How do I manage state between chain steps in LangChain?

Managing state between chain steps in LangChain involves preserving and passing data through sequential operations. The primary approach is using input and output keys to explicitly connect steps, ensuring each component receives the necessary data. For example, if a chain generates a summary in one step and uses it in the next, you define an output key (like summary) for the first step and reference it as an input key in the second. LangChain’s SequentialChain enforces this pattern, requiring developers to map outputs to inputs explicitly. This method works well for simple workflows but becomes cumbersome when handling dynamic or multi-turn interactions.

A more scalable solution is LangChain’s Memory module, which stores and retrieves state across chain executions. For instance, ConversationBufferMemory retains chat history, allowing subsequent steps to access prior messages. To use this, you initialize the memory object and pass it to the chain constructor. The memory is automatically updated after each step, and variables like history can be injected into prompts. For example, a chatbot might include {{history}} in its prompt template to maintain context. Other memory types, like ConversationSummaryMemory, compress historical data for efficiency in longer interactions. This approach simplifies state management but requires careful configuration to avoid overloading prompts with irrelevant data.

For advanced use cases, developers can implement custom state management using callbacks or middleware. Callbacks provide hooks to intercept data between steps, enabling actions like logging or modifying the state. For example, a callback could write intermediate results to a database before passing them to the next step. Alternatively, middleware can wrap chain components to inject shared state (like user IDs or session data). If built-in tools are insufficient, you might create a stateful wrapper class that tracks data across chain runs. For instance, a class could store a context dictionary that updates with each step’s output. This flexibility allows tailored solutions but requires deeper integration with LangChain’s internals and thorough testing to ensure reliability.