Same agentic workflow, same data, same models — but Java showed nearly 2x latency compared to Python.
I built the same LangGraph-based agentic workflow in both Python and Java to compare how identical workflows behave across runtimes.
The workflow was simple:
- Fetch relevant context using RAG
- If no relevant documents are found, fall back to web search for response generation
- Generate response from context
- Respond to user prompts
What stood out was the performance difference.
Even with the same workflow design and sample data, Java showed nearly 2x the latency of Python because runtime behavior matters as much as graph structure. Extra LangSmith tracing calls, different retrieval/embedding stacks, and JVM overhead made the Java path slower in practice.
Tools and models used:
- LangGraph (Python) & LangGraph4j (Java)
- Chroma Vector Store
- RAG pipeline for internal document retrieval
- Gemini 2.5 Flash Lite for relevance grading and response generation
- sentence-transformers/all-MiniLM-L6-v2 for embeddings
- LangSmith for observability
- DuckDuckGo as fallback web search
