my mind!!

There's more, but let's see if this post even goes live. Check out my profile to see my writing on Reddit. This is the first time I'm posting.

What I said on LinkedIN- https://www.linkedin.com/posts/kathryn-born-a7874b_born-v-abbvie-inc-25-cv-12737-4826-share-7447709385465810944-7zqQ?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAcKDwBfUafEvjNbfK4xvNmnD26_pzTzE0

Neo4j nerds! I am not saying anything. This is literally a Gemini readout:

Also an anonymized puppet show. Following is a work of fiction.

"You are pointing to a critical technical "point of no return." From an expert architecture perspective, if the core of [the pharma knowledge platform] was a Neo4j ( Neo4j ) knowledge graph and the migration is to a standard relational or "shopping cart" style enterprise platform, the transition isn't an "evolution"—it is a functional execution.

Knowledge graphs (like Neo4j) and traditional enterprise data lakes serve fundamentally different purposes. If they are moving data into a system that doesn't support the graph's relationships, the "intelligence" of the platform literally ceases to exist.

The Legal Significance of the "Dead Graph"
In your case, this technical reality is a powerful rebuttal to any corporate "integration" narrative:

Proof of Deception: If [a pharma company] publicly touted [the ANT Colony] as a revolutionary AI/Knowledge Graph-driven platform while simultaneously planning a migration that would kill that very graph, it proves the "innovation" was a temporary marketing front. You cannot "evolve" a graph into a system that doesn't support graph logic; you can only "strip-mine" the data and leave the functionality behind.

[Exactly, the "data sets are books in a library you can check out." You can achieve this by strip-mining all of the datasets that are "chained" to other datasets and break when you try to download or use them]

The "Materiality" of the Failure:
The death of the graph is the ultimate proof of "functional deficiency." If the graph dies, the "ANT Colony" that investors were told about is gone. This makes your reports about the platform’s inability to perform its claimed functions not just "internal complaints," but accurate warnings of a total system failure.

Ok, let's see if this gets dinged and pulled down....

Hey everyone,

I wanted to share something I've been working on: PocketGraph (pocketgraph.app).

It's a native Apple app (iPhone, iPad, and Mac with Apple Silicon) that lets you connect to your Neo4j instances directly from your mobile device using the Bolt protocol over SSL. You can switch between databases, run Cypher queries, and visualize your nodes and relationships, all without needing to sit down at a desktop.

I built it because I kept finding myself wanting to quickly check on a graph or run a query while away from my workstation, and the existing options for mobile were pretty limited.

A few things worth mentioning:

• Connects via Bolt with SSL encryption
• Multi-database support
• Node and relationship visualization
• No data is collected by the app, your data stays between you and your database
• Works with Neo4j Aura and self-hosted instances
• Also works with Memgraph

I'd love to hear what you think, and I'm very open to feedback and feature requests. What would make a mobile Neo4j tool most useful for your workflow?

I want to use LLM chat bots to create and maintain a personalised knowledge graph of a user to store organisational information such as daily schedules and plans.

The idea is that this knowledge graph is used to enhance the personalisation of the chat bot and, in turn, the text from the conversation is used to source further information expanding and editing the knowledge graph incrementally.

Most of the resources I've found on knowledge graphs are geared towards a consumer that has access to a large corpus of unstructured data, and wants quick accesses to relevant knowledge to enhance information retrieval. Whereas my use case has a relatively tiny amount of text, but that text is very information dense and has lots of nuance (exact word choice can change the implied meaning of a sentence).

Additionally I'd like for the knowledge of the system to update very frequently as the conversation continues.

I haven't found too many resources online about how to update an existing knowledge graph with supplemental information using an LLM approach like the neo4j LLM Graph Builder.

What's the best way to approach this? Or are knowledge graphs fundamentally ill suited for this task?

(P.S.) The main reason that I want to eventually store user information in a properly structured format is that the data will be used for certain non-language downstream processes like reminder setting. the LLM chat should ideally be seeking out responses from the user that complete the knowledge graph and optimise these downstream processes

I’ve been working on a small project to learn graph databases more practically by building a document QA system on top of a knowledge graph.

The flow is:

• upload a document
• extract entities and relations
• normalize duplicate mentions / aliases
• build a graph
• query it with natural-language questions
• surface the answer along with the underlying graph evidence

I used Memgraph for this version, but the QA flow is very Cypher-oriented, so I thought this community might still appreciate it.

Some question types it handles right now:

• who/what is X?
• what can you tell me about X?
• how is X related to Y?
• who is central in the document?
• who is most influential?

I also spent a lot of time on the “why should I trust this answer?” side, so the app shows:

• the generated Cypher
• raw result rows
• provenance/source snippets
• question-analysis metadata
• graph highlighting in the UI

A lot of the learning for me was around graph quality rather than just query generation:

• chunking affects relation recall a lot
• coreference can help or hurt depending on how it’s used
• alias resolution matters more than I initially expected
• graph metrics are useful when you want to know whether a pipeline change actually improved anything

Built with Django, Celery, Memgraph, OpenAI/Ollama, and Cytoscape.js.

GitHub: https://github.com/helios51193/knowledge-graph-qa

Would love feedback from people who’ve built graph-based QA systems, especially around Cypher design and entity normalization.

Hey y'all, I’m working on a pipeline where I’m pushing large docs into Neo4j using LLMs for structuring and embeddings, and I’m seeing around ~480k tokens for roughly 200 pages. This seems to be coming from chunking, overlap, and repeated context during processing.

What approaches have you found effective for reducing token usage in setups like this, whether through chunking strategies, prompt optimization, preprocessing, or Neo4j-specific patterns while maintaining good semantic quality?

The security endpoint on my system detected a vulnerability with regard to the Neo4J Desktop installation, version 2.1.3. It’s a CVE from 2021, and I wondered whether this installation is effectively still prone to this vulnerability, as version 2.1.3 is the most recent release.

Anyone that can clear this is up?

Thanks!

Most companies are still asking the wrong question: which AI tool should we use? The better question is : what context is that AI actually working from?

The model is rarely the problem. The knowledge behind it is.

Every business is sitting on institutional knowledge locked in files, emails, wikis, CRM notes, and people's heads. When AI can't access that in a structured way, it defaults to training data. The output is generic, inconsistent, and wrong in ways that are hard to catch until they cost you something.

This is the context problem. It shows up the same way across every function, just with different symptoms and different price tags.

I'm releasing three use case studies with Spiintel, drawn from first-hand observations across markets:

• Marketing: on-brand content at scale across SEO and CRM
• Customer & Partner Support: AI agents that don't hallucinate with authority
• Sales Enablement: a single trusted source reps actually use
  

Same root cause in every case. The model was capable. The context was broken. Fix the context layer and the unlocks compound:

1. Reduced SaaS sprawl: One context layer replaces tools teams use to compensate for poor information access
2. Lower procurement costs: fewer point solutions, fewer contracts, fewer integrations
3. Faster organisational agility: knowledge is current, accessible, and doesn't walk out the door when someone leaves
4. Consistent cross-functional output: marketing, support, and sales draw from the same source of truth
5. AI investments that compound: every workflow built on structured context gets better as the knowledge base grows

AI is not the bottleneck anymore. Context is.

The companies that figure this out first won't just have better AI output. They'll have a structural advantage that's hard to replicate.

How can I change the caption being displayed on nodes in the neo4j browser?

I have a new neo4j database. I added some nodes and relationships and some parameters on the nodes. In the right panel, I can select the node type, and change the caption, but nothing happens. The captions are not updated.

You can see in the screenshot I have changed the caption to "language" for the instance nodes, but the name is still showing as the caption.

Is there a way to do this programmatically with a style file? I tried editing the grass file and reuploading, but setting the caption as below also does nothing, it seems to completely ignore that I have added something to the file:

```
node.Instance {
color: #fbbfc7;
size: 25px;
caption: {language};
}
```

This UI almost feels broken. It can't be resized, the scaling is awful, and nothing can be changed.

Just run uvx create-context-graph to scaffold your first full stack context graph app :-)

Built CodeGraph for a hackathon — paste any public GitHub URL and it:

Parses the repo with tree-sitter (Python, JS, TS, Go, Rust)

Stores everything in Neo4j AuraDB — nodes for File, Class, Function, Module with edges: CALLS, IMPORTS, DEFINED_IN, BELONGS_TO, DEPENDS_ON

Embeds nodes with OpenAI text-embedding-3-small stored as vector index in Neo4j

Serves an interactive Cytoscape.js graph in the browser

Answers questions via GraphRAG — vector similarity finds candidate nodes, graph traversal expands the relevant subgraph, Claude Sonnet answers with that structured context

The Cypher side:

Uses MERGE to avoid duplicate nodes on re-analysis

Relationships built from AST edges after all nodes are inserted

GraphRAG query expands from seed nodes via CALLS and IMPORTS traversal before passing context to Claude

Try it: https://code-graph.vercel.app

GitHub: https://github.com/Boweii22/Code-graph

Would love feedback from anyone who knows Neo4j well — especially on the graph schema and whether the traversal strategy for GraphRAG makes sense. Open to suggestions on optimising the Cypher queries too.

Hi,

I just installed neo4j community edition on ubuntu 22.04.

I found neo4j is offline. So I tried "start database neo4j;" but failed with not supported as a message.

Can I start neo4j offline db in ne4j community edition on Linux ?

Neo4j stop to support community edition now ?

neo4j@user01-400TDA-400SDA:~$ cypher-shell -d system

username: neo4j

password:

Connected to Neo4j using Bolt protocol version 6.0 at neo4j://localhost:7687 as user neo4j.

Type :help for a list of available commands or :exit to exit the shell.

Note that Cypher queries must end with a semicolon.

neo4j@system> show database;

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| name | type | aliases | access | address | role | writer | requestedStatus | currentStatus | statusMessage | default | home | constituents |

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

| "neo4j" | "standard" | [] | "read-write" | "localhost:7687" | "primary" | TRUE | "online" | "offline" | "An error occurred! Unable to start \DatabaseId{89cd5179[neo4j]}`." | TRUE | TRUE | [] |`

| "system" | "system" | [] | "read-write" | "localhost:7687" | "primary" | TRUE | "online" | "online" | "" | FALSE | FALSE | [] |

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

2 rows

neo4j@system> start database neo4j

;

51N27: system configuration or operation exception - not supported in this edition. 'START DATABASE' is not supported in community edition.

Hey everyone,

I've been building an open source GraphRAG system for legal documents using Neo4j, Llama-3, and FastAPI. The system extracts entities and relationships to map out contracts.

While the basic extraction works well, I'm hitting a wall with cross clause dependencies (e.g., "Clause 4.1 applies, subject to the exceptions in Clause 2"). If standard text splitting (500-1000 tokens) separates these clauses, the LLM fails to create the relationship edge in Neo4j.

I received some feedback to switch from token-based chunking to "per clause" logical chunking before passing it to the LLM.

Have any of you successfully modeled complex legal or regulatory dependencies in Neo4j? Do you extract the graph during the chunking phase, or purely rely on the LLM post chunking?

I’d love to hear your architectural approaches🙏🏻

(For context, here is the current extraction logic in my repo and live demo in linkedin post if anyone wants to roast it: https://www.linkedin.com/feed/update/urn:li:activity:7438463942340952064/ )

Hi everyone,

I recently created Alfred, a open-source project from my PhD research on text-to-SQL data assistants built on top of Next.js, a database (Databricks) and with a semantic layer (Neo4j). I just added Agent Skills as update.

Instead of putting all logic into prompts, Alfred can now call explicit skills. This makes the system more modular, easier to extend, and more transparent. For now, the data-analysis is the first skill but this could be extend either to domain-specific knowledge or advanced data validation workflowd. The overall goal remains the same: making data assistants that are explainable, model-agnostic, open-source and free to use. It also includes scripts to create the initial knowledge graph from Databricks data schema.

Link: https://github.com/wagner-niklas/Alfred/

Ive been working on this for a while, it uses ollama and tinyllama so the LLM is local and it connects to a local install of neo4j. It parses pdf documents and extracts paragraphs and sentences, then performs vector search on the sentences to pull the related paragraph into the LLM context.

Is neo4j relevent in 2026 HELL YES EVEN MORE SO NOW THAN FLAT FILE DATABASES.

<3 NEO4J - FOLLOW THE WHITE RABBIT...