add a new Claude-based workflow for when dependabot opens a pr to have Claude review it. Base it on the claude.yml workflow and make sure to include the existing setup, just add a custom prompt. research the best way to do this with the claude github action and make it look up the change log for the dependobot for all the changed dependencies + check them for breaking changes + let us know if we're impacted

Correct Approach

The agent blocks are missing their input/output pins because the input_schema and output_schema properties are not being populated in the GraphMeta objects when flows are loaded. When these are undefined, the CustomNode component falls back to empty schemas {}, resulting in no pins being rendered.

When rendered in CustomNode.tsx (lines 132-137), agent blocks replace their schema with the hardcoded values:

The Fix Applied

Maybe try changing this line in autogpt/processing/text.py ? def split_text(text: str, max_length: int = 8192) -> Generator[str, None, None]: honestly, I'm still checking to see if that'd be it, but doubtful lol

I just deleted the three lines in autogpt/promt.py. Maybe not the nicest solution, but works so far.

Could make do_nothing a lower variablity maybe?

Maybe you can try again with: o EXECUTE_LOCAL_COMMANDS=false o RESTRICT_TO_WORKSPACE=true See if the file is written to the folder " auto_gpt_workspace folde

Its the plugins that need updating.

Try using something different than the local memory. I downloaded the code 5 days ago so I don't know if it has been changed but inside the config.py file in the scripts folder on line 75, the memory backend is hardcoded to local. Change local to pinecone and use a pinecone API key if you want.

the gist notebook executed successfully however am still getting the error on this machine :

Could you try to modify the tf.keras to keras and execute the code. I have changed some steps like modifying tf_keras/keras.Sequential instead of tf.keras.Sequential and the code was executed without error/fail. Kindly find the gist of it here. Thank you!

Also I have changed some steps like modifying tf_keras/keras.Sequential instead of tf.keras.Sequential and the code was executed without error/fail. Kindly find the gist of it here.