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.
- 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.contentReady to switch?
Free tier. No credit card. Full observability, security, and evals on your first LLM call.
50,000 free traces / month · 1,000 evals / month · 14-day retention