Sherlock Calls vs LangSmith
LangSmith is the leading LLM observability platform from LangChain — trusted by thousands of engineering teams to trace agent steps, debug failures, and monitor production AI applications. Sherlock Calls is purpose-built for the layer LangSmith doesn't reach: investigating production voice calls from Twilio, ElevenLabs, Vapi, and 12+ providers in plain English, from Slack.
TL;DR — The short answer
- 1
LangSmith is the go-to LLM observability platform for engineering teams building AI agents — providing deep tracing, evaluation, and production monitoring for any framework, not just LangChain.
- 2
Sherlock Calls is built for voice operations teams: investigating real production call failures, pulling cross-provider transcripts, and correlating costs and errors across 15+ voice providers from Slack — no instrumentation required.
- 3
If your team runs voice AI on Twilio, ElevenLabs, Vapi, or Genesys, Sherlock fills the operational gap LangSmith's agent tracing was never designed to cover.
Understanding both tools
Sherlock Calls
AI-powered voice call investigation
Sherlock Calls is a Slack-native AI investigator purpose-built for voice operations teams. Connect your existing providers — Twilio, ElevenLabs, Vapi, Genesys, and 12 more — and ask questions about your calls 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 15+ voice providers in minutes
- Investigates calls autonomously with AI
- Free tier — 100 credits per workspace
LangSmith
Know what your agents are really doing
LangSmith is an LLM observability and evaluation platform from LangChain that provides end-to-end tracing of agent steps, online quality evaluation, production monitoring with cost and latency tracking, and automated failure mode detection — compatible with any LLM framework, not just LangChain.
- End-to-end agent tracing: step-by-step visibility into every LLM call, tool invocation, and chain execution with latency, token usage, and cost per trace
- Online evaluations: LLM-as-judge and custom scorer integration to automatically score production traces for quality, hallucinations, and drift
- Automatic pattern detection: cluster analysis of production traces to surface common failure modes, usage patterns, and anomalies without manual querying
- Multi-framework support: native integrations with LangChain, OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, and any OpenTelemetry-compatible framework
Feature comparison — AI Production Observability
Sherlock Calls vs LangSmith & peers
All tools in the AI Production Observability category — so you can compare both head-to-head and within the landscape.
| Feature | SherlockCalls | LangSmiththis page | Arize AI | Fiddler AI | Helicone | InfiniteWatch | Noveum AI | Raindrop |
|---|---|---|---|---|---|---|---|---|
| AI call investigation | ||||||||
| AI agent & LLM tracing | ||||||||
| AI governance & compliance | ||||||||
| Offline LLM evaluation | ||||||||
| Provider integrations | 15+ (all voice) | Any LLM framework | ~15 (0 voice) | ~10 (0 voice) | 100+ LLM providers | ~5 (~2 voice) | ~8 (0 voice) | ~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 LangSmith to Sherlock
Voice Call Investigation vs LLM Agent Tracing
Sherlock Calls
Sherlock investigates specific voice call events — dropped calls, ElevenLabs latency spikes, Twilio billing anomalies, cross-provider transcript gaps — in plain English from Slack in under 5 seconds. No trace instrumentation. No code changes.
LangSmith
LangSmith traces LLM application steps at the code level — prompts, completions, tool calls, and chain execution. Investigating a specific voice call's transcript, cost, cross-provider timeline, and failure cause requires building custom LangSmith instrumentation that maps to telephony events, which is not its intended use case.
Operational Q&A vs Trace Dashboard Analysis
Sherlock Calls
Ask Sherlock 'Why did our Vapi agent calls fail between 2 and 4 AM Tuesday?' in Slack and get a sourced, multi-provider answer in under 5 seconds — no trace filtering, no run comparison, no engineering ticket.
LangSmith
LangSmith surfaces insights through its trace explorer and dashboard. Answering operational voice questions — which specific calls failed, what the transcript said, what the Twilio error code was — requires navigating the LangSmith UI and correlating data that LangSmith was not built to ingest.
Native Voice Integrations vs SDK Instrumentation
Sherlock Calls
Sherlock connects to Twilio, ElevenLabs, Vapi, Retell, Genesys, Amazon Connect, HubSpot, and Datadog via API key — no SDK, no code changes, no deployment. Operational in under 2 minutes.
LangSmith
LangSmith requires instrumenting your application with the LangChain SDK or OpenTelemetry to capture traces. Voice-level telemetry — Twilio call events, ElevenLabs TTS latency, per-call cost breakdowns — must be manually added as custom spans and metadata.
Which tool is right for you?
When to choose Sherlock vs LangSmith
Choose Sherlock Calls if…
- Your team operates voice AI in production and needs to investigate specific call failures without writing instrumentation or reading trace dashboards
- You want cross-provider correlation across Twilio, ElevenLabs, HubSpot, and your CRM with no SDK or code changes
- Your operations or support team needs call intelligence in Slack without LangSmith expertise
- You need per-call cost breakdowns and transcript analysis on demand across your voice provider stack
Consider LangSmith if…
- Your engineering team is building LLM-powered applications and needs deep step-by-step agent tracing, automated quality evaluation, and production monitoring within a single platform
- You need offline dataset evaluation, prompt versioning, and LLM-as-judge scoring to continuously improve agent quality before and after deployment
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
LangSmith
Free tier — $2.50/1k traces paid
LangSmith offers a free Developer plan with 5,000 traces/month and 14-day retention. Paid plans include 10,000 base traces/month with additional traces billed at $2.50 per 1,000 (base, 14-day retention) or $5.00 per 1,000 (extended, 400-day retention). Enterprise plans with BYOC and self-hosted options are available via sales.
* Pricing sourced from public information. Contact LangSmith for current rates.
FAQ
Frequently asked questions
What is LangSmith used for?
LangSmith is an LLM observability and evaluation platform that traces agent steps, monitors production AI applications, and runs automated quality evaluations. It is designed for engineering teams building LLM-powered applications — not for investigating production voice call failures or operational Q&A from Slack.
Can LangSmith investigate voice calls from Twilio or ElevenLabs?
LangSmith traces LLM application steps — it does not natively ingest Twilio call events, ElevenLabs TTS latency, or cross-provider voice data. Correlating a specific call's transcript, cost, and failure cause across voice providers would require significant custom instrumentation. Sherlock Calls provides native integrations with 15+ voice platforms out of the box.
Is Sherlock Calls a LangSmith alternative?
They solve different problems at different layers. LangSmith is right for engineering teams who need LLM agent tracing, quality evaluation, and production monitoring for AI applications. Sherlock Calls is right for voice operations teams who need to investigate production voice calls and get instant answers from their telephony stack in Slack.
How do I migrate from LangSmith to Sherlock Calls?
No migration needed — Sherlock and LangSmith serve different teams. If you use LangSmith to trace your voice AI application's LLM calls, Sherlock adds the telephony layer: specific call transcripts, cross-provider failure correlation, and per-call cost breakdowns that LangSmith traces don't expose.
Does Sherlock Calls replace LangSmith?
No. LangSmith is the right choice for engineering teams who need deep LLM agent tracing, offline evaluation, and production quality monitoring. Sherlock Calls is the right choice for voice operations teams who need to investigate voice calls and get instant answers from their provider stack — without writing instrumentation or reading trace dashboards.
Ready to investigate your calls the smarter way?
Join teams who left LangSmith for an AI-native, voice-first investigation tool. Connect in 2 minutes, no credit card required.
No credit card required · 100 free credits · Setup in 2 minutes