FluiqvsLangSmith

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.

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Free tier · No credit card · 2-minute setup

2 linesof Python to instrument any LLM app

13+framework integrations (not just LangChain)

0 msadded latency, SDK only, no proxy

Feature comparison

How Fluiq and LangSmith stack up across the features that matter in production.

Feature

Fluiq

LangSmith

2-line auto-instrumentation

requires @traceable decorators

OpenAI, Anthropic, Gemini support

~best with LangChain

LangChain & LangGraph tracing

CrewAI / Google ADK / MCP tracing

Full agent span tree

Per-node token & USD cost tracking

Prompt injection & jailbreak blocking

PII detection & redaction

Trace-driven response caching

LLM-as-judge evals

Agentic evaluation (whole-run: tools, trajectory, coordination)

~trajectory evals exist; no layered run judging or judge jury

Multi-model judge jury with audit trail

Whole-trajectory dataset capture (tools, MCP, media)

~run-linked datasets without full tool/MCP/media capture

Run-vs-run regression comparison on datasets

End-user feedback & team annotations

Transparent judge prompts (exact prompt & version stamped on every score)

evaluator prompts are definable, but scores don't record the rendered prompt

CI/CD eval gates

Prompt management

Free tier (no credit card)

~ = 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 — no black-box scoring.
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.

Migration guide

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.content

After, 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.content

Ready to switch?

Free tier. No credit card. Full observability, security, and evals on your first LLM call.

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Unlimited free traces · 1,000 evals / month · 14-day retention