Hello, I need some help because I want to link the prompts used by the LLM, but I had no success so far to do that without using the @observe decorator. I want to do it using langchain(python). Her... More on github.com

Links and Installation: GitHub repository (with complete working example): https://github.com/datagusto/agent-control-layer Install: pip install agent-control-layer More on reddit.com

This is the pattern you should be following: https://langchain-ai.github.io/langgraph/concepts/human_in_the_loop/#editing In a nutshell: you stop the graph when user input is required, add that user input to the state and resume running the graph where it left off. More on reddit.com

This is caused by that piece of text with the curly brackets trying to get formatted (when you don't want it to). There's a few ways to handle this. If you are using a ChatMessagePrompt, and the part with the curly brackets has NO other variables, then what you can do is pass that part in NOT as a template (if its part of a template, it will format it). Eg, instead of prompt = ChatPromptTemplate.from_messages([ ("system", template_str), ... ]) Do from langchain_core.messages import SystemMessage prompt = ChatPromptTemplate.from_messages([ SystemMessage(content=template_str), ... ]) 2). If the part of your prompt with curly brakets also has some other string that needs to get formatted, what you can do is treat the code with curly brackets not as part of the template, but rather as part of the input. Eg., if you have template = """ Complex code: {"foo": [1,2,3]} Answer user question: {question} """ You could change it to template = """ Complex code: {code} Answer user question: {question} """ prompt = PromptTemplate.from_template(template) prompt = prompt.partial(code="{"foo": [1,2,3]}") Hope this helps! Happy to answer more More on reddit.com