LlamaIndex vs LangChain: choosing an agent framework

Most teams evaluating orchestration layers end up weighing LlamaIndex vs LangChain for their agent builds. The two frameworks attack the same problem—composing LLM calls, tools, and retrieval into autonomous loops—but they make different tradeoffs in abstraction, performance, and footprint.

Head-to-Head Summary

Capabilities

LlamaIndex: retrieval-first agents

LlamaIndex started as a data framework. Its agent primitives assume you already have indexed documents and want to query them. The FunctionAgent and AgentWorkflow classes wrap LLM reasoning around tool calls while keeping retrieval native. Citation tracking and response synthesis are first-class.

from llama_index.core.agent import FunctionAgent
from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4o-mini")
agent = FunctionAgent.from_tools(tools=[retrieve_contract], llm=llm)
resp = await agent.achat("What are the termination clauses?")
print(resp.response, resp.sources)

The workflow engine lets you define event-driven steps without manual loop management. You can compose multiple specialized agents that hand off to each other based on intermediate events—useful for multi-stage research tasks.

LangChain: composable chains and graphs

LangChain treats everything as a runnable. You compose prompts, models, and tools with |. For stateful agents, LangGraph adds a state machine on top. This is more general but pushes more wiring onto you.

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain.tools import tool

@tool
def retrieve_contract(q: str) -> str:
    return "..."

llm = ChatOpenAI(model="gpt-4o-mini")
agent = create_tool_calling_agent(llm, [retrieve_contract], prompt)
executor = AgentExecutor(agent=agent, tools=[retrieve_contract])
result = executor.invoke({"input": "Termination clauses?"})

LangGraph supports cyclic graphs, human approval nodes, and persistent checkpoints. In the LlamaIndex vs LangChain capability split, LlamaIndex wins for RAG-native loops; LangChain wins for arbitrary topology and strict state control.

Cost Model

Neither framework charges a license fee. You pay for tokens at your LLM provider. The hidden cost is engineering time and observability.

LangChain’s LangSmith is a polished tracing UI but bills by trace volume beyond the free tier. LlamaIndex offers a managed platform but most teams self-host the OSS core. If you front either with a gateway, per-token metering can be centralized. For example, routing through a single OpenAI-compatible endpoint such as n4n.ai gives automatic fallback when a provider is rate-limited and per-token usage metering without writing custom middleware.

Provider cache-control hints matter: repeated embedding of the same document chunk wastes money. A gateway that forwards cache directives can trim redundant token spend, but that is independent of framework choice.

Latency and Throughput

Latency is dominated by model calls, but orchestration overhead is real. LlamaIndex’s Workflow runs on an async event loop with minimal intermediate objects. LangChain’s AgentExecutor constructs a new chain state per step and serializes context through prompts, adding milliseconds per hop.

For high-throughput batch agents, LlamaIndex’s async-first design holds up better. LangChain’s LangGraph mitigates this with checkpointing but introduces persistence overhead. If you need to swap models for cost or speed, a gateway that addresses 240+ models behind one OpenAI-compatible endpoint—like n4n.ai—lets you change routing without touching agent code.

Streaming is supported in both, but LlamaIndex propagates tokens from the workflow step directly to the caller, whereas LangChain requires explicit stream calls on the executor.

Ergonomics

LlamaIndex reads like idiomatic Python. Defining a workflow step is a decorated method:

from llama_index.core.workflow import Workflow, step, StartEvent, StopEvent

class QueryFlow(Workflow):
    @step
    async def run(self, ev: StartEvent) -> StopEvent:
        return StopEvent(result=await ev.agent.achat(ev.query))

Unit testing means sending a StartEvent and asserting the StopEvent. No mock client required.

LangChain’s LCEL is explicit but verbose. A simple chain is clean:

from langchain_core.prompts import ChatPromptTemplate
chain = ChatPromptTemplate.from_template("Answer: {q}") | llm

But agent wiring requires understanding prompts, tool schemas, and executor config. The LlamaIndex vs LangChain learning curve favors LlamaIndex for teams already doing RAG; LangChain’s breadth demands more upfront study. Error handling in LangChain often means catching exceptions from deep inside a runnable, while LlamaIndex surfaces workflow step failures as asyncio tasks.

Ecosystem and Integrations

LangChain lists 700+ integrations: vector stores, APIs, document loaders. If a SaaS exists, there’s likely a LangChain wrapper. LlamaIndex focuses on data connectors (Notion, Slack, PostgreSQL, MongoDB) and indexing strategies. Its community is smaller but deeply focused on retrieval quality.

For a general internal tool that calls Slack, Salesforce, and a half-dozen APIs, LangChain saves integration code. For a knowledge assistant over your docs, LlamaIndex’s connectors and chunking defaults are sharper. Both support OpenTelemetry, but LangChain’s native LangSmith integration is more turnkey.

Limits and Footguns

LlamaIndex’s agent API has shifted across minor versions; pin your version. Its non-RAG examples are sparse, so custom control flows may lack documentation. You can hit edges where the workflow event type system feels restrictive.

LangChain’s abstractions leak: you often drop to raw SDK calls to fix prompt formatting. Version churn is notorious—code from six months ago may break on upgrade. Debugging a LangGraph cycle requires reading state dumps. The sheer number of deprecated modules (llmchain, ConversationChain) confuses newcomers.

Which to Choose

Choose LlamaIndex if

• Your core problem is retrieval-augmented generation over structured or unstructured data.
• You want async workflows without hand-rolling a state machine.
• Your team prefers concise Python over declarative graphs.
• You need citation tracking and document synthesis out of the box.

Choose LangChain if

• You need a broad integration surface (CRM, messaging, custom tools).
• Your agent requires human-in-the-loop approval nodes or cyclic graphs.
• You already use LangSmith for observability and accept the cost.
• You are building a general-purpose agent product with diverse third-party actions.

Hybrid note

Both frameworks are modular. You can run a LlamaIndex retrieval node inside a LangGraph state machine, or call LangChain tools from a LlamaIndex workflow. The decision is not permanent. Whichever you pick, isolate model access behind a single client so provider swaps don’t ripple through agent code.

The verdict in the LlamaIndex vs LangChain debate is use-case driven: match the framework to the dominant constraint—data vs integration—and keep the LLM boundary clean.