If you’ve ever woken up to a surprise OpenAI bill with no idea which agent caused it, you’ve already felt the need for LLM observability. Three tools come up constantly in this space: Helicone, LangSmith, and Tracium. They solve the same core problem in very different ways, and picking the wrong one costs you hours of setup you’ll never get back.
TL;DR
| Helicone | LangSmith | Tracium | |
|---|---|---|---|
| Best for | Multi-provider teams, open source lovers | LangChain-heavy teams needing evals | Fast setup, multi-tenant SaaS, framework-agnostic |
| Setup time | ~5 min (baseURL change) | ~15–20 min (SDK + env vars + decorators) | ~2 min (1 line of Python) |
| Free tier | 100k requests/mo | 5k traces/mo, 14-day retention | 5k traces/mo, 7-day retention |
| First paid tier | $25/mo flat | $39/seat/mo | $29/mo flat |
| Framework lock-in | None | Best with LangChain | None |
| Agent grouping | Manual only (via sessions) | Yes (via runs) | Yes, per agent |
| Automatic versioning | No | No | Yes |
| Open source | Yes | No | No |
The Problem
You’re shipping an AI agent. Something breaks, or costs explode, and you have no visibility into what happened. Which call was slow? Which prompt is draining your budget? Which step in the chain failed silently? That’s the gap all three of these tools are trying to fill.
The difference is in how they fill it.
Helicone
Helicone is a proxy-based observability platform backed by Y Combinator (W23). Instead of installing an SDK that wraps your calls, Helicone routes your LLM requests through their servers, logging everything in transit.
Strengths:
- Supports 300+ models and providers out of the box: OpenAI, Anthropic, Mistral, Gemini, and more
- Open source, which matters to teams with data sovereignty concerns
- Response caching to cut repeated API costs
- Solid dashboard with cost breakdowns, latency tracking, and custom properties for segmentation
Weaknesses:
- Proxy architecture adds a network hop to every request, minor in practice, but worth knowing
- Primarily JS/TypeScript first; the Python experience is functional but secondary
- Setup requires changing your API baseURL, which can be fiddly with some frameworks and ORMs
- No automatic agent grouping. To group calls by agent you need to manually pass a session ID on every request and build your own grouping logic on top
Setup looks like this:
from openai import OpenAI
client = OpenAI(
api_key="your-openai-key",
base_url="https://oai.helicone.ai/v1",
default_headers={
"Helicone-Auth": "Bearer your-helicone-key",
}
)
Straightforward, but you’re touching your client configuration. If you use multiple LLM clients, you need to update each one.
LangSmith
LangSmith is built by the team behind LangChain. If you’re deep in the LangChain or LangGraph ecosystem, it’s the most native observability experience available.
Strengths:
- Deepest possible integration with LangChain, LangGraph, and LangServe
- Powerful eval tooling: datasets, LLM-as-judge scoring, human annotation queues
- Good team collaboration features with annotation and commenting
- Trace visualization for complex multi-step chains is genuinely excellent
Weaknesses:
- Per-seat pricing ($39/seat/month) adds up fast for small teams
- 14-day trace retention on the base plan, which is short for debugging production issues
- If you’re not using LangChain, you lose most of the value; the framework-agnostic experience is mediocre
- No self-hosted option on the standard plan
Setup looks like this:
import os
from langsmith import traceable
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = "your-langsmith-key"
os.environ["LANGCHAIN_PROJECT"] = "your-project-name"
@traceable
def my_agent_function(user_input: str):
# your agent logic here
pass
More moving parts than the others. You’re setting environment variables, importing a decorator, and annotating functions. That’s fine for a dedicated engineering setup, but less ideal if you want something running in the next five minutes.
Tracium
Tracium is a developer-first observability layer built specifically for AI agents and multi-step LLM workflows. The core design principle is radical simplicity: instrument your entire system with one line of code and get full visibility immediately.
Strengths:
- Genuinely one line of Python: no baseURL changes, no decorators
- Framework-agnostic: works with LangChain, LlamaIndex, raw OpenAI calls, or anything else
- Built-in per-tenant analytics, which matters if you’re building a multi-tenant SaaS product and need to track costs and usage by customer
- Automatic agent grouping: every call is grouped by agent out of the box, no session IDs or manual tagging required
- Automatic versioning: Tracium tracks versions of your prompts and agents automatically, so you can see exactly when behaviour changed without setting anything up
- Drift detection out of the box: catch degrading model behaviour before your users do
- Flat pricing (no per-seat) makes it predictable for small teams
Weaknesses:
- Newer product with a smaller ecosystem and fewer third-party integrations than Helicone
- No eval tooling yet. If you need LLM-as-judge scoring or annotation queues, LangSmith is still the better choice
- Not open source. If that matters to your team, Helicone has the edge
Setup looks like this:
import tracium
tracium.trace()
# That's it. Everything from here is monitored.
That’s the entire integration. No client wrapping, no decorator, no config file. Run your agent as normal and open the Tracium dashboard.
Pricing Comparison
| Helicone | LangSmith | Tracium | |
|---|---|---|---|
| Free tier | 100k requests/mo | 5k traces/mo | 5k traces/mo |
| Free retention | Not specified | 14 days | 7 days |
| First paid | $25/mo | $39/seat/mo | $29/mo |
| Team of 3 | $25/mo | $117/mo | $29/mo |
| Per-seat pricing | No | Yes | No |
LangSmith’s per-seat model is the most important thing to understand here. For a solo developer it’s competitive, but for a team of three it’s nearly 4x the cost of the alternatives. If your team is growing, that compounds quickly.
Setup Time, Head to Head
This is the most practical comparison. Here’s what it actually takes to go from pip install to seeing your first trace in the dashboard:
Helicone: Change your OpenAI client’s base_url, add an auth header. ~5 minutes. Slightly more if you use multiple providers or have an existing client abstraction layer.
LangSmith: Set three environment variables, install the SDK, import and apply the @traceable decorator to your functions. ~15–20 minutes. More if you’re not already on LangChain.
Tracium: pip install tracium, then tracium.trace() at the top of your file. ~2 minutes. Nothing else required.
If setup time is a hard constraint, whether you’re debugging something in production right now or evaluating tools and don’t want to spend half a day integrating, Tracium wins this category clearly.
Agent Grouping, Head to Head
This is where the tools diverge most sharply in practice.
Helicone has no automatic agent grouping. To see all calls from a specific agent together, you need to manually pass a Helicone-Session-Id header on every single request. If you forget it on one call, that call falls outside the group. For multi-agent systems with many concurrent runs, maintaining this manually gets messy fast.
LangSmith groups calls via its “runs” concept, which works well if you’re using LangChain. The tracing is automatic within LangChain’s execution model. Outside of it, you’re back to manual instrumentation.
Tracium groups calls by agent automatically. There’s nothing to pass, nothing to tag, nothing to maintain. Every call made by an agent is associated with that agent in the dashboard from the moment you add tracium.trace(). Combined with automatic versioning, you can see not just what each agent did but how its behaviour has changed across versions over time, without any extra setup.
When to Pick Each One
Pick Helicone if: You value open source. You’re primarily building in JavaScript/TypeScript. You want response caching to reduce API spend.
Pick LangSmith if: You’re building heavily with LangChain or LangGraph and want the deepest possible native integration. You need eval tooling: datasets, scoring, human annotation workflows. Your team can absorb the per-seat pricing.
Pick Tracium if: You want to be monitoring in under two minutes with no infrastructure changes. You’re building a multi-tenant SaaS product and need per-customer cost and usage breakdowns. You’re framework-agnostic and don’t want to be locked into LangChain’s ecosystem. You want flat, predictable pricing.
Bottom Line
All three tools will give you visibility into your LLM application. The real question is what you’re optimising for.
If you optimise for ecosystem depth and eval tooling, choose LangSmith, but be prepared to commit to the LangChain world.
If you optimise for open source and multi-provider breadth, choose Helicone.
If you optimise for speed of setup, simplicity, and per-tenant analytics, choose Tracium.
If you’re leaning toward Tracium, you can be up and running in about 2 minutes. The free tier includes 5,000 traces per month with no credit card required.