Detailed comparison of LangSmith, Helicone, and Langfuse -LLM observability platforms for agent tracing, debugging, analytics. Features, pricing, performance analysis.
TL;DR
All three: LLM observability platforms for tracing, debugging, monitoring AI agents in production.
Key question: Which provides best visibility into your agents with least setup friction?
| Feature | LangSmith | Helicone | Langfuse |
|---|---|---|---|
| Automatic tracing | ✅ (LangChain only) | ✅ (proxy-based) | ✅ (SDK-based) |
| Multi-model support | ✅ (via LangChain) | ✅ (OpenAI, Anthropic, more) | ✅ (model-agnostic) |
| Caching | ❌ No | ✅ Yes (semantic caching) | ❌ No |
| Prompt versioning | ✅ Yes | ❌ No | ✅ Yes |
| User feedback | ✅ Yes | ✅ Yes (via API) | ✅ Yes (built-in UI) |
| Datasets for evaluation | ✅ Yes | ❌ No | ✅ Yes |
| Playground (test prompts) | ✅ Yes | ❌ No | ✅ Yes |
| Self-hosting | ❌ Cloud only | ❌ Cloud only | ✅ Yes (Docker) |
| Pricing (starter) | $39/month | Free (50K req), $20/month after | Free (self-hosted), $50/month (cloud) |
If using LangChain (easiest):
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-api-key"
# All LangChain calls automatically traced
from langchain.agents import create_agent
agent = create_agent(...)
result = agent.invoke("user query") # Traced automatically
Setup time: 30 seconds (set env vars).
If NOT using LangChain (requires manual instrumentation):
from langsmith import Client
client = Client()
# Manual tracing
with client.trace("agent-run") as run:
result = my_agent.execute(query)
run.log_output(result)
Setup time: 2-3 hours (instrument all agent steps).
Proxy-based (works with any LLM, zero code changes):
import openai
# Change base URL to Helicone proxy
openai.api_base = "https://oai.helicone.ai/v1"
# Add Helicone auth header
openai.default_headers = {
"Helicone-Auth": "Bearer your-api-key"
}
# All OpenAI calls automatically logged
response = openai.ChatCompletion.create(...) # Logged to Helicone
Setup time: 2 minutes (change base URL, add header).
Works with: OpenAI, Anthropic, Cohere, Azure OpenAI, any OpenAI-compatible API.
SDK-based:
from langfuse import Langfuse
langfuse = Langfuse()
# Trace agent execution
trace = langfuse.trace(name="agent-execution")
# Log each step
span = trace.span(name="llm-call")
response = call_llm(prompt)
span.end(output=response)
trace.end()
Setup time: 1-2 hours (instrument agent steps).
Self-hosting (Docker):
docker run -p 3000:3000 langfuse/langfuse
Advantage: Full data control, no third-party cloud.
Automatic for LangChain:
Example trace (customer support agent):
customer_support_agent [3.2s total]
├─ classify_query [0.8s] - 450 tokens
├─ retrieve_context [0.3s] - 200 tokens
└─ generate_response [2.1s] - 800 tokens
Total tokens: 1,450 | Cost: $0.029
Filtering: Search by user, time range, success/failure, cost.
Model-agnostic logging:
Example log entry:
{
"timestamp": "2024-11-08T14:32:01Z",
"model": "gpt-4-turbo",
"prompt_tokens": 450,
"completion_tokens": 320,
"total_tokens": 770,
"latency_ms": 2100,
"cost_usd": 0.0154,
"status": "success"
}
Advantage: Works with any model (not just LangChain).
Limitation: Doesn't automatically connect multi-step agent flows (you see individual LLM calls, not full workflow).
Flexible tracing:
Example:
# Trace multi-step workflow
trace = langfuse.trace(name="research-agent")
# Step 1
search_span = trace.span(name="web-search")
search_results = search_web(query)
search_span.end(output=search_results)
# Step 2
llm_span = trace.span(name="summarize")
summary = call_llm(search_results)
llm_span.end(output=summary, tokens={"input": 2000, "output": 500})
trace.end()
Advantage: Works with any agent architecture.
Limitation: Requires manual instrumentation (more setup work).
Dashboards:
Filtering: By user, agent, prompt version, date range.
Best feature: Playground (test prompt changes, compare versions side-by-side).
Best analytics of the three:
Dashboards (Grafana-style):
Daily spend: $127.34 (↓ 18% vs yesterday)
Total requests: 12,450
Cache hit rate: 34% (saved $43.21)
p95 latency: 2.3s
Best feature: Semantic caching (cache similar prompts, not just exact matches).
Dashboards:
Unique feature: User feedback integration (thumbs up/down shown inline with traces).
Example:
Trace: customer_support_agent_run_123
Cost: $0.032
Latency: 3.1s
User feedback: 👍 (4/5 stars)
Comment: "Helpful but slow"
| Plan | LangSmith | Helicone | Langfuse |
|---|---|---|---|
| Free tier | 5K traces/month | 50K requests/month | Unlimited (self-hosted) |
| Starter | $39/month (50K traces) | $20/month (200K req) | Free (self-hosted) |
| Pro | $99/month (500K traces) | $100/month (2M req) | $50/month (cloud, 100K traces) |
| Enterprise | Custom | Custom | Custom (cloud) or free (self-hosted) |
Cost at scale (1M traces/month):
Winner for cost: Langfuse (self-hosted), Helicone (cloud).
Helicone's killer feature: Semantic caching.
How it works:
# First query
response1 = call_llm("What's the capital of France?") # Calls OpenAI, costs $0.01
# Similar query (cached)
response2 = call_llm("What is France's capital city?") # Returns cached response, costs $0
Caching modes:
Cost savings: 20-40% for typical workloads (user queries often similar).
Example: Customer support chatbot, common questions ("How do I reset password?") cached, reduces costs significantly.
LangSmith:
Helicone:
Langfuse:
Choose LangSmith if:
Choose Helicone if:
Choose Langfuse if:
Startup (100K requests/month):
Best choice: Helicone (free tier covers half, analytics excellent).
Enterprise (10M requests/month):
Best choice: Langfuse (self-hosted, zero cost) or Helicone (best ROI with caching).
Compliance-sensitive (HIPAA, GDPR):
Best choice: Langfuse (only option for full data control).
Bottom line: LangSmith best for LangChain users ($39/month, automatic tracing, playground). Helicone best for analytics and caching (free tier 50K req, 20-40% cost savings, model-agnostic). Langfuse best for self-hosting and open-source (free self-hosted, $50/month cloud, prompt versioning). For production: LangSmith (LangChain integration), Helicone (caching savings), Langfuse (data control).
Further reading: LangSmith docs | Helicone docs | Langfuse docs