Calling an LLM API is easy. The hard part is everything around it — feeding it the right context, chaining multiple calls together, remembering previous interactions, and deciding when the model should use a tool vs. generate text. That’s the problem LLM orchestration frameworks solve, and LangChain is the most widely adopted one.

Harrison Chase open-sourced LangChain in late 2022. It grew fast, attracting thousands of contributors, and Chase went on to raise $30M in seed funding to build a company around it.

The Core Idea

A standalone LLM call is stateless and isolated. You send a prompt, you get a response. But most real applications need more than that — they need to pull data from external sources, maintain conversation history, or pick between different actions depending on the input.

LangChain abstracts this into a modular system. Think of it as middleware between your application logic and the LLM. It connects models like GPT-4, LLaMA, or Claude to data sources like Google Drive, Notion, or a vector database, and orchestrates the flow between them.

The analogy that tends to stick: LangChain is to LLMs what Zapier is to SaaS apps. It connects things and automates the workflow between them.

Chains: Composing Multi-Step LLM Workflows

The central abstraction in LangChain is the chain — a sequence of operations that run in order. A simple chain might take user input, format it into a prompt, send it to an LLM, and parse the output. A more complex chain might query a database first, inject the results into the prompt, call the model, then store the response.

LangChain breaks this down into composable modules:

• Models — manage prompt formatting, call the LLM, and extract structured output from responses.
• Retrieval — connects the model to external data through retrieval-augmented generation (RAG). This is how you ground LLM responses in your own documents or databases.
• Memory — persists state between calls so the model can handle follow-up questions and maintain conversational context.
• Agents — the dynamic counterpart to chains. Instead of following a fixed sequence, agents let the model decide which tools to call and what actions to take at each step.
• Callbacks — hooks for logging, monitoring, and streaming intermediate results.

LangChain ships with pre-built chains for common patterns (summarization, Q&A, conversational retrieval), but developers can also compose custom chains from individual components.

What Developers Are Building With It

The most common use cases right now are chatbots, document Q&A systems, and summarization pipelines. But as LLM capabilities expand, the range of applications is growing — code generation assistants, data analysis agents, and multi-modal workflows that combine text, voice, and structured data.

Other Frameworks Worth Knowing

LangChain isn’t the only option. Depending on your use case, these alternatives might be a better fit:

• Guidance (Microsoft) — template-based control over LLM outputs with constrained generation.
• Haystack (deepset) — focused on building production-grade search and RAG pipelines.
• Hugging Face Agents — lightweight agent framework tied to the Hugging Face ecosystem.
• Griptape — emphasizes structured, predictable workflows over open-ended agent behavior.
• AutoChain (Forethought) — designed specifically for conversational AI agents.

The LLM tooling space is still early. New orchestration frameworks are appearing regularly, and existing ones are evolving fast. The best choice depends on whether you need flexible agent behavior, structured pipelines, or tight integration with a specific model ecosystem.

This article was originally published on Picovoice