Rank #7 of 8 in LLM Evals & Observability
vs Langfuse
vs Braintrust
vs Arize Phoenix
vs W&B Weave
vs Helicone
vs Galileo
vs CekuraShowcase
Products
LangChain, product by product →LangChain ships more than one product — each judged line competes in its own arena on the same stories as everyone else.
| Line | Arena | Rank | PA Score | Agent-ready |
|---|---|---|---|---|
| LangGraph | Agent Frameworks & SDKs | #8/9 | 27/100 | 43/100 |
| LangSmiththis page | LLM Evals & Observability | #7/8 | 23/100 | 36/100 |
Verified integrations
Connections to other tracked products — hover a chip for the verbatim evidence quote behind it.
By theme — the product's score on each story themeBy theme
Agenticness — how well agents can access and operate the productAgenticnessevidence →
How well agents can access and operate the product
Alerting dashboards — stories about alerting dashboards in this arenaAlerting dashboardsevidence →
Stories about alerting dashboards in this arena
Automation depth — how much of the product can run unattendedAutomation depthevidence →
How much of the product can run unattended
Cost monitoring — stories about cost monitoring in this arenaCost monitoringevidence →
Stories about cost monitoring in this arena
Data access export — stories about data access export in this arenaData access exportevidence →
Stories about data access export in this arena
Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasetsevidence →
Measuring quality — datasets, eval runs, regression tracking
Openness — open source, data portability, and self-hosting storiesOpennessevidence →
Open source, data portability, and self-hosting stories
Privacy posture — data-handling and privacy storiesPrivacy postureevidence →
Data-handling and privacy stories
Prompt management — stories about prompt management in this arenaPrompt managementevidence →
Stories about prompt management in this arena
Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentationevidence →
Instrumenting code and tracing requests end to end
Story verdicts — every judged story with its evidenceStory verdicts
What’s free: 2 free · 0 paid · 0 enterprise · 34 not stated in evidence
Follow the green: where the map greys out is where LangSmith stops today. ✓ full · ~ partial · ! disputed · — none · n/a not applicable.
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
API surface
Drive the product through a documented public API
~6/10
unlocks → Scoped API keys · Machine-readable spec · Versioning policy · Official CLI
Subscribe to events via webhooks
~6/10
Build against official SDKs
✓8/10
Issue scoped/least-privilege API credentials for an agent
—–
Connect an agent via an official MCP server
~4/10
Download a machine-readable API spec (OpenAPI or equivalent)
—0/10
Rely on versioned APIs with a documented deprecation policy
—0/10
Test against a sandbox environment without touching production data
~5/10
Explore an interactive API reference with runnable examples
—0/10
Docs for agents
Point an agent at llms.txt or agent-oriented docs
✓8/10
Agentic features
Delegate tasks to a built-in AI assistant inside the product
~3/10
unlocks → NL commands
Operate the product with natural-language commands
—0/10
Plug MCP servers into this product so it can use their tools
n/an/a
Get AI-generated insights and suggestions from my data inside the product
~5/10
Set up automations that run autonomously in the background
~6/10
Alerting dashboards — stories about alerting dashboards in this arenaAlerting dashboards
Stories about alerting dashboards in this arena
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
Cost monitoring — stories about cost monitoring in this arenaCost monitoring
Stories about cost monitoring in this arena
Data access export — stories about data access export in this arenaData access export
Stories about data access export in this arena
Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets
Measuring quality — datasets, eval runs, regression tracking
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
Prompt management — stories about prompt management in this arenaPrompt management
Stories about prompt management in this arena
Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation
Instrumenting code and tracing requests end to end
Have an agent query my traces, metrics, and eval results through an API or MCP server to debug my app
~6/10
Mask or redact sensitive data before it is stored in traces
—–
Instrument apps in both Python and JS/TS with officially supported SDKs
✓8/10
Sorted by importance (agentic first) (high → low) · 52/52 stories · click a row’s chevron for the rationale and evidence
Drive the product through a documented public API G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | partial | 6/10 | Tprobed | |
Connect an agent via an official MCP server G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | partial | 4/10 | Tprobed | |
Delegate tasks to a built-in AI assistant inside the product G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | partial | 3/10 | Cclaimed | |
Plug MCP servers into this product so it can use their tools G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | n/a | 0/10 | ||
Build against official SDKs G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 8/10 | Xcommunity | |
Point an agent at llms.txt or agent-oriented docs G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 8/10 | Tprobed | |
Run the product headlessly / in CI for automation G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 6/10 | Cclaimed | |
Set up automations that run autonomously in the background G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 6/10 | Cclaimed | |
Subscribe to events via webhooks G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 6/10 | Cclaimed | |
Get AI-generated insights and suggestions from my data inside the product G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 5/10 | Xcommunity | |
Download a machine-readable API spec (OpenAPI or equivalent) G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Explore an interactive API reference with runnable examples G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Operate the product with natural-language commands G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Rely on versioned APIs with a documented deprecation policy G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Issue scoped/least-privilege API credentials for an agent G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | untested | none yet | |
Use an official CLI G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | untested | none yet | |
Test against a sandbox environment without touching production data G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 1 | partial | 5/10 | Cclaimed | |
Capture traces of my LLM calls with inputs, outputs, latency, and token usage C Trace capture | developer | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 3 | fullfree | 8/10 | Xcommunity | |
Compare eval runs side by side to catch regressions between prompt or model versions C Offline evals | ml engineer | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 3 | full | 8/10 | Cclaimed | |
Curate datasets from production traces and run offline evaluations against them C Offline evals | ml engineer | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 3 | full | 8/10 | Cclaimed | |
Score outputs with configurable LLM-as-a-judge evaluators C Offline evals | ml engineer | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 3 | full | 8/10 | Cclaimed | |
See cost and token usage per request, model, and time period in dashboards C Cost tracking | developer | Cost monitoring — stories about cost monitoring in this arenaCost monitoring | 3 | full | 8/10 | Cclaimed | |
Send and receive traces over OpenTelemetry (OTLP) instead of a proprietary format C Trace capture | developer | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 3 | full | 7/10 | Cclaimed | |
Define rules that trigger actions automatically on events G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 3 | partial | 6/10 | Cclaimed | |
Have an agent query my traces, metrics, and eval results through an API or MCP server to debug my app C Ai observability | ai-native user | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 3 | partial | 6/10 | Tprobed | |
Self-host the core product G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 3 | partial | 6/10 | Xcommunity | |
Export all of my data in open formats and leave G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 3 | partial | 4/10 | Xcommunity | |
Version prompts and deploy changes to production without shipping code C Prompt workflow | developer | Prompt management — stories about prompt management in this arenaPrompt management | 3 | partial | 4/10 | Cclaimed | |
Prevent my data from being used to train AI models G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 3 | none | untested | none yet | |
Build custom dashboards over latency, error, cost, and eval-score metrics C Monitoring | ml engineer | Alerting dashboards — stories about alerting dashboards in this arenaAlerting dashboards | 2 | full | 8/10 | Cclaimed | |
Instrument apps in both Python and JS/TS with officially supported SDKs G Sdk coverage | developer | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 2 | full | 8/10 | Xcommunity | |
Route outputs to human annotation queues for review and labeling C Human review | ml engineer | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 2 | full | 8/10 | Cclaimed | |
Run evaluators continuously on live production traffic, not just offline datasets C Online evals | ml engineer | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 2 | full | 8/10 | Cclaimed | |
Set alerts on error rates, cost spikes, or eval-score drops and get notified in Slack, PagerDuty, or email C Monitoring | developer | Alerting dashboards — stories about alerting dashboards in this arenaAlerting dashboards | 2 | full | 8/10 | Cclaimed | |
Write custom code-based scorers and metrics for my evaluations C Offline evals | ml engineer | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 2 | full | 8/10 | Cclaimed | |
Attribute cost and usage to users, sessions, and features via custom metadata C Cost tracking | developer | Cost monitoring — stories about cost monitoring in this arenaCost monitoring | 2 | full | 7/10 | Cclaimed | |
Have an agent create a dataset, trigger an eval run programmatically, and read back the results C Ai eval ops | ai-native user | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 2 | full | 7/10 | Tprobed | |
Instrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDK C Trace capture | developer | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 2 | partial | 6/10 | Xcommunity | |
Perform bulk operations across many items at once G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 2 | partial | 6/10 | Cclaimed | |
Trace multi-step agent runs as nested spans grouped into sessions or threads C Trace capture | developer | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 2 | partialfree | 6/10 | Xcommunity | |
Do everything through the API that I can do in the UI G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 2 | partial | 5/10 | Tprobed | |
Run evals in CI and gate deployments on their results C Offline evals | developer | Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets | 2 | partial | 5/10 | Cclaimed | |
Choose where my data is stored (region/residency) G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | partial | 4/10 | Cclaimed | |
Bulk-export traces and datasets to blob storage or my data warehouse C Data export | developer | Data access export — stories about data access export in this arenaData access export | 2 | partial | 3/10 | Cclaimed | |
Iterate on prompts in a playground against real models and variables C Prompt workflow | developer | Prompt management — stories about prompt management in this arenaPrompt management | 2 | none | 0/10 | ||
Opt out of telemetry and usage tracking G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | 0/10 | ||
Read the product's source under an open license G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 2 | none | 0/10 | ||
Schedule recurring jobs or workflows G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 2 | none | 0/10 | ||
Control data retention and deletion G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | untested | none yet | |
Mask or redact sensitive data before it is stored in traces C Data controls | developer | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 2 | none | untested | none yet | |
Version, review, and roll back my automations G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 1 | none | 0/10 | ||
Capture multimodal payloads (images, audio, files) inside my traces C Trace capture | developer | Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation | 1 | none | untested | none yet |
Opportunities — the stories that would move this product's scores, from its own judged verdictsOpportunitiestop 8 of 35 stories with headroom
What would move LangSmith’s scores — derived from its own judged verdicts, biggest headroom first. Each line quotes what the judge found missing; shipping it (or evidencing it publicly) is the fix.
Agenticness — how well agents can access and operate the productDelegate tasks to a built-in AI assistant inside the product
partialq3/10moves Built-in AIimpact 31.5
Missing: detailed documentation of assistant capabilities/UX, examples of delegated task execution, independent/hands-on confirmation.
Privacy posture — data-handling and privacy storiesPrevent my data from being used to train AI models
nonemoves PA Scoreimpact 30
The evidence pack contains no mention of a data-training opt-out, privacy policy, or commitment regarding use of customer trace data for model training; all evidence is about tracing, evaluation, dashboards, and self-hosting features, not privacy/training-data posture.
Agenticness — how well agents can access and operate the productOperate the product with natural-language commands
nonemoves Built-in AIimpact 30
Missing: any documented NL command interface, chat-based control of dashboards/alerts/experiments, or evidence of conversational operation.
Agenticness — how well agents can access and operate the productUse an official CLI
nonemoves agent-readyimpact 30
No evidence pack item mentions an official LangSmith CLI tool; the SDKs (Python/TS/Go/Java) and APIs are referenced but not a dedicated CLI for AI-native workflows.
Agenticness — how well agents can access and operate the productIssue scoped/least-privilege API credentials for an agent
nonemoves agent-readyimpact 30
The evidence pack covers tracing, evaluation, dashboards, alerts, and self-hosting, but contains no mention of API key scoping, permissions, roles, or least-privilege credential issuance for agents.
Agenticness — how well agents can access and operate the productExplore an interactive API reference with runnable examples
nonemoves API qualityimpact 30
No evidence of an interactive API reference with runnable examples; the OpenAPI probe explicitly returned 404s at all candidate paths, and no docs mention a Swagger/Redoc-style interactive reference or embedded runnable code snippets.
Agenticness — how well agents can access and operate the productDownload a machine-readable API spec (OpenAPI or equivalent)
nonemoves API qualityimpact 30
LangSmith exposes a REST API (referenced for filtering/exporting traces) but the evidence pack shows a direct probe for OpenAPI/swagger specs at the docs site returned 404 on all candidate paths, and no other citation points to a downloadable machine-readable API spec.
Agenticness — how well agents can access and operate the productRely on versioned APIs with a documented deprecation policy
nonemoves API qualityimpact 30
No evidence pack item documents API versioning scheme or a deprecation policy; the OpenAPI probe returned 404s and no docs page addresses version support lifecycle or breaking-change policy.
Showing the top 8 of 35 — every none/partial verdict in the story verdicts table is headroom.
Think a verdict is wrong? Every verdicts-table row has a Flag link — see the methodology.
Coverage map — which docs area, API section, or community source covers which judged storiesCoverage map6 surfaces · 36 covered stories
Where the cited evidence behind each covered verdict came from — the same citations the verdicts table shows, no extra judging.
Langsmith docs35 stories
- Point an agent at llms.txt or agent-oriented docs
- Run the product headlessly / in CI for automation
- Drive the product through a documented public API
- Build against official SDKs
- Subscribe to events via webhooks
- Get AI-generated insights and suggestions from my data inside the product
- Set up automations that run autonomously in the background
- Delegate tasks to a built-in AI assistant inside the product
- Test against a sandbox environment without touching production data
- Build custom dashboards over latency, error, cost, and eval-score metrics
- Set alerts on error rates, cost spikes, or eval-score drops and get notified in Slack, PagerDuty, or email
- Perform bulk operations across many items at once
- Define rules that trigger actions automatically on events
- Attribute cost and usage to users, sessions, and features via custom metadata
- See cost and token usage per request, model, and time period in dashboards
- Bulk-export traces and datasets to blob storage or my data warehouse
- Have an agent create a dataset, trigger an eval run programmatically, and read back the results
- Route outputs to human annotation queues for review and labeling
- Run evals in CI and gate deployments on their results
- Write custom code-based scorers and metrics for my evaluations
- Compare eval runs side by side to catch regressions between prompt or model versions
- Score outputs with configurable LLM-as-a-judge evaluators
- Curate datasets from production traces and run offline evaluations against them
- Run evaluators continuously on live production traffic, not just offline datasets
- Do everything through the API that I can do in the UI
- Export all of my data in open formats and leave
- Self-host the core product
- Choose where my data is stored (region/residency)
- Version prompts and deploy changes to production without shipping code
- Have an agent query my traces, metrics, and eval results through an API or MCP server to debug my app
- Instrument apps in both Python and JS/TS with officially supported SDKs
- Trace multi-step agent runs as nested spans grouped into sessions or threads
- Instrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDK
- Send and receive traces over OpenTelemetry (OTLP) instead of a proprietary format
- Capture traces of my LLM calls with inputs, outputs, latency, and token usage
GitHub README10 stories
- Run the product headlessly / in CI for automation
- Connect an agent via an official MCP server
- Build against official SDKs
- Perform bulk operations across many items at once
- Have an agent create a dataset, trigger an eval run programmatically, and read back the results
- Have an agent query my traces, metrics, and eval results through an API or MCP server to debug my app
- Instrument apps in both Python and JS/TS with officially supported SDKs
- Trace multi-step agent runs as nested spans grouped into sessions or threads
- Instrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDK
- Capture traces of my LLM calls with inputs, outputs, latency, and token usage
Hacker News9 stories
- Build against official SDKs
- Get AI-generated insights and suggestions from my data inside the product
- Do everything through the API that I can do in the UI
- Export all of my data in open formats and leave
- Self-host the core product
- Instrument apps in both Python and JS/TS with officially supported SDKs
- Trace multi-step agent runs as nested spans grouped into sessions or threads
- Instrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDK
- Capture traces of my LLM calls with inputs, outputs, latency, and token usage
Langsmith docs7 stories
- Run the product headlessly / in CI for automation
- Drive the product through a documented public API
- Build against official SDKs
- Do everything through the API that I can do in the UI
- Have an agent query my traces, metrics, and eval results through an API or MCP server to debug my app
- Instrument apps in both Python and JS/TS with officially supported SDKs
- Instrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDK
OpenAPI spec2 stories
Claims vs evidence — vendor claims reconciled against independent verdictsClaims vs evidence
6 of 21 testable claims verified · 0 contradicted → integrity 29/100
22 distinct capability claims found in LangSmith’s own claimed-docs/GitHub materials, reconciled against our judge’s independent verdicts.
6
Verified
15
Unverified
0
Contradicted
15
Undersold
Verified (7)
“Traces capture what an agent did in production, used for debugging, monitoring, and building eval datasets”
Capture traces of my LLM calls with inputs, outputs, latency, and token usagefullproof ↗
“LangSmith Engine automatically detects recurring issues in traces, diagnoses root cause, and helps resolve them”
Get AI-generated insights and suggestions from my data inside the productpartialproof ↗
“LangSmith can be self-hosted in your own infrastructure for observability, evaluation, and prompt engineering, with optional managed agent deployment”
“Provides an OpenAI client wrapper for automatic instrumentation of OpenAI calls”
Instrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDKpartialproof ↗
“Tracing supported via Python, TypeScript, Go, and Java SDKs, or integration with any agent stack/framework”
Instrument apps in both Python and JS/TS with officially supported SDKsfullproof ↗
“Tracing supported via Python, TypeScript, Go, and Java SDKs, or integration with any agent stack/framework”
“Tracing supported via Python, TypeScript, Go, and Java SDKs, or integration with any agent stack/framework”
Instrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDKpartialproof ↗
Unverified (23)
“Traces can be filtered, exported, shared, and compared via UI or API”
Bulk-export traces and datasets to blob storage or my data warehousepartialproof ↗
“Can build dashboards and set alerts to track quality and catch issues early”
Build custom dashboards over latency, error, cost, and eval-score metricsfullproof ↗
“Can build dashboards and set alerts to track quality and catch issues early”
Set alerts on error rates, cost spikes, or eval-score drops and get notified in Slack, PagerDuty, or emailfullproof ↗
“Workflows can be automated with rules, webhooks, and online evaluations triggered on events”
Define rules that trigger actions automatically on eventspartialproof ↗
“Workflows can be automated with rules, webhooks, and online evaluations triggered on events”
“Workflows can be automated with rules, webhooks, and online evaluations triggered on events”
Run evaluators continuously on live production traffic, not just offline datasetsfullproof ↗
“Outputs can be annotated and user feedback gathered via annotation queues or inline annotation”
Route outputs to human annotation queues for review and labelingfullproof ↗
“Datasets can be created from curated test cases, historical production traces, or synthetic data generation”
Curate datasets from production traces and run offline evaluations against themfullproof ↗
“Evaluators can be created to score performance via human review, code rules, LLM-as-judge, or pairwise comparison”
Score outputs with configurable LLM-as-a-judge evaluatorsfullproof ↗
“Evaluators can be created to score performance via human review, code rules, LLM-as-judge, or pairwise comparison”
Write custom code-based scorers and metrics for my evaluationsfullproof ↗
“Applications can be run against a dataset to create an experiment with configurable repetitions, concurrency, and caching”
Compare eval runs side by side to catch regressions between prompt or model versionsfullproof ↗
“Evaluators can run automatically on prod traces for safety, format, quality checks, with filters/sampling for cost control”
Run evaluators continuously on live production traffic, not just offline datasetsfullproof ↗
“Supports OpenTelemetry-based tracing so traces can be sent from any OTel-compatible application”
Send and receive traces over OpenTelemetry (OTLP) instead of a proprietary formatfullproof ↗
“Provides threshold-based alerting on metrics like run count, cost, errors, feedback score, and latency”
Set alerts on error rates, cost spikes, or eval-score drops and get notified in Slack, PagerDuty, or emailfullproof ↗
“Alerts can route to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook”
Set alerts on error rates, cost spikes, or eval-score drops and get notified in Slack, PagerDuty, or emailfullproof ↗
“Alerts can route to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook”
“Prebuilt dashboards are auto-created per project covering trace count, error rates, token usage, and more”
Build custom dashboards over latency, error, cost, and eval-score metricsfullproof ↗
“Prebuilt dashboards are auto-created per project covering trace count, error rates, token usage, and more”
See cost and token usage per request, model, and time period in dashboardsfullproof ↗
“Custom dashboards let users build tailored collections of charts for metrics that matter to them”
Build custom dashboards over latency, error, cost, and eval-score metricsfullproof ↗
“Data can be grouped by run tag or metadata to split metrics across attributes”
Attribute cost and usage to users, sessions, and features via custom metadatafullproof ↗
“Evaluations can be run on curated datasets during development to compare versions, benchmark performance, and catch regressions”
Run evals in CI and gate deployments on their resultspartialproof ↗
“Evaluations can be run on curated datasets during development to compare versions, benchmark performance, and catch regressions”
Compare eval runs side by side to catch regressions between prompt or model versionsfullproof ↗
“Real user interactions can be evaluated in real-time to detect issues and measure quality on live traffic”
Run evaluators continuously on live production traffic, not just offline datasetsfullproof ↗
Undersold (15)
Point an agent at llms.txt or agent-oriented docsfullproof ↗
Run the product headlessly / in CI for automationpartialproof ↗
Drive the product through a documented public APIpartialproof ↗
Set up automations that run autonomously in the backgroundpartialproof ↗
Delegate tasks to a built-in AI assistant inside the productpartialproof ↗
Test against a sandbox environment without touching production datapartialproof ↗
Perform bulk operations across many items at oncepartialproof ↗
Have an agent create a dataset, trigger an eval run programmatically, and read back the resultsfullproof ↗
Do everything through the API that I can do in the UIpartialproof ↗
Export all of my data in open formats and leavepartialproof ↗
Choose where my data is stored (region/residency)partialproof ↗
Version prompts and deploy changes to production without shipping codepartialproof ↗
Have an agent query my traces, metrics, and eval results through an API or MCP server to debug my apppartialproof ↗
Trace multi-step agent runs as nested spans grouped into sessions or threadspartialproof ↗
Claims outside our story set (1)
Real capability claims found in LangSmith’s own materials, but no story in this arena’s taxonomy covers them yet — that’s feedback on the taxonomy, not a mark against the product.
“Users can sign up and log in via Google, GitHub, or email without a credit card”
source ↗
Business model
Free Developer tier with one seat and 5k base traces/month; Plus is priced per seat plus usage-based trace ingestion; Enterprise (including self-hosted) is custom.
pricing ↗Score trend
How this product’s scores have moved as evidence and verdicts are re-derived — a point per change, not per day.
Flag
⚑ Flag a verdictThink a verdict is wrong? Opens a prefilled GitHub issue — or use the ⚑ next to any verdict above.
For agents
Agent surface uptime llms.txt 100% (30d, checked every 6h since Sep 8 '26)