deepagents v0.6 is about performance
the first level at which we can control that is the model layer: how can you squeeze perf out of a model?
tweaking prompts, tool names, and tool descriptions in accordance with the provider’s prompting guide can lead to substantial perf gains. we observed 10-20 jumps on subsets of the tau2 bench with just these tweaks alone.
deepagents ships with a builtin set of profiles so that you can match the harness to the model with no extra effort
@masondrxy and @Vtrivedy10 wrote a great blog on this!
https://www.langchain.com/blog/tuning-deep-agents-different-models