Top 10 LangChain Patterns Every Developer Should Know in 2026

Top 10 LangChain Patterns Every Developer Should Know in 2026

The world of Artificial Intelligence is moving incredibly fast. As a developer, keeping up with the latest tools and techniques is crucial for building smart applications. In 2026, LangChain continues to be a go-to framework for creating powerful applications powered by large language models (LLMs).

Mastering key LangChain patterns will give you a significant advantage. These patterns are like blueprints, helping you solve common problems efficiently. They allow you to build complex AI systems with less effort.

Why LangChain Patterns Matter in 2026

By 2026, LLMs have become even more capable and integrated into everyday tools. Developing effective AI applications means knowing how to orchestrate these models. LangChain provides the structure needed to connect LLMs with data, other tools, and user interfaces. Understanding core LangChain patterns helps you create reliable, scalable, and intelligent applications.

These patterns are not just theoretical; they are practical solutions to real-world challenges. They help you avoid common pitfalls and accelerate your development process. Let’s explore the essential LangChain patterns you need to master.

The Top 10 LangChain Patterns You Need to Master

This guide will walk you through the most important LangChain patterns in 2026. Each pattern comes with an explanation, practical use cases, and a simple code example. You will also learn about their pros and cons and when to best use them.

Pattern 1: Simple LLM Chain

Explanation

The Simple LLM Chain is the most basic building block in LangChain. It connects a prompt template directly to a language model. You provide an input, the prompt formats it, and the LLM generates an output. It’s like having a direct conversation with the AI, but with a pre-defined instruction.

This is often the first step when you’re just starting to use LangChain. It helps you understand how inputs become outputs. It’s the foundation for many more complex interactions.

Use Cases

You can use a Simple LLM Chain for basic question answering. It’s perfect for simple text generation tasks, like writing a short email. Summarizing a single piece of text also fits this pattern well.

Code Example

# Assuming you have LangChain installed and your OpenAI API key set up
# For 2026, we're using hypothetical advanced models.
# Make sure to install: pip install langchain-openai

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain

# Initialize the LLM (using a hypothetical 2026 model)
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Create a prompt template
prompt_template = ChatPromptTemplate.from_template("What is a good name for a company that makes {product}?")

# Create the LLM chain
chain = LLMChain(llm=llm, prompt=prompt_template)

# Run the chain
response = chain.invoke({"product": "eco-friendly water bottles"})
print(response['text'])

Pros & Cons

Pros

• Easy to understand and implement.
• Great for quick prototyping and simple tasks.
• Good starting point for learning LangChain fundamentals.

Cons

• Lacks complex logic or multiple steps.
• Not suitable for tasks requiring memory or tool use.
• Limited to a single input-output transformation.

When to Use

Use this pattern when you need a straightforward interaction with an LLM. If your task is a single turn, like generating a creative idea or answering a simple fact question, this is your go-to. It’s ideal for initial experiments and very specific, isolated prompts.

Pattern 2: Sequential Chain

Explanation

The Sequential Chain allows you to run multiple LLM chains in a specific order. The output of one chain becomes the input for the next. Imagine a production line where each machine performs a step and then passes its work to the next. This lets you break down complex problems into smaller, manageable steps.

There are two main types: SimpleSequentialChain (which passes all output directly) and SequentialChain (which allows mapping specific outputs to specific inputs). For most uses, SequentialChain is more flexible.

Use Cases

You can use a Sequential Chain for tasks like summarizing a document and then translating the summary. Another use is extracting key information from text, then generating a report based on that information. It’s great for multi-step content creation.

Code Example

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain, SequentialChain

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Chain 1: Generate product name
prompt_1 = ChatPromptTemplate.from_template("What is a creative name for a company that makes {product_type}?")
chain_1 = LLMChain(llm=llm, prompt=prompt_1, output_key="company_name")

# Chain 2: Generate slogan
prompt_2 = ChatPromptTemplate.from_template("Write a catchy slogan for a company named {company_name}. The product is {product_type}.")
chain_2 = LLMChain(llm=llm, prompt=prompt_2, output_key="slogan")

# Combine into a Sequential Chain
overall_chain = SequentialChain(
    chains=[chain_1, chain_2],
    input_variables=["product_type"],
    output_variables=["company_name", "slogan"],
    verbose=True
)

# Run the overall chain
response = overall_chain.invoke({"product_type": "smart home devices"})
print(f"Company Name: {response['company_name']}")
print(f"Slogan: {response['slogan']}")

Pros & Cons

Pros

• Breaks down complex tasks into manageable steps.
• Improves accuracy by refining output through multiple LLM calls.
• Enhances reusability of individual chain components.

Cons

• Can be slower due to multiple LLM calls.
• Error propagation: an error in one step affects subsequent steps.
• Can be more complex to debug if not well-structured.

When to Use

Employ this pattern when your task naturally involves a sequence of operations. If you need to transform or enrich information progressively, a Sequential Chain is very effective. It’s perfect for workflows like “summarize, then analyze” or “extract, then generate.” You can learn more about chaining operations in our article on advanced LangChain workflows.

Pattern 3: Router Chain

Explanation

The Router Chain acts as a traffic controller for your LLM application. It takes an incoming query and decides which specialized “sub-chain” or “expert” LLM to send it to. This is incredibly useful for applications that handle diverse types of questions. Instead of one LLM trying to be good at everything, different LLMs or chains can specialize.

This pattern uses an initial LLM (the “router”) to choose the best path. It’s like a smart switchboard for your AI.

Use Cases

Imagine a customer service chatbot that can answer questions about products, shipping, or technical support. A Router Chain can direct a product question to a “product info chain” and a shipping question to a “shipping policy chain.” It’s ideal for multi-domain assistants.

Code Example

from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain, RouterChain
from langchain.prompts import PromptTemplate
from langchain.chains.router import MultiPromptChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_init import DEFAULT_RESPONSE_PROMPT

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Define prompt templates for different experts
product_template = """You are a product expert. Answer the user's question about product features.
Question: {input}
Answer:"""

shipping_template = """You are a shipping expert. Answer the user's question about shipping policies and delivery.
Question: {input}
Answer:"""

general_template = """You are a general assistant. Answer the user's question.
Question: {input}
Answer:"""

# Create individual LLM chains for each expert
product_chain = LLMChain(llm=llm, prompt=PromptTemplate.from_template(product_template))
shipping_chain = LLMChain(llm=llm, prompt=PromptTemplate.from_template(shipping_template))
general_chain = LLMChain(llm=llm, prompt=PromptTemplate.from_template(general_template))

# Define the routes for the router
destination_chains = {
    "product_expert": product_chain,
    "shipping_expert": shipping_chain,
    "general_assistant": general_chain
}

# Define the router prompt
router_template = """Given a user query, determine which of the following tools is most appropriate to answer the question.
Choose one of the following tools: 'product_expert', 'shipping_expert', 'general_assistant'.

User query: {input}
Tool choice:"""

router_prompt = PromptTemplate(
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)

# Create the Router Chain
router_chain = LLMRouterChain.from_llm(llm, router_prompt, verbose=True)

# Combine into a MultiPromptChain
multi_prompt_chain = MultiPromptChain(
    router_chain=router_chain,
    destination_chains=destination_chains,
    default_chain=general_chain,
    verbose=True
)

# Test the router
print(multi_prompt_chain.run("What are the features of the new AI speaker?"))
print("---")
print(multi_prompt_chain.run("How long does standard shipping take?"))
print("---")
print(multi_prompt_chain.run("Tell me a fun fact about giraffes."))

Pros & Cons

Pros

• Improves efficiency by routing queries to specialized models.
• Enhances accuracy by using experts for specific domains.
• Easier to maintain and scale by adding or removing expert chains.

Cons

• Adds overhead due to an extra LLM call for routing.
• Router’s decision quality is crucial; a bad route leads to a poor answer.
• Requires careful design of routing prompts and expert chains.

When to Use

This LangChain pattern is best when your application needs to handle diverse queries that fall into distinct categories. If you have different knowledge bases or specific functions for different types of questions, a Router Chain is ideal. It’s perfect for complex chatbots and assistants.

Pattern 4: Map-Reduce for Documents

Explanation

The Map-Reduce pattern is a powerful way to process many documents or long pieces of text. It works in two main steps. First, the “Map” step processes each document independently (e.g., summarizes each one). Then, the “Reduce” step combines the results from all documents into a single, cohesive answer. This allows you to handle very large inputs that wouldn’t fit into a single LLM call.

It’s excellent for summarizing an entire book or answering questions across many articles. You can explore more document processing techniques in our LangChain document loaders guide.

Use Cases

You can use Map-Reduce for summarizing a collection of research papers. It’s also useful for answering questions based on many different sections of a long legal document. Analyzing customer feedback from hundreds of reviews is another great use case.

Code Example

from langchain_openai import ChatOpenAI
from langchain.chains.summarize import load_summarize_chain
from langchain.docstore.document import Document

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Sample documents (representing parts of a larger text or separate documents)
docs = [
    Document(page_content="The first paragraph discusses the history of AI, focusing on early symbolic AI and expert systems. It highlights their limitations in handling complex, unstructured data and the high cost of knowledge engineering."),
    Document(page_content="The second paragraph introduces the rise of machine learning, especially neural networks, and their breakthrough in pattern recognition. It mentions key events like the ImageNet challenge and the development of deep learning architectures."),
    Document(page_content="The third paragraph details the current era of large language models (LLMs). It explains their transformer architecture, vast training data, and emergent capabilities like natural language understanding and generation, paving the way for advanced applications."),
    Document(page_content="The fourth paragraph speculates on future trends in AI for 2026. It predicts more integrated multimodal AI, increased focus on ethical AI, and the widespread adoption of AI agents in various industries. Quantum computing's potential impact is also noted.")
]

# Create the Map-Reduce summarization chain
# 'map_reduce' is the strategy; 'stuff' for combining the final summaries
chain = load_summarize_chain(llm, chain_type="map_reduce", verbose=True)

# Run the chain
summary = chain.run(docs)
print(summary)

Pros & Cons

Pros

• Handles very large inputs that exceed single LLM context windows.
• Parallelizable: Map step can be run on documents simultaneously.
• Effective for synthesizing information from multiple sources.

Cons

• Can be slower due to multiple LLM calls.
• Potential for information loss if the ‘map’ summaries are too brief.
• More complex to implement and optimize than simple chains.

When to Use

Choose this pattern when you need to process a large volume of text or many individual documents. If your task involves summarizing a long book, answering questions over an entire database of articles, or analyzing widespread data, Map-Reduce is your solution. It’s one of the core LangChain patterns for data aggregation.

Pattern 5: Conversational Chain with Memory

Explanation

A Conversational Chain with Memory allows an LLM to remember past interactions. Without memory, an LLM treats each query as a brand new conversation. With memory, it can recall previous turns, allowing for natural, flowing dialogues. This is essential for building chatbots and interactive assistants.

LangChain offers various types of memory, from simple buffer memory to more advanced options that summarize conversations. This ensures the AI understands context across multiple messages.

Use Cases

This pattern is perfect for building a customer support chatbot that remembers previous issues. It’s also great for a personal assistant that understands your preferences over time. Any application requiring a continuous dialogue benefits from conversational memory.

Code Example

from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Initialize memory
memory = ConversationBufferMemory()

# Create a custom prompt template for the conversation
# 'history' will be filled by the memory module
prompt = PromptTemplate(
    input_variables=["history", "input"],
    template="""The following is a friendly conversation between a human and an AI.
The AI is talkative and provides lots of specific details from its context.
If the AI does not know the answer to a question, it truthfully says it does not know.

Current conversation:
{history}
Human: {input}
AI:"""
)

# Create the conversational chain
conversation = ConversationChain(
    llm=llm,
    memory=memory,
    prompt=prompt,
    verbose=True
)

# Interact with the chain
print(conversation.predict(input="Hi there! What's your name?"))
print(conversation.predict(input="What can you do for me today?"))
print(conversation.predict(input="Can you remind me of my first question?"))

Pros & Cons

Pros

• Enables natural, multi-turn conversations.
• Improves user experience by maintaining context.
• Makes chatbots feel more intelligent and responsive.

Cons

• Can increase token usage and cost for long conversations.
• Memory management can become complex for very long dialogues.
• Privacy concerns if sensitive information is stored in memory.

When to Use

This LangChain pattern is indispensable for any application that involves back-and-forth interaction with a user. If you are building chatbots, virtual assistants, or any system where context from previous turns is crucial, you must use a Conversational Chain with Memory. It’s a cornerstone for interactive AI applications in 2026.

Pattern 6: Agent with Tools

Explanation

An Agent with Tools is a sophisticated LangChain pattern where an LLM acts as a reasoning engine. It observes a user’s request, decides which tools (like a calculator, search engine, or database query) it needs to use, and then uses them. After getting results from the tools, it uses its reasoning again to formulate a final answer. This gives LLMs the ability to go beyond their training data and interact with the real world.

The agent pattern is about giving the LLM agency – the power to choose and act.

Use Cases

You can use an Agent for answering complex questions that require current information (e.g., “What’s the weather like in Paris right now?”). It’s also great for performing calculations (“What’s 15% of 345?”). An Agent can even interact with APIs to book flights or retrieve live stock prices.

Code Example

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent, Tool
from langchain import hub
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Define tools
wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
tools = [
    Tool(
        name="Wikipedia",
        func=wikipedia.run,
        description="Useful for when you need to answer questions about general knowledge, history, or people. Input should be a search query."
    ),
    # You can add more tools here, e.g., for calculations, current weather, etc.
    # Tool(
    #     name="Calculator",
    #     func=lambda x: eval(x), # WARNING: Eval is dangerous in production. Use a safe calculator tool.
    #     description="Useful for when you need to perform mathematical calculations. Input should be a mathematical expression."
    # )
]

# Get the prompt for the ReAct agent (standard pattern)
prompt = hub.pull("hwchase17/react")

# Create the agent
agent = create_react_agent(llm, tools, prompt)

# Create the agent executor
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run the agent
print(agent_executor.invoke({"input": "What is the capital of France and who painted the Mona Lisa?"}))
print("\n--- Next query ---")
print(agent_executor.invoke({"input": "Tell me about the history of the internet."}))

Pros & Cons

Pros

• Enables LLMs to perform actions and access external, up-to-date information.
• Highly flexible and can solve complex problems by combining multiple tools.
• Extends LLM capabilities beyond their training data.

Cons

• Can be slower due to multiple LLM calls and tool executions.
• Debugging can be challenging as the LLM’s reasoning path is dynamic.
• Requires careful tool selection and description for optimal performance.

When to Use

Use the Agent with Tools LangChain pattern when your application needs to perform actions or access real-time information. If the LLM needs to make decisions about how to answer a question, rather than just generating text, this is your solution. It’s a key pattern for building truly intelligent LangChain applications in 2026.

Pattern 7: RAG Pattern (Retrieval Augmented Generation)

Explanation

The RAG (Retrieval Augmented Generation) pattern combines the power of LLMs with external knowledge bases. When a user asks a question, the system first retrieves relevant documents or information from a database (the “Retrieval” step). Then, it feeds this retrieved context along with the user’s question to the LLM to generate a factual and grounded answer (the “Generation” step). This prevents the LLM from “hallucinating” and ensures answers are based on specific, verifiable data.

RAG is a cornerstone of enterprise LLM applications. You can delve deeper into this pattern with our guide on implementing RAG in LangChain.

Use Cases

This pattern is perfect for building an internal knowledge base chatbot that answers questions based on company documents. It’s also great for a legal research assistant that pulls information from legal texts. An educational tool that answers questions from textbooks also benefits from RAG.

Code Example

# Assuming you have LangChain installed and relevant packages
# pip install langchain-openai langchain-community chromadb tiktoken unstructured
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.prompts import ChatPromptTemplate
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter

# 1. Load Documents (example using a simple text loader)
# In a real scenario, this could be from PDFs, websites, databases, etc.
with open("company_policy.txt", "w") as f:
    f.write("Our company policy on vacation states that full-time employees accrue 10 days of paid vacation per year. After 5 years of service, this increases to 15 days. Unused vacation days can roll over up to 5 days into the next year. Sick leave is separate and accrues at 5 days per year.")
    f.write("\nOur company policy on remote work states that employees can work remotely up to 3 days a week with manager approval. Core office hours are 10 AM to 3 PM local time. All remote employees must maintain a secure internet connection.")
    f.write("\nOur company policy on expense reimbursement requires all expenses to be submitted within 30 days of incurrence. Receipts are mandatory for any expense over $25. Travel expenses must be pre-approved.")

loader = TextLoader("company_policy.txt")
documents = loader.load()

# 2. Split Documents
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# 3. Create Embeddings and Vector Store (using Chroma DB)
embeddings = OpenAIEmbeddings(model="text-embedding-3-small") # A modern embedding model
vectorstore = Chroma.from_documents(texts, embeddings)
retriever = vectorstore.as_retriever()

# 4. Define the Prompt
prompt = ChatPromptTemplate.from_template("""Answer the following question based only on the provided context:
<context>
{context}
</context>
Question: {input}""")

# 5. Create the document chain (combines retrieved docs with prompt)
document_chain = create_stuff_documents_chain(llm, prompt)

# 6. Create the retrieval chain (retrieves docs then passes to document chain)
retrieval_chain = create_retrieval_chain(retriever, document_chain)

# Run the RAG chain
response = retrieval_chain.invoke({"input": "How many vacation days do I get after working for 6 years?"})
print(response["answer"])

response = retrieval_chain.invoke({"input": "What is the policy for remote work?"})
print(response["answer"])

response = retrieval_chain.invoke({"input": "What's the best cafe in London?"}) # Should not answer as it's not in context
print(response["answer"])

Pros & Cons

Pros

• Reduces LLM hallucinations by grounding answers in factual data.
• Enables LLMs to access and use up-to-date, specific knowledge.
• Improves trust and reliability of generated responses.
• Allows for easy updating of the knowledge base without retraining the LLM.

Cons

• Requires building and maintaining a retrieval system (vector database).
• Retrieval quality directly impacts answer quality.
• Can be slower due to the additional retrieval step.

When to Use

The RAG pattern is a must-have LangChain pattern when your application needs to answer questions accurately from a specific corpus of documents. If you need to build a Q&A system over your own data, RAG is the most effective approach. This is crucial for nearly all knowledge-based LangChain applications in 2026.

Pattern 8: Self-Ask with Search

Explanation

The Self-Ask with Search pattern is a specialized agent technique. It teaches an LLM to break down a complex question into smaller, answerable sub-questions. For each sub-question, the LLM uses a search tool to find the answer. Finally, it synthesizes all the gathered information to provide a comprehensive response to the original complex query. This mimics how a human might research a topic.

It is particularly powerful for questions that require multiple steps of information gathering.

Use Cases

This pattern is ideal for answering complex historical questions that require combining several facts. It’s also great for explaining scientific concepts that involve multiple interconnected ideas. Anytime a question can be naturally decomposed into smaller search tasks, Self-Ask shines.

Code Example

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, Tool, create_self_ask_with_search_agent
from langchain_community.tools import DuckDuckGoSearchRun
from langchain import hub

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Define tools (using DuckDuckGo search)
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Intermediate Answer",
        func=search.run,
        description="Useful for when you need to answer questions that require searching a knowledge base. The input to this tool should be a single search query.",
    )
]

# Get the prompt for the Self-Ask with Search agent
prompt = hub.pull("hwchase17/self-ask-with-search")

# Create the agent
agent = create_self_ask_with_search_agent(llm, tools, prompt)

# Create the agent executor
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# Run the agent
print(agent_executor.invoke({"input": "Who was the first person to walk on the moon, and what year did that happen?"}))
print("\n--- Next query ---")
print(agent_executor.invoke({"input": "What is the capital of Canada and what is its population?"}))

Pros & Cons

Pros

• Handles complex, multi-faceted questions effectively.
• Breaks down problems into verifiable search queries.
• Reduces the chance of hallucination by relying on search results.

Cons

• Can be slow due to multiple search queries and LLM calls.
• Dependent on the quality and availability of search results.
• More computationally expensive than simpler chains.

When to Use

Utilize this LangChain pattern when your users frequently ask open-ended, complex questions that likely require multiple steps of research. If you need an LLM to act as a research assistant, intelligently breaking down a problem and using a search engine, Self-Ask with Search is highly effective. It is a powerful tool for complex information retrieval tasks in 2026.

Pattern 9: Plan and Execute

Explanation

The Plan and Execute pattern provides an LLM with a more structured approach to problem-solving. First, a planning LLM creates a detailed plan of steps to achieve a goal. Then, an execution LLM (often an agent with tools) follows these steps, performing actions and observing results. The planning LLM can also reflect on the execution and adjust the plan if needed. This brings robust, multi-step reasoning to LLM applications.

It’s particularly useful for long-running, multi-step tasks that might involve human feedback or dynamic changes.

Use Cases

You can use Plan and Execute for automating complex workflows, such as generating a marketing campaign from scratch (plan: define audience, create content, schedule posts; execute: use tools for each step). It’s also suitable for data analysis tasks that require multiple queries and transformations. Automating software development tasks, like writing small code snippets and testing them, is another advanced use.

Code Example

# This pattern is more involved and often requires `langchain_experimental`
# pip install langchain_openai langchain_community langchain_experimental

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain import hub
from langchain_community.tools import DuckDuckGoSearchRun
from langchain.chains import LLMMathChain
from langchain.tools import Tool
from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Define tools for the executor
search = DuckDuckGoSearchRun()
llm_math_chain = LLMMathChain(llm=llm, verbose=True)

tools = [
    Tool(
        name="Search",
        func=search.run,
        description="useful for when you need to answer questions about current events, facts, or general knowledge"
    ),
    Tool(
        name="Calculator",
        func=llm_math_chain.run,
        description="useful for when you need to answer questions about math"
    ),
]

# Create the planner (an LLM that generates a plan)
planner = load_chat_planner(llm)

# Create the executor (an agent that executes the plan using tools)
executor_agent = create_react_agent(llm, tools, hub.pull("hwchase17/react"))
executor = load_agent_executor(executor_agent, tools, verbose=True)

# Combine into the PlanAndExecute chain
agent_executor_chain = PlanAndExecute(planner=planner, executor=executor, verbose=True)

# Run the chain
print(agent_executor_chain.invoke({"input": "What is the square root of the current population of the United States? Find the population first using search."}))

Pros & Cons

Pros

• Provides robust, multi-step reasoning for complex tasks.
• More resilient to errors, as the planner can adapt based on execution feedback.
• Enables LLMs to tackle longer, more involved problems.

Cons

• Increased complexity in setup and debugging due to multiple interacting components.
• Higher latency and cost due to multiple LLM calls for planning and execution.
• Requires careful prompt engineering for both planner and executor.

When to Use

This LangChain pattern is best suited for complex, long-running tasks that demand strategic planning and execution. If your application needs to break down big goals into actionable steps, possibly involving external tools and dynamic adjustments, Plan and Execute is a powerful choice. It’s a key pattern for advanced automation with LangChain in 2026.

Pattern 10: Multi-Agent Collaboration

Explanation

Multi-Agent Collaboration is an advanced LangChain pattern where multiple specialized LLM agents work together to solve a complex problem. Each agent has a specific role, set of tools, and often a unique perspective. They communicate and pass information between themselves, debating ideas, refining solutions, or working on different parts of a problem simultaneously. This mimics human teams collaborating on projects.

This pattern leverages the strengths of diverse AI agents to achieve better outcomes than a single agent could alone.

Use Cases

You can use multi-agent collaboration for complex content creation, where one agent drafts an article, another reviews it for grammar, and a third optimizes it for SEO. It’s also great for research projects, with agents specializing in different sub-topics. Even for software development, one agent might plan, another code, and a third test.

Code Example

# This is a conceptual example, as full multi-agent frameworks like CrewAI or AutoGen
# often build *on top* of LangChain but abstract away some of the direct LangChain chaining.
# Here, we show a simplified LangChain-native interpretation.
# pip install langchain_openai langchain_community crewai

from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.agents import AgentExecutor, create_react_agent, Tool
from langchain_community.tools import DuckDuckGoSearchRun
from langchain.chains import LLMChain
from langchain import hub

# Initialize LLM
llm = ChatOpenAI(model_name="gpt-4-turbo-2026-preview", temperature=0.7)

# Define a shared search tool for all agents
search_tool = Tool(
    name="Search",
    func=DuckDuckGoSearchRun().run,
    description="useful for answering questions by searching the internet"
)
tools = [search_tool]

# Agent 1: Researcher Agent
researcher_prompt = PromptTemplate.from_template("""You are a meticulous researcher. Your goal is to gather facts about a given topic.
User query: {input}
Research and provide concise facts.""")
researcher_agent_chain = LLMChain(llm=llm, prompt=researcher_prompt)

# Agent 2: Summarizer Agent
summarizer_prompt = PromptTemplate.from_template("""You are an expert summarizer. Take the provided research facts and create a cohesive summary.
Research facts: {research_facts}
Summarize:""")
summarizer_agent_chain = LLMChain(llm=llm, prompt=summarizer_prompt)

# Agent 3: Creative Writer Agent
writer_prompt = PromptTemplate.from_template("""You are a creative content writer. Take the summary and write an engaging short article.
Summary: {summary}
Write article:""")
writer_agent_chain = LLMChain(llm=llm, prompt=writer_prompt)

# Simulate collaboration using a manual sequence (for demonstration)
# In a true multi-agent system, this orchestration would be more dynamic/automated
def collaborative_workflow(query):
    print("--- Researcher Agent at work ---")
    research_output = researcher_agent_chain.invoke({"input": query})
    facts = research_output['text']
    print(f"Researcher found:\n{facts}\n")

    print("--- Summarizer Agent at work ---")
    summary_output = summarizer_agent_chain.invoke({"research_facts": facts})
    summary = summary_output['text']
    print(f"Summarizer created:\n{summary}\n")

    print("--- Writer Agent at work ---")
    article_output = writer_agent_chain.invoke({"summary": summary})
    article = article_output['text']
    print(f"Writer produced:\n{article}\n")
    return article

# Run the collaborative workflow
collaborative_workflow("The impact of quantum computing on cybersecurity in the next 5 years.")

Pros & Cons

Pros

• Solves highly complex, multi-faceted problems that single agents cannot.
• Mimics human team collaboration, leading to diverse perspectives and robust solutions.
• Enables dynamic problem-solving and adaptation.

Cons

• High complexity in design, orchestration, and debugging.
• Can be very slow and expensive due to numerous LLM calls and inter-agent communication.
• Requires careful role definition and communication protocols to avoid conflicts or redundancies.

When to Use

This advanced LangChain pattern is suitable for very complex, open-ended problems that benefit from diverse expertise. If your task requires different AI “personalities” or “specialists” to contribute to a solution, Multi-Agent Collaboration is a powerful, albeit challenging, approach. It represents the cutting edge of LangChain patterns for intricate automation in 2026.

Looking Ahead: LangChain in 2026 and Beyond

As we move further into 2026, the LangChain framework continues to evolve. New models, tools, and integration methods will emerge, but these core LangChain patterns will remain fundamental. They provide the architectural blueprints for building intelligent, adaptable, and powerful AI applications. Mastering them today ensures you’re ready for the innovations of tomorrow.

Keep experimenting with these patterns and combining them in creative ways. The future of AI development is bright, and with LangChain, you are at the forefront.

Conclusion

You’ve now explored the top 10 LangChain patterns every developer should know in 2026. From the foundational Simple LLM Chain to the advanced Multi-Agent Collaboration, these patterns equip you with the knowledge to build sophisticated AI applications. Each pattern solves a unique problem, enabling you to create more powerful, efficient, and intelligent systems. By understanding and applying these LangChain patterns, you are well-prepared to tackle the challenges and opportunities of AI development in the coming years. Happy building!