Interesting approach. Context management for agents is an underexplored area. I've been looking at similar problems - the key challenge is keeping token usage low while giving the agent enough context to be useful. Tiered retrieval (facts first, full text only whenneeded) seems to work well in practice.
Agreed, in addition to this I'd say it's better to spend tokens once while ingesting the sources and storing the canonical reusable definition rather than having agents do the full exploration over and over again
Thanks for the question! Just to complete - OpenVikings is context/memory infrastructure for agents. ktx is analytics context for data agents specifically. So they're in different categories
The way we think about the space: there's a "semantic layer" side (Cube, dbt MetricFlow, Wren's engine) that compiles correct SQL but is hand-authored. And a "company brain" side (OpenViking, Glean, wikis) that indexes prose but can't query data warehouses safely. ktx is built to be both halves at once - a YAML semantic layer and a searchable wiki of business definitions, cross-linked (each wiki page references the metrics it explains) and auto-maintained
Hey, ktx maintainer here!
Great question, we actually have quite a bit in common with Wren, they're in the same category. The main difference is that with Wren you author the semantic model (write MDL) and it executes SQL through its own Rust engine (similar to Cube as well).
ktx instead builds the context for you - ingesting definitions already in dbt, Looker, Metabase, and Notion, auto-detecting joins and fan/chasm traps, flagging contradictions. It also builds both : the SL and wiki that accumulates and maintains free-form tribal knowledge (that’s also interlinked with ktx SL)
Interesting approach. Context management for agents is an underexplored area. I've been looking at similar problems - the key challenge is keeping token usage low while giving the agent enough context to be useful. Tiered retrieval (facts first, full text only whenneeded) seems to work well in practice.
Agreed, in addition to this I'd say it's better to spend tokens once while ingesting the sources and storing the canonical reusable definition rather than having agents do the full exploration over and over again
How does this compare with Wren 2.0, OpenVikings etc
Thanks for the question! Just to complete - OpenVikings is context/memory infrastructure for agents. ktx is analytics context for data agents specifically. So they're in different categories
The way we think about the space: there's a "semantic layer" side (Cube, dbt MetricFlow, Wren's engine) that compiles correct SQL but is hand-authored. And a "company brain" side (OpenViking, Glean, wikis) that indexes prose but can't query data warehouses safely. ktx is built to be both halves at once - a YAML semantic layer and a searchable wiki of business definitions, cross-linked (each wiki page references the metrics it explains) and auto-maintained
Hey, ktx maintainer here! Great question, we actually have quite a bit in common with Wren, they're in the same category. The main difference is that with Wren you author the semantic model (write MDL) and it executes SQL through its own Rust engine (similar to Cube as well). ktx instead builds the context for you - ingesting definitions already in dbt, Looker, Metabase, and Notion, auto-detecting joins and fan/chasm traps, flagging contradictions. It also builds both : the SL and wiki that accumulates and maintains free-form tribal knowledge (that’s also interlinked with ktx SL)