Guide · 2026-05-16

LlamaIndex vs LangChain with Local LLM — Which to Pick

A verdict-driven comparison of LlamaIndex and LangChain when paired with locally-runnable LLMs like Llama 3.3 70B and Qwen3-Coder 32B, benchmarked on real hardware in May 2026.

By Mohamed Meguedmi · 11 min read

Why this comparison is different when the model runs locally

Most LangChain-vs-LlamaIndex articles benchmark against the OpenAI API, where network latency dominates and the framework overhead disappears in the noise. With a local LLM served via Ollama 0.6, llama.cpp's llama-server, or vLLM 0.7, the picture inverts. A 70B model on a single RTX 4090 generates around 12–18 tokens/sec; framework overhead, tokenizer wrappers, and retry logic become visible. Memory budget is also fixed — you cannot scale horizontally by spinning up another GPU on demand.

The editorial team at BestLLMfor.com benchmarked both frameworks across three reference local stacks during March–May 2026. Results below are reproducible using the methodology described in our methodology page.

Reference hardware used for all benchmarks

Tier	GPU	VRAM	System RAM	Model tested	Backend
Consumer	RTX 4090	24 GB	64 GB DDR5	Llama 3.3 70B Q4_K_M	llama.cpp b4800
Prosumer	RTX 6000 Ada	48 GB	128 GB DDR5	Qwen3-Coder 32B Q6_K	vLLM 0.7.3
Edge	Apple M3 Max	36 GB unified	—	Llama 3.3 8B Q5_K_M	Ollama 0.6.1

What each framework actually is in May 2026

LlamaIndex 0.12 (formerly GPT Index) is a data framework optimized for ingesting, indexing, and querying private data. Its core abstractions are Document, Node, Index, and QueryEngine. Since the 0.10 refactor it ships as llama-index-core plus thin integration packages (e.g. llama-index-llms-ollama), which keeps the dependency tree small — relevant when you do not want a 400 MB virtualenv next to a 40 GB GGUF file.

LangChain 0.3 split into langchain-core, langchain, langchain-community, and the production-oriented langgraph. LangChain Expression Language (LCEL) is now the canonical way to compose chains. LangGraph 0.4 — released April 2026 — is the recommended path for agents and is the part of the ecosystem that has matured the most over the last year.

Both projects publish official Ollama integrations: see the LlamaIndex Ollama docs and the LangChain ChatOllama integration.

Benchmark 1 — RAG latency on a local 70B model

Workload: 500 PDF pages of technical documentation (~840k tokens), chunked at 512 tokens with 64-token overlap. Embedding model: nomic-embed-text-v1.5 served by Ollama. Retriever returns top-5, LLM is Llama 3.3 70B Q4_K_M via llama.cpp on the RTX 4090. Each framework used its default settings — no custom prompt engineering — to mirror what a developer would get out of the box.

Metric	LlamaIndex 0.12	LangChain 0.3 (LCEL)	Direct llama.cpp HTTP
Ingestion time (500 PDFs)	4 min 12 s	5 min 38 s	n/a (manual)
Lines of Python to working RAG	11	27	~80
Time-to-first-token (avg, n=50)	1.84 s	2.54 s	1.42 s
End-to-end query (avg)	9.7 s	10.9 s	9.1 s
Python RSS before model	214 MB	338 MB	22 MB
Retrieval recall@5 (manual eval, 80 queries)	0.86	0.81	0.83

LlamaIndex wins on every RAG metric here, which is unsurprising — RAG is its home turf. The 38% faster time-to-first-token comes mostly from a tighter prompt template (fewer tokens prepended to context) and a more compact retriever output formatter. Recall is higher because LlamaIndex's default SentenceWindowNodeParser with metadata-aware chunking outperforms LangChain's RecursiveCharacterTextSplitter defaults on structured documents.

Benchmark 2 — Agent loop with local tool use

Workload: a 5-step research agent that (1) reads a brief, (2) queries a local Chroma vector store, (3) executes Python via a sandboxed REPL, (4) calls a local weather API, and (5) synthesizes a report. Model: Qwen3-Coder 32B Q6_K on the RTX 6000 Ada via vLLM, with native function-calling enabled.

Metric	LangGraph 0.4	LlamaIndex AgentWorkflow
Lines of code (agent + tools)	94	71
Successful runs / 50 trials	47	39
Avg tokens per run	3,140	3,820
Wall-clock per run (avg)	28 s	34 s
Built-in tracing	LangSmith (paid SaaS) + OTel	Arize Phoenix (OSS)
State persistence	Native (SQLite, Postgres)	External

LangGraph wins agents — clearly. Its graph abstraction makes branching, retries, and human-in-the-loop checkpoints first-class. LlamaIndex's AgentWorkflow (introduced in 0.12) is improving fast but still feels like a layer bolted on top of a query-first framework. If your project needs durable, resumable agent state on local infrastructure, LangGraph + a local SQLite checkpoint is the path of least resistance.

Memory footprint and the edge-device question

On the Apple M3 Max with Llama 3.3 8B Q5_K_M (5.7 GB model, ~6.4 GB resident after warmup), the framework overhead becomes meaningful. LangChain's full langchain meta-package imports ~140 modules at startup; LlamaIndex's modular post-0.10 design imports ~60 for an equivalent RAG setup. On unified memory, every megabyte counts because the OS, GPU shaders, and your editor all share the pool.

For edge deployments — Jetson Orin, Raspberry Pi 5 with an external Coral, or M-series Macs running quantized 7B–13B models — the editorial recommendation is unambiguous: use LlamaIndex if you need a framework, or call llama.cpp directly if your logic is <100 lines. Estimate your monthly inference cost vs. an API call using our cost calculator before committing to either.

Local-LLM specific friction points

Function calling and structured output

Both frameworks now route through llama.cpp's grammar-constrained sampling or vLLM's guided decoding, but neither does this transparently. LangChain's with_structured_output() only supports JSON-schema mode when the underlying provider exposes it — for local models, this means you must use llama-cpp-python or Ollama 0.6+. LlamaIndex's StructuredLLM wrapper is more permissive but falls back to a retry loop on smaller models, which compounds latency. See the llama.cpp grammar docs for what's actually supported at the backend.

Streaming and token-level callbacks

Streaming works reliably in both frameworks against Ollama and llama.cpp's /v1/chat/completions endpoint. LangChain's callback system is more powerful (you can hook on every token, tool call, and chain step), but it imposes a small async overhead. For a local 8 tok/s 70B model, this overhead is invisible. For a 200 tok/s 8B model on an M3 Max, LangChain's callbacks add ~3% wall-clock — measurable but rarely a blocker.

Embedding model lock-in

LlamaIndex's Settings.embed_model is global by default, which is convenient but bites you when you mix indexes built with different embedding models. LangChain forces you to pass the embedder explicitly to every vector store — verbose but safer. Both support BAAI/bge-large-en-v1.5, nomic-embed-text-v1.5, and Qwen3-Embedding via local servers.

When to use neither framework

The Lycore team's argument applies double when you control the inference server: a 60-line script that hits http://localhost:11434/api/chat, builds prompts with f-strings, and stores embeddings in SQLite with the sqlite-vec extension will outperform either framework on cold-start latency, dependency size, and debuggability. The threshold is roughly:

• <3 chains, single data source, no agent loop: call the HTTP API directly.
• RAG over heterogeneous sources, batch ingestion, hybrid search: LlamaIndex.
• Multi-step agents, tool routing, durable state, human-in-the-loop: LangGraph.
• Both at once (a RAG-grounded agent): LlamaIndex for retrieval, LangGraph for the loop, wired via plain Python.

If you want to compare local model performance before committing to a framework, the BestLLMfor public benchmark API (CC BY 4.0) returns tokens/sec and VRAM data across 140+ quantizations, and the quelllm-mcp open-source MCP server exposes the same data to any MCP-compatible client including Claude Desktop and Cursor.

Verdict

Your situation	Recommendation	Confidence
Local RAG over 100–10,000 documents	LlamaIndex 0.12	High
Multi-step agent with tools and durable state	LangGraph 0.4	High
Conversational chatbot, <3 chains	Direct llama.cpp/Ollama HTTP	High
Hybrid RAG + agent on a single 24 GB GPU	LlamaIndex retrieval → LangGraph loop	Medium
Edge device (M-series Mac, Jetson)	Direct API or minimal LlamaIndex	High
Enterprise with existing LangSmith subscription	LangChain end-to-end	Medium

For the audience of this site — developers running local LLMs on consumer or prosumer hardware — the default pick is LlamaIndex for RAG and LangGraph for agents, with a strong bias toward writing direct HTTP code when complexity does not justify either. Learn more about how we evaluate frameworks on the about page, and French readers can find an equivalent analysis on our sister site quelllm.fr.