Prompt

I need LLM observability that works with multiple providers and custom tools

Technology / Observability1 observationLast seen Oct 9, 2026

Latest observation

Oct 9, 2026OpenAI APIWeb search: off

If you need LLM observability across multiple providers and custom tools, look for a platform that supports:

  • OpenTelemetry or SDK-based tracing
  • Vendor-neutral instrumentation
  • Model/provider agnostic logging
  • Custom tool / function-call traces
  • Prompt, response, token, latency, error, and cost tracking
  • Evaluation / feedback hooks
  • PII redaction and access controls

Good options to consider

1. Langfuse

  • Works with multiple LLM providers
  • Strong support for traces, spans, scores, and evaluations
  • Good fit for custom tools / workflows
  • Can self-host
  • Open-source and commonly used for product observability

2. LangSmith

  • Strong tracing for chains/agents/tools
  • Works well with LangChain-based apps
  • Supports custom runs and metadata
  • Best if your stack is already in the LangChain ecosystem

3. Helicone

  • Easy proxy-based observability
  • Supports multiple providers
  • Tracks latency, usage, costs, and request/response data
  • Good for quick setup and API-level observability

4. Arize Phoenix

  • More focused on evaluation, debugging, and experimentation
  • Useful for tracing LLM apps and analyzing quality issues
  • Good if you want stronger analysis workflows

5. OpenTelemetry + your observability backend

  • Best if you want full control and already use OTEL
  • More setup effort, but very flexible
  • Great for capturing custom tools, non-LLM spans, and app-level context

My quick recommendation

  • Best all-around open-source choice: Langfuse
  • Best if you want easiest API proxy setup: Helicone
  • Best if you’re deeply in LangChain: LangSmith
  • Best if you want maximum flexibility: OpenTelemetry

What to verify before choosing

Make sure the product supports:

  • OpenAI, Anthropic, Gemini, Azure OpenAI, Bedrock, local models
  • Custom spans for tools/functions
  • Conversation/session grouping
  • Prompt versioning
  • Feedback and evals
  • Self-hosting if needed
  • Data retention and privacy controls

If you want, I can give you a side-by-side comparison table or recommend the best option for your stack if you tell me:

  1. which providers you use,
  2. whether you need self-hosting, and
  3. whether you’re using LangChain/LlamaIndex/custom code.

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.