Sherlock Calls vs Langfuse
Langfuse traces LLM calls and evaluation runs at the code level. Sherlock Calls investigates real production voice call failures across Twilio, ElevenLabs, and 13+ more providers — in Slack, in under 5 seconds.
TL;DR — The short answer
- 1
Langfuse is purpose-built for tracing LLM calls inside your application — prompts, completions, tool calls, and evaluation scores at the code level.
- 2
Sherlock Calls investigates voice call failures across your entire provider stack — Twilio telephony events, ElevenLabs TTS latency, Vapi agent behavior — correlated in one timeline and delivered in Slack.
- 3
If your team builds LLM applications, Langfuse is excellent. If your team runs voice AI agents in production and needs call-level forensics, Sherlock is purpose-built for that workflow.
Understanding both tools
Sherlock Calls
AI-powered voice call investigation
Sherlock Calls is a Slack-native AI investigator for operations teams. Connect your existing providers — Twilio, ElevenLabs, Vapi, Genesys, and 20+ more — and ask questions in plain English. Sherlock autonomously gathers data across all connected services, correlates events, and delivers a sourced answer in under 5 seconds. No new dashboards. No SDK. No code changes.
- Works inside Slack — no new UI to learn
- Connects to 20+ providers in minutes
- Investigates calls autonomously with AI
- Free tier — 100 credits per workspace
Langfuse
Open-source LLM observability and analytics
Langfuse is an open-source LLM observability platform with 22K+ GitHub stars. It traces LLM calls, evaluation runs, and user sessions for AI application teams.
- Step-by-step LLM trace visualization across prompts, completions, and tool calls
- Online evaluations and automated quality scoring for LLM outputs
- Open-source and self-hostable — free tier plus cloud plans
- 22K+ GitHub stars with broad framework support (LangChain, OpenAI SDK, Anthropic SDK, LlamaIndex)
Feature comparison — AI Production Observability
Sherlock Calls vs Langfuse & peers
All tools in the AI Production Observability category — so you can compare both head-to-head and within the landscape.
| Feature | SherlockCalls | Langfusethis page | Arize AI | Fiddler AI | Helicone | InfiniteWatch | LangSmith | Noveum AI | Plura | Raindrop |
|---|---|---|---|---|---|---|---|---|---|---|
| AI call investigation | ||||||||||
| AI agent & LLM tracing | ||||||||||
| AI governance & compliance | ||||||||||
| Offline LLM evaluation | ||||||||||
| Provider integrations | 20+ | 40+ (LLM frameworks, no voice) | ~15 (0 voice) | ~10 (0 voice) | 100+ LLM providers | ~5 (~2 voice) | Any LLM framework | ~8 (0 voice) | Voice AI builder (Twilio/ElevenLabs abstraction) | ~8 (0 voice) |
| Cross-provider correlation | ||||||||||
| Natural language queries | ||||||||||
| Zero-code setup | ||||||||||
| Per-call cost tracking | ||||||||||
| Free tier available |
Scroll horizontally to compare all tools →
Key differences
Why teams switch from Langfuse to Sherlock
Voice Provider Coverage vs LLM Framework Coverage
Sherlock Calls
Sherlock natively connects to Twilio, ElevenLabs, Vapi, Retell, Genesys, Amazon Connect, and 9+ more voice providers via API key — no instrumentation, no code changes. A voice call failure investigation starts in 2 minutes.
Langfuse
Langfuse's 40+ integrations are all LLM frameworks (LangChain, OpenAI SDK, LlamaIndex). It has no native connectors for Twilio telephony events, ElevenLabs TTS latency, or Vapi call data — the layers where most voice AI failures actually happen.
Call-Level Forensics vs LLM-Level Tracing
Sherlock Calls
Sherlock correlates telephony events (call setup, DTMF, webhooks), TTS latency, ASR transcripts, and agent behavior across providers into a single incident timeline with a root cause hypothesis.
Langfuse
Langfuse traces what happens inside your LLM — prompts, completions, and tool calls. A dropped Twilio call that shows no LLM exception, or a silent ElevenLabs TTS failure that returns HTTP 200, is invisible to Langfuse.
Slack-Native vs Developer Dashboard
Sherlock Calls
Ask Sherlock a question in your existing Slack channel. No dashboard login, no trace ID to look up, no query language to learn. Your operations team gets call answers where they already work.
Langfuse
Langfuse is a developer tool — engineers navigate a web dashboard to explore traces, build evaluations, and analyze LLM session data. Operations managers and on-call engineers benefit less without a developer intermediary.
Which tool is right for you?
When to choose Sherlock vs Langfuse
Choose Sherlock Calls if…
- Your team needs to investigate specific voice call failures across Twilio, ElevenLabs, Vapi, or Retell
- Operations or on-call teams need call intelligence from Slack without developer intermediaries
- You want cross-provider correlation — telephony + TTS + ASR + CRM in one query
- You need per-call cost breakdowns across multiple voice providers
Consider Langfuse if…
- Your team builds LLM applications and needs step-by-step trace visualization
- You want offline evaluation and quality scoring for LLM outputs
- You prefer open-source and self-hosted observability infrastructure
- Your debugging happens at the LLM framework layer, not the telephony layer
Pricing
Cost comparison
Sherlock Calls
Free to start
100 credits per Slack workspace. Team plans from $50/month. No credit card required to start.
- Free tier — 100 credits/workspace
- Team: $50–$5,000/month (usage-based)
- Enterprise: custom pricing
- No sales call required to start
- Cancel anytime
Langfuse
Free (open-source) + cloud plans from ~$49/month
Langfuse offers a generous open-source self-hosted option and a cloud free tier. Paid cloud plans add team features and higher retention.
* Pricing sourced from public information. Contact Langfuse for current rates.
FAQ
Frequently asked questions
What is the difference between Sherlock Calls and Langfuse?
Langfuse traces LLM calls at the application layer — prompts, completions, tool calls, and evaluation scores. Sherlock Calls investigates voice call failures at the provider layer — Twilio telephony events, ElevenLabs TTS latency, Vapi agent behavior — correlated across providers and delivered in Slack. They solve different problems in the AI observability stack.
Can Langfuse trace Twilio or ElevenLabs calls?
No. Langfuse integrates with LLM frameworks (LangChain, OpenAI SDK, Anthropic SDK) and traces LLM application logic. It does not ingest Twilio telephony events, ElevenLabs TTS data, or Vapi call records. Sherlock Calls natively connects to all three via API key.
Is Sherlock Calls a good Langfuse alternative?
They are complementary, not alternatives. If you need LLM trace visualization and offline evaluation, Langfuse is excellent. If you need to investigate why voice calls failed in production across your telephony and TTS stack, Sherlock is purpose-built for that. Many voice AI teams use both.
How do I migrate from Langfuse to Sherlock Calls?
No migration needed. Sherlock connects to your existing Twilio, ElevenLabs, or Vapi accounts via API key — no code changes required. Langfuse and Sherlock address different layers of the observability stack and can run simultaneously.
Does Sherlock Calls replace Langfuse?
Not necessarily. Langfuse is the right choice for teams that need step-by-step LLM trace visualization and offline evaluation. Sherlock is the right choice for teams that need to investigate real production voice call failures across their telephony and TTS provider stack in Slack.
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