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How to use LangChain for LLM application development
LangChain helps developers do more with language models, linking them to tools, APIs and live data to build more capable and dynamic AI applications.
To create advanced AI applications, developers need large language models that can integrate with diverse data sets. However, an LLM's ability to access the software stack in standardized ways remains a hurdle.
LangChain, an open source framework for building AI applications, has become a de facto standard for working with LLMs and integrating APIs. The tool serves as a critical intermediary, enabling a targeted LLM to interface with traditional software.
First introduced in 2022, LangChain has evolved quickly. Explore its core capabilities and key features, and learn how to use the tool for AI application development.
Understanding LangChain: Prompts, tools and chains
LangChain enables developers to integrate AI models with standard IT components such as software utilities, APIs and databases. Within LangChain, developers use a combination of prompts, tools and chains to manage LLMs and create desired AI interactions.
Prompts
All processes in LangChain revolve around prompts, which initiate the tasks that AI applications rely on.
Although building simple LLMs is relatively easy, the process becomes more complex for more advanced use cases, such as manipulating prompts and adding memory for models to recall past interactions and context. To further fine-tune and customize models, LangChain provides prompt templates -- reusable text strings that developers can populate with dynamic input.
Tools
Tools are the individual modules that comprise a chain. Developers can connect these modules to execute LLM tasks. LangChain provides multiple built-in tools that users can access:
- Tavily Search API.
- Python REPL (read-eval-print loop) for executing code.
- SerpAPI for search engine access.
- Plugins for Wolfram Alpha's computational knowledge engine.
Chains
A chain consists of multiple steps, or links, with each tool representing a link. The most basic chain joins a prompt template with an LLM instance. More complex chains contain multiple links, where the output of one becomes the input of the next. For example, an LLM instance can be linked with several utility tools and APIs to achieve a desired outcome.
LangChain offers three types of chains:
- Generic chains. To build other chains.
- Utility chains. To combine multiple tools.
- Asynchronous chains. To execute tasks concurrently.
In a generic LLM chain, a prompt template formats the input and passes it to the LLM. A variant, the transform chain, modifies input data before passing it to another chain or LLM to produce a specific result. Modules like APIChain enable developers to build API interfaces so their LLMs can interact with external data.
Utility chains power AI applications, automate tasks and generate dynamic content. Thanks to widespread developer support, these chains are constantly evolving. They include components such as program-aided language models (PAL) for code-based reasoning, SQL database chains, bash chains, APIs and request chains.
Developers building AI applications often create multistep workflows that involve multiple LLMs and external data sources. A complex LangChain workflow might incorporate tools and agents that retrieve and process data to achieve specific outcomes, such as launching a new commercial product or building a high-level reasoning chatbot. LangChain supports these goals by enabling retrieval-augmented generation, which improves LLM accuracy by providing context and reducing hallucinations.
Using LangChain with Spark and Kafka
LangChain excels at managing LLM workflows and integrating language models with APIs, tools and software utilities. But AI applications often require more than just model orchestration.
Developers building enterprise AI tools frequently need to gather, process and stream large volumes of data in real time or near real time -- for example, for financial monitoring tools or predictive maintenance systems. In these scenarios, integrating LangChain with scalable data processing platforms, such as Apache Spark and Apache Kafka, can bridge the gap between language models and high-throughput data infrastructure.
Apache Spark
Apache Spark is an open source processing system that provides a distributed computing framework for data extraction and processing. It supports SQL analytics, distributed machine learning tasks and fast streaming data processing.
Spark can process data in memory and lets developers add files directly, insert links and text, or connect to data sources. Users can work in different languages, including Scala, R, C# and Python.
Note that while Spark is well suited for high-volume, high-velocity data environments common in large enterprises, it might be less relevant for smaller-scale AI initiatives.
Apache Kafka
Apache Kafka is an event streaming and data integration platform often used alongside Spark. Together, the two tools enable developers to build end-to-end pipelines for data publishing and processing.
Kafka is well suited for event-driven applications such as batch processing of sensor data. It is highly fault tolerant and ensures data integrity by publishing information even if some brokers fail. Kafka minimizes the overhead on brokers by offloading message delivery tracking, enabling it to support a high number of parallel clients and achieve massive data throughput.
However, Kafka can be challenging to operate without specialized expertise. If misconfigured, the platform can also be particularly inefficient. To simplify operations, developers can turn to managed services and Kafka expertise from providers including Amazon Managed Streaming for Kafka (MSK), Confluent, Redpanda, WarpStream and Aiven.
3 LangChain best practices
Some users find LangChain unnecessarily complicated. Experienced developers, in particular, often contend that they can build AI applications more easily with plain Python and the OpenAI library, writing their own wrappers as needed. Still, LangChain remains a valuable asset for developers seeking an extensible framework, especially those who prefer low-code tools or are building sophisticated workflows without deep programming expertise.
These three best practices can help new users get started with LangChain:
- Use LangServe to deploy chains as REST APIs. LangServe, part of the LangChain ecosystem, makes it easier to serve chains via REST endpoints, enabling batch processing, consistent testing and streamlined integration with other systems.
- Use LangSmith for evaluation and debugging. LangSmith, a companion platform to LangChain, helps developers monitor, test and evaluate chains. It supports experiment tracking and structured debugging for more reliable outputs.
- Automate feedback loops to improve application performance. Iteration is essential for effective AI workflows. Set up feedback mechanisms, such as logging outputs and tracking user inputs, to refine applications over time. Because LangChain's documentation is still evolving, developers might need to rely on community resources and custom workarounds to implement this effectively.
Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.
