Build reproducible evaluation pipelines for LangChain 1.0 chains and LangGraph 1.0
agents — golden datasets, LangSmith evaluate(), ragas RAG metrics, deepeval
LLM-as-judge, agent trajectory analysis, and CI gating on quality regressions.
Use when setting up quality measurement for a new chain, diagnosing regression
after a model switch, or building an evaluation gate for a pull request.
Trigger with "langchain eval", "langsmith evaluate", "ragas", "llm-as-judge",
"agent trajectory eval", "eval regression gate".
Build reproducible evaluation pipelines for LangChain 1.0 chains and LangGraph 1.0
agents — golden datasets, LangSmith evaluate(), ragas RAG metrics, deepeval
LLM-as-judge, agent trajectory analysis, and CI gating on quality regressions.
Use when setting up quality measurement for a new chain, diagnosing regression
after a model switch, or building an evaluation gate for a pull request.
Trigger with "langchain eval", "langsmith evaluate", "ragas", "llm-as-judge",
"agent trajectory eval", "eval regression gate".
A team swapped gpt-4o for claude-sonnet-4-6 to save money and a week later CS
noticed answer quality dropped on 15% of refund tickets — the regression was
invisible in code review and invisible in CI because no golden set existed.
Fix: a versioned golden set, a stacked eval pipeline (LangSmith +
ragas + deepeval + custom trajectory), and a PR-blocking regression gate
with paired Wilcoxon significance. The tooling exists; the patterns for
wiring it into a statistically honest loop are scattered across five doc sites.
Build a 100-example JSONL golden set, wire LangSmith evaluate() with a
custom correctness evaluator, add a ragas quartet (faithfulness, answer
relevance, context precision/recall) for RAG, add deepeval LLM-as-judge
with N=3 judge quorum, score LangGraph trajectories on coverage/precision/
order, and gate PRs on a 2% aggregate drop or 5% per-example drop. Pin:
langchain-core 1.0.x, langgraph 1.0.x, langsmith>=0.2, ragas>=0.2,
deepeval>=2.0. Pain-catalog anchors: P01, P11, P12, P22, P33.
Prerequisites
Python 3.10+
langchain-core >= 1.0, < 2.0, langgraph >= 1.0, < 2.0 for the system under eval
LangSmith account + LANGSMITH_API_KEY (free tier is sufficient for dataset versioning)
Provider API keys for the judge LLM: OPENAI_API_KEY and/or ANTHROPIC_API_KEY
Instructions
Step 1 — Build a versioned golden set
Format: JSONL, one example per line, with a dataset_version tag. Minimum 20
examples to start; grow to 100 for PR gating, 200+ for absolute-metric claims.
Sample from real traffic (redacted), not imagination. Stratify by tag and
difficulty (aim for 30% hard). Two annotators per example, disagreements
reconciled — reconciliation rate under 90% means your task definition is
ambiguous. Treat the file as immutable within a version; bump the version
to refresh. See Golden Set Curation for
sourcing strategy, annotation tool options, and the refresh cadence.
Step 2 — Wire LangSmith evaluate() with a custom evaluator
from langsmith import Client
from langsmith.evaluation import evaluate, EvaluationResult
from langchain_anthropic import ChatAnthropic
client = Client()
DATASET_VERSION = "2026.04"# One-time: upload golden set as a versioned datasetdefupload_golden_set(jsonl_path, dataset_name):
examples = [json.loads(line) for line inopen(jsonl_path)]
client.create_dataset(dataset_name)
client.create_examples(
inputs=[{"input": e["input"]} for e in examples],
outputs=[{"expected": e["expected"]} for e in examples],
metadata=[{"id": e["id"], "tags": e["tags"]} for e in examples],
dataset_name=dataset_name,
)
chain = ChatAnthropic(model="claude-sonnet-4-6", temperature=0, timeout=30)
deftarget(inputs):
return {"answer": chain.invoke(inputs["input"]).content}
defcorrectness(outputs, reference_outputs):
"""Deterministic exact-match floor — baseline, not ceiling."""match = outputs["answer"].strip().lower() == reference_outputs["expected"].strip().lower()
return EvaluationResult(key="exact_match", score=float(match))
results = evaluate(
target,
data=f"golden-set-v{DATASET_VERSION}",
evaluators=[correctness],
experiment_prefix="refund-bot-v3",
max_concurrency=10, # Avoid 429s on judge LLM (P22)
)
Free-form outputs need semantic scoring (ragas, deepeval, or LLM-as-judge — Step 4).
Step 3 — Add ragas metrics for RAG pipelines
For a RAG chain returning {answer, contexts}, ragas scores four standard
dimensions. The default judge is gpt-4o-mini; override to pin model +
cost:
from ragas import evaluate as ragas_evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision, context_recall
from langchain_openai import ChatOpenAI
from langchain_openai import OpenAIEmbeddings
from datasets import Dataset
judge = ChatOpenAI(model="gpt-4o-mini", temperature=0)
embed = OpenAIEmbeddings(model="text-embedding-3-small")
# Prepare rows — ragas wants HuggingFace Dataset shape
rows = []
for ex in golden_examples:
result = rag_chain.invoke({"question": ex["input"]})
rows.append({
"question": ex["input"],
"answer": result["answer"],
"contexts": [d.page_content for d in result["source_documents"]],
"ground_truth": ex["expected"],
})
ragas_results = ragas_evaluate(
Dataset.from_list(rows),
metrics=[faithfulness, answer_relevancy, context_precision, context_recall],
llm=judge,
embeddings=embed,
)
# ragas_results is a dict of per-metric means; call .to_pandas() for per-row
Do not use ragas on non-RAG chains — context_precision against an empty
context list returns 0 and looks like a regression. See
Framework Comparison for when each
tool fits.
Step 4 — Add deepeval LLM-as-judge for free-form outputs
deepeval is pytest-shaped — each example is an LLMTestCase asserting against
metrics. Run N=3 judge invocations per example and take the median to tame
LLM-as-judge variance (±5-15% across runs; single-run scores are not CI-ready):
import statistics
from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase, LLMTestCaseParams
defeval_with_quorum(test_case, metric, n=3):
scores = []
for _ inrange(n):
metric.measure(test_case)
scores.append(metric.score)
return statistics.median(scores), statistics.stdev(scores) if n > 1else0.0
correctness = GEval(
name="Correctness",
criteria="Does the actual output match the expected output in meaning?",
evaluation_params=[LLMTestCaseParams.INPUT, LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.EXPECTED_OUTPUT],
model="gpt-4o-mini",
)
for ex in golden_examples:
result = chain.invoke({"input": ex["input"]})
case = LLMTestCase(input=ex["input"], actual_output=result, expected_output=ex["expected"])
median, sd = eval_with_quorum(case, correctness, n=3)
if sd > 0.2: # judge disagreeing with itself — flag, don't gate
flag_for_review(ex["id"], median, sd)
Step 5 — LangGraph agent trajectory eval
For agents, final-answer correctness misses the process. Score the tool-call
sequence on three axes — coverage (did required tools run?), precision
(were extra tools used?), and order (Kendall's tau on shared tools):
from langchain_core.messages import AIMessage
defextract_trajectory(final_state: dict) -> list[dict]:
return [
{"tool": tc["name"], "args": tc["args"]}
for msg in final_state["messages"] ifisinstance(msg, AIMessage)
for tc in (msg.tool_calls or [])
]
deftrajectory_score(expected: list[str], actual: list[str]) -> dict:
e_set, a_set = set(expected), set(actual)
coverage = len(e_set & a_set) / len(e_set) if e_set else1.0
precision = len(e_set & a_set) / len(a_set) if a_set else0.0
shared = [t for t in actual if t in e_set]
order = _kendall_tau(expected, shared) iflen(shared) >= 2else1.0return {"coverage": coverage, "precision": precision, "order": order}
# Composite: 0.5 * coverage + 0.3 * precision + 0.2 * order
Set temperature=0 for the agent during eval — temperature > 0 produces
different trajectories across runs (P11) and makes paired comparison
statistically invalid. See Agent Trajectory Eval
for args-level matching, efficiency/safety scoring, and the LLM-as-judge
fallback for non-deterministic trajectories.
Step 6 — Gate PRs on regression
A PR touching prompts, chain code, or model config runs the eval suite on
PR branch and main, then blocks merge on any of: aggregate mean drop > 2.0%,
any single-example drop > 5.0%, or paired Wilcoxon signed-rank p < 0.05
with negative mean delta.
from scipy.stats import wilcoxon
defpaired_regression_check(baseline, candidate, alpha=0.05):
"""Wilcoxon — right test when metric distribution is non-normal (most LLM metrics)."""
n = len(baseline)
if n < 50:
return {"verdict": "too_small_n", "n": n}
diffs = [c - b for b, c inzip(baseline, candidate)]
_, p = wilcoxon(diffs, alternative="less")
return {"n": n, "mean_delta": sum(diffs) / n, "p_value": float(p),
"regression": p < alpha andsum(diffs) < 0}
At n=100 and α=0.05 this detects a ~3-5% true regression at ~80% power. See
CI Integration for the GitHub Actions workflow,
PR-comment delta table, bootstrap CI, and spend/rate-limit safety rails.
Output
JSONL golden set at evals/golden_set/v2026.04.jsonl with an immutable version tag
LangSmith dataset uploaded and versioned; experiment runs linked to traces
Ragas scores (faithfulness, answer relevance, context precision/recall) on RAG chains
Deepeval LLMTestCase assertions in pytest, with median-of-3 judge quorum
LangGraph trajectory scores (coverage, precision, order) with composite summary
GitHub Actions workflow gating PRs on 2% aggregate / 5% per-example / Wilcoxon p < 0.05
PR-comment delta table posted on every eval run
Framework selection at a glance
Use case
LangSmith
ragas
deepeval
Custom
RAG metrics (faithfulness, context recall)
—
Primary
Fallback
—
Pytest-style assertion in CI
Secondary
—
Primary
—
Trace capture + dataset versioning
Primary
Complementary
Complementary
—
Agent trajectory (tool-call sequence)
Secondary (traces)
—
—
Primary
Exact match / JSON schema / structured output
—
—
—
Primary
Free-form paraphrase scoring
Via custom evaluator
—
Primary (G-Eval)
—
Most real pipelines stack two or three. The anti-pattern is running all four
on every example — you pay $10-30 per run for signal you are not using. See
Framework Comparison for the full
decision tree and dependency weight comparison.
Error Handling
Error / Failure mode
Cause
Fix
TimeoutError on eval runs > 20 min
Long agent trajectories on slow models; 100 examples × 30s each exceeds default GH Actions job timeout
Cap max_concurrency=10, use asyncio.gather with asyncio.Semaphore, split eval into sharded jobs
Judge disagreement (stdev > 0.2 on [0,1] scale across N=3 runs)
LLM-as-judge variance on ambiguous examples
Flag example for manual review; do not use that row's score for gating
ValidationError: missing 'contexts' in ragas
Chain does not return retrieved docs
Modify chain to surface source_documents, or switch to non-RAG evaluator
Wilcoxon p-value is NaN
All paired diffs are 0 (identical outputs)
Expected when the PR did not change behavior — no regression, skip the stat test
LangSmith 429 rate limit during upload
> 50 examples/sec to create_examples
Batch with client.create_examples(..., batch_size=20) and sleep between batches
Spend overrun ($50+ per run)
Judge calls scaling with N_examples × N_metrics × N_judge_runs
Use gpt-4o-mini not gpt-4o for judge; cache per (dataset_version, chain_version)
AttributeError: 'list' has no attribute 'lower' in custom evaluator
Claude AIMessage.content is list[dict] not str (P02 — see langchain-model-inference)
Use msg.text() or iterate content blocks
Trajectory comparison drifts week-over-week on unchanged agent
temperature > 0 non-determinism (P11)
Set temperature=0 for all eval runs; pin seed where supported
Examples
Setting up eval for a new RAG chain
Start with 20 production-sampled golden examples, wire up ragas_evaluate
with four metrics, record scores to evals/baselines/ as the reference,
and promote to LangSmith dataset versioning once two engineers annotate in
parallel. See Golden Set Curation.
Diagnosing regression after a model swap
Run the main-branch chain on the golden set, then swap the model and rerun.
Diff per-example scores sorted by delta — the top-10 regressions usually
cluster by tag (long contexts, one-shot lookups). Report paired Wilcoxon
and per-tag breakdown before deciding to ship. See CI Integration.
Evaluating a LangGraph tool-calling agent
Record expected tool-call sequences for 50 tasks, capture actual trajectories
via extract_trajectory, and score on coverage/precision/order. Composite
drops indicate a policy change — diff sequences to find the drift. See
Agent Trajectory Eval.
