LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
apm install @orchestra-research/langsmith-observability---
name: langsmith-observability
description: LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Observability, LangSmith, Tracing, Evaluation, Monitoring, Debugging, Testing, LLM Ops, Production]
dependencies: [langsmith>=0.2.0]
---
# LangSmith - LLM Observability Platform
Development platform for debugging, evaluating, and monitoring language models and AI applications.
## When to use LangSmith
**Use LangSmith when:**
- Debugging LLM application issues (prompts, chains, agents)
- Evaluating model outputs systematically against datasets
- Monitoring production LLM systems
- Building regression testing for AI features
- Analyzing latency, token usage, and costs
- Collaborating on prompt engineering
**Key features:**
- **Tracing**: Capture inputs, outputs, latency for all LLM calls
- **Evaluation**: Systematic testing with built-in and custom evaluators
- **Datasets**: Create test sets from production traces or manually
- **Monitoring**: Track metrics, errors, and costs in production
- **Integrations**: Works with OpenAI, Anthropic, LangChain, LlamaIndex
**Use alternatives instead:**
- **Weights & Biases**: Deep learning experiment tracking, model training
- **MLflow**: General ML lifecycle, model registry focus
- **Arize/WhyLabs**: ML monitoring, data drift detection
## Quick start
### Installation
```bash
pip install langsmith
# Set environment variables
export LANGSMITH_API_KEY="your-api-key"
export LANGSMITH_TRACING=true
```
### Basic tracing with @traceable
```python
from langsmith import traceable
from openai import OpenAI
client = OpenAI()
@traceable
def generate_response(prompt: str) -> str:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
# Automatically traced to LangSmith
result = generate_response("What is machine learning?")
```
### OpenAI wrapper (automatic tracing)
```python
from langsmith.wrappers import wrap_openai
from openai import OpenAI
# Wrap client for automatic tracing
client = wrap_openai(OpenAI())
# All calls automatically traced
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello!"}]
)
```
## Core concepts
### Runs and traces
A **run** is a single execution unit (LLM call, chain, tool). Runs form hierarchical **traces** showing the full execution flow.
```python
from langsmith import traceable
@traceable(run_type="chain")
def process_query(query: str) -> str:
# Parent run
context = retrieve_context(query) # Child run
response = generate_answer(query, context) # Child run
return response
@traceable(run_type="retriever")
def retrieve_context(query: str) -> list:
return vector_store.search(query)
@traceable(run_type="llm")
def generate_answer(query: str, context: list) -> str:
return llm.invoke(f"Context: {context}\n\nQuestion: {query}")
```
### Projects
Projects organize related runs. Set via environment or code:
```python
import os
os.environ["LANGSMITH_PROJECT"] = "my-project"
# Or per-function
@traceable(project_name="my-project")
def my_function():
pass
```
## Client API
```python
from langsmith import Client
client = Client()
# List runs
runs = list(client.list_runs(
project_name="my-project",
filter='eq(status, "success")',
limit=100
))
# Get run details
run = client.read_run(run_id="...")
# Create feedback
client.create_feedback(
run_id="...",
key="correctness",
score=0.9,
comment="Good answer"
)
```
## Datasets and evaluation
### Create dataset
```python
from langsmith import Client
client = Client()
# Create dataset
dataset = client.create_dataset("qa-test-set", description="QA evaluation")
# Add examples
client.create_examples(
inputs=[
{"question": "What is Python?"},
{"question": "What is ML?"}
],
outputs=[
{"answer": "A programming language"},
{"answer": "Machine learning"}
],
dataset_id=dataset.id
)
```
### Run evaluation
```python
from langsmith import evaluate
def my_model(inputs: dict) -> dict:
# Your model logic
return {"answer": generate_answer(inputs["question"])}
def correctness_evaluator(run, example):
prediction = run.outputs["answer"]
reference = example.outputs["answer"]
score = 1.0 if reference.lower() in prediction.lower() else 0.0
return {"key": "correctness", "score": score}
results = evaluate(
my_model,
data="qa-test-set",
evaluators=[correctness_evaluator],
experiment_prefix="v1"
)
print(f"Average score: {results.aggregate_metrics['correctness']}")
```
### Built-in evaluators
```python
from langsmith.evaluation import LangChainStringEvaluator
# Use LangChain evaluators
results = evaluate(
my_model,
data="qa-test-set",
evaluators=[
LangChainStringEvaluator("qa"),
LangChainStringEvaluator("cot_qa")
]
)
```
## Advanced tracing
### Tracing context
```python
from langsmith import tracing_context
with tracing_context(
project_name="experiment-1",
tags=["production", "v2"],
metadata={"version": "2.0"}
):
# All traceable calls inherit context
result = my_function()
```
### Manual runs
```python
from langsmith import trace
with trace(
name="custom_operation",
run_type="tool",
inputs={"query": "test"}
) as run:
result = do_something()
run.end(outputs={"result": result})
```
### Process inputs/outputs
```python
def sanitize_inputs(inputs: dict) -> dict:
if "password" in inputs:
inputs["password"] = "***"
return inputs
@traceable(process_inputs=sanitize_inputs)
def login(username: str, password: str):
return authenticate(username, password)
```
### Sampling
```python
import os
os.environ["LANGSMITH_TRACING_SAMPLING_RATE"] = "0.1" # 10% sampling
```
## LangChain integration
```python
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
# Tracing enabled automatically with LANGSMITH_TRACING=true
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant."),
("user", "{input}")
])
chain = prompt | llm
# All chain runs traced automatically
response = chain.invoke({"input": "Hello!"})
```
## Production monitoring
### Hub prompts
```python
from langsmith import Client
client = Client()
# Pull prompt from hub
prompt = client.pull_prompt("my-org/qa-prompt")
# Use in application
result = prompt.invoke({"question": "What is AI?"})
```
### Async client
```python
from langsmith import AsyncClient
async def main():
client = AsyncClient()
runs = []
async for run in client.list_runs(project_name="my-project"):
runs.append(run)
return runs
```
### Feedback collection
```python
from langsmith import Client
client = Client()
# Collect user feedback
def record_feedback(run_id: str, user_rating: int, comment: str = None):
client.create_feedback(
run_id=run_id,
key="user_rating",
score=user_rating / 5.0, # Normalize to 0-1
comment=comment
)
# In your application
record_feedback(run_id="...", user_rating=4, comment="Helpful response")
```
## Testing integration
### Pytest integration
```python
from langsmith import test
@test
def test_qa_accuracy():
result = my_qa_function("What is Python?")
assert "programming" in result.lower()
```
### Evaluation in CI/CD
```python
from langsmith import evaluate
def run_evaluation():
results = evaluate(
my_model,
data="regression-test-set",
evaluators=[accuracy_evaluator]
)
# Fail CI if accuracy drops
assert results.aggregate_metrics["accuracy"] >= 0.9, \
f"Accuracy {results.aggregate_metrics['accuracy']} below threshold"
```
## Best practices
1. **Structured naming** - Use consistent project/run naming conventions
2. **Add metadata** - Include version, environment, user info
3. **Sample in production** - Use sampling rate to control volume
4. **Create datasets** - Build test sets from interesting production cases
5. **Automate evaluation** - Run evaluations in CI/CD pipelines
6. **Monitor costs** - Track token usage and latency trends
## Common issues
**Traces not appearing:**
```python
import os
# Ensure tracing is enabled
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = "your-key"
# Verify connection
from langsmith import Client
client = Client()
print(client.list_projects()) # Should work
```
**High latency from tracing:**
```python
# Enable background batching (default)
from langsmith import Client
client = Client(auto_batch_tracing=True)
# Or use sampling
os.environ["LANGSMITH_TRACING_SAMPLING_RATE"] = "0.1"
```
**Large payloads:**
```python
# Hide sensitive/large fields
@traceable(
process_inputs=lambda x: {k: v for k, v in x.items() if k != "large_field"}
)
def my_function(data):
pass
```
## References
- **[Advanced Usage](references/advanced-usage.md)** - Custom evaluators, distributed tracing, hub prompts
- **[Troubleshooting](references/troubleshooting.md)** - Common issues, debugging, performance
## Resources
- **Documentation**: https://docs.smith.langchain.com
- **Python SDK**: https://github.com/langchain-ai/langsmith-sdk
- **Web App**: https://smith.langchain.com
- **Version**: 0.2.0+
- **License**: MIT