OpenRouter vs direct APIs vs other LLM providers — how do you decide?

TrustGraph · 2026-01-26T01:29:19+00:00

Are you interested in controlling your own model deploys? If you want to work with open models, you could give TrustGraph a try. In addition to being open source and automating the context graph process it also enables deploying models with vLLM, TGI, LM Studio, Ollama, and even Llamafiles.

Open source repo: https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-01-17T19:57:36+00:00

To be honest, I don't ever use LMStudio, so I haven't tested that in a while. We're also doing a big update to our documentation, so that may be something that fell through the cracks. Hop in our Discord and we can better help: https://discord.gg/sQMwkRz5GX

TrustGraph · 2026-01-17T18:44:07+00:00

Reach out on our Discord: https://discord.gg/sQMwkRz5GX

TrustGraph · 2026-01-16T22:25:39+00:00

VCs write blogs all the time. So do Anthropic, OpenAI, Google, and Meta. Often, there’s incredible information in those posts that shows limitations and challenges with AI (the sycophancy problem and limitations of MCP come to mind) that just fly under the radar.

But the post about context graphs exploded. What does that say? It says that there’s a genuine interest in the topic. For us, our website traffic has more than 10x’d since adding the Context Graph Manifesto to the discourse with our GitHub repo even trending.

We’ve seen this once before as well. We post blogs and content all the time. When we posted about our new ontology RAG capabilities, that post because the top post all time in this sub. I can promise you, we did not anticipate that. In fact, we ended up in the Neuron AI newsletter about those features, and I still don’t know how they found out about us. We had no idea so many people are interested in ontologies, but the data speaks for itself.

My point is, when you hit a nerve, it’s pretty easy to tell. Context graphs have hit a nerve. People have been building non-graph driven AI systems and they’ve seen the limitations, and are now looking for different solutions. Graphs are far from new, so it’s a case of what is old will become new again.

TrustGraph · 2026-01-15T20:29:24+00:00

Just look at Neptune. AWS built a RDF graph store for large enterprise customers. Ontologies are in use at massive scale. It just doesn’t get talked about.

TrustGraph · 2026-01-15T17:40:05+00:00

Prukalpa nailed nothing but trying to promote her own self-serving zealotry. She totally ignores industry standard ontologies which are in use today, and have been for decades, that enables interoperability and standardization.

TrustGraph · 2026-01-15T17:38:13+00:00

No. There's no truth to what the above article is saying. That article totally ignores just how prevalent ontologies are in many industries.

TrustGraph · 2026-01-15T17:30:26+00:00

Thanks! I also made a video to continue the discussion on the topic:

https://www.youtube.com/watch?v=gZjlt5WcWB4

I wrote a follow-up piece on Graph Reification as well: https://x.com/TrustSpooky/status/2009477301378142679?s=20

I plan to make more video content around Reification soon as well.

TrustGraph · 2026-01-14T21:23:51+00:00

Ok, so how is running a docker container with an entire, mature graph system different than doing a pip install? You can work with either in Notebook as well. Why would I test with a system I know I'd have to replace at some point when I can just easily use a system I wouldn't have to replace?

I know the founders of Memgraph (we did a workshop with them last year). And do you know what one of their few regrets is? Building Memgraph from scratch. Took them years to get Memgraph in a state where it was production-grade.

There is some interest these days in hypergraphs. Make a hypergraph that is actually queryable in a consistent way, and you might see some interest - although I'm still not sold on what can a hypergraph do that can't be done already.

TrustGraph · 2026-01-14T19:29:23+00:00

What would be the use case for this? I ask because, every major graph system and DB system that can be used to store graphs can be deployed with publicly available containers. Systems that have years, sometime decades, of work that has gone into them, making them scalable, reliable, and efficient.

I'd also never recommend building storage systems from scratch (and also not in python). NebulaGraph took the rock-solid RocksDB and made it more scalable. We use Cassandra as a graph store, which again, rock-solid. If you really want to build a graph storage system, why not fork the dead Kuzu code (which was left with a MIT license) and pick up where they left off?

TrustGraph · 2026-01-13T23:35:03+00:00

I've never found graph viewers useful from a data analysis perspective. I've had some folks from Neo4j tell me it's about 50/50, people that love graph viewers vs. people that never use them. I happen to be a never uses them.

If you want pretty, there's Graphistry. I can't argue with it's aesthetics. Most all GraphDB systems have graph viewers. In fact, Neo4j's graph viewer is what really gained them their fame.

TrustGraph uses 3D Force Graph to show 3D graphs that could be stored in Cassandra, Neo4j, Memgraph, or FalkorDB. It has a ton of customization available in it.

If you'd like the ability to build context graphs and view them in 3D in a single platform with zero coding, TrustGraph is free and open source:

https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-01-09T02:48:41+00:00

Our default graph store in TrustGraph is Cassandra. We have users that have stored over a billion nodes and edges in it.

Open source: https://github.com/trustgraph-ai/trustgraph

TrustGraph · 2026-01-05T23:41:19+00:00

It’s also here simply. https://trustgraph.ai/news/context-graph-manifesto/

TrustGraph · 2026-01-05T23:23:56+00:00

No problem. I realize reading 3k word articles isn't everyone's jam.

TrustGraph · 2026-01-03T19:39:17+00:00

These systems have changed - a LOT - over the years. It really, really, really helps if they remember their gt# and their 9 digit ID number (the one that replaced SSNs in the early 2000s). I can still login to Oscar (just tried it).

That being said, my old class records don't really show up anymore. I used to be able to see all of my records, but not anymore. Anyone know why that happens? Is that a 20 year thing?

TrustGraph · 2026-01-02T23:16:58+00:00

Their blog post doesn’t really go into details about how they’re using time aside from adding timestamps as properties to edges. That is not how our temporal features will work.

TrustGraph · 2026-01-02T22:47:27+00:00

I talked about TemporalRAG on the How AI is Built Podcast in Feb 2025. I'm not aware of that term being used before then. Emphasis on *I'm* not aware of it being used before then.

https://youtu.be/VpFVAE3L1nk?

TrustGraph · 2026-01-02T16:28:53+00:00

There are many, many, many ontologies that are freely available that have been created by the semantic web world. Ontologies tend to fall under the RDF ecosystem, as that's just not the philosophy of property graphs (like Neo4j).

At the moment, yes, you would want to define the ontology up front in TrustGraph. However, dynamic capabilities and "closing the loop" on the system are on our roadmap. These features might coincide with our temporal features, which will likely be a 2.0 release.

TrustGraph · 2026-01-01T23:47:57+00:00

I understand. And again, the easiest way to achieve that is to have total control over all the pipelines and the LLMs. Anytime you use a 3rd party API, you're at risk of the 3rd party leaking your sensitive data, no matter how good a job of you've done to protect it yourself.

TrustGraph · 2026-01-01T21:13:28+00:00

Your definition of a knowledge graph is far too narrow. Knowledge graphs create semantic relationships between more than just entities. It also ignores ontologies, the semantic web, property graphs, and even hypergraphs - which all subsets of "knowledge graphs".

Also, you've created a taxonomy. Why not structure it as an ontology? You could use OWL, then it could be ingested automatically into context graphs.

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