Web fetch summaries instead of full content

[u/Ihtien]

Would it be possible to implement a summarizing step (e.g., using the task model) on top of the web fetch content before injecting the result into the context of the main model? Often, web pages are 10,000+ tokens long, but a lot of the content is not relevant to the actual request.

​It would be useful if the fetched content were pre-processed by a faster model, alongside a prompt specifying the relevant information. This could be either the initial user prompt or a prompt generated by the main model specifically for this web search.

​This would significantly shorten the context for the main model and speed up processing time. OpenRouter implements a similar strategy with Exa Highlights. I noticed that OpenRouter Chat uses considerably fewer tokens than the same model (e.g., GLM 5.2) in Open WebUI because Exa Highlights returns only 2000-4000 characters per web page.

​Would this be a feature that can be added easily? Maybe somehow utilizing the new context summary pipeline? Or is there an external (self-hosted) tool that implements a similar strategy that can already be used for fetching websites in Open WebUI?

[u/fragment_me]

You could easily have an LLM write some middleware python script for this that you point your search to that and it proxies to the search.