LangChain vs LangGraph: Choosing Between a Toolkit and an Orchestration Layer

What the Two Frameworks Do

LangChain is a modular toolkit that supplies prompt templates, document loaders, retrievers, output parsers, and memory abstractions, which are chained together in a linear A → B → C fashion, like a conveyor belt where data moves forward step by step.

LangGraph sits on top of LangChain as an orchestration layer. It models a workflow as a state machine: nodes are functions, edges are transitions that can be conditional. The graph can loop, branch, retry, or pause for human input, essentially forming a flowchart.

Since LangChain v1.0 (2025) builds its Agent abstraction on top of LangGraph, the two are not competing products but different abstraction layers of the same system.

Experiment: The Same Pipeline in Both Frameworks

The test case is a three‑stage code‑review pipeline:

Context agent – fetch PR diff and repository history.

Analysis agent – locate issues in the changed code.

Review agent – output a structured feedback with severity scores.

In the real system (BugLens) the analysis agent sometimes needs to re‑fetch context when confidence is low, which forces a decision point.

LangChain Implementation

from langchain_core.prompts import ChatPromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.output_parsers import StrOutputParser

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash")

context_chain = (
    ChatPromptTemplate.from_template("Analyze this PR diff: {diff}")
    | llm
    | StrOutputParser()
)

analysis_chain = (
    ChatPromptTemplate.from_template("Find issues in: {context}")
    | llm
    | StrOutputParser()
)

review_chain = (
    ChatPromptTemplate.from_template("Write review for: {analysis}")
    | llm
    | StrOutputParser()
)

# Linear execution
def run_pipeline(diff: str):
    context = context_chain.invoke({"diff": diff})
    analysis = analysis_chain.invoke({"context": context})
    review = review_chain.invoke({"analysis": analysis})
    return review

The code is clean and can be written in about five minutes. However, if the analysis step returns low confidence, the whole chain fails and must be manually wrapped with if/else logic to re‑invoke the analysis and stitch the state back together.

LangGraph Implementation

from langgraph.graph import StateGraph, END
from typing import TypedDict

class ReviewState(TypedDict):
    diff: str
    context: str
    analysis: str
    review: str
    confidence: float
    iterations: int

def context_node(state: ReviewState) -> ReviewState:
    context = fetch_context(state["diff"])
    return {**state, "context": context}

def analysis_node(state: ReviewState) -> ReviewState:
    result = run_analysis(state["context"])
    return {
        **state,
        "analysis": result["content"],
        "confidence": result["confidence"],
        "iterations": state.get("iterations", 0) + 1,
    }

def review_node(state: ReviewState) -> ReviewState:
    review = write_review(state["analysis"])
    return {**state, "review": review}

def should_loop(state: ReviewState) -> str:
    if state["confidence"] < 0.75 and state["iterations"] < 3:
        return "fetch_more_context"
    return "write_review"

graph = StateGraph(ReviewState)
graph.add_node("get_context", context_node)
graph.add_node("analyze", analysis_node)
graph.add_node("review", review_node)
graph.set_entry_point("get_context")
graph.add_edge("get_context", "analyze")
graph.add_conditional_edges("analyze", should_loop, {
    "fetch_more_context": "get_context",
    "write_review": "review",
})
graph.add_edge("review", END)
pipeline = graph.compile()

The state flows automatically between nodes, and a confidence‑threshold triggers a conditional edge that loops back to fetch more context. Adding a human‑in‑the‑loop step is as simple as inserting interrupt() – LangGraph supports it natively.

When LangChain Still Wins

For straightforward linear pipelines (e.g., a simple RAG flow or a single‑step LLM task), LangChain’s pipe syntax chain1 | chain2 | chain3 is concise and clean. Its ecosystem offers 600+ ready‑made integrations (vector stores, PDF loaders, external APIs), which currently have no comparable alternative.

Why LangGraph Is the Only Option for Complex Workflows

When a workflow involves multiple agents, conditional logic, retries, or persistent state, LangGraph becomes essential. Trying to emulate this control flow inside pure LangChain results in the same stitched‑together state management that LangGraph was designed to replace.

Decision Framework

Use LangChain’s high‑level API first. If you hit a wall—need loops, retries, conditional branches, or persistence—drop down to LangGraph. Many production systems combine both: outer orchestration (GitHub webhook routing, retry management) with LangGraph, inner data processing (diff formatting, file summarization) with LangChain’s LCEL.

Conclusion

Build simple RAG pipelines or single‑step LLM flows with LangChain for speed and readability. As soon as the problem requires multi‑agent coordination, conditional branching, or state persistence, LangGraph is not just “better” but the only viable solution; attempting to replicate its capabilities in pure LangChain leads back to the same graph‑based design LangGraph provides.