How do I design a custom chain of tasks in LangChain?

To design a custom chain of tasks in LangChain, you need to structure sequential or conditional steps that combine language model (LLM) calls, data processing, and external tools. A chain in LangChain is built by defining a sequence of components (like prompts, LLMs, or utility functions) and specifying how they pass data between each other. Start by identifying the tasks your chain must perform—for example, fetching data, processing text, and formatting output—then use LangChain’s primitives to link them logically.

First, create a subclass of LangChain’s Chain class or use the SequentialChain/TransformChain utilities for simpler workflows. For custom logic, implement the _call method to define how inputs are processed step-by-step. For instance, a chain that summarizes text and extracts keywords might first call an LLM to generate a summary, then use a Python function to identify key terms. Use input_variables and output_variables to declare dependencies, ensuring each step receives the correct data. If your chain requires conditional logic (e.g., rerouting based on user input), incorporate tools like RouterChain or write custom logic to branch execution. For example, a customer support chain might route technical queries to a troubleshooting step and billing questions to a payment handler.

Next, integrate LangChain’s built-in components, such as prompt templates, memory systems, or external API wrappers. Use PromptTemplate to standardize inputs to LLMs, ensuring consistency across tasks. For instance, a chain that generates product descriptions could use a template like “Write a 50-word description for {product_name} targeting {audience}.” If your chain requires context from previous steps (e.g., retaining user preferences), add memory using ConversationBufferMemory or similar classes. To include external services, wrap APIs as tools—like fetching weather data via a REST client—and call them within your chain. Test each component in isolation before combining them, using LangChain’s debugging features to inspect intermediate outputs. Finally, validate the entire chain with diverse inputs to handle edge cases, such as empty responses or unexpected data formats, ensuring robustness.