Table of Contents
Framework Overview
LangChain and LlamaIndex are the two dominant frameworks for building AI applications with retrieval-augmented generation. Both connect to local LLM endpoints on your dedicated GPU server, but they take fundamentally different approaches to the problem. GigaGPU provides hosting optimised for both: LangChain hosting and LlamaIndex hosting with pre-configured GPU infrastructure.
| Aspect | LangChain | LlamaIndex |
|---|---|---|
| Primary focus | General-purpose AI chains and agents | Data indexing and retrieval |
| Core abstraction | Chains, agents, tools | Indexes, query engines, retrievers |
| Best for | Complex multi-step workflows | Document QA, knowledge bases |
| Learning curve | Steeper (many abstractions) | Moderate (focused API) |
| LLM integration | 100+ providers | 50+ providers |
| Vector DB support | 40+ stores | 30+ stores |
Architecture Comparison
LangChain is a general-purpose framework for building AI applications. It provides abstractions for chains (sequential LLM calls), agents (dynamic tool-calling loops), and memory (conversation history). RAG is one use case among many. The breadth of LangChain means you can build chatbots, agents, data extraction pipelines, and more within a single framework.
LlamaIndex is purpose-built for connecting LLMs to your data. It excels at indexing documents, building query engines, and optimising retrieval. If your application is primarily about turning unstructured data into queryable knowledge, LlamaIndex’s focused API is often more ergonomic than LangChain’s general-purpose abstractions.
Both frameworks pair with inference engines like vLLM or Ollama running on your GPU server. For engine selection, see our vLLM vs TGI vs Ollama comparison.
RAG Pipeline Performance
We built equivalent RAG pipelines in both frameworks, using the same LLM (LLaMA 3 8B via vLLM), embedding model (BGE-large), and vector store (Qdrant). We measured query latency on an RTX 3090.
| Metric | LangChain | LlamaIndex | Difference |
|---|---|---|---|
| End-to-end query latency | 7.2 sec | 6.9 sec | LlamaIndex 4% faster |
| LLM calls per query | 1 | 1 | Same |
| Embedding calls per query | 1 | 1 | Same |
| Framework overhead | ~120 ms | ~85 ms | LlamaIndex 30% less |
| Index build time (50K docs) | 9.4 min | 9.1 min | Comparable |
For simple RAG, both frameworks perform nearly identically because the GPU handles 95%+ of the compute. LlamaIndex has slightly less framework overhead due to its streamlined query engine path. For detailed GPU benchmarks, see best GPU for RAG pipelines.
Multi-Step RAG (with reranking and citation)
| Metric | LangChain | LlamaIndex |
|---|---|---|
| End-to-end latency (RTX 3090) | 14.8 sec | 13.5 sec |
| LLM calls | 2-3 | 2 |
| Built-in reranking | Via retriever chain | Native node postprocessor |
| Citation generation | Custom chain | Built-in CitationQueryEngine |
LlamaIndex’s built-in reranking and citation modules reduce the number of LLM calls needed, which matters more on slower GPUs. See our LlamaIndex GPU guide for detailed benchmarks.
Agent Capabilities
LangChain has a significantly more mature agent ecosystem. LangGraph provides stateful, graph-based agent execution with persistence and human-in-the-loop support. LlamaIndex’s agent framework is capable but less feature-rich.
| Agent Feature | LangChain | LlamaIndex |
|---|---|---|
| ReAct agents | Yes | Yes |
| Multi-agent orchestration | LangGraph | Basic |
| Tool ecosystem | 100+ built-in tools | 30+ built-in tools |
| State persistence | LangGraph checkpoints | Limited |
| Streaming agent output | Yes | Yes |
If your primary use case involves complex agents, LangChain is the stronger choice. For GPU requirements of agent workloads, see our best GPU for AI agents guide. Agent frameworks like AutoGen and CrewAI also integrate with both LangChain and LlamaIndex.
GPU Requirements for Each Framework
Both frameworks have identical GPU requirements because the compute bottleneck is the LLM and embedding model, not the framework code. The table below shows recommended GPUs for common configurations.
| Use Case | Models Needed | Min VRAM | Recommended GPU | $/hr |
|---|---|---|---|---|
| Simple RAG | 7B LLM + embedding | 16 GB | RTX 3090 | $0.45 |
| Multi-doc RAG with reranking | 7B LLM + embedding + reranker | 20 GB | RTX 3090 | $0.45 |
| Agent with tools | 8B LLM + embedding | 16 GB | RTX 5080 | $0.85 |
| Budget RAG | 7B LLM (4-bit) + embedding | 6 GB | RTX 4060 | $0.20 |
For detailed GPU selection, see our framework-specific guides: best GPU for LangChain and best GPU for LlamaIndex.
Ecosystem and Integrations
Both frameworks integrate with all major vector databases. For our comparison of vector stores, see FAISS vs Qdrant vs Weaviate vs ChromaDB.
| Integration | LangChain | LlamaIndex |
|---|---|---|
| FAISS | Yes | Yes |
| Qdrant | Yes | Yes |
| Weaviate | Yes | Yes |
| ChromaDB | Yes | Yes |
| vLLM | Yes (OpenAI wrapper) | Yes (OpenAILike) |
| Ollama | Yes (native) | Yes (native) |
| Observability | LangSmith | Arize, LlamaTrace |
Which Framework Should You Choose?
Choose LlamaIndex if: Your primary use case is document QA, knowledge base querying, or data-centric RAG. LlamaIndex’s focused API makes simple RAG pipelines faster to build and slightly more efficient. It is the better starting point for teams new to RAG.
Choose LangChain if: You need complex multi-step workflows, sophisticated agents, or a broad tool ecosystem. LangChain’s generality makes it the better foundation for applications that go beyond pure retrieval, such as AI agents, data extraction, and multi-modal pipelines.
Use both together: Many production deployments use LlamaIndex for the retrieval layer and LangChain for orchestration. LlamaIndex query engines can be wrapped as LangChain tools, combining the strengths of both frameworks.
Whichever framework you choose, the GPU is the performance bottleneck. Start with an RTX 3090 for development and production, and scale to an RTX 5090 or multi-GPU clusters as traffic grows. See our embedding generation and vector database GPU guides for stack-level optimisation.
Run LangChain or LlamaIndex on Dedicated GPUs
GigaGPU provides pre-configured servers for both frameworks with vLLM, Ollama, and vector databases ready to deploy. Build RAG pipelines on bare-metal GPUs with no per-query fees.
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