Kritin Vongthongsri

Posted on Jun 23

The Best 6 LangSmith Competitors in 2026 🛠️🧪

⚡ Top 6 LangSmith Alternatives & Competitors in 2026 🧪🔍

TL;DR 📚

LangSmith is a strong choice if you're all-in on LangChain and your AI quality process is primarily engineering-driven. If you need deeper evals, cross-functional workflows for non-technical team members, or consistent evaluation depth across multiple frameworks — there are platforms better suited to those needs. For evaluation-first observability, Confident AI is the strongest pick. For open-source self-hosting, Langfuse is worth a look. For multi-provider gateway, check out Helicone.

Let's get into it!

What Do People Like & Don't Like About LangSmith?

LangSmith is an observability and debugging platform built by the LangChain team. If your stack is LangChain and LangGraph, the native integration is excellent — traces are detailed, setup is minimal, and it just works.

That said, depending on your use case, LangSmith may not cover everything you need 🚩. While it does support tracing for non-LangChain frameworks like OpenAI SDK, Vercel AI SDK, and LlamaIndex via wrappers, evaluation depth and feature support are notably weaker outside LangChain — teams using other frameworks will find fewer native integrations and shallower eval coverage. Evaluation workflows require engineering involvement at every step, with no no-code interface for product or QA teams. Multi-turn evals were added in late 2025 but are limited to online thread scoring — automated simulation and scenario generation are not available. Red teaming is not included. For teams larger than ~10 seats, annual commitments apply at $39/seat/month.

In this article we'll cover the top 6 alternatives worth considering before committing to LangSmith.

1. Confident AI — Eval-First Observability That Works for Your Whole Team

Confident AI is an eval-first cloud platform for LLM observability, powered by DeepEval — one of the world's most popular open-source LLM evaluation frameworks (3M+ monthly PyPI downloads, 10k+ GitHub stars).

Key differences

One key difference between Confident AI and LangSmith is accessibility. LangSmith is designed primarily for engineering teams. Confident AI is built for the whole organization — PMs can upload CSV datasets and trigger evals directly against your production app via HTTP, domain experts can annotate traces, and QA teams can own regression testing, all without requiring engineering support.

The other major gap is evaluation consistency across frameworks. LangSmith traces any framework, but its deepest eval features — agent execution trees, native prompt management, evaluation templates — are designed for LangChain and LangGraph. Confident AI delivers consistent evaluation depth across OpenAI, Pydantic AI, CrewAI, LlamaIndex, Vercel AI SDK, and more via OpenTelemetry — no drop-off outside a preferred ecosystem.

This means:

Your whole team can run evals, not just engineers
50+ research-backed metrics out of the box vs. custom setup on LangSmith
Multi-turn simulation compresses hours of manual testing into minutes
Red teaming and safety testing built in — no separate vendor needed

Side by side comparison summary

We'll go through the key areas so you can make a more informed decision.

Framework & Evaluation Consistency

Feature	Confident AI	LangSmith
Traces any framework	✅	✅
Native LangChain / LangGraph support	✅	✅
Consistent eval depth across all frameworks	✅	❌
Eval depth drops outside LangChain	❌	✅
Can test your actual AI app via HTTP	✅	❌
OpenTelemetry native	✅	✅

LangSmith traces work across frameworks, but the evaluation features — templates, agent execution trees, prompt management integration — are meaningfully stronger inside LangChain. Confident AI delivers the same eval depth regardless of what you're building with.

Evaluation Depth

Feature	Confident AI	LangSmith
Out-of-the-box eval metrics	50+	30+ templates (custom setup required)
RAG-specific metrics	✅	Limited
Conversation / chatbot metrics	✅	❌
Agent metrics	✅	Limited
Research-backed metrics	✅	❌
Deterministic LLM-as-a-judge	✅	❌
Open-source metrics	✅ (DeepEval)	❌
Works with any LLM for evaluation	✅	❌
Run evals locally in code	✅	❌
Scales to 100s of test cases easily	✅	🚧
Publicly sharable testing reports	✅	❌

LangSmith added 30+ evaluator templates in April 2026, but these still require custom implementation per use case. Confident AI ships 50+ metrics that work on day one.

🌟 Visit Confident AI Website

Cross-functional Workflows

Feature	Confident AI	LangSmith
No-code eval workflows for PMs / QA	✅	❌
Trigger evals against production app (no code)	✅	❌
Domain expert annotation on traces	✅	Limited
Annotation on spans and threads	✅	Traces only
Regression testing without engineering	✅	❌
CSV dataset upload without code	✅	✅
Shareable dashboards for stakeholders	✅	❌

LangSmith is primarily designed for engineering workflows. Confident AI is built for engineering, product, and QA teams working together — which matters as AI quality increasingly becomes a cross-functional responsibility.

Multi-turn & Safety

Feature	Confident AI	LangSmith
Multi-turn simulation	✅	❌
Online multi-turn evals (thread scoring)	✅	🚧
Multi-turn dataset format	✅	❌
Red teaming	✅	❌
Safety metrics (toxicity, bias, PII)	✅	❌
OWASP Top 10 / NIST AI RMF coverage	✅	❌

LangSmith added online multi-turn evals in October 2025 for scoring existing conversation threads. Confident AI goes further — generating realistic conversations with tool use and branching paths from scratch, not just scoring threads after the fact.

Prompt Management

Feature	Confident AI	LangSmith
Prompt editor	✅	✅
Auto versioning	✅	✅
Dynamic variables	✅	✅
Git-style branching	✅	❌
Pull requests & approval workflows	✅	❌
Automated eval triggered on prompt changes	✅	❌
Prompt drift detection & alerting	✅	❌
Linked to evals and observability	✅	Limited

LangSmith's Prompt Hub covers versioning and a playground well. Confident AI treats prompts like code — with branching, PRs, approvals, and automated evaluation on every change, so a prompt that degrades faithfulness gets caught before it ships.

LLM Observability

Feature	Confident AI	LangSmith
Production tracing	✅	✅
Online evals on production traces	✅	✅
Quality-aware alerting (PagerDuty, Slack, Teams)	✅	❌
Per-use-case drift detection	✅	Limited
Chatbot-specific monitoring	✅	❌
Auto-curate datasets from production traces	✅	Limited
Advanced filtering for custom properties	✅	❌

Security & Pricing

Feature	Confident AI (Premium)	LangSmith (Plus)
Pricing	$49.99/seat/month	$39/seat/month
SOC2 Type II	✅	✅
HIPAA	✅	Enterprise only (BAA required)
SSO	Team plan+	Enterprise only
RBAC	Team plan+	Enterprise only
Data retention	Unlimited (paid)	14 days (free)
Support	Dedicated	Community + email
On-prem deployment	Enterprise	Enterprise

LangSmith is SOC2 Type II certified and HIPAA compliant, but only signs Business Associate Agreements with Enterprise customers. Confident AI offers HIPAA, SSO, and RBAC from the Team plan.

Which One Should You Choose?

If your team is purely engineers on LangChain and that won't change, LangSmith is a reasonable choice.
If anyone beyond engineering needs to be involved in AI quality — PMs, QA, domain experts — Confident AI is the better choice.
If you need multi-turn simulation, red teaming, or consistent eval depth across any framework, Confident AI is the only option on this list that covers all three.

🌟 Visit Confident AI Website

2. Arize AI — Framework-Agnostic Observability at Scale

Primary Use Case: Large-scale LLM and ML observability
Features:
- Originally built for ML engineers, expanded into LLMs through Phoenix (~8k GitHub stars)
- Fully framework-agnostic following OpenTelemetry standards — no LangChain dependency
- Strong operational telemetry: latency, error rates, token consumption at scale
- Evaluation requires building custom evaluators — no out-of-the-box LLM-specific metrics
- Engineer-only workflows; no non-technical access
Ideal for: Large technical teams needing framework-agnostic observability at scale, or those monitoring traditional ML models alongside LLMs in the same platform.

Key Differences

Feature	Arize AI	LangSmith
LLM tracing	✅	✅
Framework agnostic	✅	🚧
Built-in LLM eval metrics	Limited	Limited
Multi-turn evals	❌	🚧
No-code eval workflows	Limited	❌
Open-source component	✅ (Phoenix)	❌
ML model monitoring	✅	❌
Red teaming	❌	❌

Which One Should You Choose?

Choose Arize AI if you need truly framework-agnostic observability at scale, or you're monitoring both traditional ML models and LLMs in the same platform.
Choose LangSmith if you're all-in on LangChain and just need tracing and debugging within that ecosystem.
If you need eval depth or non-technical workflows alongside observability, neither platform delivers — look at Confident AI.

Visit Arize AI Website

3. Langfuse — The Best Open-Source Alternative

Primary Use Case: Self-hosted LLM tracing, prompt management, and evaluation
Features:
- 100% open-source and self-hostable — full data control on your own infrastructure
- Unlimited users across all pricing tiers — no per-seat charges
- Native integrations for OpenAI SDK, Pydantic AI, Vercel AI SDK, LlamaIndex — broader than LangSmith outside LangChain
- Basic evaluation scoring on traces, but no built-in research-backed metrics
- No multi-turn simulation, no red teaming
Ideal for: Engineering teams that need on-prem deployment, full data ownership, or want to avoid security and procurement friction.

Key Differences

Feature	Langfuse	LangSmith
Open-source	✅ (100%)	❌
Self-hostable	✅	❌
Unlimited users	✅	❌
LLM tracing	✅	✅
Framework agnostic (native integrations)	✅	🚧
Built-in LLM metrics	❌	❌
Multi-turn evals	❌	🚧
Data masking & sampling	✅	Limited
Red teaming	❌	❌

Which One Should You Choose?

Choose Langfuse if on-prem deployment, data privacy, or procurement friction are hard requirements. It's essentially LangSmith but fully open-source with native integrations across more frameworks and no seat limits.
For teams that don't have those constraints and need eval depth or cross-functional workflows, there are better-valued alternatives.

Visit Langfuse Website

4. Helicone — Best for Multi-Provider AI Gateway

Primary Use Case: Unified AI gateway + lightweight model-layer observability
Features:
- AI gateway routing calls to 100+ LLM providers through a single OpenAI-compatible API
- Lightweight model-layer observability for cost, error rates, and usage tracking
- Prompt management with direct deployment via the gateway
- Operates at the model layer, not the application layer — different positioning from LangSmith
- Open-source and popular among early-stage startups
Ideal for: Teams managing multiple LLM providers who need unified cost visibility and fast setup without heavyweight observability infrastructure.

Key Differences

Feature	Helicone	LangSmith
AI gateway (100+ LLMs)	✅	❌
Cost & token tracking	✅	✅
Application-level tracing	Limited	✅
Framework agnostic	✅	🚧
Built-in LLM eval metrics	❌	Limited
Multi-turn evals	❌	🚧
Open-source	✅	❌
Red teaming	❌	❌

Which One Should You Choose?

Choose Helicone if you need unified access to multiple LLM providers and lightweight cost-focused observability. Open-source and fast to get running.
Skip it if you need deep application tracing or any kind of evaluation workflow.

Visit Helicone Website

5. Braintrust — Best Playground UI for Non-Technical Teams

Primary Use Case: UI-driven prompt evaluation and testing for cross-functional teams
Features:
- Playground-first approach with a cleaner UI than LangSmith for non-technical users
- External stakeholders can test prompt and model variations without coding
- Supports multi-turn scoring via custom setup, but no automated simulation or built-in scenario generation
- No open-source component
- Pricing moves from a free tier to $249/month — no mid-tier option currently
Ideal for: Teams where non-technical stakeholders drive prompt testing and a polished UI is the primary unlock.

Key Differences

Feature	Braintrust	LangSmith
No-code playground UI	✅ (stronger)	Limited
Non-technical workflows	✅	❌
LLM tracing	✅	✅
Built-in eval metrics	Limited	Limited
Multi-turn evals	🚧	🚧
Framework agnostic	✅	🚧
Open-source	❌	❌
Red teaming	❌	❌
Mid-tier pricing	$249/month (unlimited users)	$39/seat/month

Which One Should You Choose?

Choose Braintrust if non-technical stakeholders are your primary users and a polished playground UI is the main unlock.
For automated multi-turn simulation, red teaming, or framework-consistent eval depth — Confident AI covers all three.

Visit Braintrust Website

6. Giskard — Best for Pre-Deployment Safety & Bias Testing

Primary Use Case: Pre-deployment LLM red teaming, bias detection, and compliance validation
Features:
- Focused on catching problems before your model goes live — bias, vulnerabilities, hallucinations
- Automated vulnerability scanning aligned with OWASP Top 10 for LLMs and NIST AI RMF
- Structured post-assessment reports with severity scoring (CVSS)
- SOC 2 Type II, HIPAA, and GDPR compliant
- Not a production monitoring replacement — a pre-deployment quality gate
Ideal for: LLM teams with regulatory requirements or safety obligations that need structured validation before deployment.

Key Differences

Feature	Giskard	LangSmith
Pre-deployment safety testing	✅	❌
Bias & fairness testing	✅	❌
Automated vulnerability scanning	✅	❌
OWASP / NIST AI RMF coverage	✅	❌
Compliance & safety reports	✅	❌
Production monitoring	Limited	✅
LLM tracing	Limited	✅
Open-source	✅	❌
Multi-turn evals	Limited	🚧

Which One Should You Choose?

Choose Giskard if pre-deployment model validation, bias testing, and compliance are non-negotiables. It fills a gap LangSmith doesn't attempt to address.
It's not a LangSmith replacement — think of it as a quality gate before deployment, not an observability layer after.
For teams that need pre-deployment safety testing and production monitoring in one platform, Confident AI's built-in red teaming covers a lot of the same ground without a separate vendor.

Visit Giskard Website

Why Confident AI is Our Top Pick for the Best LangSmith Alternative 🏆

LangSmith is a solid platform for what it is designed to do — tracing and debugging LangChain applications. As AI quality requirements expand though, many teams find they need more than observability. They need their entire team — product, QA, domain experts — to participate in evaluation. They need evals that run on the actual application. They need multi-turn simulation for conversational AI. And they need red teaming without adding a separate vendor.

Confident AI is the only platform on this list that covers all of that in one place:

No-code eval workflows mean product managers and QA teams run evaluation cycles independently — without engineering involvement at every step.
50+ research-backed metrics out of the box mean you're evaluating on day one, not after weeks of custom evaluator setup.
Multi-turn simulation automates conversational AI testing end-to-end — instead of manually prompting through scenarios, the platform generates and scores realistic conversations automatically.
Built-in red teaming based on OWASP Top 10 for LLMs and NIST AI RMF means safety testing is part of the same workflow, with no separate tool or vendor needed.
Framework-agnostic eval depth means you get consistent evaluation quality whether you're using LangChain, Pydantic AI, CrewAI, or any other framework.

Teams adopting Confident AI typically do so because it lets non-engineers own AI quality for the first time — replacing fragmented spreadsheets and manual testing with a single collaborative workspace for evaluation, multi-turn testing, and bias testing.

Confident AI is built on top of DeepEval, so teams already using the open-source framework get the same evaluation logic extended to a full cloud platform — datasets, observability, annotation, and prompt management all in one place.

🌟 Visit Confident AI Website