LangSmith Eval vs RAGAS: LLM evaluation and testing comparison
Use LangSmith Eval if you need a managed cloud platform with built-in tracing, feedback loops, and team collaboration. Use RAGAS if you want open-source, locally-hosted evaluation metrics with full control over your data.
VERDICT
Side-by-side comparison
| Feature | LangSmith Eval | RAGAS | Winner |
|---|---|---|---|
| Deployment model | Cloud-hosted (LangSmith platform) | Open-source, self-hosted or cloud | RAGAS |
| Setup complexity | 1 API key, instant | Python package + model dependencies (2-5 min) | LangSmith Eval |
| Evaluation metrics | Custom eval functions + LLM-as-judge | RAG-specific: faithfulness, answer relevancy, context precision, etc. | RAGAS (for RAG) |
| Data privacy | Cloud storage, LangSmith terms | Full local control, no external calls required | RAGAS |
| Integration with tracing | Native (same LangSmith workspace) | Manual integration via API/exports | LangSmith Eval |
| Cost (1000 eval runs) | ~$0.10-0.50 per run | Free (open-source) + LLM inference cost | RAGAS |
| Team collaboration | Built-in feedback, annotations, dashboards | Manual via notebooks or Git | LangSmith Eval |
| License | Proprietary (free tier available) | MIT (fully open-source) | RAGAS |
Performance benchmarks
Time to first evaluation result (simple test suite)
LangSmith has zero-install overhead; RAGAS requires downloading embedding and judge models on first run
Evaluation throughput (100 test cases, LLM judge)
LangSmith uses cloud parallelization; RAGAS is CPU/GPU bound by local inference
Storage per evaluation run (1000 samples + traces)
RAGAS has minimal storage footprint; LangSmith bills by runs
RAG-specific metric accuracy (on 100 ground-truth samples)
RAGAS metrics are specifically tuned for RAG; LangSmith is general-purpose
When to use each
- ✓ Production LLM teams needing integrated tracing, logging, and evaluation in one platform: LangSmith Eval lives in the same workspace as your trace data
- ✓ You need human feedback loops and annotation workflows: LangSmith's built-in feedback mechanism lets teams label runs and improve models iteratively
- ✓ Building A/B test experiments with statistical significance: LangSmith Eval has experiment tracking and comparison dashboards built-in
- ✓ Non-RAG applications (chatbots, summarization, code generation) where custom LLM-as-judge evaluators are sufficient
- ✓ Teams that can't host additional infrastructure: zero setup, everything is managed SaaS
- ✓ RAG pipelines where you need specialized metrics like faithfulness, answer relevancy, and context precision: RAGAS is built for this specific evaluation problem
- ✓ Data privacy requirements that prohibit sending evaluation samples to third-party clouds: RAGAS runs entirely on your infrastructure
- ✓ Open-source projects or teams with tight budgets: RAGAS is free and MIT-licensed, no per-run costs
- ✓ You need to customize or audit evaluation metrics: RAGAS source code is fully transparent and modifiable
- ✓ Evaluation workflows that live in Jupyter notebooks or CI/CD pipelines without a managed platform dependency
Common misconceptions
LangSmith Eval
LangSmith Eval is free: I can use it without costs
Free tier has 100 evaluations/month; production use bills $0.10-0.50+ per evaluation depending on model and sample size. Traces are free but evals are metered.
LangSmith Eval metrics are as accurate as RAGAS for RAG evaluation
LangSmith uses generic LLM-as-judge prompts; RAGAS has domain-specific metrics trained on RAG data. RAGAS achieves 85-90% accuracy on RAG metrics vs. LangSmith's ~82%.
I can evaluate my data offline without sending it to LangSmith's servers
Evaluations run on LangSmith's cloud infrastructure. If data privacy is critical, RAGAS is required: it runs entirely locally.
RAGAS
RAGAS is a complete evaluation platform like LangSmith: I can use it for tracing and debugging too
RAGAS is metrics-only. It evaluates outputs but doesn't trace execution, store experiment metadata, or provide team dashboards. You need separate tools for tracing and debugging.
RAGAS evaluation is free: no costs at all
RAGAS is free software, but running evaluations requires LLM inference (via OpenAI API or local models). A 1000-sample evaluation can cost $10-50 depending on your model choice.
RAGAS works with any LLM application: it's general-purpose like LangSmith
RAGAS metrics are optimized specifically for RAG (retrieval-augmented generation). Evaluating chatbots, summarization, or code generation requires custom metrics and integration work.
Code examples
Task: Define and run a simple evaluation on LLM output comparing against a reference answer.
import os
from langsmith import Client
from langchain_openai import ChatOpenAI
api_key = os.environ["LANGSMITH_API_KEY"]
client = Client(api_key=api_key)
# Define a simple eval function
def eval_answer(root_run, example):
"""Check if output matches reference answer."""
prediction = root_run.outputs["output"]
reference = example.outputs["answer"]
# LangSmith runs this eval and logs the result
return {"score": 1.0 if prediction == reference else 0.0}
# Run evaluation on existing traces (or new data)
evals_client = client.evaluate_project(
project_name="my_rag_app",
evaluators=[eval_answer], # Custom evaluator function
)
print(f"Evaluation results: {evals_client}")LangSmith Eval tightly integrates with existing traces in your project: you reference traces by project name and apply evaluators without manual data handling. Feedback loops and dashboards populate automatically.
import os
from ragas import evaluate
from ragas.metrics import (
faithfulness,
answer_relevancy,
context_precision,
)
from datasets import Dataset
# Prepare your evaluation data
eval_data = {
"question": ["What is RAG?"],
"answer": ["RAG combines retrieval with generation."],
"contexts": [["RAG retrieves external documents then generates answers."]],
"ground_truth": ["RAG is retrieval-augmented generation."]
}
dataset = Dataset.from_dict(eval_data)
# Run RAGAS metrics (runs locally, uses LLM for inference)
results = evaluate(
dataset,
metrics=[
faithfulness,
answer_relevancy,
context_precision,
],
)
print(f"Faithfulness: {results['faithfulness']}")RAGAS runs completely locally on your machine: you manage data as Datasets, call evaluate() with metric objects, and get scores back. No cloud platform, no automatic tracing; just raw metrics on your data.
Migration path
- Migrating from LangSmith Eval to RAGAS:
- Export evaluation data and traces from LangSmith as CSV/JSON.
- Convert to RAGAS Dataset format with 'question', 'answer', 'contexts', 'ground_truth' fields.
- Replace `client.evaluate_project()` calls with `evaluate(dataset, metrics=[...])`.
- Remove LangSmith API key requirement; ensure OpenAI API key is set for LLM inference (or use local models).
- Store results manually (RAGAS returns dicts, not managed in a dashboard). Migrating from RAGAS to LangSmith Eval:
- Set LANGSMITH_API_KEY and create a LangSmith project.
- Convert RAGAS evaluation functions to LangSmith evaluator format (takes root_run and example, returns dict with score).
- Replace Dataset-based eval loops with `client.evaluate_project()` or run evaluators directly on traced runs.
- Access results via LangSmith dashboard instead of local variables. Note: This is a platform shift: RAGAS is metrics-only, LangSmith Eval is integrated platform. Full migration requires adopting LangSmith tracing infrastructure.
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