LangSmith Alternatives: Choosing the Right LLM Observability Platform

While LangSmith is popular, it’s not the only option for LLM observability. Let’s explore alternatives and understand when each makes sense.

Why Consider Alternatives?

# Reasons to look beyond LangSmith
CONSIDERATIONS = {
    "vendor_lock_in": "LangSmith is tightly coupled to LangChain",
    "pricing": "May be expensive for high-volume applications",
    "data_privacy": "Data goes to external servers",
    "self_hosting": "Limited self-hosting options",
    "ecosystem": "May not fit non-LangChain stacks"
}

Alternative Comparison

Phoenix (Arize) - Open Source, Local First

# Phoenix: Great for local development and privacy
# pip install arize-phoenix

import phoenix as px
from phoenix.trace import SpanProcessor
from opentelemetry import trace

# Launch local Phoenix server
px.launch_app()

# Create a custom span processor
class PhoenixProcessor(SpanProcessor):
    def on_start(self, span, parent_context):
        pass

    def on_end(self, span):
        # Spans automatically sent to local Phoenix
        pass

# Advantages:
# - Free and open source
# - Data stays local
# - Beautiful UI for exploration
# - OpenTelemetry compatible

# Disadvantages:
# - No cloud option (self-host only)
# - Limited team collaboration features
# - Requires running local server

Weights & Biases Weave

# W&B Weave: Great for ML teams already using W&B
# pip install weave wandb

import weave
from openai import OpenAI

# Initialize Weave project
weave.init("my-llm-project")

# Decorator-based tracing
@weave.op()
def chat(prompt: str) -> str:
    client = OpenAI()
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

# Evaluation integration
@weave.op()
def evaluate_response(response: str, expected: str) -> float:
    # Custom evaluation logic
    return 1.0 if expected in response else 0.0

# Advantages:
# - Tight integration with W&B ecosystem
# - Excellent experiment tracking
# - Built-in evaluation framework
# - Team collaboration features

# Disadvantages:
# - Learning curve if not using W&B
# - Pricing can scale quickly

Helicone - Proxy-Based, Zero Code Changes

# Helicone: Easiest setup, just change the URL
from openai import OpenAI
import os

# Option 1: Direct URL change
client = OpenAI(
    base_url="https://oai.helicone.ai/v1",
    default_headers={
        "Helicone-Auth": f"Bearer {os.environ['HELICONE_API_KEY']}"
    }
)

# Option 2: Using the Helicone SDK
# pip install helicone
from helicone.openai_proxy import openai

# Use exactly like regular OpenAI
response = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Advantages:
# - Zero code changes required
# - Built-in caching and rate limiting
# - Cost tracking out of the box
# - Works with any LLM provider

# Disadvantages:
# - Proxy adds latency
# - Less deep integration
# - Limited evaluation features

Langfuse - Open Source LangSmith Alternative

# Langfuse: Open source, self-hostable LangSmith alternative
# pip install langfuse

from langfuse import Langfuse
from langfuse.decorators import observe

# Initialize
langfuse = Langfuse(
    public_key="pk-xxx",
    secret_key="sk-xxx",
    host="https://cloud.langfuse.com"  # Or self-hosted URL
)

# Decorator-based tracing
@observe()
def process_request(prompt: str) -> str:
    # Your LLM logic here
    pass

# Manual tracing
trace = langfuse.trace(name="my-trace")
span = trace.span(name="llm-call")
span.end(output={"response": "Hello!"})

# Advantages:
# - Open source (MIT license)
# - Self-hosting option
# - Similar API to LangSmith
# - Growing community

# Disadvantages:
# - Smaller ecosystem than LangSmith
# - Fewer integrations

PromptLayer - Prompt Management Focus

# PromptLayer: Focus on prompt versioning and management
# pip install promptlayer

import promptlayer

# Wrap OpenAI
promptlayer.api_key = "your_api_key"
OpenAI = promptlayer.openai.OpenAI

# Use normally - all calls are tracked
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
    pl_tags=["production", "v2"]  # Add tags for filtering
)

# Advantages:
# - Strong prompt versioning
# - A/B testing built-in
# - Simple integration
# - Template management

# Disadvantages:
# - Less focus on observability
# - Limited tracing depth

Decision Framework

from dataclasses import dataclass
from typing import List

@dataclass
class RequirementProfile:
    """Profile to match against tools"""
    framework: str  # "langchain", "llamaindex", "custom"
    deployment: str  # "cloud", "self-hosted", "hybrid"
    team_size: str  # "solo", "small", "large"
    budget: str  # "free", "limited", "enterprise"
    priority: str  # "observability", "evaluation", "cost_tracking"

def recommend_alternative(profile: RequirementProfile) -> str:
    """Recommend the best alternative based on requirements"""

    # LangChain users might still want LangSmith
    if profile.framework == "langchain" and profile.budget == "enterprise":
        return "LangSmith - Best LangChain integration"

    # Privacy-focused or self-hosted needs
    if profile.deployment == "self-hosted":
        if profile.priority == "observability":
            return "Phoenix - Open source, local-first"
        else:
            return "Langfuse - Self-hostable, full-featured"

    # Cost-conscious with any framework
    if profile.budget == "free":
        return "Phoenix - Completely free, local"

    # ML team with existing W&B
    if profile.priority == "evaluation" and profile.team_size != "solo":
        return "Weights & Biases Weave - Best evaluation features"

    # Quick setup, minimal changes
    if profile.framework == "custom" and profile.priority == "cost_tracking":
        return "Helicone - Proxy-based, zero code changes"

    # Default recommendation
    return "Langfuse - Good balance of features and flexibility"

# Example usage
profile = RequirementProfile(
    framework="custom",
    deployment="cloud",
    team_size="small",
    budget="limited",
    priority="observability"
)

recommendation = recommend_alternative(profile)
print(f"Recommended: {recommendation}")

Migration Example: LangSmith to Langfuse

# Before: LangSmith
from langchain.callbacks import LangSmithCallbackHandler
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="gpt-4o",
    callbacks=[LangSmithCallbackHandler()]
)

# After: Langfuse
from langfuse.callback import CallbackHandler as LangfuseCallbackHandler
from langchain_openai import ChatOpenAI

langfuse_handler = LangfuseCallbackHandler(
    public_key="pk-xxx",
    secret_key="sk-xxx"
)

llm = ChatOpenAI(
    model="gpt-4o",
    callbacks=[langfuse_handler]  # Just swap the callback
)

# Migration is often this simple!

The best tool depends on your specific needs. Start with the simplest option that meets your requirements, and upgrade as your needs grow.