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LangChain helps you connect a language model to an application, then compose prompts, retrieved information, and tools into useful workflows. Start with a prompt-and-model chain for a straightforward task; add retrieval when the model needs your documents, and use an agent or LangGraph when the application must choose and control multiple steps.
What LangChain does in an application
A language model can generate a response from an input, but an application often needs more: a prompt tailored to the task, relevant information from a knowledge source, or the ability to call a tool. LangChain provides integrations and building blocks for composing those parts.
The right starting point is the job you want the application to perform. For example, a PDF question-answering tool needs a way to find relevant passages, while a support-request router needs to classify a request and direct it to the right next step. LangChain’s current learning material includes tutorials for semantic search, retrieval-augmented generation (RAG), SQL agents, and custom agent workflows.
Start with a prompt-and-model chain
A basic chain combines a prompt template with a model. The prompt defines how the model should handle the task, and the chain accepts structured input when invoked. This is a useful first version when the task is a direct transformation or response and does not require the application to search data or select among tools.
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Python example using OpenAI
This example follows LangChain’s documented Python OpenAI integration. It is provider-specific: it uses the separate langchain-openai package and an OpenAI API key. Model identifiers and APIs can change, so choose a currently supported model from the live integration documentation rather than assuming an older example name remains valid.
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Create an OpenAI account and API key, then set the
OPENAI_API_KEYenvironment variable in the environment where the application will run. Keep the key out of source code and public repositories.Rank #2
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Install the provider integration:
pip install langchain-openai. This example also uses LangChain’s core prompt and chain components; follow the current setup instructions in the official OpenAI integration guide for the complete package requirements and supported model options. -
Compose and invoke a chat-model chain:
import os from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate # Set OPENAI_API_KEY in the environment before running this script. model = ChatOpenAI(model="YOUR_CURRENT_CHAT_MODEL") prompt = ChatPromptTemplate.from_messages([ ("system", "Answer clearly and concisely."), ("human", "Explain {topic} in one paragraph."), ]) chain = prompt | model response = chain.invoke({"topic": "retrieval-augmented generation"}) print(response.content)
Replace YOUR_CURRENT_CHAT_MODEL with a model currently supported by the integration. The response is a chat message, so the example prints its text content. LangChain’s guide distinguishes text-completion and chat-completion models and describes chat models as the more popular choice; use the integration documentation that matches the model type and API you select.
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Add your own information with retrieval
A model call only has the context provided to it or otherwise available to that model. If an application should answer from a PDF or another knowledge source, retrieval adds a step that finds relevant information and supplies it to the model. This is the central idea behind retrieval-augmented generation: retrieve material pertinent to the question, then use it as context for a response.
Semantic search and RAG are related but distinct application patterns. A semantic-search application focuses on finding meaningfully related passages; a RAG application uses retrieved passages as part of generating an answer. LangChain’s tutorials offer separate paths to build a semantic search engine over a PDF and create a RAG agent. The appropriate implementation depends on the source data and the behavior you need, so a simple chain should not be mistaken for a complete document-search system.
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Use tools when the application must take action
A tool gives an agent a capability beyond generating text, such as querying a database or invoking an application function. An agent can decide whether and when to use available tools, making it suitable when the next action depends on the user’s request or on intermediate results. For a fixed sequence of steps, a directly composed chain or workflow can be simpler and easier to reason about.
LangChain’s Learn documentation says, “LangChain’s agent implementations use LangGraph primitives.” Its learning material positions LangChain agents as an easy way to start, while allowing developers to implement agents directly in LangGraph when they need deeper customization. See the LangChain Learn documentation for the current tutorials and entry points.
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When to build the workflow in LangGraph
Use LangGraph when you need more explicit control over workflow steps, transitions, or shared state than a simple chain or agent provides. Its guide says, “When you build an agent with LangGraph, you will first break it apart into discrete steps called nodes.” Nodes perform work; connections describe what happens next, including conditional decisions.
The LangGraph design sequence is to map the workflow, define what each step does, decide what information belongs in shared state, implement nodes, and wire the flow together. A customer-support process, for example, might classify an email, search documentation, draft a response, and route an issue for escalation or follow-up. That is a workflow-design pattern, not a requirement to use every step in every application. The LangGraph concepts guide explains the framework’s workflow model.
| Approach | Setup and control | Good fit |
|---|---|---|
| Prompt-and-model chain | Few moving parts; the application defines the prompt and invokes the model. | A direct response or transformation with no retrieval or tool choice. |
| LangChain agent | A practical starting point for tool-using behavior; the agent can decide which available action to take. | Simpler applications where the model needs to choose among tools. |
| Direct LangGraph workflow | More explicit control over nodes, transitions, and shared state. | Workflows requiring conditional routing, defined stages, or more customized control. |
Test and observe the application
Check more than whether a response sounds plausible. Test representative inputs, including cases where the needed information is missing or a tool fails; for retrieval, verify that the application finds relevant passages; for an agent, check that it chooses an appropriate action. Tracing the steps can help identify whether a problem comes from the prompt, retrieved context, tool behavior, or workflow routing.
LangChain describes LangSmith as a product for debugging, testing, and monitoring LLM applications. It is an optional observability product, not a prerequisite for building a LangChain application.
Choose the smallest design that meets the need
Build the simplest version that can satisfy the application’s requirements: a chain for a direct model task, retrieval when answers should draw on your data, and an agent or LangGraph workflow when the application must select tools or manage explicit steps. For a provider integration, confirm the current package, credentials, model capabilities, and API requirements in that provider’s documentation before relying on executable setup details.
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