Prompt

LangSmith alternative for tracing and evals

Technology / Observability1 observationLast seen Oct 9, 2026

Latest observation

Oct 9, 2026OpenAI APIWeb search: off

If you’re looking for LangSmith alternatives for tracing and evals, here are the main options depending on what you need:

Strong alternatives

  • OpenTelemetry + Grafana/Tempo/Loki

    • Best if you want vendor-neutral tracing and full observability control.
    • Good for distributed traces, logs, metrics, and can be adapted for LLM apps.
    • Evals usually need to be built separately or integrated via custom pipelines.
  • Arize Phoenix

    • One of the best open-source options for LLM tracing, debugging, and evals.
    • Supports prompt/response inspection, embeddings, drift, retrieval analysis, and evaluation workflows.
    • Good fit if you want something closer to LangSmith’s LLM-focused experience.
  • Weights & Biases Weave

    • Good for LLM tracing, experiment tracking, and evals.
    • Especially useful if you already use W&B for ML experiments.
    • Better for teams doing systematic experimentation and comparison.
  • Helicone

    • Focused on LLM observability and usage analytics.
    • Great for tracing, request logs, cost tracking, latency, and prompt monitoring.
    • Evals are more limited than LangSmith/Phoenix, but still useful.
  • Traceloop

    • Open-source tracing for LLM apps built around OpenTelemetry.
    • Good if you want tracing with a more standards-based approach.
    • Evals are less mature than some dedicated tools.
  • Braintrust

    • Strong for evals, prompt testing, and experiment management.
    • Offers datasets, scoring, comparison, and tracing features.
    • Good if your main pain point is evaluation rather than pure observability.

If you want the closest match to LangSmith

  1. Arize Phoenix — best open-source LLM tracing + evals
  2. Braintrust — best for eval workflows and prompt experimentation
  3. Weave — good for tracing + experiment tracking

Quick decision guide

  • Need open-source and LLM-native tracing/evals? → Phoenix
  • Need enterprise eval workflows and prompt versioning? → Braintrust
  • Need standard observability stack integration? → OpenTelemetry
  • Need cost/latency/request monitoring for LLM APIs? → Helicone
  • Already use W&B? → Weave

If you want, I can also give you:

  • a feature comparison table
  • open-source only alternatives
  • or recommendations based on your stack, like Python / JS / LangChain / LlamaIndex / OpenAI / Anthropic.

Observation history

How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, Meta AI, Qwen.

How can brands improve their AEO?

Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.

Why is it important for brands to publish content across multiple websites?

Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.

Want this interpreted for your brand?

Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.