LangChain vs LlamaIndex

Side-by-side — pricing, strengths, tradeoffs, and fit.

LangChain

Build applications with LLMs through composability

LlamaIndex

Leading framework for building LLM-powered agents over your data.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Build applications with LLMs through composability

One-liner

Leading framework for building LLM-powered agents over your data.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Developers looking to integrate large language models into their applications quickly and efficiently
Projects requiring modular, composable components for building complex AI systems
Teams that need flexibility in choosing different LLMs and APIs

✓ Best for

Teams building RAG applications who need a flexible framework to integrate large language models with their datasets.
Data science teams looking for an easy way to leverage LLMs in their projects without extensive setup.

Not ideal for

✕ Not for

Scenarios where real-time performance is critical (LangChain may introduce additional latency)
Use cases requiring a fully managed service without self-hosting capabilities

✕ Not for

Projects requiring real-time data processing and streaming capabilities, as the framework is optimized for batch operations.
Teams that prefer a fully managed service over self-hosting solutions.

Strengths

↑ Strengths

Modular architecture for LLM integration
Supports a wide range of language models and APIs
Easily composable components for building complex applications
Extensive documentation and community support

↑ Strengths

Simplifies integration of large language models with data.
Supports building intelligent agents over structured and unstructured data.
Flexible architecture for customizing agent behavior.

Weaknesses

↓ Weaknesses

LangChain lacks native support for MCP code execution and lazy result loading.
LangChain does not support dynamic tool addition or removal after agent creation.
LangChain's OpenAI compatibility layer struggles with third-party providers like OpenRouter.
LangChain's output parsing does not reliably honor Pydantic field datatypes, causing random exceptions.

↓ Weaknesses

The project's primary development focus has shifted away from the open-source orchestration framework toward commercial document parsing products.
Full LangChain profile →Full LlamaIndex profile →