Automated Evaluation with LLMs in LangChain for Efficient AI Assessment

Introduction

Automated evaluation is a cornerstone of developing reliable AI-driven applications, enabling developers to assess performance at scale without manual intervention. LangChain, a versatile framework for building applications powered by large language models (LLMs), provides robust tools within its langchain.evaluation module to automate the evaluation of components like chains, agents, and retrievers using LLMs as judges. Accessible under the /langchain/evaluation/auto-evaluation-with-llms path, this automated evaluation leverages LLMs to assess output quality, correctness, relevance, and other criteria, streamlining the validation process. This comprehensive guide explores automated evaluation with LLMs in LangChain, covering setup, core techniques, best practices, practical applications, and advanced configurations, empowering developers to efficiently assess and optimize their AI systems.

To understand LangChain’s broader evaluation ecosystem, start with LangChain Evaluation Introduction.

What is Automated Evaluation with LLMs in LangChain?

Automated evaluation with LLMs in LangChain involves using language models to assess the performance of LangChain components by scoring outputs against predefined metrics or criteria. The langchain.evaluation module provides evaluators that leverage LLMs to judge qualities like factual correctness, relevance, coherence, or custom attributes (e.g., tone, specificity) without human intervention. These evaluators can compare outputs to ground truth, evaluate open-ended responses, or perform pairwise comparisons, making them ideal for scalable testing. Integrated with LangSmith, automated evaluations enable dataset-driven assessments and performance tracking, supporting iterative development and production monitoring.

For related concepts, see LangChain Metrics Overview and LangSmith Evaluation.

Why Use Automated Evaluation with LLMs?

Automated evaluation with LLMs is essential for:

• Scalability: Evaluate large datasets or frequent runs efficiently.
• Consistency: Reduce subjectivity compared to human evaluation.
• Speed: Accelerate development with rapid, automated feedback.
• Flexibility: Assess both objective (e.g., correctness) and subjective (e.g., coherence) qualities.

Explore evaluation capabilities at the LangChain Evaluation Documentation.

Setting Up Automated Evaluation with LLMs

To set up automated evaluation with LLMs in LangChain, you need to install the required packages, configure evaluators, and integrate them with your application. Below is a setup for evaluating a RetrievalQA chain using LLM-based evaluators with LangSmith:

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.documents import Document
from langchain.evaluation import load_evaluator, EvaluatorType
from langchain.chains import RetrievalQA
from langchain_core.prompts import PromptTemplate
from langsmith import Client
from langsmith.evaluation import evaluate
import os
import logging
from typing import Dict, Any

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set LangSmith environment variables
os.environ["LANGSMITH_API_KEY"] = ""
os.environ["LANGSMITH_PROJECT"] = "auto-evaluation"

# Initialize embeddings and language model
embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Create sample documents
documents = [
    Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
    Document(page_content="The Eiffel Tower is in Paris.", metadata={"source": "landmark"})
]

# Initialize Chroma vector store
vector_store = Chroma.from_documents(
    documents,
    embedding_function,
    collection_name="langchain_example",
    persist_directory="./chroma_db"
)

# Set up RetrievalQA chain
prompt = PromptTemplate.from_template("Answer: {question}")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vector_store.as_retriever()
)

# Initialize LangSmith client
client = Client()

# Create or load a dataset
dataset_name = "qa_auto_dataset"
try:
    dataset = client.create_dataset(dataset_name=dataset_name)
except:
    dataset = client.get_dataset(dataset_name=dataset_name)

# Add examples to dataset
examples = [
    {"input": "What is the capital of France?", "output": "Paris"},
    {"input": "Where is the Eiffel Tower?", "output": "Paris"}
]
for example in examples:
    client.create_example(
        inputs={"question": example["input"]},
        outputs={"answer": example["output"]},
        dataset_id=dataset.id
    )

# Define LLM-based evaluators
def evaluate_correctness(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("result", "")
    reference = example.outputs.get("answer", "")
    question = example.inputs.get("question", "")
    evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        reference=reference,
        input=question
    )
    return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

def evaluate_relevance(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("result", "")
    question = example.inputs.get("question", "")
    evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="relevance", llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        input=question
    )
    return {"key": "relevance", "score": result["score"], "comment": result.get("reasoning", "")}

def evaluate_coherence(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("result", "")
    question = example.inputs.get("question", "")
    evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="coherence", llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        input=question
    )
    return {"key": "coherence", "score": result["score"], "comment": result.get("reasoning", "")}

# Run automated evaluation
import time
start_time = time.time()
results = evaluate(
    lambda inputs: {"result": qa_chain.invoke({"query": inputs["question"]})["result"]},
    data=dataset_name,
    evaluators=[evaluate_correctness, evaluate_relevance, evaluate_coherence],
    experiment_prefix="qa_auto_evaluation",
    metadata={"version": "1.0", "evaluated_at": "2025-05-15T15:22:00Z"}
)

# Log results
logger.info(f"Evaluation completed in {time.time() - start_time:.2f} seconds")
print(f"Evaluation Results: {results}")
print("View detailed results in LangSmith dashboard under 'qa_auto_evaluation' experiment.")

This setup creates a RetrievalQA chain, uploads a dataset to LangSmith, and evaluates outputs using LLM-based evaluators for correctness, relevance, and coherence. Results are logged in the LangSmith dashboard for analysis.

Installation

Install the core packages for LangChain, LangSmith, and evaluation:

pip install langchain langchain-chroma langchain-openai chromadb langsmith

For specific metrics, install additional dependencies:

• NLP Metrics: pip install nltk rouge-score for BLEU/ROUGE scores.
• Embedding Metrics: Included with langchain-openai.

Example:

pip install nltk rouge-score

For detailed installation guidance, see LangSmith Documentation.

Configuration Options

Customize automated evaluation during setup:

• Dataset Configuration:
• Create datasets with input-output pairs or open-ended inputs.
• Example:

• dataset = client.create_dataset(dataset_name="open_ended_qa")
      client.create_example(
          inputs={"question": "Describe Paris landmarks."},
          outputs={},
          dataset_id=dataset.id
      )

• Evaluators:
• Use LLM-based evaluators (QA, CRITERIA, PAIRWISE_STRING) or custom functions.
• Example:

• evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="conciseness", llm=llm)

• Experiment Settings:
• Define experiment prefixes and metadata for tracking.
• Example:

• evaluate(..., experiment_prefix="test_run", metadata={"version": "1.0"})

• LLM for Evaluation:
• Use a reliable LLM (e.g., gpt-3.5-turbo or gpt-4) for judgment-based metrics.
• Example:

• llm = ChatOpenAI(model="gpt-4", temperature=0)

Core Evaluation Techniques

1. Correctness Evaluation

Assess whether LLM outputs are factually accurate compared to ground truth.

• QA Evaluator:
• Uses an LLM to compare predicted outputs to reference answers.
• Use Case: Validating factual responses in question-answering tasks.
• Example:

• def evaluate_correctness(run, example):
          prediction = run.outputs.get("result", "")
          reference = example.outputs.get("answer", "")
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
          result = evaluator.evaluate_strings(
              prediction=prediction,
              reference=reference,
              input=question
          )
          return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

2. Relevance Evaluation

Measure how well outputs align with input queries or task objectives.

• Criteria Evaluator (Relevance):
• Uses an LLM to score relevance to the input.
• Use Case: Ensuring responses address user intent.
• Example:

• def evaluate_relevance(run, example):
          prediction = run.outputs.get("result", "")
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="relevance", llm=llm)
          result = evaluator.evaluate_strings(
              prediction=prediction,
              input=question
          )
          return {"key": "relevance", "score": result["score"], "comment": result.get("reasoning", "")}

3. Coherence Evaluation

Assess the logical flow and clarity of LLM outputs.

• Criteria Evaluator (Coherence):
• Evaluates whether outputs are logically structured and clear.
• Use Case: Validating conversational or multi-step responses.
• Example:

• def evaluate_coherence(run, example):
          prediction = run.outputs.get("result", "")
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="coherence", llm=llm)
          result = evaluator.evaluate_strings(
              prediction=prediction,
              input=question
          )
          return {"key": "coherence", "score": result["score"], "comment": result.get("reasoning", "")}

4. Custom LLM-Based Metrics

Define custom evaluators for project-specific needs.

• Custom Criteria Evaluator:
• Create LLM-based metrics for attributes like tone or specificity.
• Example:

• def evaluate_conciseness(run, example):
          prediction = run.outputs.get("result", "")
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(
              EvaluatorType.CRITERIA,
              criteria={"conciseness": "Is the response brief yet informative?"},
              llm=llm
          )
          result = evaluator.evaluate_strings(
              prediction=prediction,
              input=question
          )
          return {"key": "conciseness", "score": result["score"], "comment": result.get("reasoning", "")}

• Custom String Evaluator:
• Extend StringEvaluator for bespoke logic.
• Example:

• from langchain.evaluation import StringEvaluator
      class ToneEvaluator(StringEvaluator):
          def _evaluate_strings(self, prediction: str, **kwargs) -> dict:
              score = 1.0 if "formal" in prediction.lower() or "dear" in prediction.lower() else 0.5
              return {"score": score, "reasoning": "Checks for formal tone."}

5. Pairwise Comparison

Compare two LLM outputs to determine which is better for a given input.

• Pairwise String Evaluator:
• Uses an LLM to judge which output is superior.
• Use Case: Comparing different prompts or model configurations.
• Example:

• def evaluate_pairwise(run, example):
          prediction = run.outputs.get("result", "")
          prediction_b = "Alternative response from another run"  # Example placeholder
          question = example.inputs.get("question", "")
          evaluator = load_evaluator(EvaluatorType.PAIRWISE_STRING, llm=llm)
          result = evaluator.evaluate_string_pairs(
              prediction=prediction,
              prediction_b=prediction_b,
              input=question
          )
          return {"key": "pairwise", "score": result["score"], "comment": result.get("reasoning", "")}

Comprehensive Example

Here’s a complete system evaluating a LangChain agent with automated LLM-based metrics using LangSmith, integrated with Chroma and MongoDB Atlas, and including dataset evaluation:

from langchain_chroma import Chroma
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.documents import Document
from langchain.evaluation import load_evaluator, EvaluatorType
from langchain.agents import initialize_agent, AgentType
from langchain.tools import Tool
from langchain_community.tools import DuckDuckGoSearchRun
from langsmith import Client
from langsmith.evaluation import evaluate
from pymongo import MongoClient
import os
import logging
import time
from typing import Dict, Any

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Set LangSmith environment variables
os.environ["LANGSMITH_API_KEY"] = ""
os.environ["LANGSMITH_PROJECT"] = "agent-auto-evaluation"

# Initialize embeddings and language model
embedding_function = OpenAIEmbeddings(model="text-embedding-3-small")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

# Create sample documents
documents = [
    Document(page_content="The capital of France is Paris.", metadata={"source": "geo"}),
    Document(page_content="The Eiffel Tower is in Paris.", metadata={"source": "landmark"})
]

# Initialize Chroma and MongoDB Atlas vector stores
chroma_store = Chroma.from_documents(
    documents,
    embedding_function,
    collection_name="langchain_example",
    persist_directory="./chroma_db"
)
client = MongoClient("mongodb+srv://:@.mongodb.net/")
collection = client["langchain_db"]["example_collection"]
mongo_store = MongoDBAtlasVectorSearch.from_documents(
    documents,
    embedding_function,
    collection=collection,
    index_name="vector_index"
)

# Set up search tool and agent
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="Useful for answering questions about recent events or general knowledge."
    )
]
agent = initialize_agent(
    tools=tools,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

# Initialize LangSmith client
ls_client = Client()

# Create or load dataset
dataset_name = "agent_qa_auto_dataset"
try:
    dataset = ls_client.create_dataset(dataset_name=dataset_name)
except:
    dataset = ls_client.get_dataset(dataset_name=dataset_name)

# Add examples to dataset
examples = [
    {"input": "What is the capital of France?", "output": "Paris"},
    {"input": "Describe Paris landmarks.", "output": ""}  # Open-ended for coherence/relevance
]
for example in examples:
    ls_client.create_example(
        inputs={"question": example["input"]},
        outputs={"answer": example["output"]},
        dataset_id=dataset.id
    )

# Define LLM-based evaluators
def evaluate_correctness(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("output", "")
    reference = example.outputs.get("answer", "")
    question = example.inputs.get("question", "")
    if not reference:
        return {"key": "correctness", "score": None, "comment": "No reference provided."}
    evaluator = load_evaluator(EvaluatorType.QA, llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        reference=reference,
        input=question
    )
    return {"key": "correctness", "score": result["score"], "comment": result.get("reasoning", "")}

def evaluate_relevance(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("output", "")
    question = example.inputs.get("question", "")
    evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="relevance", llm=llm)
    result = evaluator.evaluate_strings(
        prediction=prediction,
        input=question
    )
    return {"key": "relevance", "score": result["score"], "comment": result.get("reasoning", "")}

def evaluate_tool_usage(run, example) -> Dict[str, Any]:
    prediction = run.outputs.get("output", "")
    question = example.inputs.get("question", "")
    evaluator = load_evaluator(
        EvaluatorType.CRITERIA,
        criteria={"tool_usage": "Did the agent choose and use the correct tool effectively?"},
        llm=llm
    )
    result = evaluator.evaluate_strings(
        prediction=prediction,
        input=question
    )
    return {"key": "tool_usage", "score": result["score"], "comment": result.get("reasoning", "")}

# Run automated evaluation
start_time = time.time()
results = evaluate(
    lambda inputs: {"output": agent.run(inputs["question"])},
    data=dataset_name,
    evaluators=[evaluate_correctness, evaluate_relevance, evaluate_tool_usage],
    experiment_prefix="agent_auto_evaluation",
    metadata={"version": "1.0", "evaluated_at": "2025-05-15T15:22:00Z"}
)

# Log results
logger.info(f"Evaluation completed in {time.time() - start_time:.2f} seconds")
print(f"Evaluation Results: {results}")
print("View detailed results in LangSmith dashboard under 'agent_auto_evaluation' experiment.")

Output:

Evaluation completed in 7.12 seconds
Evaluation Results: 
View detailed results in LangSmith dashboard under 'agent_auto_evaluation' experiment.

The evaluation runs automated LLM-based metrics for correctness, relevance, and tool usage, logging results in LangSmith. The dashboard provides detailed scores, comments, and analytics for each example.

Best Practices

1. Select Appropriate Metrics: Use QA for factual tasks, CRITERIA for subjective qualities, and custom metrics for agent-specific behaviors.
2. Curate Diverse Datasets: Include factual, open-ended, and edge-case inputs to ensure comprehensive evaluation.
3. Optimize LLM Selection: Use cost-effective models (e.g., gpt-3.5-turbo) for evaluation to balance accuracy and cost.
4. Combine with Human Feedback: Supplement automated evaluation with human-in-the-loop review for subjective or ambiguous tasks (see Human-in-the-Loop Evaluation).
5. Iterate on Results: Refine prompts, tools, or models based on evaluation comments and scores.
6. Monitor Performance: Use LangSmith’s dashboard to track metrics over time and detect regressions.

Error Handling

• LLM Failures: Implement retries or fallback models for evaluation errors.
• Dataset Issues: Validate dataset format to avoid parsing errors.
• Tool Errors: Handle tool execution failures by logging and skipping invalid responses.
• Resource Limits: Batch evaluations to manage API costs and rate limits.

See Troubleshooting.

Limitations

• LLM Bias: Judgment-based metrics may vary by model or prompt design.
• Cost: LLM-based evaluations can be expensive for large datasets.
• Subjectivity: Metrics like coherence or relevance depend on LLM interpretation.
• Ground Truth Dependency: Correctness metrics require accurate references, limiting applicability for open-ended tasks.

Recent Developments

• 2025 Updates: LangSmith enhanced automated evaluation with support for dynamic evaluator prompts and batch processing.
• Community Feedback: X posts highlight LLM-based evaluations for optimizing agent tool usage in enterprise workflows.
• LangSmith UI: Improved analytics for visualizing automated evaluation trends and comparing experiments.

Conclusion

Automated evaluation with LLMs in LangChain, powered by LangSmith, enables scalable, consistent, and insightful assessment of AI-driven applications. By leveraging LLM-based evaluators, developers can assess correctness, relevance, coherence, and custom metrics, optimizing components for reliability and performance. Start using automated evaluation to enhance your LangChain projects, ensuring high-quality outputs with efficient, data-driven validation.

For official documentation, visit LangSmith Documentation.