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Having LLM model per-agent is even more flexible than per-flow.
We can have some more complex tasks during patch generation with the most elaborate model,
but also some simpler ones with less elaborate models.
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We may want to use a weaker model for some workflows.
Allow to use different models for different workflows.
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Some syzbot dashboard pages are guarded by an AppEngine login page. The
-download flag helps generate an input.json out of a bug report but it
currently uses simple HTTP GET commands without any authentication. When
called on a bug behind a login page, it received a login page HTML
instead of a json payload and fails in confusing ways.
The authentication page can be skipped using an authentication token.
It's easy to retrieve a token from the gcloud auth print-access-token
command.
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This is useful to save time when iterating on syz-aflow changes.
Note that this also sets a cache size of 10GB by default
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syz-aflow tool can be used to invoke any agentic workflow registered with pkg/aflow.
For example, to run the patching workflow use:
go run ./tools/syz-aflow -input=input.json -download-bug=d8fd35fa6177afa8c92b
go run ./tools/syz-aflow -input=input.json -workflow=patching-baseline -workdir=workdir
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