Click a benchmark to explore the data, methodology, and what it means in practice.
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#8 — RAG from PDF
Metric: total lines of code (imports + functional) · lower is better
Winner: SynapseKit
SynapseKit
7
3.0 pts
LlamaIndex
11
2.0 pts
LangChain
13
1.0 pts
SynapseKit loads a PDF into a retrieval pipeline in 2 functional lines. LangChain requires a PyPDFLoader, RecursiveCharacterTextSplitter, vector store, and retriever — each a separate abstraction, each a separate line. The gap is 2x on lines of code and 4 distinct objects to manage vs 1.
#9 — Chunking Strategies
Metric: built-in splitter count · higher is better
Winner: LlamaIndex
LlamaIndex
9
3.0 pts
LangChain
7
2.0 pts
SynapseKit
4
1.0 pts
LlamaIndex wins clearly here. The two decisive splitters: SentenceWindowNodeParser (retrieves surrounding sentence context, not just the matched chunk) and HierarchicalNodeParser (builds chunk trees at multiple granularities). Neither exists in SynapseKit or LangChain. If retrieval quality depends on chunk context window, this is the right tool.
#10 — Built-in BM25
Metric: extra packages required · lower is better
Winner: SynapseKit
SynapseKit
0
3.0 pts
LangChain
1
1.5 pts
LlamaIndex
1
1.5 pts
SynapseKit bundles rank_bm25 as a core dependency — BM25 retrieval is available the moment you install the package. LangChain requires rank-bm25 separately. LlamaIndex requires llama-index-retrievers-bm25. One extra pip install sounds trivial. In a locked production environment, dependency additions require security review and deployment cycles. Zero extras wins.
#11 — Hybrid Search RRF
Metric: RRF configurability score (out of 5) · higher is better
Winner: LangChain
LangChain
5/5
3.0 pts
SynapseKit
4/5
2.0 pts
LlamaIndex
3/5
1.0 pts
LangChain's EnsembleRetriever accepts unlimited retrievers with per-retriever weights — you can blend 3 different retrievers with custom weighting in one constructor call. SynapseKit supports two retrievers with a fixed alpha weight. LlamaIndex has no weight control at all. If you need custom retrieval fusion (e.g. 60% dense + 30% BM25 + 10% knowledge graph), LangChain is the only option.
#12 — Streaming TTFT
Metric: median framework overhead to first token (ms) · lower is better
Winner: SynapseKit (but read the caveat)
SynapseKit
0.001
3.0 pts — ms overhead
LlamaIndex
0.184
2.0 pts — ms overhead
LangChain
0.236
1.0 pts — ms overhead
All three frameworks add sub-millisecond overhead between calling .stream() and receiving the first token. SynapseKit's async generator is fastest. But in any real deployment, network latency to an LLM API is 300–2000ms. Framework overhead is noise. This benchmark's winner should not influence your framework choice. It measures the wrong bottleneck.
#13 — Conversation Memory
Metric: lines of code to add memory to RAG · lower is better
Winner: SynapseKit
SynapseKit
4
3.0 pts
LlamaIndex
6
2.0 pts
LangChain
12
1.0 pts
SynapseKit exposes conversation memory as one constructor parameter: memory=True. LangChain's RunnableWithMessageHistory requires a message store object, a getter function, session ID binding, and LCEL chain wiring — 12 lines before history is injected. LangChain's approach is more flexible (supports any storage backend), but you pay for that flexibility in boilerplate every time.