LangChain lessons and observations

This is mostly a scratch for me to refer to, but figured it might also be helpful someone else.

Chains in a nutshell

A chain is an abstraction that encapsulates a prompt template + variable injection + a model + any memory-related operations.

A model chain (LLMChain) takes:

  • an input key aliasing the initial input to the chain (input key input by default).
  • (optionally) a memory class (memory key history).
  • a prompt template.
  • a list of input variables expected by the prompt as input_variables.
  • a model to use for predictions.
  • (optionally) an output parser.
  • an output variable which is text by default, response in a ConversationChain, or a custom variable if set.
  • (optionally) a list of callbacks to execute on different chain events.

When run, the chain:

  • takes the initial input aliased as input, or **kwargs if multiple inputs.
  • (optionally) loads the memory, if set on the chain, aliased as the memory key history.
  • injects the input into the prompt template.
  • sends the prompt template in to the model.
  • (optionally) parses the model’s output.
  • (optionally) writes the output to memory, if set on the chain.
  • which optionally writes the output to a data store, if set on the memory.
  • returns the model’s output as the output variable.

Miscellaneous lessons


To get visibility into what each chain is getting for input and output, add a breakpoint in the Chain._call and Chain.prep_inputs methods.

It can be difficult to understand what’s happening under the hood, why you’re getting errors related to input/output keys, etc. This is especially true once you start connecting multiple chains and other components.

If you have chat memory with a data store hooked up, saving to the data store occurs in BaseChatMemory.save_context.


Prefer Jinja2 templates over f-string templates, because f-string doesn’t support iteration over a list or other operations that Jinja2 does for you. For example, with f-strings you can’t pass a list of values into a prompt and have it expanded into a bullet list.

Finding answers

Because this stuff is so new, there isn’t a lot of documentation. If you’re stuck on a bug, try searching all of Github for the class name that you’re using, or a unique snippet of the function signature. This can help surface ideas about how others are using the same class.

Need something and don’t see it? Build it!

If you need LangChain to do something it doesn’t do yet, open a PR and contribute.

It’s open source, and they release a new minor or patch version every day or two. Make life easier for the reviewers by including an explicit branch name, and a good description of exactly what your PR does, including use cases. This will expedite your contribution.

Common errors and what they mean


Invalid prompt schema; check for mismatched or missing input parameters. 'history' (type=value_error)


Your PromptTemplate is receiving a history variable in input_variables which isn’t in the template. Add the variable to your prompt, or check variable names to make sure they’re the same.


Missing some input keys: {'some_input_key'}


Your PromptTemplate specifies an input key some_input_key that isn’t being passed in when the chain is invoked with .run.