Does LangChain support parallel processing or batch operations?

LangChain supports both parallel processing and batch operations, though the implementation depends on the specific components and use cases. The framework is designed to handle multiple tasks efficiently by leveraging asynchronous execution and built-in batching features in certain modules. While not every part of LangChain natively supports parallelism, developers can implement strategies to achieve concurrent processing or batch data handling, especially when interacting with language models or external APIs.

For parallel processing, LangChain enables asynchronous execution through Python’s asyncio library. For example, when using an LLMChain (a core component for chaining language model calls), you can run multiple chains concurrently by wrapping calls in asyncio.gather(). This is useful for tasks like processing user queries in parallel or generating responses for multiple inputs simultaneously. Additionally, some integrations, like the ChatOpenAI class, support asynchronous methods such as agenerate(), which allow non-blocking API requests to services like OpenAI. Developers can also use tools like threads or multiprocessing for CPU-bound tasks, though LangChain itself doesn’t enforce a specific parallelism model, leaving flexibility for implementation.

Batch operations are supported in scenarios where models or tools can process multiple inputs at once. For instance, when using an LLM via LangChain’s generate() method, you can pass a list of prompts to the model in a single call, which is more efficient than making individual requests. This is particularly effective with APIs like OpenAI, where batch endpoints reduce latency and cost. However, not all integrations support batching natively—some may process batches by iterating sequentially under the hood. Developers can also build custom batching logic using LangChain’s Runnable interfaces or by combining chains with tools like BatchProcessor. For example, a translation pipeline could accept a list of text inputs, process them in batches via an LLM, and return translated outputs in bulk, optimizing resource usage.