The LangSmith Alternative Built Beyond LangChain
LangSmith is excellent if your stack is LangChain. Fluiq auto-instruments OpenAI, Anthropic, Gemini, LangGraph, CrewAI, Google ADK, and 7 more, plus adds security scanning and response caching LangSmith doesn't touch.
Free tier · No credit card · 2-minute setup
Feature comparison
How Fluiq and LangSmith stack up across the features that matter in production.
Fluiq~ = partial support · - = not available
An honest take
We'll be straight. Here's where LangSmith genuinely excels, and where Fluiq goes further.
Where LangSmith shines
- Native, battle-tested integration with LangChain and LangGraph, if your entire stack lives there, it's the deepest option.
- Mature debugging tools with side-by-side trace comparison and annotation workflows.
- Strong evaluation suite including human feedback loops and dataset versioning.
- Widely adopted with a large community and extensive documentation.
Where Fluiq pulls ahead
- Auto-instruments every major LLM framework, OpenAI, Anthropic, Gemini, LangChain, LangGraph, CrewAI, Google ADK, MCP, and more, not just the LangChain ecosystem.
- Two lines to full instrumentation. No @traceable decorators, no manual span management.
- Built-in security layer: prompt injection blocking, PII redaction, and jailbreak detection on every call, LangSmith has none of this.
- Trace-driven Redis caching reduces repeated LLM calls to milliseconds and cuts API costs.
- Every eval score records the exact judge prompt and version that produced it, and each org can edit the judge prompts their evaluations use, so no score is a black box.
- Closed eval loop out of the box: grade datasets against expected outputs, diff any two runs to catch regressions, collect end-user feedback with fluiq.feedback(), and gate CI with python -m fluiq.ci.
- Single SDK covers observability, security, caching, and evals, not four separate integrations.
Switch from LangSmith in minutes
Remove wrap_openai and @traceable. Fluiq patches the SDK at the import level, your existing code works unchanged.
Before, LangSmith
from langsmith import traceable
from langsmith.wrappers import wrap_openai
from openai import OpenAI
# Must wrap the client AND decorate every function
client = wrap_openai(OpenAI())
@traceable
def run_pipeline(query: str) -> str:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}],
)
return response.choices[0].message.contentAfter, Fluiq
import fluiq
fluiq.instrument(api_key="fl_...") # that's it
# Use the standard OpenAI client, no wrappers, no decorators
from openai import OpenAI
client = OpenAI()
def run_pipeline(query: str) -> str:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}],
)
return response.choices[0].message.contentPricing
What each one costs
LangSmith charges per seat and per trace, and its evaluators run on an API key you paste in, so judge tokens land on your OpenAI bill separately. Fluiq does not charge per seat or per trace at all: tracing is unlimited on every plan and you pay for evaluation volume, with judge tokens included. A five-person team on LangSmith Plus starts at $195/mo before traces; the equivalent on Fluiq is $149 flat.
Free
$0
Unlimited traces, 100 evals and 1,000 security scans a month. Bring your own provider keys.
Starter
$29/mo
2,000 evals, 50k security scans, unlimited retention, multi-model judge jury.
Team
$149/mo
10,000 evals, 500k security scans, response caching, SSO.
Growth
$499/mo
50,000 evals, 2M security scans, priority support.
Developer
$0
One seat, up to 5,000 base traces a month, then pay-as-you-go.
Plus
$39/seat/mo
Up to 10,000 base traces a month, then pay-as-you-go.
Enterprise
Custom
Negotiated volume, self-hosting available.
LangSmith pricing from their public pricing page, checked July 2026. Plans change; check theirs before deciding.
Ready to switch?
Free tier. No credit card. Full observability, security, and evals on your first LLM call.
Unlimited free traces · 100 evals & 1,000 security scans / month · 14-day retention