How to access nested dictionary values from one LangChain chain output as input for the next chain?

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1

I’m working with a SequentialChain setup in LangChain where my first chain returns a nested dictionary structure like this:

{
  'user_data': {
    'name': 'john_doe',
    'email': '[email protected]'
  },
  'status': 'active'
}

I know I can set the output_variables to include 'user_data' or 'status'. However, I want to pass the nested values like 'name' and 'email' directly to my second chain as separate inputs. Is there an easy way to extract these nested values without having to manually flatten the dictionary structure first? I’m looking for a clean solution that doesn’t require additional preprocessing steps.

2

try using a TransformationChain between your chains. just define a lambda like lambda x: {'name': x['user_data']['name'], 'email': x['user_data']['email']}. not the cleanest way, but it gets the job done without extra stuff or big changes.

3

Been there too. LangChain’s a pain with nested dictionaries, gets worse when you scale up multiple chains.

Custom output parsers that flatten everything? Sure, but you’re just piling complexity on top of complexity.

I ditched this whole approach for Latenode. Built it as a visual workflow instead of fighting LangChain’s sequential chains.

One node processes the initial data and spits out the nested structure. Then I drag connections from specific outputs straight to the next node’s inputs. No flattening, no custom parsers, no preprocessing headaches.

The visual setup shows exactly what data goes where. Need to modify structure or add processing steps later? Just reconnect nodes instead of rewriting chain logic.

Saved me hours debugging dictionary key errors and made everything way more maintainable.

4

Here’s what I’ve done in production systems for this.

Create a simple output parser that handles the extraction. Make one that implements BaseOutputParser and grabs what you need:

class NestedOutputParser(BaseOutputParser):
    def parse(self, text):
        # assuming your chain returns the dict structure
        data = eval(text)  # or json.loads if it's JSON
        return {
            'name': data['user_data']['name'],
            'email': data['user_data']['email']
        }

Then attach this parser to your first chain with output_parser=NestedOutputParser().

I’ve used this pattern across multiple microservices where we needed to reshape data between chain stages. Works better than transformation chains since you keep parsing logic separate from chain logic.

Just make sure your first chain returns a string representation the parser can handle. Most LLM chains do this automatically but custom chains might need tweaking.

5

I hit this exact problem last month. Here’s what worked for me - I made a custom chain class that inherits from Chain and overrides the _call method. You can grab the nested values and restructure the output dictionary right there:

class NestedExtractorChain(Chain):
    def _call(self, inputs):
        # Your existing chain logic here
        result = your_first_chain.run(inputs)
        # Extract nested values
        flattened = {
            'name': result['user_data']['name'],
            'email': result['user_data']['email'],
            'status': result['status']
        }
        return flattened

Just use this custom chain as your first chain in the sequence. More setup upfront but you get full control over output structure without messy preprocessing.