LangChain vs LangGraph: How to Choose the Right AI Framework!

Source: Dev.to

Why This Comparison Matters – LangChain vs LangGraph

I build practical LLM‑powered software and have seen two patterns emerge: straightforward, linear pipelines and stateful, agentic workflows. The question “LangChain vs LangGraph” is not academic; it determines architecture, maintenance, and how the system reasons over time.

When I say “LangChain vs LangGraph” I mean comparing two different design philosophies:

• LangChain – optimized for linear sequences: take input, run one or more LLM calls in order, store or return the result.
• LangGraph – optimized for graphs: nodes, edges, loops, and persistent state across many steps.

LangChain

Core Concepts

• Prompt templates – reusable templates that accept variables and generate consistent LLM inputs.
• LLM‑agnostic connectors – easy swaps between OpenAI, Anthropic, Mistral, Hugging Face models, and more.
• Chains – the core abstraction: compose multiple steps so each output feeds the next.
• Memory – short‑term or long‑term conversational context, useful for stateful chat but limited compared to full state machines.
• Agents and tools – let models call APIs, calculators, or external services in a structured way.

When to Use LangChain

• Prototyping prompts, building simple RAG systems, or creating a question‑answering pipeline that reads from a vector store and returns a single response.
• Text‑transformation pipelines (summarize, translate, extract information).
• Single‑turn user interactions such as customer‑support responses.
• Basic RAG systems that perform retrieval from a vector store and return a single synthesized answer.

LangChain makes developers productive fast. It provides plug‑and‑play components—prompt templates, retrievers, and chain combinators—letting you ship quickly without building orchestration primitives yourself.

LangGraph

Core Concepts

• Nodes – discrete tasks: call an LLM, fetch from a database, run a web search, or invoke a summarizer.
• Edges – define conditional transitions, parallel branches, or loop‑back paths.
• State – dynamic context that evolves across nodes: messages, episodic memory, and checkpoints.
• Decision nodes – native support for conditional logic and routing to specialist agents.

LangGraph treats the application as a state machine. Nodes can loop, revisit earlier steps, and perform multi‑turn tool calls. This enables agentic behaviors such as reflection, iterative retrieval, or progressive refinement of answers.

When to Use LangGraph

• Multi‑step decision making that can loop until an exit condition is met.
• Routing queries to specialist agents depending on context.
• Persistent state across many LLM calls and user interactions.
• Sophisticated tool usage, including multi‑turn web searches, summarization, and aggregation of external sources.

Example: an email‑drafting agent that retrieves user preferences, consults a calendar, drafts an email, asks for clarifications, and iteratively refines the draft maps naturally to LangGraph.

Practical Comparison Checklist

When weighing “LangChain vs LangGraph,” consider not only current needs but expected future complexity. If the app might grow into a multi‑agent orchestration or needs persistent state and retries, starting with LangGraph can save refactors.

Example: RAG with LangChain (Linear)

1. Install the required packages and configure API keys.
2. Create prompt templates that accept variables such as objective and topic.
3. Initialize an LLM or local model connector via Hugging Face, OpenAI, or other providers.
4. Store documents in a vector database and create a retriever.
5. Build a retrieval‑augmented generation chain that retrieves context and synthesizes answers.

This pattern stays linear: retrieve relevant docs → generate an answer. It suits many FAQ bots, documentation assistants, and single‑pass pipelines. The code is compact and easy to iterate on.

# Example LangChain RAG pipeline
from langchain import PromptTemplate, LLMChain
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI

# 1. Prompt template
prompt = PromptTemplate(
    input_variables=["question", "context"],
    template="Answer the question based on the context:\n\nContext: {context}\n\nQuestion: {question}"
)

# 2. LLM
llm = OpenAI(model_name="gpt-4")

# 3. Chain
chain = LLMChain(llm=llm, prompt=prompt)

# 4. Retrieval
vector_store = FAISS.load_local("my_index")
retriever = vector_store.as_retriever()

def answer_question(question: str):
    docs = retriever.get_relevant_documents(question)
    context = "\n".join([doc.page_content for doc in docs])
    return chain.run({"question": question, "context": context})

Example: RAG with LangGraph (Graph‑Based)

1. Load static content into a vector store from URLs or documents.
2. Create graph nodes: retrieve, web_search, decision, and generate.
3. Define state: track whether the retrieved results answered the user, store interim summaries, and record tool outputs.
4. Connect nodes with conditional edges:
   • If local retrieval fails → route to web search.
   • If web search yields noisy results → ask clarifying questions.
   • Loop back as needed.
5. Run the graph until a stop condition is met, then return the final synthesis.

This pattern enables multi‑turn tool use and agentic reasoning. In tests, asking a LangGraph agent about “latest AI developments this month” triggers a web‑search node when the local knowledge is stale, fetches, summarizes, and checks adequacy before presenting the answer.

# Example LangGraph workflow (pseudo‑code)
from langgraph import Graph, Node, Edge, State

# Nodes
def retrieve(state: State):
    docs = vector_store.as_retriever().get_relevant_documents(state["question"])
    state["retrieved"] = docs
    return state

def web_search(state: State):
    results = search_api(state["question"])
    state["web_results"] = results
    return state

def decide(state: State):
    if not state["retrieved"]:
        return "web_search"
    if not is_sufficient(state["retrieved"]):
        return "web_search"
    return "generate"

def generate(state: State):
    context = combine(state.get("retrieved", []), state.get("web_results", []))
    answer = llm.generate(prompt=prompt, context=context)
    state["answer"] = answer
    return state

# Graph definition
graph = Graph()
graph.add_node("retrieve", retrieve)
graph.add_node("web_search", web_search)
graph.add_node("decision", decide)
graph.add_node("generate", generate)

graph.add_edge("retrieve", "decision")
graph.add_edge("web_search", "decision")
graph.add_edge("decision", "generate", condition=lambda s: s == "generate")
graph.add_edge("decision", "web_search", condition=lambda s: s == "web_search")
graph.set_entry_point("retrieve")

Decision Heuristics

• Pattern: Start simple – If the problem is single‑pass, build with LangChain to validate prompts quickly.
• Pattern: Evolve to graph – If your single‑pass pipeline accumulates conditionals and stateful checkpoints, refactor into a LangGraph graph incrementally.
• Anti‑pattern: Premature complexity – Avoid implementing a full graph when no loops or persistent state are needed. Over‑engineering reduces clarity and increases maintenance cost.
• Anti‑pattern: One‑off tool calls – If you need repeated or multi‑stage tool orchestration, a linear chain becomes fragile. LangGraph’s native edges and state are better suited.

Reusable Templates

Migrating from LangChain to LangGraph

1. Identify branching points in your LangChain where decision logic begins to appear.
2. Extract prompt templates and retrieval components into reusable nodes.
3. Define a state schema that captures intermediate results, tool outputs, and memory.
4. Replace linear chain execution with a graph that connects nodes via conditional edges.
5. Add checkpointing or human‑in‑the‑loop nodes as needed.

Conclusion

• Choose LangChain when you need rapid development, your workflow is linear, and state management is minimal.
• Choose LangGraph when your application requires loops, rich persistent state, conditional routing, or multi‑agent orchestration.

Evaluating the current and future complexity of your AI system will guide you to the right framework and avoid costly refactors later.