TrustGraph

We've been building TrustGraph for a while now and just cut the v2.1 release. Wanted to share it here because explainability in RAG pipelines is something I don't see talked about enough, and we've put a lot of work into making it actually useful.

What is TrustGraph?
It's an open-source context development platform — graph-native infrastructure for storing, enriching, and retrieving structured knowledge. Think Supabase but built around knowledge graphs instead of relational tables. Self-hostable, no mandatory API keys, works locally or in the cloud.

What's new in v2:

The big one is end-to-end explainability. Most RAG setups are a black box — you get an answer and you have no

We've been building a context graph layer on top of LLMs (TrustGraph, which is open source) for the past 2 years and we hit something during testing that I think a lot of people building RAG pipelines will recognize.

We ran two queries against the same context graph:

"Where can I drink craft beer?"
"What pub serves craft beer?"

Different answers. And both were correct.

The first question is semantically open — "where" could mean a pub, a brewery, a taproom, a festival. The context graph followed the relationships and returned a broader set of results.

The second question is semantically constrained — "pub" is a specific concept with s

We've been building a context graph layer on top of LLMs (TrustGraph, which is open source) and we hit something during testing that I think a lot of people building RAG pipelines will recognize.

We ran two queries against the same context graph:

"Where can I drink craft beer?"
"What pub serves craft beer?"

Different answers. And both were correct.

The first question is semantically open — "where" could mean a pub, a brewery, a taproom, a festival. The context graph followed the relationships and returned a broader set of results.

The second question is semantically constrained — "pub" is a specific concept with specific relationships

With all the new interest in context graphs in AI, I've seen increased discussions around graph building. There's also been a lot of talk around the need for creating multiple graphs.

But you don't have to. The power of graph structures is being able to find unknown relationships that occur when seemingly disconnected data is added to the graph. Of course, this approach is easier with an RDF approach, especially when using ontologies. And there are tools for managing graph segments and modularity for access controls, multi-tenancy, and cost-efficiencies.

Here is an article that dives into this topic:
X: [https://x.com/TrustSpooky/status/2020344717486219759](https://x.com/TrustSpooky/sta

With all of the talk around decision traces and systems of records with context graphs, I felt it was important to discuss how we can actually accomplish this: reification.

In this article:

- Why “AI decisions” are a category error
- How behavioral economics exposes the limits of decision framing
- Why reification is the real missing concept behind context graphs
- How reification enables true systems of record
- Why this matters for auditability, governance, and liability—not just explainability

I also dive into the tradeoffs of RDF graphs vs. property graphs for reification. Traditionally, property graphs, while not being well-suited for ontologies, have been the most strai

For anyone that hates long reads, I created a video discussion of the Context Graph Manifesto, going into a bit more detail on a few topics.

Some of the things I discuss:
- Structuring simple data as RDF
- The subjectivity of "ground truth"
- Why "the sky is blue" isn't so simple
- Using time as a measure of information "freshness"
- Information encoded in structures
- What are ontologies?

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

If you'd like to read the original article:
https://x.com/TrustSpooky/status/2006481858289361339
[https://trustgra

It seems that the term "context graph" is generating a lot of buzz these days. We've been working on this problem for over 2 years now, as we strongly believed then - and still believe even more strongly now - that graphs will be instrumental in unlocking the potential of LLMs.

Why were so confident about this 2 years ago? We've been working with graph tech a long time. There are so many gotchas about operationalizing graphs, starting with common misunderstandings about what knowledge graphs are.

To help create some common understanding, we wrote the Context Graph Manifesto:

https://x.com/TrustSpooky/status/2006481858289361339

(or

You may have seen a lot of talk about "context graphs" lately and how they're the next "trillion dollar opportunity" according to Foundation Capital. I don't know about that, but we - at TrustGraph - have strongly believed for over 2 years that graphs would be at the heart of realizing the potential of LLMs.

To provide more context to "Context Graphs" (ha!), we've written the Context Graph Manifesto that we hope will give some insight into how to approach graphs for AI and the potential areas of development.

In our Context Graph Manifesto, I dig into:
- The fundamental building block of graphs: the triple
- The Semantic Web, RDF, and how they compare to property graphs
- What onto