LLM Evals & Observability Arena
LangSmith vs Cekura
LangSmith wins · 18–17 (15 drawn)
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
Agent access
ai-native userPoint an agent at llms.txt or agent-oriented docs
weight 2 · round to CekuraDirect probe evidence confirms LangSmith hosts a working llms.txt file (HTTP 200) plus markdown-formatted docs pages that explicitly point agents to the llms.txt index, making the docs agent-consumable by design. missing for 10: no independent/community confirmation that agents actually consume this successfully in practice, and no explicit vendor statement framing this as an intentional agent-facing feature.
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.langchain.com/llms.txt # Docs by LangChain > Documentation for LangSmith, Fleet, and our open sour…”
- [probe] “PROBE docs-md: HTTP 200 at https://docs.langchain.com/langsmith/home.md > ## Documentation Index > Fetch the complete documentation index at…”
Cekura has a confirmed live llms.txt endpoint (HTTP 200) plus per-page .md documentation format and an index pointing agents to llms.txt, directly enabling agent-oriented doc consumption; this is corroborated by both docs and live probes. Missing for 10: independent third-party confirmation of an agent actually consuming llms.txt successfully in the wild.
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.cekura.ai/llms.txt # Cekura > Cekura is the testing and observability platform for voice AI agents…”
- [probe] “PROBE docs-md: HTTP 200 at https://docs.cekura.ai/documentation/introduction.md > ## Documentation Index > Fetch the complete documentation …”
- [claimed-docs] “create a comprehensive plan to integrate cekura observability”
ai-native userRun the product headlessly / in CI for automation
weight 2 · round to CekuraLangSmith exposes SDKs (Python/TS/Go/Java) and OpenTelemetry-based tracing that let you programmatically run evaluations and send traces without the UI, and evaluation experiments can be scripted with configurable concurrency/repetitions, which is consistent with running as part of automated pipelines. However there is no explicit CI/CD example, GitHub Actions template, or documented CLI for headless automation. missing for 10: explicit CI/CD integration guide or example, a dedicated headless CLI, and independent confirmation of automated pipeline usage.
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
- [claimed-docs] “LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application.”
- [claimed-docs] “Run evaluations on curated datasets during development to compare versions, benchmark performance, and catch regressions.”
- [claimed-docs] “Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.”
- [github] “client = wrap_openai(openai.Client())”
Cekura documents a CLI/SDK for programmatic and terminal-based agent/scenario/run management, a REST API with OpenAPI spec and API keys, cron job scheduling for automated evaluation workflows, and explicit GitHub Actions CI/CD integration for testing agents on every change—together these clearly support headless/CI automation. Missing for 10: independent hands-on confirmation of the GitHub Actions or cron setup actually working in a real pipeline beyond docs.
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Learn how to set up and manage automated cron jobs for your AI agent testing and evaluation workflows.”
- [claimed-docs] “Learn how to set up GitHub Actions workflows to automatically test your agents with each change”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
ai-native userConnect an agent via an official MCP server
weight 3 · round to CekuraThere is direct evidence of an official LangSmith MCP server repository (langsmith-mcp-server), confirming LangSmith ships a first-party MCP server that agents could connect to. However, the evidence pack has no further detail on setup, supported tools/capabilities, or independent confirmation of it working end-to-end with an agent, so the story is only thinly supported. missing for 10: detailed docs on MCP server capabilities/setup, independent/hands-on verification that an agent successfully connects and uses it, and integration guidance beyond the bare repo reference.
- [probe] “official MCP server documented at https://github.com/langchain-ai/langsmith-mcp-server”
Cekura is not itself an agent but a testing/observability platform, so the axis applies; it documents an official MCP server ("Install Cekura Skills and MCP so your AI assistant can design, run, and improve voice-agent evaluations") confirmed live via probe. Missing for 10: independent hands-on verification of the MCP server working end-to-end and more detail on supported tool/resource surface.
- [claimed-docs] “Install Cekura Skills and MCP so your AI assistant can design, run, and improve voice-agent evaluations.”
- [probe] “official MCP server documented at https://docs.cekura.ai/mcp/overview”
ai-native userUse an official CLI
weight 2 · round to CekuraLangSmithnone0/10No 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.
Cekura documents an official CLI ('Manage agents, scenarios, runs, and call data from your terminal') alongside an SDK, confirmed by both docs and probe evidence. Missing for 10: independent hands-on review of the CLI's usability/completeness beyond first-party docs.
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
ai-native userDrive the product through a documented public API
weight 3 · round to CekuraLangSmith explicitly advertises programmatic access ('Filter, export, share, and compare traces... via the UI or API') and ships official SDKs for Python, TypeScript, Go, and Java plus webhook-based automation, indicating a documented public API surface. However, a probe for a discoverable OpenAPI/swagger spec returned 404s across standard paths, suggesting no easily found canonical API reference, and no independent developer confirmation of full API coverage was found. missing for 10: a public OpenAPI/reference doc, independent/hands-on confirmation of API completeness.
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
- [claimed-docs] “Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.”
- [claimed-docs] “Automate workflows with rules, webhooks, and online evaluations.”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.langchain.com/openapi.json, https://docs.langchain.com/swagger.json, https://docs.langc…”
Cekura publishes a documented public API with OpenAPI spec (probe confirms live openapi.json), API key auth, dedicated API reference endpoints (e.g. send-calls), plus SDKs (sync/async) and a CLI for programmatic/terminal control—clearly enabling an AI-native user to drive the product via documented API access. Missing for 10: independent third-party review/hands-on validation of the API beyond vendor docs and probes.
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
ai-native userIssue scoped/least-privilege API credentials for an agent
weight 2 · round to CekuraLangSmithnone0/10The 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. No documentation of scoped/limited API keys or RBAC-style credential controls is present.
Cekura documents API key creation restricted to project-level scope, showing some credential scoping capability, but there's no evidence of finer-grained least-privilege controls like read/write permission tiers, resource-specific scoping, or per-agent credential issuance. missing for 10: granular permission scopes (read-only vs write), per-agent or per-resource credential issuance, role-based access control documentation.
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
ai-native userBuild against official SDKs
weight 2 · round drawnLangSmith documents official SDKs in Python, TypeScript, Go, and Java, backed by a public GitHub SDK repo with working code examples (e.g., wrap_openai), and also supports OpenTelemetry-based tracing for any compatible stack. Missing for 10: independent hands-on verification of SDK completeness across all four languages, and community notes (langsmith-comm-3) indicate weaker support for non-LangChain frameworks like crewai, which slightly tempers confidence in universal SDK-native coverage.
- [claimed-docs] “Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.”
- [github] “client = wrap_openai(openai.Client())”
- [claimed-docs] “LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application.”
- [community] “Langsmith only shows the llm inferences. Also it hardly works with other frameworks like crewai for instance.”
Cekura documents official sync/async SDKs and a CLI for programmatic access ('Manage agents, scenarios, runs, and call data from your terminal', 'sync and async clients for application code'), plus an OpenAPI spec, backed by probe confirmation of live docs and endpoints. missing for 10: independent hands-on developer reports specifically about SDK usage (only community praise for the platform generally, not SDK-specific).
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
ai-native userSubscribe to events via webhooks
weight 2 · round to LangSmithLangSmith docs explicitly state that alerts and automation rules can route to webhooks/any HTTP endpoint, letting users subscribe to events like errors, cost, or feedback thresholds via webhook. However, this is scoped to alert/automation rules rather than a general-purpose event subscription API, and there's no independent or hands-on confirmation of webhook reliability or payload structure. missing for 10: broader documentation of webhook event types/payloads beyond alert thresholds, independent/hands-on validation of webhook delivery.
- [claimed-docs] “Automate workflows with rules, webhooks, and online evaluations.”
- [claimed-docs] “Alerts can route to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
- [claimed-docs] “Alerts can [route](#step-4-configure-notification-channel) to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
Cekuranone0/10Evidence shows Cekura receiving inbound webhooks (agent's provider posts call data to Cekura) but nothing about Cekura emitting outbound webhooks that a user could subscribe to for events like evaluation completion or drift detection. Missing for 10: documented outbound webhook/event subscription mechanism, webhook configuration UI/API, event types list.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
Agentic features
ai-native userGet AI-generated insights and suggestions from my data inside the product
weight 2 · round to CekuraLangSmith offers LLM-as-judge evaluators, automated root-cause detection via 'LangSmith Engine' (langsmith-docs-6), and alerting/dashboards that surface quality signals, which constitute AI-generated insight generation embedded in the product. However, evidence doesn't show a general-purpose AI assistant proactively surfacing free-form suggestions across the whole product, and community feedback notes trace visibility is LLM-inference-only and framework-limited (langsmith-comm-3). Missing for 10: broader proactive AI-generated recommendations beyond eval/root-cause detection, first-hand demonstration of LangSmith Engine's insight quality, and independent corroboration of its effectiveness.
- [claimed-docs] “Automatically detect recurring issues in your traces, diagnose their root cause, and resolve them with LangSmith Engine.”
- [claimed-docs] “Create evaluators to score performance: Human review, Code rules, LLM-as-judge, Pairwise comparison”
- [claimed-docs] “Set up evaluators to run automatically on production traces: safety checks, format validation, quality heuristics, and reference-free LLM-as…”
- [claimed-docs] “Human review * Code rules * LLM-as-judge * Pairwise comparison”
- [community] “Langsmith only shows the llm inferences. Also it hardly works with other frameworks like crewai for instance.”
Cekura provides LLM-judge evaluation metrics, live drift detection, and auto-generated fix suggestions ('flags issues → reproduces in simulation → suggests fixes automatically'), showing AI-generated insights derived from call/agent data. However, this is narrowly scoped to voice-agent testing/observability rather than broad, ad-hoc AI-generated insights across arbitrary product data, and there's no independent hands-on evidence of the 'suggests fixes automatically' claim beyond marketing copy. Missing for 10: independent/hands-on verification of the auto-fix-suggestion feature, broader insight/analytics generation beyond voice-agent metrics, and detail on how suggestions are surfaced to users.
- [claimed-docs] “Evaluate AI voice agent calls using natural language criteria with LLM-powered judging”
- [claimed-docs] “Live drift detection across every call.”
- [claimed-docs] “Cekura flags issues → reproduces in simulation → suggests fixes automatically.”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
ai-native userSet up automations that run autonomously in the background
weight 2 · round drawnLangSmith supports background automations via rules/webhooks, alerts that route to Slack/PagerDuty/HTTP endpoints, and online evaluators that run automatically on live production traces without user intervention (docs-4, docs-10, docs-12/13). It also offers optional 'LangSmith Deployment' to run and manage agents (docs-17), suggesting some autonomous agent execution capability, but this is a secondary/optional feature and detail is thin. Missing for 10: independent/hands-on evidence that these automations actually run unattended over time, more detail on scheduling/triggers beyond alerts, and clearer scope of 'LangSmith Deployment' for autonomous agent runs.
- [claimed-docs] “Automate workflows with rules, webhooks, and online evaluations.”
- [claimed-docs] “Set up evaluators to run automatically on production traces: safety checks, format validation, quality heuristics, and reference-free LLM-as…”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
- [claimed-docs] “Alerts can route to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
Cekura supports autonomous background automation via cron jobs for scheduled testing/evaluation runs, GitHub Actions CI/CD integration, and live drift detection across calls, plus automatic flagging/reproduction/fix-suggestion pipelines. However, evidence is entirely first-party docs describing scheduling/triggering mechanisms rather than a broad general-purpose 'automation builder' for arbitrary agentic workflows. missing for 10: independent/hands-on confirmation that cron/CI automations run reliably unattended, evidence of more flexible trigger types beyond cron/CI/webhooks, and detail on monitoring/error-handling for long-running autonomous automations.
- [claimed-docs] “Learn how to set up and manage automated cron jobs for your AI agent testing and evaluation workflows.”
- [claimed-docs] “Learn how to set up GitHub Actions workflows to automatically test your agents with each change”
- [claimed-docs] “Live drift detection across every call.”
- [claimed-docs] “Cekura flags issues → reproduces in simulation → suggests fixes automatically.”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
ai-native userDelegate tasks to a built-in AI assistant inside the product
weight 3 · round to LangSmithLangSmith Engine is described as automatically detecting recurring issues, diagnosing root causes, and resolving them, which functions like a built-in AI assistant for debugging tasks, but this is only a single, thin doc line with no further elaboration, UI walkthrough, or independent corroboration of how tasks are 'delegated' to it. missing for 10: detailed documentation of assistant capabilities/UX, examples of delegated task execution, independent/hands-on confirmation.
- [claimed-docs] “Automatically detect recurring issues in your traces, diagnose their root cause, and resolve them with LangSmith Engine.”
Cekuranone0/10Cekura's AI-assistant integrations (Skills, MCP, CLI) are designed so external AI assistants like Claude or Cursor can drive Cekura's testing/evaluation features — this is the reverse relationship of an AI-native user delegating tasks to a built-in assistant inside Cekura itself. The 'flags issues → reproduces → suggests fixes automatically' pipeline is an automated feature, not a delegatable in-product assistant, so there is no evidence of a built-in AI assistant you can converse with or delegate open-ended tasks to.
- [claimed-docs] “Cekura flags issues → reproduces in simulation → suggests fixes automatically.”
- [claimed-docs] “Install Cekura Skills and MCP so your AI assistant can design, run, and improve voice-agent evaluations.”
- [claimed-docs] “npx skills add cekura-ai/cekura-skills --all”
ai-native userOperate the product with natural-language commands
weight 2 · round to CekuraLangSmithnone0/10LangSmith is an observability/evaluation platform with a UI, API, and SDKs, but there is no evidence of natural-language command interfaces for operating the product itself (e.g., a chat-based control plane). The MCP server exposes tools to agents but that's a different axis from a user issuing NL commands to operate the product. missing for 10: any documented NL command interface, chat-based control of dashboards/alerts/experiments, or evidence of conversational operation.
- [probe] “official MCP server documented at https://github.com/langchain-ai/langsmith-mcp-server”
Cekura ships an MCP server and Skills package so an AI assistant can design, run, and improve voice-agent evaluations via natural-language commands, plus an LLM-judge metric for natural-language evaluation criteria; this supports agentic/NL operation for a subset of workflows. missing for 10: independent hands-on evidence of natural-language command execution beyond vendor docs, and confirmation that the full breadth of platform actions (not just evaluation/testing tasks) can be driven purely by NL commands.
- [claimed-docs] “npx skills add cekura-ai/cekura-skills --all”
- [claimed-docs] “Install Cekura Skills and MCP so your AI assistant can design, run, and improve voice-agent evaluations.”
- [github] “13 Skills, 14 Commands in one plugin”
- [claimed-docs] “Evaluate AI voice agent calls using natural language criteria with LLM-powered judging”
- [probe] “official MCP server documented at https://docs.cekura.ai/mcp/overview”
Api quality
ai-native userExplore an interactive API reference with runnable examples
weight 2 · round to CekuraLangSmithnone0/10No 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.
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.langchain.com/openapi.json, https://docs.langchain.com/swagger.json, https://docs.langc…”
Cekura publishes an OpenAPI spec and structured API-reference docs (e.g., send-calls, create-api-key endpoints) confirming a machine-readable interactive reference exists, but no evidence shows runnable 'try it' examples or a sandboxed console in the docs. Missing for 10: explicit runnable/try-it example UI, evidence of live request execution, independent confirmation of interactivity.
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
ai-native userDownload a machine-readable API spec (OpenAPI or equivalent)
weight 2 · round to CekuraLangSmithnone0/10LangSmith 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.
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.langchain.com/openapi.json, https://docs.langchain.com/swagger.json, https://docs.langc…”
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
A direct probe confirms an OpenAPI spec is publicly downloadable at https://docs.cekura.ai/openapi.json, returning HTTP 200 with a valid "openapi" key, corroborated by API reference docs built from it. Missing for 10: no independent third-party confirmation of spec completeness/versioning beyond the probe.
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
ai-native userTest against a sandbox environment without touching production data
weight 1 · round drawnLangSmith supports datasets with curated/synthetic examples and running evaluations/experiments against them separately from production traces, which functions like a sandbox testing workflow rather than testing directly on live production data. However, there's no explicit documentation of an isolated 'sandbox environment' concept, staging/prod environment separation, or data isolation guarantees — evaluation datasets can be built from historical production traces, blurring the separation. missing for 10: explicit sandbox/staging environment documentation, guarantees of production data isolation, and independent confirmation that dataset-based testing avoids touching production systems.
- [claimed-docs] “Create a dataset with examples from manually curated test cases, historical production traces, or synthetic data generation.”
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
- [claimed-docs] “Run evaluations on curated datasets during development to compare versions, benchmark performance, and catch regressions.”
- [claimed-docs] “Create a [dataset](/langsmith/manage-datasets) with [examples](/langsmith/evaluation-concepts#examples) from manually curated test cases, hi…”
- [claimed-docs] “Execute your application on the dataset to create an [experiment](/langsmith/evaluation-concepts#experiment). Configure [repetitions, concur…”
Cekura explicitly supports testing voice agents via synthetic/simulated conversations 'before go-live' and running scenarios without external API keys, which implies a sandbox-like testing flow separate from live production calls. However, there is no explicit documentation of a dedicated sandbox environment, data isolation guarantees, or explicit statements that test runs never touch production data/logs. missing for 10: explicit sandbox/staging environment documentation, data isolation guarantees between test and production calls, independent confirmation that simulated runs are fully separated from production data.
- [claimed-docs] “Learn how to quickly create and test voice agents on Cekura without requiring any external API keys or integrations.”
- [claimed-docs] “Run thousands of synthetic conversations before go-live.”
- [claimed-docs] “Run the same scenarios across platforms and models. Pick the one that actually performs.”
ai-native userRely on versioned APIs with a documented deprecation policy
weight 2 · round drawnLangSmithnone0/10No 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.
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.langchain.com/openapi.json, https://docs.langchain.com/swagger.json, https://docs.langc…”
Cekuranone0/10There is an OpenAPI spec and API-key auth docs, but no evidence of API versioning scheme or a documented deprecation policy anywhere in the pack. missing for 10: versioning scheme documentation, deprecation policy, changelog/migration guides.
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
Alerting dashboards — stories about alerting dashboards in this arenaAlerting dashboards
Stories about alerting dashboards in this arena
Monitoring
ml engineerBuild custom dashboards over latency, error, cost, and eval-score metrics
weight 2 · round to LangSmithLangSmith docs explicitly describe building custom dashboards ('tailored collections of charts') and prebuilt dashboards covering trace count, error rates, token usage, plus threshold alerts on Run Count, Cost, Errors, Feedback Score, and Latency, covering latency, error, cost, and eval-score metrics with group-by tag/metadata support for slicing. Missing for 10: independent/hands-on confirmation of dashboard-building UX and eval-score charting specifically.
- [claimed-docs] “Prebuilt dashboards are created automatically for each project and cover essential metrics, such as trace count, error rates, token usage, a…”
- [claimed-docs] “Create tailored collections of charts for tracking metrics that matter most for your application.”
- [claimed-docs] “Custom dashboards: Collections of charts you can configure to your needs.”
- [claimed-docs] “You can use group by [run tag or metadata](/langsmith/add-metadata-tags) to split data over attributes that are important to your applicatio…”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
- [claimed-docs] “You can use group by run tag or metadata to split data over attributes that are important to your application.”
Cekura docs explicitly describe building custom dashboards with widgets to visualize call data, metrics, and metadata, which covers eval-score and general metric visualization for an ML engineer's needs. However, the docs don't explicitly enumerate latency, error rate, or cost as first-class dashboard metrics, and there's no independent/hands-on corroboration of dashboard flexibility. Missing for 10: explicit mention of latency/error/cost metric widgets, and third-party evidence of dashboard customization in practice.
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
developerSet alerts on error rates, cost spikes, or eval-score drops and get notified in Slack, PagerDuty, or email
weight 2 · round to LangSmithLangSmith docs explicitly describe threshold-based alerting on Errors, Cost, Feedback Score (eval score), Run Count, and Latency, with routing to Slack, PagerDuty, Dynatrace, or any HTTP webhook (which can cover email/other integrations), directly matching the story. Missing for 10: independent/hands-on confirmation of alert delivery reliability and no explicit mention of native email notification channel beyond webhook routing.
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
- [claimed-docs] “Alerts can route to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics:”
- [claimed-docs] “Alerts can [route](#step-4-configure-notification-channel) to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
- [claimed-docs] “Build dashboards and set alerts to track quality and catch issues early.”
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
ai-native userPerform bulk operations across many items at once
weight 2 · round drawnLangSmith documents dataset-wide evaluation runs (execute across many examples with configurable concurrency/repetitions), bulk trace filter/export/share/compare via API, and bulk dataset creation from historical traces or synthetic generation—supporting operations across many items at once. However, there's no explicit documentation of general bulk actions (e.g., bulk delete/tag/annotate across arbitrary trace sets) beyond evaluation and dataset contexts, and no independent/hands-on confirmation of bulk-op reliability at scale. Missing for 10: explicit bulk edit/delete/tag API documentation, independent hands-on verification of large-scale bulk operations.
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
- [claimed-docs] “Create a dataset with examples from manually curated test cases, historical production traces, or synthetic data generation.”
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
- [claimed-docs] “Create a [dataset](/langsmith/manage-datasets) with [examples](/langsmith/evaluation-concepts#examples) from manually curated test cases, hi…”
- [claimed-docs] “Execute your application on the dataset to create an [experiment](/langsmith/evaluation-concepts#experiment). Configure [repetitions, concur…”
- [github] “client = wrap_openai(openai.Client())”
Cekura's docs advertise running thousands of synthetic conversations/scenarios at once and provide a CLI/SDK to manage agents, scenarios, runs, and call data programmatically, which supports bulk test execution and automation via cron/CI. However, there's no explicit documentation of bulk edit/delete/update operations across many existing items (e.g., batch API endpoints for updating many scenarios or calls at once). Missing for 10: explicit bulk CRUD API/CLI commands operating on many items simultaneously, and any hands-on evidence of large-scale batch throughput.
- [claimed-docs] “Run thousands of synthetic conversations before go-live.”
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Learn how to set up and manage automated cron jobs for your AI agent testing and evaluation workflows.”
- [claimed-docs] “Learn how to set up GitHub Actions workflows to automatically test your agents with each change”
ai-native userDefine rules that trigger actions automatically on events
weight 3 · round drawnLangSmith documents rule-based automation via threshold alerts (run count, cost, errors, feedback score, latency) that route to Slack, PagerDuty, Dynatrace, or webhooks, and separately mentions 'rules, webhooks, and online evaluations' as automation primitives. This covers automatic action-on-event but is scoped mainly to monitoring/alerting metrics rather than a general-purpose rule engine for arbitrary events. Missing for 10: detailed documentation of the 'rules' feature itself (beyond the one-line mention), broader event types beyond the five alert metrics, and independent/hands-on confirmation that rule-triggered actions work reliably in practice.
- [claimed-docs] “Automate workflows with rules, webhooks, and online evaluations.”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
- [claimed-docs] “Alerts can route to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics:”
- [claimed-docs] “Alerts can [route](#step-4-configure-notification-channel) to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
Cekura supports automation triggers like scheduled cron jobs for test workflows, GitHub Actions CI/CD hooks that fire tests on code changes, and an automatic flag→reproduce→suggest-fix pipeline triggered by detected issues, which are event-driven automations. However, there's no evidence of a general-purpose user-defined rules engine (e.g., 'if X metric drops, then do Y') beyond these fixed automation patterns. Missing for 10: a documented custom rule/condition builder for arbitrary events, and independent confirmation these automations behave as advertised.
- [claimed-docs] “Learn how to set up and manage automated cron jobs for your AI agent testing and evaluation workflows.”
- [claimed-docs] “Learn how to set up GitHub Actions workflows to automatically test your agents with each change”
- [claimed-docs] “Cekura flags issues → reproduces in simulation → suggests fixes automatically.”
- [claimed-docs] “Live drift detection across every call.”
ai-native userSchedule recurring jobs or workflows
weight 2 · round to CekuraLangSmithnone0/10LangSmith documents automation via rules, webhooks, and online evaluations triggered on production traces, but there is no evidence of a scheduler or cron-like capability for recurring jobs/workflows (e.g., scheduled dataset evaluations or periodic report runs). Since automation is otherwise a supported theme for this platform, the absence of scheduling evidence makes this 'none' rather than 'na'.
- [claimed-docs] “Automate workflows with rules, webhooks, and online evaluations.”
- [claimed-docs] “Set up evaluators to run automatically on production traces: safety checks, format validation, quality heuristics, and reference-free LLM-as…”
- [claimed-docs] “Evaluate real user interactions in real-time to detect issues and measure quality on live traffic.”
Cekura documents dedicated cron job support for scheduling automated testing/evaluation workflows (cekura-docs-13), plus GitHub Actions integration for triggering workflows on changes (cekura-docs-14), giving AI-native users concrete recurring automation options via CLI/SDK/API. Missing for 10: independent/hands-on confirmation that cron scheduling works reliably in production and more detail on scheduling granularity/limits.
- [claimed-docs] “Learn how to set up and manage automated cron jobs for your AI agent testing and evaluation workflows.”
- [claimed-docs] “Learn how to set up GitHub Actions workflows to automatically test your agents with each change”
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
ai-native userVersion, review, and roll back my automations
weight 1 · round to CekuraLangSmithnone0/10LangSmith documents automation rules, webhooks, and dashboards but no evidence describes versioning, review workflows, or rollback capabilities for these automations. Missing for 10: version history for rules/automations, review/approval workflow, rollback mechanism, and any documentation or community confirmation of these features.
- [claimed-docs] “Automate workflows with rules, webhooks, and online evaluations.”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
- [claimed-docs] “Alerts can route to Slack, PagerDuty, Dynatrace, or any HTTP endpoint via webhook.”
Cekura supports comparing two versions of an agent side-by-side (A/B testing) and provides dashboards/metrics for reviewing agent performance, which touches 'version' and 'review' aspects, but there is no evidence of a rollback mechanism to revert an automation/agent to a prior version. Missing for 10: explicit version history/versioning system, rollback/undo capability, and any documented workflow for reverting agent configurations.
- [claimed-docs] “Compare two versions of your agent side-by-side to measure prompt, model, or configuration changes.”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
Cost monitoring — stories about cost monitoring in this arenaCost monitoring
Stories about cost monitoring in this arena
Cost tracking
developerAttribute cost and usage to users, sessions, and features via custom metadata
weight 2 · round to LangSmithLangSmith docs show custom metadata/tags can be attached to traces and used to group dashboard metrics (including token usage) by attribute, and alerts can be set on cost, enabling attribution of cost/usage to arbitrary dimensions like user, session, or feature via metadata. Missing for 10: a concrete worked example of per-user/session/feature cost breakdown and independent/hands-on corroboration that metadata-based cost attribution works well in practice.
- [claimed-docs] “You can use group by run tag or metadata to split data over attributes that are important to your application.”
- [claimed-docs] “You can use group by [run tag or metadata](/langsmith/add-metadata-tags) to split data over attributes that are important to your applicatio…”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics:”
- [claimed-docs] “Prebuilt dashboards are created automatically for each project and cover essential metrics, such as trace count, error rates, token usage, a…”
- [claimed-docs] “Custom dashboards: Collections of charts you can configure to your needs.”
Cekuranone0/10Cekura's docs show metadata being sent with calls and custom dashboards for visualizing metrics/metadata, but there is no evidence of cost tracking, cost attribution, or usage-cost breakdown by user/session/feature — the platform is framed purely around voice-agent evaluation and observability, not cost accounting.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
developerSee cost and token usage per request, model, and time period in dashboards
weight 3 · round to LangSmithLangSmith documents prebuilt per-project dashboards covering token usage and cost, threshold-based cost alerting, and custom dashboards with group-by tag/metadata to slice metrics by model or time period, directly matching the story. missing for 10: independent/hands-on confirmation of the cost-per-model breakdown specifically and no third-party validation of dashboard accuracy.
- [claimed-docs] “LangSmith provides threshold-based alerting on the following metrics: Run Count, Cost, Errors, Feedback Score, Latency”
- [claimed-docs] “Prebuilt dashboards are created automatically for each project and cover essential metrics, such as trace count, error rates, token usage, a…”
- [claimed-docs] “Create tailored collections of charts for tracking metrics that matter most for your application.”
- [claimed-docs] “You can use group by run tag or metadata to split data over attributes that are important to your application.”
- [claimed-docs] “Custom dashboards: Collections of charts you can configure to your needs.”
- [claimed-docs] “You can use group by [run tag or metadata](/langsmith/add-metadata-tags) to split data over attributes that are important to your applicatio…”
Cekuranone0/10Cekura documents customizable dashboards for call data, metrics, and metadata (cekura-docs-11), but nothing in the evidence pack mentions cost or token usage tracking per request, model, or time period. As an observability platform for voice agents, this axis plausibly applies, but no evidence supports the specific cost/token dashboard capability.
Data access export — stories about data access export in this arenaData access export
Stories about data access export in this arena
Data export
developerBulk-export traces and datasets to blob storage or my data warehouse
weight 2 · round to LangSmithDocs mention the ability to 'export' traces via UI or API, but there is no evidence of bulk export tooling, blob storage integration (S3/GCS), or data warehouse connectors (BigQuery/Snowflake) specifically designed for large-scale export. missing for 10: dedicated bulk-export API/CLI docs, blob storage integration, data warehouse connector documentation, and any hands-on confirmation of large-scale export workflows.
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
Cekuranone0/10Evidence shows Cekura has an API, CLI, SDK, and dashboards for accessing call data, but there is no mention of bulk-export functionality to blob storage (S3/GCS) or data warehouse integrations (Snowflake, BigQuery, etc). Missing for 10: bulk export feature, blob storage integration, data warehouse connectors/sinks, any documentation or API endpoint for exporting datasets in bulk.
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
Evals datasets — measuring quality — datasets, eval runs, regression trackingEvals datasets
Measuring quality — datasets, eval runs, regression tracking
Ai eval ops
ai-native userHave an agent create a dataset, trigger an eval run programmatically, and read back the results
weight 2 · round to CekuraLangSmith docs and SDK show programmatic dataset creation (from curated data, traces, or synthetic generation), programmatic evaluator/experiment execution (`Execute your application on the dataset to create an experiment`), and API/SDK access to trace/experiment results, all reachable from Python/TS/Go/Java SDKs, and an official MCP server exists so an agent can invoke these operations as tools. Missing for 10: a concrete end-to-end code sample showing an agent creating a dataset, kicking off an eval, and parsing results back, plus independent hands-on confirmation of the MCP server workflow.
- [claimed-docs] “Create a dataset with examples from manually curated test cases, historical production traces, or synthetic data generation.”
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
- [claimed-docs] “Create evaluators to score performance: Human review, Code rules, LLM-as-judge, Pairwise comparison”
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
- [github] “client = wrap_openai(openai.Client())”
- [probe] “official MCP server documented at https://github.com/langchain-ai/langsmith-mcp-server”
Cekura ships a CLI/SDK explicitly for managing agents, scenarios (datasets), runs, and call data from code, plus an official MCP server letting an AI assistant 'design, run, and improve' evaluations, backed by a documented OpenAPI spec and API-key auth for programmatic access — together covering dataset creation, triggering eval runs, and reading back results. missing for 10: independent/hands-on confirmation that an agent using the MCP/CLI actually completes the full create-dataset-to-trigger-run-to-read-results loop end-to-end.
- [claimed-docs] “Install Cekura Skills and MCP so your AI assistant can design, run, and improve voice-agent evaluations.”
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [probe] “official MCP server documented at https://docs.cekura.ai/mcp/overview”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
Human review
ml engineerRoute outputs to human annotation queues for review and labeling
weight 2 · round to LangSmithLangSmith explicitly supports 'Annotate outputs and gather user feedback using queues or inline annotation' and lists 'Human review' as a first-class evaluator type, directly matching the routing-to-annotation-queue story. missing for 10: no independent/hands-on corroboration of the annotation queue workflow beyond first-party docs, and no detail on queue assignment/collaboration features.
- [claimed-docs] “Annotate outputs and gather user feedback using queues or inline annotation.”
- [claimed-docs] “Create evaluators to score performance: Human review, Code rules, LLM-as-judge, Pairwise comparison”
- [claimed-docs] “Human review * Code rules * LLM-as-judge * Pairwise comparison”
Cekuranone0/10Cekura's evidence pack covers voice-agent testing, evaluation metrics (LLM-judge, Python), dashboards, PII redaction, and observability, but there is no mention of routing outputs to human annotation queues, labeling workflows, or human-in-the-loop review pipelines. Absence of evidence for this applicable ML-eval capability yields none. Missing for 10: any mention of human annotation queues, labeling UI/workflow, or reviewer assignment features.
Offline evals
developerRun evals in CI and gate deployments on their results
weight 2 · round to CekuraLangSmith supports running evaluations on datasets to compare versions and catch regressions, and can execute experiments programmatically, which could be invoked from a CI pipeline, but there is no documented CI integration, GitHub Actions example, exit-code/pass-fail gating mechanism, or deployment-gating workflow in the evidence pack. missing for 10: explicit CI/CD integration docs, pass/fail threshold gating mechanism, deployment-blocking example, independent confirmation of CI usage.
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
- [claimed-docs] “Run evaluations on curated datasets during development to compare versions, benchmark performance, and catch regressions.”
- [claimed-docs] “Execute your application on the dataset to create an [experiment](/langsmith/evaluation-concepts#experiment). Configure [repetitions, concur…”
Cekura provides a dedicated GitHub Actions CI/CD guide plus CLI/SDK and cron job automation for testing agents on each change, indicating CI integration is a first-class capability. However, the evidence pack only shows the guide's title/description, not concrete details on exit-code/gating behavior for deployments, and there is no independent corroboration of this specific workflow. Missing for 10: explicit documentation of deployment-gating logic (pass/fail thresholds blocking merges/deploys), and independent/hands-on confirmation of the GitHub Actions integration.
- [claimed-docs] “Learn how to set up GitHub Actions workflows to automatically test your agents with each change”
- [claimed-docs] “Learn how to set up and manage automated cron jobs for your AI agent testing and evaluation workflows.”
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
ml engineerWrite custom code-based scorers and metrics for my evaluations
weight 2 · round drawnLangSmith docs explicitly list 'Code rules' as an evaluator type alongside LLM-as-judge, human review, and pairwise comparison, confirming support for custom code-based scorers/metrics in evaluations. Missing for 10: no concrete code example of writing a custom Python scorer function and no independent/hands-on corroboration of code-based evaluator usage.
- [claimed-docs] “Create evaluators to score performance: Human review, Code rules, LLM-as-judge, Pairwise comparison”
- [claimed-docs] “Human review * Code rules * LLM-as-judge * Pairwise comparison”
- [claimed-docs] “Create [evaluators](/langsmith/evaluation-concepts#evaluators) to score performance:”
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
Cekura explicitly documents Python metrics that let engineers 'write custom evaluation logic in Python to evaluate your AI agent's performance with complete control', alongside LLM-judge metrics as an alternative, giving ML engineers code-based scorer capability. missing for 10: no independent hands-on example of a custom Python metric in use, and no detail on SDK/CLI invocation of custom scorers beyond the docs page.
- [claimed-docs] “Write custom evaluation logic in Python to evaluate your AI agent's performance with complete control”
- [claimed-docs] “Evaluate AI voice agent calls using natural language criteria with LLM-powered judging”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
ml engineerCompare eval runs side by side to catch regressions between prompt or model versions
weight 3 · round to LangSmithLangSmith's evaluation docs explicitly describe running evaluations on curated datasets to 'compare versions, benchmark performance, and catch regressions,' with experiments generated per run and pairwise comparison evaluators supported, directly matching the story. Missing for 10: independent/hands-on confirmation of the side-by-side comparison UI experience and any community validation specific to regression-catching workflows.
- [claimed-docs] “Run evaluations on curated datasets during development to compare versions, benchmark performance, and catch regressions.”
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
- [claimed-docs] “Create evaluators to score performance: Human review, Code rules, LLM-as-judge, Pairwise comparison”
- [claimed-docs] “Human review * Code rules * LLM-as-judge * Pairwise comparison”
- [claimed-docs] “Execute your application on the dataset to create an [experiment](/langsmith/evaluation-concepts#experiment). Configure [repetitions, concur…”
Cekura has an explicit A/B testing feature to compare two agent versions side-by-side to measure prompt, model, or configuration changes, plus dashboards for visualizing metrics/call data, which supports regression detection between versions. However, the story specifically frames this as an ML-engineer 'eval run' comparison workflow, and evidence is limited to a single docs page description without detail on run history diffing, metric-level regression flags, or independent hands-on confirmation. Missing for 10: detailed UI/workflow evidence of comparing full eval-run history (not just two agent versions), regression-flagging mechanics, and independent/community corroboration of the comparison feature itself.
- [claimed-docs] “Compare two versions of your agent side-by-side to measure prompt, model, or configuration changes.”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
- [claimed-docs] “Run the same scenarios across platforms and models. Pick the one that actually performs.”
ml engineerScore outputs with configurable LLM-as-a-judge evaluators
weight 3 · round drawnLangSmith's docs explicitly document creating LLM-as-judge evaluators (alongside human review, code rules, pairwise comparison) for both offline dataset experiments and online production traces, with configurable filters/sampling for cost control. Missing for 10: independent hands-on validation of LLM-as-judge accuracy/configurability and no first-party code snippet showing evaluator configuration in this pack.
- [claimed-docs] “Create evaluators to score performance: Human review, Code rules, LLM-as-judge, Pairwise comparison”
- [claimed-docs] “Set up evaluators to run automatically on production traces: safety checks, format validation, quality heuristics, and reference-free LLM-as…”
- [claimed-docs] “Run evaluations on curated datasets during development to compare versions, benchmark performance, and catch regressions.”
- [claimed-docs] “Evaluate real user interactions in real-time to detect issues and measure quality on live traffic.”
- [claimed-docs] “Human review * Code rules * LLM-as-judge * Pairwise comparison”
- [claimed-docs] “Set up [evaluators](/langsmith/online-evaluations-llm-as-judge) to run automatically on production traces: safety checks, format validation,…”
Cekura documents an LLM-judge metric type that evaluates calls using natural language criteria, alongside custom Python metrics for full control, giving configurable LLM-as-a-judge evaluators for scoring agent outputs. This is corroborated by dashboards/A-B testing docs showing these metrics feed into broader evaluation workflows. Missing for 10: no independent hands-on benchmark of judge accuracy/configurability, and no detail on rubric customization depth or multi-model judge support.
- [claimed-docs] “Evaluate AI voice agent calls using natural language criteria with LLM-powered judging”
- [claimed-docs] “Write custom evaluation logic in Python to evaluate your AI agent's performance with complete control”
- [claimed-docs] “Compare two versions of your agent side-by-side to measure prompt, model, or configuration changes.”
ml engineerCurate datasets from production traces and run offline evaluations against them
weight 3 · round to LangSmithDocs explicitly describe creating datasets from historical production traces and running offline experiments/evaluations with configurable evaluators (human review, code rules, LLM-as-judge, pairwise comparison) to compare versions and catch regressions. Missing for 10: independent/hands-on corroboration of the dataset-curation-from-traces workflow specifically (community comments focus on tracing/observability, not dataset curation).
- [claimed-docs] “Create a dataset with examples from manually curated test cases, historical production traces, or synthetic data generation.”
- [claimed-docs] “Run evaluations on curated datasets during development to compare versions, benchmark performance, and catch regressions.”
- [claimed-docs] “Create evaluators to score performance: Human review, Code rules, LLM-as-judge, Pairwise comparison”
- [claimed-docs] “Execute your application on the dataset to create an experiment. Configure repetitions, concurrency, and caching to optimize runs.”
- [claimed-docs] “Traces are the record of what your agents did in production. Use them to debug failures, monitor quality, and build the datasets you evaluat…”
Cekura stores production call data as CallLog objects via its observability API and lets teams run metric evaluations (Python/LLM-judge) and scenario-based tests, which supports building and evaluating datasets from real traces, but there is no explicit documentation of a 'dataset curation' workflow (filtering/tagging/versioning production traces into an eval set) or an offline batch-eval mode distinct from live/simulated runs. missing for 10: dedicated dataset-curation/versioning feature for production traces, explicit offline-evaluation-on-curated-dataset workflow, independent evidence of this specific use case.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Write custom evaluation logic in Python to evaluate your AI agent's performance with complete control”
- [claimed-docs] “Evaluate AI voice agent calls using natural language criteria with LLM-powered judging”
- [claimed-docs] “Learn how to set up and manage automated cron jobs for your AI agent testing and evaluation workflows.”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
Online evals
ml engineerRun evaluators continuously on live production traffic, not just offline datasets
weight 2 · round drawnDocs explicitly describe online evaluators that run automatically on production traces (safety checks, format validation, quality heuristics, reference-free LLM-as-judge) with filters/sampling to control cost, and separately describe real-time evaluation of live traffic distinct from offline dataset evals. This directly matches the story. Missing for 10: independent/hands-on confirmation of continuous online evaluators in practice (community evidence only covers tracing, not online eval specifically).
- [claimed-docs] “Set up evaluators to run automatically on production traces: safety checks, format validation, quality heuristics, and reference-free LLM-as…”
- [claimed-docs] “Evaluate real user interactions in real-time to detect issues and measure quality on live traffic.”
- [claimed-docs] “Set up [evaluators](/langsmith/online-evaluations-llm-as-judge) to run automatically on production traces: safety checks, format validation,…”
- [claimed-docs] “Automate workflows with rules, webhooks, and online evaluations.”
Cekura's observability pipeline explicitly ingests live production calls via webhook/API, stores them as CallLogs, and 'schedules metric evaluation' automatically, with marketing copy specifically citing 'Live drift detection across every call' as a continuous production-monitoring feature distinct from offline simulation runs. missing for 10: independent/hands-on confirmation of evaluator behavior on live traffic at scale (only vendor docs/marketing cited).
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Live drift detection across every call.”
- [claimed-docs] “Automatically detect and remove sensitive information from transcripts and audio recordings”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.cekura.ai/llms.txt # Cekura > Cekura is the testing and observability platform for voice AI agents…”
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
ai-native userDo everything through the API that I can do in the UI
weight 2 · round to CekuraDocs explicitly state traces can be filtered, exported, shared, and compared 'via the UI or API', and SDKs (Python/TS/Go/Java) support tracing, dataset creation, and evaluation programmatically, suggesting real API parity for core workflows. However, there's no evidence of a full OpenAPI spec (probe for openapi.json returned 404s) or explicit API support for dashboard/alert creation, and community notes describe friction (e.g., needing a cloud account just to view your own traces) suggesting the API surface isn't fully self-serve/parity for all UI actions. missing for 10: published OpenAPI/API reference showing full UI-equivalent endpoints, explicit API methods for dashboards/alerts management, independent confirmation that all UI actions are scriptable.
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
- [claimed-docs] “Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.”
- [probe] “PROBE openapi: all candidate paths 404 (https://docs.langchain.com/openapi.json, https://docs.langchain.com/swagger.json, https://docs.langc…”
- [community] “I built this because LangSmith needs a cloud account to see my own traces.”
Cekura ships a full OpenAPI spec, CLI, and SDK that cover core workflows—managing agents, scenarios, runs, call data, sending calls, and metrics—giving strong programmatic parity with the UI (cekura-docs-4, cekura-docs-5, cekura-docs-6, cekura-probe-3, cekura-probe-5). However, some UI-specific features like custom dashboard building (cekura-docs-11) and cron/CI setup aren't clearly confirmed as fully API-driven rather than UI-configured. Missing for 10: explicit confirmation that dashboard creation and all UI-configurable settings are exposed via API/CLI, and independent verification of complete feature parity.
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
ai-native userExport all of my data in open formats and leave
weight 3 · round to LangSmithLangSmith allows exporting traces via UI/API (langsmith-docs-2) and self-hosting your own instance (langsmith-docs-17), which gives some data portability, but there's no documented bulk-export tool, no explicit open-format guarantee (e.g., standard OTLP export of full trace history), and community comments note traces require a cloud account and lock-in concerns (langsmith-comm-5, langsmith-comm-1). missing for 10: a documented full-account data export/dump feature, explicit open-format (e.g., JSON/OTLP) export guarantees, and independent confirmation that exported data is complete and portable without vendor lock-in.
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
- [community] “I built this because LangSmith needs a cloud account to see my own traces.”
- [community] “LangSmith is excellent, but my usage is quite minimal, and I would prefer a locally hosted version that is easy to customize.”
ai-native userRead the product's source under an open license
weight 2 · round drawnLangSmithnone0/10Evidence shows an open-source client SDK (langsmith-sdk) and MCP server, but no indication that the core LangSmith platform/product itself is released under an open license — self-hosting docs describe running LangSmith in your own infra, not viewing or auditing its source code.
- [github] “client = wrap_openai(openai.Client())”
- [probe] “official MCP server documented at https://github.com/langchain-ai/langsmith-mcp-server”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
Cekuranone0/10Cekura is a closed SaaS platform with a GitHub repo only for a skills/plugin package (cekura-skills), not the product's core source code, and no evidence of an open-source license for the platform itself. No license file or open-source repo for the core product is mentioned anywhere in the evidence.
ai-native userSelf-host the core product
weight 3 · round to LangSmithLangSmith explicitly documents self-hosting the core platform in your own infrastructure for observability, evaluation, and prompt engineering (with optional deployment management), which directly matches the story. However, community evidence shows at least one user built a workaround specifically because default LangSmith usage requires a cloud account to view traces, suggesting self-hosting is not the default/easy path and likely gated behind enterprise tiers rather than freely available like a fully open-source self-host. Missing for 10: independent hands-on confirmation of a successful self-hosted deployment, and clarity on licensing/tier restrictions for self-hosting.
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering.”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for [observability](/langsmith/observability), [evaluation](/langsmith/evaluation),…”
- [community] “I built this because LangSmith needs a cloud account to see my own traces.”
Cekuranone0/10Cekura is presented as a hosted SaaS platform (API keys, cloud dashboards, webhooks) with no mention of a self-hosted or on-premises deployment option anywhere in the docs, CLI, SDK, or website copy. No evidence of Docker images, self-hosting instructions, or open-source core.
- [claimed-docs] “Create a new API key. When using API key authentication, only project-level API keys can be created.”
- [claimed-docs] “create a comprehensive plan to integrate cekura observability”
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.cekura.ai/llms.txt # Cekura > Cekura is the testing and observability platform for voice AI agents…”
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
ai-native userChoose where my data is stored (region/residency)
weight 2 · round to LangSmithLangSmith supports self-hosting your own instance in your own infrastructure (docs-17/23/32), which lets an organization control where data physically resides, but there is no documented cloud region-selection or data-residency feature (e.g., EU vs US region choice) for the hosted SaaS offering. missing for 10: explicit cloud data-residency/region selection options, documentation of supported regions, and any compliance certifications tied to residency.
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering.”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for [observability](/langsmith/observability), [evaluation](/langsmith/evaluation),…”
ai-native userPrevent my data from being used to train AI models
weight 3 · round drawnLangSmithnone0/10The 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.
ai-native userControl data retention and deletion
weight 2 · round drawnLangSmithnone0/10The evidence pack contains no mention of data retention windows, deletion APIs, or user-controlled data lifecycle settings for traces/datasets in LangSmith. Self-hosting (langsmith-docs-17/23/32) offers indirect infrastructure control but is not evidence of an explicit retention/deletion feature.
Cekuranone0/10Cekura documents PII redaction to remove sensitive info from transcripts, but there is no evidence of user-facing data retention controls or deletion policies/APIs (e.g., data export, retention windows, right-to-delete). missing for 10: documented retention policy, deletion/export mechanism, user control over stored call data lifecycle.
- [claimed-docs] “Automatically detect and remove sensitive information from transcripts and audio recordings”
ai-native userOpt out of telemetry and usage tracking
weight 2 · round drawnLangSmithnone0/10No evidence in the pack of any telemetry/usage-tracking opt-out setting for LangSmith itself; self-hosting (langsmith-docs-17/23/32) addresses data locality but doesn't address opting out of product telemetry, and community comments (langsmith-comm-1, langsmith-comm-5) actually highlight lack of control, needing a cloud account to view one's own traces.
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
- [community] “LangSmith is excellent, but my usage is quite minimal, and I would prefer a locally hosted version that is easy to customize.”
- [community] “I built this because LangSmith needs a cloud account to see my own traces.”
Cekuranone0/10No evidence of any telemetry opt-out or usage tracking controls; Cekura's docs focus on PII redaction for customer call data, not on the vendor's own product telemetry. Missing for 10: any mention of telemetry/analytics collection, opt-out mechanism, or privacy settings for usage data.
Prompt management — stories about prompt management in this arenaPrompt management
Stories about prompt management in this arena
Prompt workflow
developerIterate on prompts in a playground against real models and variables
weight 2 · round drawnLangSmithnone0/10The evidence pack mentions 'prompt engineering' and a 'prompt-context-hub#prompts' link in passing (langsmith-docs-17/23/32) but contains no description of a playground UI, testing against real models, or variable substitution for prompt iteration. Without concrete documentation of this specific capability, it cannot be credited as delivered.
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering.”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for [observability](/langsmith/observability), [evaluation](/langsmith/evaluation),…”
Cekuranone0/10Cekura's docs describe testing/evaluation workflows (scenario runs, A/B testing agent versions, synthetic conversations) but there is no evidence of an interactive playground where a developer iterates on prompt text against real models with variables in real time — the closest items (ab-testing, testing-agents/overview) describe comparing pre-built agent versions, not a live prompt-editing sandbox.
- [claimed-docs] “Compare two versions of your agent side-by-side to measure prompt, model, or configuration changes.”
- [claimed-docs] “Learn how to quickly create and test voice agents on Cekura without requiring any external API keys or integrations.”
- [claimed-docs] “Run the same scenarios across platforms and models. Pick the one that actually performs.”
Tracing instrumentation — instrumenting code and tracing requests end to endTracing instrumentation
Instrumenting code and tracing requests end to end
Ai observability
ai-native userHave an agent query my traces, metrics, and eval results through an API or MCP server to debug my app
weight 3 · round drawnLangSmith exposes traces via a documented API (filter/export via API) and has an official MCP server repo (langsmith-mcp-server) confirming agent-queryable access, but the evidence pack lacks detail on whether the MCP server or API surfaces metrics and eval results specifically, and no OpenAPI spec was found. Missing for 10: documented API/MCP coverage of metrics dashboards and eval-result querying, and independent confirmation the MCP server works well in practice.
- [probe] “official MCP server documented at https://github.com/langchain-ai/langsmith-mcp-server”
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
- [claimed-docs] “Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.”
Cekura has documented API access (OpenAPI spec, send-calls, SDK/CLI for agents/scenarios/runs/call data) and an official MCP server, but the MCP server's documented purpose is to 'design, run, and improve voice-agent evaluations' rather than explicitly querying traces/metrics/eval results for debugging — no direct evidence the MCP tools expose trace/metric/eval query endpoints for agent-driven debugging. missing for 10: explicit MCP tool list showing trace/metric/eval query capabilities, independent hands-on confirmation of an agent using MCP or API to debug via traces/metrics/evals.
- [claimed-docs] “Install Cekura Skills and MCP so your AI assistant can design, run, and improve voice-agent evaluations.”
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [probe] “PROBE openapi: HTTP 200 at https://docs.cekura.ai/openapi.json — contains "openapi" key”
- [probe] “official MCP server documented at https://docs.cekura.ai/mcp/overview”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
Data controls
developerMask or redact sensitive data before it is stored in traces
weight 2 · round to CekuraLangSmithnone0/10No evidence in the pack mentions PII masking, redaction, or data filtering before trace storage; the docs cover tracing, evaluation, dashboards, alerts, and self-hosting but nothing about redacting sensitive data in traces.
Cekura has a dedicated PII redaction feature that automatically detects and removes sensitive information from transcripts and audio recordings before/at storage, directly addressing masking/redaction in traces. missing for 10: no independent/hands-on corroboration of redaction accuracy or configurability details beyond the doc summary.
- [claimed-docs] “Automatically detect and remove sensitive information from transcripts and audio recordings”
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
Sdk coverage
developerInstrument apps in both Python and JS/TS with officially supported SDKs
weight 2 · round to LangSmithDocs and GitHub SDK repo confirm official Python and TypeScript SDKs (plus Go/Java) for instrumenting apps, with wrap_openai example and OpenTelemetry-based tracing support as alternatives. missing for 10: independent hands-on corroboration of JS/TS SDK parity and depth beyond docs, and community feedback is mixed on framework compatibility outside core LangChain use cases.
- [claimed-docs] “Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.”
- [github] “client = wrap_openai(openai.Client())”
- [claimed-docs] “LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application.”
- [community] “Langsmith only shows the llm inferences. Also it hardly works with other frameworks like crewai for instance.”
- [community] “This is primarily based on the LangChain framework so it's fairly turnkey, but has no integration with the rest of your application... It's …”
Cekura documents a general SDK ('sync and async clients for application code') and a Python-specific custom metric guide, implying Python SDK support, but there is no explicit mention of an officially supported JS/TS SDK anywhere in the evidence pack. missing for 10: explicit JS/TS SDK documentation, parity of features across languages, and confirmation the generic SDK is multi-language rather than Python-only.
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Write custom evaluation logic in Python to evaluate your AI agent's performance with complete control”
- [probe] “official CLI documented at https://docs.cekura.ai/cli-sdk/cli”
Trace capture
developerTrace multi-step agent runs as nested spans grouped into sessions or threads
weight 2 · round to LangSmithDocs and community comments confirm LangSmith traces multi-step agent runs and lets you inspect each step in a graph/execution ('full traces of moving through the graph ... inputs and outputs for each step'), and OpenTelemetry-based tracing implies nested span semantics. However, the evidence pack never explicitly documents grouping into 'sessions' or 'threads' as a first-class concept, and one hands-on report claims tracing is limited to LLM inferences and works poorly with non-LangChain frameworks like CrewAI, undercutting universal multi-step nested tracing. missing for 10: explicit docs on session/thread grouping of traces, and stronger cross-framework corroboration of full nested-span coverage beyond LLM calls.
- [claimed-docs] “Traces are the record of what your agents did in production. Use them to debug failures, monitor quality, and build the datasets you evaluat…”
- [claimed-docs] “LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application.”
- [github] “client = wrap_openai(openai.Client())”
- [community] “The power of langsmith is seeing full traces of moving through the graph and being able to inspect the inputs and outputs for each step... l…”
- [community] “Langsmith only shows the llm inferences. Also it hardly works with other frameworks like crewai for instance.”
Cekuranone0/10Cekura's observability evidence centers on capturing call-level logs (CallLog with transcript, recording, metadata) and LiveKit SDK tracing, plus dashboards and PII redaction, but nothing in the evidence describes a hierarchical trace model with nested spans grouped into sessions or threads for multi-step agent runs. Missing for 10: explicit span/trace data model, parent-child span nesting, and session/thread grouping documentation.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Enhanced observability for LiveKit agents using the Cekura SDK”
- [claimed-docs] “Build custom dashboards with widgets to visualize and analyze your call data, metrics, and metadata.”
developerInstrument my app through existing integrations for frameworks like LangChain, the OpenAI SDK, or the Vercel AI SDK
weight 2 · round to LangSmithLangSmith is built natively for LangChain and has an explicit OpenAI SDK wrapper (wrap_openai) plus OTel-based tracing and generic SDKs (Python/TS/Go/Java) for other stacks, but no evidence names a Vercel AI SDK integration specifically, and community feedback notes friction integrating non-LangChain frameworks (e.g. 'hardly works with other frameworks like crewai', 'no integration with the rest of your application'). missing for 10: explicit Vercel AI SDK integration documentation, broader evidence that non-LangChain framework instrumentation works smoothly.
- [github] “client = wrap_openai(openai.Client())”
- [claimed-docs] “LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application.”
- [claimed-docs] “Trace your preferred framework or integrate LangSmith with any agent stack using our Python, Typescript, Go, or Java SDKs.”
- [community] “Langsmith only shows the llm inferences. Also it hardly works with other frameworks like crewai for instance.”
- [community] “This is primarily based on the LangChain framework so it's fairly turnkey, but has no integration with the rest of your application... It's …”
Cekura documents a generic observability API/webhook (send-calls) and SDK/CLI that any agent could POST to, plus a named integration for LiveKit voice agents, but there is no documented integration for LangChain, the OpenAI SDK, or the Vercel AI SDK specifically as the story requests. missing for 10: named LangChain integration, named OpenAI SDK integration, named Vercel AI SDK integration.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Enhanced observability for LiveKit agents using the Cekura SDK”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
developerCapture multimodal payloads (images, audio, files) inside my traces
weight 1 · round to CekuraLangSmithnone0/10No evidence in the pack mentions multimodal payload capture (images, audio, files) within traces; documentation covers general tracing, evaluation, dashboards, alerts, and SDK integrations but never addresses multimodal content in traces. Missing for 10: any mention of image/audio/file attachment support in trace payloads, UI rendering of multimodal content, or SDK helpers for multimodal logging.
Cekura's observability pipeline explicitly captures audio (recording URL) and transcript/metadata as part of CallLog traces, and PII redaction applies to both transcripts and audio recordings, showing audio is a first-class multimodal payload. However, there is no evidence of support for images or generic file attachments within traces — the product is voice-agent focused and docs never mention image/file capture. Missing for 10: documented support for image payloads, documented support for arbitrary file attachments in traces.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Automatically detect and remove sensitive information from transcripts and audio recordings”
developerSend and receive traces over OpenTelemetry (OTLP) instead of a proprietary format
weight 3 · round to LangSmithLangSmith's docs explicitly state OpenTelemetry-based tracing support, letting any OTel-compatible app send traces to LangSmith instead of using a proprietary SDK format. However, evidence is limited to a single doc page with no independent/hands-on corroboration or detail on receiving/exporting via OTLP endpoints. Missing for 10: independent verification of OTLP ingestion working in practice, and documentation on OTLP export/interop beyond a single sentence.
- [claimed-docs] “LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application.”
Cekuranone0/10Cekura's observability ingestion uses a proprietary POST endpoint (transcript, recording URL, metadata) and its own API/CLI/SDK, with no mention of OpenTelemetry or OTLP support anywhere in the evidence pack.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Programmatic access to Cekura — sync and async clients for application code”
- [claimed-docs] “Manage agents, scenarios, runs, and call data from your terminal”
developerCapture traces of my LLM calls with inputs, outputs, latency, and token usage
weight 3 · round to LangSmithDocs and SDK examples (wrap_openai) confirm capturing full traces with inputs/outputs, and dashboards explicitly track token usage, latency, and error rates; OpenTelemetry support extends this beyond native SDKs. Community feedback corroborates seeing full input/output traces per step, though one user notes it mainly captures LLM inferences and integration with non-LangChain frameworks (e.g., CrewAI) is weaker. missing for 10: independent benchmark of latency/token accuracy, broader non-LangChain framework validation.
- [claimed-docs] “Traces are the record of what your agents did in production. Use them to debug failures, monitor quality, and build the datasets you evaluat…”
- [claimed-docs] “Filter, export, share, and compare traces via the UI or API.”
- [claimed-docs] “LangSmith supports OpenTelemetry-based tracing, allowing you to send traces from any OpenTelemetry-compatible application.”
- [claimed-docs] “Prebuilt dashboards are created automatically for each project and cover essential metrics, such as trace count, error rates, token usage, a…”
- [github] “client = wrap_openai(openai.Client())”
- [community] “The power of langsmith is seeing full traces of moving through the graph and being able to inspect the inputs and outputs for each step... l…”
- [community] “Langsmith only shows the llm inferences. Also it hardly works with other frameworks like crewai for instance.”
Cekura's observability captures call-level data (transcript, recording URL, metadata) via its send-calls API and stores it as a CallLog, and offers 'enhanced observability' for LiveKit agents, but the docs never explicitly mention capturing per-LLM-call inputs/outputs, latency, or token usage — the focus is on transcript/audio-level call logging for voice-agent evaluation rather than granular LLM tracing. Missing for 10: explicit documentation of input/output payload capture per LLM call, latency metrics, and token usage tracking.
- [claimed-docs] “Your agent (or its provider's post-call webhook) POSTs here with the transcript, recording URL, and metadata; Cekura stores it as a `CallLog…”
- [claimed-docs] “Enhanced observability for LiveKit agents using the Cekura SDK”
- [probe] “official MCP server documented at https://docs.cekura.ai/mcp/overview”
Not comparable on these axes
ai-native userPlug MCP servers into this product so it can use their tools
weight 3 · not comparableLangSmithn/aLangSmith is an observability/evaluation platform for LLM applications, not an agent that executes tasks using tools; the only MCP evidence found (langsmith-probe-4) shows LangSmith exposes itself AS an MCP server for others to query traces/evals, which is the opposite direction from the story's ask (LangSmith consuming external MCP servers' tools). There is no indication LangSmith has an agent runtime that plugs into MCP servers as a client, so this axis does not apply to this product category.
- [probe] “official MCP server documented at https://github.com/langchain-ai/langsmith-mcp-server”
Cekuranone0/10All Cekura MCP evidence describes Cekura exposing its own MCP server so external AI assistants can call Cekura's tools (docs-3, probe-4), not Cekura itself acting as an MCP client that consumes third-party MCP servers' tools. No evidence shows Cekura can plug in and use external MCP servers.
- [claimed-docs] “Install Cekura Skills and MCP so your AI assistant can design, run, and improve voice-agent evaluations.”
- [probe] “official MCP server documented at https://docs.cekura.ai/mcp/overview”
developerVersion prompts and deploy changes to production without shipping code
weight 3 · not comparableThe evidence only references 'prompt engineering' and a 'prompt-context-hub#prompts' link within self-hosted/observability docs, implying a prompt management feature exists, but there is no detail on versioning, promotion workflows, or deploying prompt changes to production without code changes. Missing for 10: explicit docs on prompt version history, tagging/promotion to production, and evidence that apps pull prompts at runtime without redeploy.
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering. You can optionally enable La…”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for observability, evaluation, and prompt engineering.”
- [claimed-docs] “Host an instance of LangSmith in your own infrastructure for [observability](/langsmith/observability), [evaluation](/langsmith/evaluation),…”
Cekuran/aCekura is a testing/observability platform for voice AI agents (simulated calls, metrics, A/B comparison of agent versions), not a prompt-management or deployment system. Prompt versioning and production deployment without code changes belongs to the underlying agent-building platform, not to a third-party evaluation/observability tool like Cekura, making this axis a category error for this product type.
- [claimed-docs] “Compare two versions of your agent side-by-side to measure prompt, model, or configuration changes.”
- [probe] “PROBE llms.txt: HTTP 200 at https://docs.cekura.ai/llms.txt # Cekura > Cekura is the testing and observability platform for voice AI agents…”