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September 18, 2026Using TypeSafe's Jev for evalsA specialized decision model for routing, classification, and eval verdicts — and how to score Langfuse traces with it.Annabell Schäfer TypeSafe launched Jev mid-September 2026. It makes decisions, cannot write a single sentence, and is 20 to 200x faster and 40 to 400x cheaper than frontier models according to TypeSafe. You send a state, a string or JSON, plus typed questions. You get typed answers with probabilities. It gives you no reasoning back. Useless at other tasks. Decisions matter a lot for AI agents and AI-powered workflows. AI decides between different categories, whether something is true or not, which tool or path to take. And in the case of evals, AI classifies or simply decides if an output is right or wrong. Over the past years we forced this out of LLMs with structured outputs and JSON schemas, and still paid generation prices for a yes/no. Jev is deliberately built for that job instead of adapted to it. The flipside: it will not write code, summaries, or tell you why it answered the way it did. What Jev is good for Jev can help anywhere in your pipeline or eval harness where decisions are repeated, high volume, and t…

Used by 21 of Fortune 5090B+ observations/month100,000+ engineers building on LangfuseUsed by 21 of Fortune 5090B+ observations/month100,000+ engineers building on LangfuseUsed by 21 of Fortune 5090B+ observations/month100,000+ engineers building on LangfuseOpen Source Agent Evals &ObservabilityTrace, evaluate, and improve AI agents with one open platform. Use production data to understand behavior, collaborate on fixes, and ship better quality at lower cost and latency.Start freeSDocumentationDOnboard with AIRead storyRead storyRead storyRead storyRead storyRead storyLaunch, observe, improve — repeat.Langfuse connects tracing, monitoring, datasets, experiments, and evaluation in one continuous loop. Use production signals to understand behavior, test improvements, and ship better agents with confidence.The full AI engineering loopSee how observability, prompts, evals, experiments, and human feedback work together.Learn in AcademyAll the tools, oneintegrated platform.One integrated platform to trace, manage prompts, evaluate, and experiment from prototype to production scale.ObservabilityHierarchical traces capture every LLM call, tool invocation, and retrieval step. Filter by user…

HandbookChaptersWho are our customers?Copy pageWho are our customers? We are building Langfuse for engineering teams working on impactful AI applications/features that are core to their product strategy, constantly iterating and working around model limitations. We are working with 50,000+ companies using Langfuse across industries, AI use cases, company sizes and geographies. Some of our customers and what they do: Ramp - Smart financial infrastructure; self-hosted agent observability (Watch Story) Canva - Online design platform for creating visual content (Read Story) Intuit - Financial software company (TurboTax, QuickBooks, Mint) Juicebox.ai - AI recruiting platform that powers outbound talent sourcing Samsara - Fleet management and IoT platform for physical operations Twilio - Cloud communications platform (SMS, voice, video APIs) SumUp - Point-of-sale and payment processing solutions for small businesses (Read Story) Khan Academy - Free online educational platform with courses and lessons (Read Story) Magic Patterns - AI-powered design tool for generating UI components and prototypes (Read Story) Merck - Global pharmaceutical and life sciences company (Read Story) Hugging Fac…

DocsObservabilityOverviewCopy pageObservability & Application Tracing Generative AI systems are inherently non-deterministic. Therefore, debugging your application without any observability tool is more like guesswork. Implementing comprehensive AI observability gives you the tools to understand what's happening inside your application and why. It is the foundation for continuously evaluating and improving your system. The core of this is application tracing — structured logs of every request that capture the exact prompt sent, the model's response, token usage, latency, and any tools or retrieval steps in between. Here's an example of a trace in the Langfuse UI: 🚀Want to see it in action? Create a free account and explore Langfuse Observability in the interactive example project. Getting Started Start by setting up your first trace, and compare it against our best practices guide so you're set up for success. If you're new to AI observability, take a look at the core concepts too. Use your traces Once traces are coming in, the next step is to make sense of them and use what you learn to improve your agent. Read the chapter on Monitoring in the Langfuse Academy to learn how to uti…

DocsExample ProjectCopy pageExample Project The Langfuse example project is a live, shared project that lets you explore Langfuse's features with real data before setting up your own account. Step 01Generate your first demo traceRun a demo interaction below to generate real data.Step 02Find your trace in LangfuseOpen Tracing and select your latest request to inspect its trace.Step 03Explore the whole demo projectOpen the live shared project in Langfuse. Generate your first demo trace Q&A ChatbotChatVoice AgentVoiceImage GeneratorImageSentiment ClassifierSentimentRock Paper ScissorsRPS👋 Do you have any questions about Langfuse?Ask me!What can I use Langfuse for?How do I link my prompts to my traces? My code is in pythonHow do I get started with tracing?⚠️ Warning: Do not enter sensitive information. All chat messages can be viewed in the public example project. Responses may be inaccurate. Please check the documentation for details or reach out to us via the chat widget.Click to start a voice conversationStore audio recording on the traceStart a voice conversation with the AI agent. It runs on OpenAI GPT-Live, and every turn, tool call, and the recording is traced in Langfuse via L…

DocsEvaluationOverviewCopy pageEvaluation Overview Evals give you a repeatable check of your LLM application's behavior. You replace guesswork with data, and catch regressions before you ship a change. Evaluation runs across most of the AI engineering loop: you score live traces in production, turn interesting examples into datasets, run experiments to compare changes, and judge the results with manual or automated evaluators. It happens both online, on live production traces, and offline, before you ship a change. DeployOnlineTracetraces · sessions · agents · promptsOnlineMonitordashboards · LLM-as-judge · feedbackOfflineBuild datasetsdatasets · features-as-testsOfflineExperimentprompts · models · code variantsOfflineEvaluatejudges · custom evals · annotation 🚀Want to see it in action? Create a free account and explore Langfuse Evaluation in the interactive example project. Getting Started You can evaluate both: live incoming traces to measure quality on production data and track trends over time. your existing application on a pre-defined dataset, to make sure your changes are ready for production. For more information on how evaluators, scores, datasets, and experiments fit tog…

DocsOverviewCopy pageLangfuse Overview Langfuse is an open-source AI engineering platform (GitHub) that helps teams collaboratively debug, analyze, and iterate on their AI agent applications. All platform features are natively integrated to accelerate the development workflow. Langfuse is open, self-hostable, and extensible (why Langfuse?). ObservabilityLog tracesLowest level transparencyUnderstand cost and latencyPromptsVersion control and deployCollaborate on promptsTest prompts and modelsEvaluationMeasure output qualityMonitor production healthTest changes in developmentPlatformAPI-first architectureData exports to blob storageEnterprise security and administration Observability Observability is essential for understanding and debugging AI agent applications. Unlike traditional software, AI agent applications involve complex, non-deterministic interactions that can be challenging to monitor and debug. Langfuse provides comprehensive tracing capabilities that help you understand exactly what's happening in your application. Traces include all LLM and non-LLM calls, including retrieval, embedding, API calls, and more Support for tracking multi-turn conversations as sessions and us…

DocsPrompt ManagementOverviewCopy pagePrompt Management Prompt management is a systematic approach to storing, versioning, and retrieving prompts for your LLM application. Instead of hardcoding prompts in your application code, you manage them centrally in Langfuse. 🚀Want to see it in action? Create a free account and explore Langfuse Prompt Management in the interactive example project. Decouple Prompt Updates from Code Deployment In most LLM applications, prompt iteration and code deployment are managed by different people. Product managers and domain experts iterate on prompts, while engineers manage deployments. With prompts in code, a simple text change requires engineering involvement, code review, and a full deployment cycle, turning a 2-minute update into hours or days of waiting. When prompts live in Langfuse, non-technical team members update them directly in the UI while your application automatically fetches the latest version. This separation of concerns means prompt updates deploy instantly, without needing to involve engineering or triggering a deployment. No latency, no availability risk Langfuse Prompt Management adds no latency to your application. Prompts are ca…

← Back to changelogAugust 26, 2025New End-to-End Walkthrough VideosMarc KlingenWe've released new comprehensive walkthrough videos covering observability, prompt management, and evaluation to help you get up to speed quickly with Langfuse. These walkthroughs are currently being refreshed. In the meantime, explore Langfuse yourself in the interactive example project.Was this page helpful?GoodBadSupportPreviousFull-Text Search Across Dataset ItemsNextAdditional Observation Types for More Meaningful Span ContextGitHubDiscordXYouTubeLinkedInProductObservabilityPrompt ManagementEvaluationsMetricsLangfuse for AgentsChat agentsCoding agentsWorkflow automationPlaygroundPricingEnterpriseDevelopersDocumentationSelf-HostingSDKsIntegrationsAPI ReferenceStatusTalk to UsResourcesBlogChangelogEventsRoadmapInteractive DemoCustomersCompareAI Engineering LibraryWorkshopGuides & CookbooksCompanyAbout UsPartnersCareersHandbookPressSecuritySupportOpen sourceTermsPrivacyImprintCookie Policy© 2022–2026 Langfuse GmbH / Finto Technologies Inc.Design by Altalogy

August 31, 2025Langfuse August UpdateLangfuse August Update: New Observation Types, End-to-End Walkthrough Videos, Agent Demo Project, and more.Marc Klingen This month, we've shipped updates to improve tracing for complex agents, enhance team collaboration, and expanded the integration library. Here's what's new: New Observation Types We've introduced new observation types to give you deeper insight into your agent's behavior. You can now semantically label spans as Agent, Tool, Chain, Retriever, Embedding, and Guardrail, making it easier to debug and analyze complex traces. For most agent framework integrations, these types are detected automatically. You can also set them manually via the Langfuse Tracing SDKs. → Learn more in the docs End-to-End Walkthrough Videos We've released many new capabilities over recent months. We recorded four new short videos covering observability, prompt management, and evaluation to help you get up to speed quickly. These have since been consolidated into a single end-to-end walkthrough. → Watch the walkthrough Agent Example Project Experiment with our revamped interactive example project (langfuse.com/demo) that uses the Langfuse Docs MCP server t…

GuidesGuidesCopy pageGuides End-to-end examples of how to use Langfuse: tutorials, runnable cookbooks, and video walkthroughs. Introduction to Langfuse If you are new to Langfuse, watch this walkthrough to get a thorough introduction to the Langfuse platform. Introduction to LangfuseGet an overview of the complete Langfuse platform and learn how it helps teams build better LLM applications through observability, prompt management, and evaluation.Technical documentation Evaluation Tutorials Start with the fundamentals and progress through advanced techniques as your application complexity grows. Read our blog post for an overview. Set Up a User Feedback LoopCapture explicit and implicit user feedback as scores on your traces, and use it to surface the responses worth reviewing.Error AnalysisReview traces to classify issues like hallucinations, irrelevance, and formatting errors. Turn raw logs into actionable insights.Automated EvaluationsAutomated evaluators give you a scalable way to measure and monitor your application’s failure modes.Testing LLM ApplicationsBuild a testing foundation with deterministic checks and probabilistic ones using LLM judges to prevent regressions.CI/CD fo…

GuidesVideosOverviewCopy pageLangfuse Videos Introduction to Langfuse If you are new to Langfuse, watch the end-to-end walkthrough to get a thorough introduction to the Langfuse platform. More Videos Beginner's Guide to RAG Evaluation with Langfuse and RagasDebug a Fan-Out RAG Agent With TracingHeadless Langfuse From Your Coding AgentReplace Vibes With Evals: Build an Evaluation PipelineWas this page helpful?GoodBadSupportLast updated on PreviousExample: Langfuse Prompt Management for OpenAI functions (Python)NextHeadless Langfuse From Your Coding AgentGitHubDiscordXYouTubeLinkedInProductObservabilityPrompt ManagementEvaluationsMetricsLangfuse for AgentsChat agentsCoding agentsWorkflow automationPlaygroundPricingEnterpriseDevelopersDocumentationSelf-HostingSDKsIntegrationsAPI ReferenceStatusTalk to UsResourcesBlogChangelogEventsRoadmapInteractive DemoCustomersCompareAI Engineering LibraryWorkshopGuides & CookbooksCompanyAbout UsPartnersCareersHandbookPressSecuritySupportOpen sourceTermsPrivacyImprintCookie Policy© 2022–2026 Langfuse GmbH / Finto Technologies Inc.Design by Altalogy

← Back to changelogMay 15, 2026Trace context on /api/public/v2/observationsNiklas Semmler, PhDFetch a trace's tags, release, and trace name directly on each observation row /api/public/v2/observations is significantly faster than the v1 endpoint at scale — it uses cursor-based pagination and selective field retrieval so you only pay for the columns you actually need. See the Langfuse v4 data model for the full picture of what changed. The API /api/public/v2/observations now supports the trace_context field group. Add it to your fields query parameter and each observation comes back with tags, release, and traceName — denormalized from the parent trace, no extra /traces/{id} calls needed. GET /api/public/v2/observations?fields=core,basic,trace_context Common cases this simplifies: Filter observations by trace tag in a downstream pipeline without keeping a trace lookup table. Group observations by release for regression analysis directly on the observations response. Render trace names in an analytics view that paginates observations. trace_context joins the existing groups (core, basic, time, io, metadata, model, usage, prompt, metrics) on the v2 endpoint. The full table is in the O…

← Back to changelogMarch 10, 2026Simplify Langfuse for ScaleLangfuse now delivers faster product performance at scale. See the overview page for rollout details, access, and migration steps. Langfuse is rolling out a simplified architecture built for significantly faster product performance at scale. Charts, filters, APIs are becoming much faster across Langfuse Cloud. Open the Langfuse v4 Docs To avoid delays in the new experience and see your data in real time, upgrade to Python SDK v4.7.0+ and JS/TS SDK v5.4.0+. What changes for you The main table in Langfuse is now observations — every LLM call, tool execution, and agent step is a row you can query directly. As agentic applications grow more complex, a single trace can contain thousands of operations, and the interesting ones are rarely at the top level. trace_id works like any other filter column (session_id, user_id, score) to group related observations together. Filter by observation name, type, or model to surface the operations that matter most — then save that view for one-click access. See the guide on working with observations in the next version of Langfuse (v4) for common workflows and saved views to get started. What…

DocsAPI & Data PlatformFeaturesPublic APICopy pagePublic API Langfuse is open and meant to be extended via custom workflows and integrations. All Langfuse data and features are available via the API. There are 3 different groups of APIs: This page -> Project-level APIs: CRUD traces/evals/prompts/configuration within a project Organization-level APIs: provision projects, users (SCIM), and permissions Instance Management API: administer organizations on self-hosted installations API reference This page covers concepts and workflows. For the complete request and response contract of every endpoint — parameters, schemas, and interactive examples — see the API reference: API Reference: https://api.reference.langfuse.com OpenAPI spec: https://cloud.langfuse.com/generated/api/openapi.yml Quickstart Obtain credentialsThe public and secret keys are available in the Langfuse project settings.Select the regional base URLPathCloud USCloud EUCloud JapanHIPAA US/api/publichttps://us.cloud.langfuse.com/api/publichttps://cloud.langfuse.com/api/publichttps://jp.cloud.langfuse.com/api/publichttps://hipaa.cloud.langfuse.com/api/publicMake an authenticated requestExample:curl -u public-key:secret-key …

DocsAPI & Data PlatformFeaturesQuery via SDKsCopy pageQuery Data via SDKs Langfuse is open-source and data tracked with Langfuse is open. Use the Python and JS/TS SDKs to query the same public APIs without writing raw HTTP requests. Common use cases: Query row-level observations for evaluation pipelines, few-shot examples, or fine-tuning datasets. Query aggregate cost, usage, latency, volume, and score metrics for dashboards or billing workflows. Programmatically create datasets. If you are new to Langfuse, we recommend familiarizing yourself with the Langfuse data model. New data is typically available for querying within 15-30 seconds of ingestion, though processing times may vary at times. Please visit status.langfuse.com if you encounter any issues. SDKs Via the SDKs for Python and JS/TS you can easily query the API without having to write the HTTP requests yourself. The api namespace is auto-generated from the Public API (OpenAPI). Method names mirror REST resources and support filters and pagination. From Python SDK v4 and JS/TS SDK v5 onward, the high-performance observations and metrics APIs are the defaults: api.observations (formerly api.observations_v_2 / api.observation…

← Back to changelogAugust 17, 2026Langfuse v4 is live: faster at scale, with more ways to search, monitor, and evaluateMax DeichmannQuery and evaluate every agent step directly, with initial table loads in milliseconds and at least 10x faster dashboards for large projects. Langfuse v4 is live on Langfuse Cloud and generally available for self-hosted deployments. It makes it faster to debug, evaluate, and monitor complex LLM applications by letting you work with every LLM call, tool execution, and agent step directly. Initial table loads over large datasets drop from seconds to milliseconds, and dashboards over longer time ranges load at least 10x faster in large projects. Langfuse Cloud becomes v4-only on November 16, 2026 (2026-11-16). Most projects need no migration. If the Migration Assistant shows actions for your project, complete them before this date. New to LangfuseThere is nothing to migrate. Start tracing with the v4 data model.Using Langfuse CloudOpen the Migration status page and complete only the checks detected for your project.Self-hosting LangfusePlan the upgrade on your schedule and review the infrastructure requirements first. What we shipped in Langfuse v4 Langfu…

FaqAll FAQsDashboard changes in Langfuse v4Copy pageDashboard changes in Langfuse v4 This page explains how dashboards behave differently in Langfuse v4 compared to Langfuse v3. If you are on the new experience — via the toggle on Langfuse Cloud or a self-hosted v4 deployment — use this as a reference for understanding any differences you observe. Langfuse has two types of dashboards: Home dashboard — the built-in dashboard shown on the project home page with fixed tiles (trace counts, latency percentiles, score histograms, model usage, etc.). Custom dashboards — user-created dashboards with configurable widgets. Some changes below affect only one type; others affect both. Each section notes which dashboards it applies to. Background: from traces to observations In Langfuse, observations (also known as "spans" in OpenTelemetry) are the individual operations within a trace — LLM calls, tool executions, retrieval steps, and so on. (span is also a specific observation type.) In Langfuse v3, a trace is a first-class record in its own table, and observations are separate records that reference it via a foreign key. Queries JOIN these tables at read time to combine trace metadata with ob…

FaqAll FAQsDeprecated API migrationCopy pageMigration of deprecated APIs This page maps every deprecated Langfuse REST endpoint and the Python and JS/TS SDK methods that call it to their supported replacements. It includes parameter mappings, semantic differences, and before/after examples. Many of these changes follow from the observations-first data model: traces and observations are no longer separate entities, and reads are consolidated onto fewer, faster endpoints. If you access these endpoints through a Langfuse SDK, each section includes the methods in Python SDK v4 and JS/TS SDK v5 that call them. Deprecated Public API methods remain callable in these SDK majors, so replacing them is separate from upgrading the SDK itself. If you still need to upgrade, first follow Python v3 to v4 or JS/TS v4 to v5, then return here to replace deprecated API methods. Still need the deprecated endpoints? They are documented in the reference section below. This page is also served as plain markdown at https://langfuse.com/faq/all/deprecated-api-migration.md for programmatic use, e.g. by coding agents. The section anchors below are stable. Quick reference Deprecated endpointReplacementDetailsG…

FaqAll FAQsWhy do I see 524 errors on Langfuse API calls?Copy pageWhy do I see 524 errors on Langfuse API calls? The 524 error class on Langfuse Cloud indicates that your request timed out because ClickHouse ran out of resources or out of time while processing your query. This typically occurs when queries scan too much data due to overly broad filter conditions. For older data retrieval endpoints, the recommended fix is to move to the v2 data APIs instead of tuning the old query, as the older APIs are less performant at scale. General Approaches To prevent 524 errors across API endpoints: Use v2 data APIs for extraction: Follow the Observations API v2 upgrade guidance for row-level data and use Metrics API v2 for aggregates. Always use the endpoint's timestamp filters: v2 Observations uses fromStartTime and toStartTime; Metrics API v2 uses fromTimestamp and toTimestamp inside the JSON query; older trace reads use fromTimestamp and toTimestamp URL parameters. Add specific filters: Use userId, sessionId, name, tags, or other filters to narrow your query Older trace reads The older trace read endpoints remain available on Langfuse Cloud (see the Deprecated APIs reference), but are de…

FaqAll FAQsAre there any limits to the Langfuse API?Copy pageWhat are the limits to the Langfuse API? While the Langfuse API is extremely open and flexible, there are some limits to ensure the stability and performance of the platform. Langfuse Cloud If you run into unexpected limits or need to increase your limits, please reach out to us via the chat widget. Payloads 5MB per request and 5MB per response Request Constraints Trace Deletion: We advise strongly against trying to send more than 30-50 trace ids in a single DELETE request. Consider using data retention to automatically delete old traces instead of manual deletion. Rate Limits Langfuse Cloud groups API requests into per-organization resource buckets shared by all projects and API keys in the organization. Resources with dedicated buckets are listed separately from the General API bucket. Closely related buckets with identical limits may be combined in one row. Resource bucketApplies toHobbyCorePro/Team/EnterpriseTracingBatched /ingestion and /otel endpoints used by SDKs and integrations to ingest traces.1,000 req/min4,000 req/min20,000 req/minDeprecated tracingDeprecated ingestion endpoints.100 req/min400 req/min400 req/m…

DocsObservabilityFeaturesCorrectionsCopy pageCorrected Outputs Corrections allow you to capture improved versions of LLM outputs directly in trace and observation views. Domain experts can document what the model should have generated, creating a foundation for fine-tuning datasets and continuous improvement. Why Use Corrections? Domain expert feedback: Subject matter experts provide what the model should have output based on their expertise Fine-tuning datasets: Export corrected outputs alongside original inputs to create high-quality training data from production traces Quality benchmarking: Compare actual vs expected outputs across your production traces to identify systematic issues Human-in-the-loop workflows: Capture corrections during review processes, especially useful in annotation queues How It Works Add corrected outputs to any trace or observation through the UI or API. Corrections appear alongside the original output with a diff view showing what changed. Each trace or observation can have one corrected output. Adding Corrections Langfuse UIAPI/SDKVia the UINavigate to any trace or observation detail page: Find the "Corrected Output" field below the original output Cli…

DocsObservabilityFeaturesFull-Text SearchCopy pageFull-Text Search Full-text search lets you find all occurrences of a specific keyword or phrase across the inputs, outputs, and metadata of your traces and observations. This is especially useful when debugging complex applications where you remember a piece of content but not which trace it belongs to. Search in the UI Use the search bar above the traces and observations tables to search across input and output content. Matching traces and observations are returned so you can quickly locate the run you are looking for and combine search with the existing filters and time range selectors. On the v4 Observations and Traces tables, full-text search is also available inline in the Filter Search Bar, alongside structured filters like level:ERROR and latency:>2. Performance Search uses ClickHouse full-text search. Text indexes let Langfuse skip over data that cannot match a query before reading full observation payloads, which keeps search fast even for large projects with high-volume traces. You can read more in the ClickHouse GA announcement. Because these indexes are token-based, a query matches whole words rather than substrings with…

DocsMetricsFeaturesMetrics APICopy pageMetrics API GET /api/public/v2/metrics The Metrics API enables you to retrieve customized analytics from your Langfuse data. This endpoint allows you to specify dimensions, metrics, filters, and time granularity to build powerful custom reports and dashboards for your LLM applications. What you can do Use the Metrics API to: Aggregate cost, token usage, volume, latency, and score data. Group results by supported dimensions, such as model or trace attributes. Filter data and analyze trends over time. Power custom reports, dashboards, billing, and monitoring workflows. For supported views, fields, query parameters, response schemas, and interactive examples, see the v2 Metrics API Reference. For practical Python examples, see the Metrics API v2 cookbook. The deprecated GET /api/public/metrics and GET /api/public/metrics/daily endpoints are documented, with migration steps, in Migration of deprecated APIs. Metrics API v2 Where is this feature available?HobbyAvailableCoreAvailableProAvailableEnterpriseAvailableSelf HostedLangfuse v4+ Data availability: Data from older SDKs (langfuse-python < 4.7.0, langfuse-js < 5.4.0) or direct OpenTelemetry expo…

DocsEvaluationExperimentsExperiments via OpenTelemetryCopy pageExperiments via OpenTelemetry Attach experiment metadata to OpenTelemetry spans so Langfuse groups those traces as an experiment run. Using Python or TypeScript? Use Experiments via SDK. The Langfuse SDKs set these attributes automatically. See the Experiments data model for how datasets, experiment runs, items, traces, and scores relate. Ingest experiment spans Set experiment and item attributes on your OTEL spans so Langfuse can synthesize them as one experiment. The attribute list, baggage propagation pattern, and per-item loop live in the OpenTelemetry integration docs: Ingest experiment spans with OpenTelemetry After traces land, use the Experiments API to list runs and fetch items.Was this page helpful?GoodBadSupportLast updated on PreviousExperiments in CI/CDNextCompare ExperimentsGitHubDiscordXYouTubeLinkedInProductObservabilityPrompt ManagementEvaluationsMetricsLangfuse for AgentsChat agentsCoding agentsWorkflow automationPlaygroundPricingEnterpriseDevelopersDocumentationSelf-HostingSDKsIntegrationsAPI ReferenceStatusTalk to UsResourcesBlogChangelogEventsRoadmapInteractive DemoCustomersCompareAI Engineering Lib…