Prompt

LangSmith alternatives for tracing prompts and tool calls

Technology / Observability1 observationLast seen Oct 9, 2026

Latest observation

Oct 9, 2026OpenAI APIWeb search: off

Here are solid LangSmith alternatives for tracing prompts, LLM calls, and tool/function calls, grouped by what they’re best at:

Managed observability platforms

These are the closest to LangSmith in terms of end-to-end tracing, debugging, and evaluation.

  • Helicone

    • OpenAI-compatible proxy for logging/tracing LLM requests
    • Good dashboards, cost tracking, latency, prompt history
    • Easy to add to many stacks
  • Arize Phoenix

    • Strong for LLM observability and evaluation
    • Great for tracing, embeddings, retrieval/debugging, and experiments
    • Open-source, can self-host
  • OpenTelemetry + vendor backend

    • If you want full control, instrument with OTel and export to:
      • Datadog
      • Honeycomb
      • Grafana Cloud
      • New Relic
    • Best if you already use standard observability tooling
  • WhyLabs

    • Focus on monitoring, drift, and production observability
    • Useful for model/application monitoring at scale
  • Traceloop

    • LLM tracing built around OpenTelemetry
    • Good for distributed traces across app, model, retrieval, and tools
  • Braintrust

    • Strong for prompt testing, evals, and experiment tracking
    • Also supports tracing and collaboration

Open-source / self-hostable

Good if you want more control, lower cost, or local deployment.

  • Arize Phoenix

    • Open-source leader here
    • Best mix of tracing + evaluation + retrieval analysis
  • OpenLLMetry

    • OpenTelemetry-based instrumentation for LLM apps
    • Works well as a standard tracing layer
  • Langfuse

    • Very popular LangSmith alternative
    • Traces, prompt management, scores, evals, user feedback
    • Self-host or cloud
  • Opik

    • Tracing and evals for LLM apps
    • Good developer experience and prompt/version tracking

If your main goal is just tracing tool calls

If you want to trace agent steps, tool invocations, and nested spans, these are especially relevant:

  • Langfuse
  • Arize Phoenix
  • Traceloop
  • OpenTelemetry with any backend
  • Datadog / Honeycomb if you already have infra tracing

Quick recommendations

  • Best overall replacement for LangSmith: Langfuse
  • Best open-source observability + evals: Arize Phoenix
  • Best if you want standard distributed tracing: OpenTelemetry / OpenLLMetry
  • Best if you want simple logging/proxy for OpenAI calls: Helicone
  • Best for prompt testing/evals and team workflows: Braintrust

What to choose by use case

  • Debugging one app with tool calls: Langfuse or Phoenix
  • Enterprise tracing with existing infra: OpenTelemetry + Datadog/Honeycomb
  • Prompt engineering and eval-heavy workflows: Braintrust or Langfuse
  • Retrieval-augmented systems: Phoenix
  • Low-friction OpenAI request logging: Helicone

If you want, I can also give you a feature comparison table for these tools, or narrow it down based on your stack, like Python/LangChain/LlamaIndex/OpenAI/agents.

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.

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