Helicone vs. LangSmith vs. Langfuse: LLM Observability for Production Claude Agents

If you’re running Claude agents in production and you’re not logging every request, you’re flying blind. You don’t know which prompts are costing you the most, which tool calls are silently failing, or where latency is spiking at 2am. Choosing the right LLM observability platform comparison matters more than most teams realize — until something breaks in production and you have no trace data to debug with.

I’ve used all three of the platforms covered here — Helicone, LangSmith, and Langfuse — on real production deployments. Each has a distinct philosophy and clear sweet spots. This article breaks down exactly where each one wins, where each one frustrates, and which one you should reach for depending on your setup.

What You Actually Need from an LLM Observability Platform

Before comparing tools, let’s get specific about what “observability” actually means for Claude agents. It’s not just logging. At minimum you need:

• Request/response logging — full prompt + completion capture with metadata
• Cost tracking — per-request token counts mapped to dollar values
• Latency tracing — end-to-end timing, especially for multi-step chains
• Error surfacing — failed completions, rate limit hits, malformed outputs
• Evaluation and scoring — human or automated quality feedback on outputs

Multi-step agents add extra complexity. When your Claude agent is calling tools across multiple steps, you need trace grouping that shows the full chain — not just individual API calls. That’s where the platforms diverge significantly.

Helicone: Minimal Friction, Proxy-First Architecture

Helicone takes the simplest approach of the three: it’s a transparent HTTP proxy that sits between your code and the Anthropic API. You change one URL, and you’re logging everything. Zero SDK changes required.

import anthropic

# Before: direct API call
# client = anthropic.Anthropic(api_key="sk-ant-...")

# After: route through Helicone proxy
client = anthropic.Anthropic(
    api_key="sk-ant-...",
    base_url="https://anthropic.helicone.ai",
    default_headers={
        "Helicone-Auth": "Bearer sk-helicone-...",
        # Optional: tag requests for filtering
        "Helicone-Property-AgentName": "lead-qualifier",
        "Helicone-Property-Environment": "production",
    }
)

# All subsequent calls are automatically logged
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Summarize this document..."}]
)

That’s genuinely it for basic logging. The dashboard gives you cost per request, latency percentiles, error rates, and token usage broken down by model and custom properties you add via headers.

Where Helicone Excels

The proxy architecture means it works with any Claude integration regardless of framework — raw HTTP, the official SDK, LangChain, or anything else. No vendor lock-in on the instrumentation side.

Cost visibility is excellent. You can filter by custom properties (e.g., customer ID, agent type, environment) and see spend broken down at any level. For a team running multiple Claude-powered products, this is immediately useful.

Helicone also has a caching feature built in — add a header and identical requests return cached responses. This can meaningfully cut costs on development workloads or agent runs with repeated context.

Where Helicone Falls Short

Tracing multi-step agent chains is the weak point. You can group requests with a session ID header, but the visualization is basic compared to LangSmith or Langfuse. If your agent runs five tool calls before returning a final answer, you’ll see five logged requests — there’s no native waterfall trace view showing how they relate.

Evaluation features exist but feel bolted on. You can add scores via API, but there’s no native prompt playground with A/B comparison or structured eval datasets.

Pricing: Free tier covers 10,000 requests/month. Pro starts at $20/month for 100,000 requests. Enterprise is custom. At current usage, most small-to-mid production deployments fit in the $20-50/month range.

LangSmith: Deep LangChain Integration, Built for Evaluation

LangSmith is LangChain’s observability product. If your Claude agents are built on LangChain, the integration is nearly automatic — set two environment variables and you get full trace trees with almost no code changes. If you’re not using LangChain, the story is more complicated.

import os
from langsmith import traceable
from langsmith.wrappers import wrap_anthropic
import anthropic

# Set these once (e.g., in your .env file)
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "ls__..."
os.environ["LANGCHAIN_PROJECT"] = "claude-production-agents"

# Wrap the Anthropic client for automatic tracing
client = wrap_anthropic(anthropic.Anthropic())

# Decorate your functions to create trace spans
@traceable(name="document-summarizer")
def summarize_document(content: str, doc_id: str) -> str:
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=512,
        messages=[{
            "role": "user",
            "content": f"Summarize this document:\n\n{content}"
        }],
        # Pass metadata for filtering in UI
        extra_headers={"X-Doc-ID": doc_id}
    )
    return response.content[0].text

The `@traceable` decorator is where LangSmith shines. Nest them inside a parent trace and you get a full execution tree — parent function, each LLM call within it, any sub-calls, timing for each node, and the exact inputs/outputs at every level.

Where LangSmith Excels

The evaluation suite is the strongest of the three. You can create datasets of reference examples, run your agent against them, score outputs with LLM-as-judge or human review, and track regressions across versions. For teams iterating on prompt quality, this is genuinely useful — not a checkbox feature.

The prompt playground is also well-built. You can pull any logged prompt, modify it, and run it against historical inputs directly from the UI. This dramatically speeds up debugging when you’re trying to figure out why a particular class of input produces bad output — a problem that comes up constantly when you’re working to reduce hallucinations in production systems.

Where LangSmith Falls Short

Outside LangChain, the ergonomics get rougher. The `wrap_anthropic` approach works but doesn’t capture everything automatically — you need to be deliberate about which functions get `@traceable`. It’s not onerous, but it’s more work than Helicone’s proxy approach.

The free tier is genuinely stingy: 5,000 traces/month. That disappears fast in production. The Plus plan is $39/user/month, which adds up quickly for teams. There’s no flat per-request pricing — it’s seat-based, which is painful if you have several developers all needing dashboard access.

Pricing: Free tier: 5,000 traces/month. Plus: $39/user/month. Enterprise: custom. If you have 3 developers and moderate production traffic, you’re looking at ~$120/month minimum.

Langfuse: Open-Source First, Best Self-Hosting Story

Langfuse is the open-source option and it’s the one I’d reach for if data residency or cost at scale matters. You can self-host the entire stack on your own infrastructure, and the managed cloud version is competitively priced. The SDK is framework-agnostic and the tracing model is more flexible than either competitor.

from langfuse import Langfuse
from langfuse.decorators import observe, langfuse_context
import anthropic

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

client = anthropic.Anthropic()

@observe()  # Creates a trace automatically
def run_research_agent(query: str, session_id: str) -> str:
    # Add custom metadata to this trace
    langfuse_context.update_current_trace(
        session_id=session_id,
        tags=["research-agent", "production"],
        user_id="user-123"
    )

    # Manual span for the LLM call
    generation = langfuse.generation(
        name="research-synthesis",
        model="claude-3-5-sonnet-20241022",
        input=[{"role": "user", "content": query}]
    )

    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1024,
        messages=[{"role": "user", "content": query}]
    )

    output = response.content[0].text

    # Close the generation span with output and token usage
    generation.end(
        output=output,
        usage={
            "input": response.usage.input_tokens,
            "output": response.usage.output_tokens
        }
    )

    return output

The tracing model in Langfuse is the most explicit of the three — you have fine-grained control over trace structure, span nesting, and metadata. More boilerplate than Helicone, but you’re never guessing what got logged.

Where Langfuse Excels

Self-hosting is a first-class citizen. The Docker Compose setup works in under 10 minutes, and there’s a Helm chart for Kubernetes. If you’re processing sensitive data or need your logs in your own VPC, this is the only realistic option of the three.

Session-level analytics are strong. You can track entire user conversations across multiple turns, see aggregate metrics by user or session, and filter traces by custom tags. This maps well to how Claude agents with persistent memory across sessions actually behave in production.

The cost is also the most predictable at scale. Self-hosted is essentially free (your infra costs). Cloud pricing is event-based rather than seat-based: the Hobby tier is free up to 50,000 observations/month, Pro is $59/month for up to 1M observations. That’s far cheaper than LangSmith for high-volume workloads.

Where Langfuse Falls Short

The evaluation UI is less polished than LangSmith’s. The annotation workflow exists but feels rougher. LLM-as-judge evaluation is supported but requires more manual setup. If systematic prompt evaluation is your primary need, LangSmith is ahead here.

The SDK requires more intentional instrumentation than Helicone. There’s no “change one URL” approach — you’re writing tracing code throughout your application. For teams who want to add observability to an existing codebase quickly, this has real friction.

Pricing: Self-hosted: free. Cloud Hobby: free (50k observations/month). Cloud Pro: $59/month (1M observations). Enterprise: custom. This is the best pricing of the three for high-volume production workloads.

Side-by-Side Comparison

Real-World Cost Examples

Let me make this concrete. Assume a production agent handling 50,000 requests/month — a reasonable number for a B2B SaaS feature or an internal automation tool. This is also roughly where batch processing workflows start to generate meaningful observability overhead.

• Helicone: ~$50/month (at $0.001 per logged request above free tier, roughly)
• LangSmith: $78-156/month depending on team size (2-4 seats at $39/user). The 5k free trace limit is gone by day 3 of the month.
• Langfuse Cloud: $59/month flat (50k requests = 50k+ observations depending on tracing depth). Self-hosted: your infra cost, probably $10-30/month on a small VM.

At higher volumes — 500k+ requests/month — Langfuse self-hosted wins on cost by a large margin. Helicone’s per-request model becomes expensive. LangSmith’s seat model stays flat but the per-seat cost remains.

Debugging Multi-Step Chains: Where Each Tool Gets Tested

This is the scenario where the differences are most visible. Consider a Claude agent that: (1) retrieves context via vector search, (2) calls a tool to fetch live data, (3) synthesizes a response, and (4) calls another tool to write output. You need to see all four steps as a single coherent trace.

LangSmith renders this beautifully if you’re using LangChain or have wrapped each step with `@traceable`. The waterfall view shows step durations, inputs/outputs, and lets you click into any node. When something fails in step 3, you see exactly what input it received from steps 1 and 2.

Langfuse handles this equally well with explicit span nesting. The parent trace wraps child spans, each with their own metadata. The manual instrumentation pays off here — you have precise control over what gets grouped.

Helicone shows you the four API calls, tagged with a session ID if you set it. You can filter to see them together, but there’s no hierarchical view showing how they nest or depend on each other. For simple agents this is fine. For complex multi-agent workflows, it becomes a real limitation — especially when you’re dealing with the kind of fallback and retry patterns that add branching to your trace graphs.

Verdict: Choose Based on Your Actual Constraints

Choose Helicone if: You need observability up and running in under an hour, you care primarily about cost tracking and basic logging, and your agents are relatively simple (single-step or shallow chains). Also the right pick if you want built-in caching with zero extra code. Best for solo founders and small teams moving fast.

Choose LangSmith if: You’re building on LangChain, your team actively iterates on prompt quality, and you need a structured evaluation workflow with datasets and regression tracking. The seat-based pricing is painful for large teams but acceptable for a 2-3 person team where evaluation quality matters more than cost optimization.

Choose Langfuse if: You need self-hosting for data residency, you’re running high request volumes where per-seat pricing doesn’t make sense, or you want an open-source foundation you can extend. Also the right call for teams building complex multi-agent systems who need flexible trace instrumentation. This is my default recommendation for production deployments at any meaningful scale.

For the most common use case — a small team running a Claude-powered product in production with moderate traffic: Start with Helicone to get visibility fast, then migrate to Langfuse when you hit the limits of its tracing capabilities or when volume makes the per-request pricing significant. This LLM observability platform comparison consistently points to Langfuse as the long-term winner for teams who treat observability as infrastructure rather than an afterthought.