Building applications with large language models (LLMs) is easier than ever thanks to frameworks like LangChain, LangGraph, and a growing ecosystem. This guide walks you through how to start with AI development and use multiple frameworks effectively.
Why use a framework?
Raw API calls to OpenAI or other providers work, but frameworks give you prompt management, chaining, memory, tool use, and agent patterns out of the box. They also make it easier to switch providers or add RAG (Retrieval-Augmented Generation) and agents.
LangChain
LangChain is one of the most popular frameworks for building LLM applications. It provides components for prompts, chains, retrievers, and agents, with support for Python and JavaScript.
Core concepts
- LCEL (LangChain Expression Language): Compose chains with
|for a clean, declarative pipeline. - Runnable interface: Standard interface for prompts, models, output parsers, and tools.
- Retrievers & vector stores: Integrate your own data (e.g. Weaviate, Pinecone) for RAG.
Start by defining a prompt template, piping it to an LLM, then to an output parser. Add a retriever in the middle for RAG, or wrap the LLM with tools for agentic behavior.
LangGraph
LangGraph extends LangChain with stateful, graph-based workflows. You define nodes (e.g. “call LLM”, “call tool”, “decide”) and edges (including conditional branches), and the runtime runs the graph until an end state.
This is ideal for agents that need loops (e.g. plan → act → observe → repeat), multi-step reasoning, or human-in-the-loop. The graph is explicit, so debugging and control flow are clearer than with a single “agent” black box.
Other frameworks and when to use them
- LlamaIndex: Strong focus on RAG and data indexing; great when your app is retrieval-centric.
- CrewAI / AutoGen: Multi-agent systems; use when you need several specialized agents collaborating.
- Semantic Kernel (Microsoft): Plugins, planning, and integration with Microsoft stack; good in .NET or Azure-heavy environments.
You can combine them: e.g. LangChain or LangGraph for orchestration and agents, and LlamaIndex or a vector DB for retrieval.
Practical first steps
- Pick one stack (e.g. LangChain + OpenAI) and run a simple chain: prompt → LLM → output.
- Add a vector store and retriever for RAG so the model can use your documents.
- Introduce tools and a simple agent (or a small LangGraph) so the model can take actions.
- Iterate on prompts, chunking, and tool design based on quality and cost.
Conclusion
Starting with AI development is about choosing a framework that matches your use case—LangChain and LangGraph for chaining and agents, and complementary tools for RAG or multi-agent systems. Begin with a minimal pipeline, then add retrieval and agentic behavior as needed.