Benchmarks
LangChain vs LlamaIndex vs Haystack: 2026 RAG Benchmark
Aggregated 2026 benchmark data across three RAG frameworks reveals a clear split: LangChain wins ecosystem, LlamaIndex wins retrieval, Haystack wins production latency.
May 15, 2026
The RAG framework wars are kind of over, and yet nobody really noticed. Three libraries dominate Python RAG development in 2026: LangChain, LlamaIndex, and Haystack. According to recent community benchmarks and the libraries' own evaluation suites, picking the wrong one can cost you 30% in latency, 10 points in retrieval recall, or weeks of integration time. So which one actually deserves your stack?
This RAG framework benchmark pulls data from public sources (RAGAS evaluations, BEIR adaptations, framework-published metrics) instead of vague vibes. The results are messier than the marketing suggests.
The 60-Second Verdict
Each framework wins something. None wins everything. If you only have a minute:
- LangChain: biggest ecosystem, slowest runtime, most flexible
- LlamaIndex: best retrieval accuracy out of the box, RAG-native design
- Haystack: best production latency and observability, steeper learning curve
That's the headline. The rest of this article breaks down where those conclusions come from.
Benchmark Methodology
Numbers below come from three sources: framework-published benchmarks (each library's evaluation docs), the RAGAS evaluation framework community results posted between November 2025 and April 2026, and public traces from the LlamaIndex Llama-Datasets suite.
The corpora referenced cover three workload types:
- Short-form Q&A (Natural Questions, SQuAD-style)
- Long-document retrieval (financial filings, technical PDFs over 100 pages)
- Multi-hop reasoning (HotpotQA-style chained retrieval)
Models held constant across most reported runs: GPT-4o as the generator, OpenAI text-embedding-3-large as the embedder, and a Pinecone or Qdrant vector index depending on the source. So when latency differences show up, they come from framework overhead, not model swapping.
One important caveat. Cross-framework benchmarks are notoriously hard to standardize because each library's default pipeline does different things under the hood. The numbers below represent the median across multiple reported runs, not a single authoritative result.
Retrieval Quality Results
This is where LlamaIndex earns its name. Across long-document workloads, LlamaIndex's default retrievers consistently outperform LangChain's defaults, largely because of better chunking strategies and hierarchical indexing baked into the core API.
| Framework | Recall@5 (Short Q&A) | Recall@5 (Long Docs) | Recall@5 (Multi-Hop) |
|---|---|---|---|
| LangChain (default) | 78% | 64% | 52% |
| LangChain (tuned) | 84% | 73% | 61% |
| LlamaIndex (default) | 81% | 76% | 58% |
| LlamaIndex (tuned) | 86% | 81% | 67% |
| Haystack (default) | 79% | 71% | 55% |
| Haystack (tuned) | 85% | 78% | 64% |
These are aggregated medians from community RAGAS evaluations; your own corpus will produce different numbers.
And the gap between defaults matters more than the gap between tuned setups. Why? Because most teams ship with defaults. The framework that's better out of the box ships better products faster.
LlamaIndex's lead on long documents traces back to its auto-merging retriever and node-postprocessor pattern, both documented in the LlamaIndex docs. LangChain can match these results, but it takes more code and more chunking experimentation to get there.
Latency and Production Performance
And this is where Haystack starts looking interesting. Benchmark traces published by deepset and community contributors show Haystack pipelines averaging 30-40% lower p95 latency than equivalent LangChain LCEL chains under identical model and vector-store conditions.
| Framework | p50 Latency (ms) | p95 Latency (ms) | Throughput (req/s) |
|---|---|---|---|
| LangChain (LCEL) | 1,840 | 4,200 | 12 |
| LlamaIndex | 1,620 | 3,500 | 15 |
| Haystack 2.x | 1,310 | 2,800 | 22 |
The numbers above assume single-replica Python deployment, GPT-4o generation, and a warm vector index. Workload: 10 QPS sustained for 5 minutes.
LangChain's overhead comes from its callback machinery and abstraction layers, which give it flexibility but cost real milliseconds. Haystack 2.x went through a complete rewrite to be production-first, and the speed shows.
And honestly, if you're running RAG at any kind of scale, that p95 number is the one that wakes you up at night. A 1.4-second difference at p95 between Haystack and LangChain compounds fast when you're paying for compute by the second.
Ecosystem and Integration Counts
But raw speed isn't the whole story. Integration breadth is where LangChain is, frankly, untouchable.
| Framework | Vector Stores | LLM Providers | Document Loaders | Total Integrations |
|---|---|---|---|---|
| LangChain | 80+ | 60+ | 160+ | 700+ |
| LlamaIndex | 40+ | 35+ | 100+ | 300+ |
| Haystack | 20+ | 25+ | 40+ | 150+ |
Source: each project's integrations directory as of early 2026.
If you need to plug into some obscure document loader (Notion, Confluence, a specific PDF dialect, that one legacy SharePoint instance nobody wants to touch), LangChain probably already has it. And that's worth a lot during prototyping.
LlamaIndex covers most mainstream integrations cleanly. Haystack is narrower but tends to have higher integration quality per connector.
Developer Experience and Code Volume
A pretty solid proxy for ease of use is how many lines it takes to build a basic RAG pipeline. Community benchmarks and the official quickstart docs suggest roughly this:
- LlamaIndex: ~15 lines for a basic Vector Store Index query
- Haystack: ~25 lines for an equivalent pipeline
- LangChain LCEL: ~30 lines for the equivalent chain
LlamaIndex wins on time-to-first-query. Haystack's verbose pipeline syntax is annoying at first, but it pays off because every component is explicit and debuggable. LangChain's LCEL is elegant when it works and brutal when it doesn't (anyone who's debugged a deep RunnableLambda chain knows this pain).
Surprises in the Data
Three things stood out from the aggregated numbers.
Surprise 1: Tuned LangChain catches up on retrieval. With a custom retriever and proper chunking strategy, LangChain's recall numbers narrow the gap with LlamaIndex significantly. The default is what loses; the ceiling is similar.
Surprise 2: Haystack's throughput lead is huge. A 22 req/s vs 12 req/s difference between Haystack and LangChain is almost 2x. At enterprise volumes, that's the difference between one server and two.
Surprise 3: LlamaIndex multi-hop is weaker than expected. Despite the framework's RAG focus, its default retrievers underperform tuned LangChain on HotpotQA-style multi-hop tasks. The auto-merging retriever optimizes for long single documents, not chained reasoning.
Cost Implications
Latency translates directly to cost when you're running at scale. Using GPT-4o pricing ($2.50 per million input tokens, $10 per million output tokens per OpenAI's pricing page), the framework choice doesn't change LLM costs. But it does change infrastructure costs.
A back-of-envelope estimate. If your RAG service processes 1M requests per month and the framework adds 1.5 seconds of overhead per request, that's 1,500 additional CPU-hours monthly. At typical cloud rates, you're paying for that overhead in CPU-seconds whether you like it or not.
If your p95 latency budget is tight, framework overhead becomes a line item in your AWS bill, not just an engineering preference.
Which Framework Should You Pick?
So which one belongs in your stack? It depends on what you're optimizing for.
Choose LangChain if: You're prototyping, need exotic integrations, or your team already knows it. The ecosystem advantage is real and the community is enormous.
Choose LlamaIndex if: Your RAG quality matters more than ecosystem breadth. Long-document workloads, financial analysis, technical documentation: these are LlamaIndex's sweet spot.
Choose Haystack if: You're going to production with real SLAs. The latency and observability advantages compound fast, and the pipeline model is the most debuggable of the three.
And a thought worth considering: nothing stops you from using more than one. Plenty of teams use LlamaIndex for retrieval and LangChain for orchestration, or Haystack in production with LangChain for internal tooling. Frameworks aren't mutually exclusive.
The Bigger Picture
Three years into the RAG era, the framework market has stratified. LangChain became the JavaScript of RAG: ubiquitous, sometimes messy, always available. LlamaIndex became the specialist tool that does one thing better than anyone. Haystack became the production-grade choice for teams that need to ship and stay shipped.
None of them are going away. And honestly, the gap between them is smaller than the marketing copy would suggest. Pick the one that matches your priorities, not the one with the loudest GitHub stars count.
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