{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"langsmith","slug":"langsmith","name":"LangSmith","type":"platform","url":"https://smith.langchain.com","page_url":"https://unfragile.ai/langsmith","categories":["observability","model-training"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":"$39/mo"},"status":"active","verified":false},"capabilities":[{"id":"langsmith__cap_0","uri":"capability://automation.workflow.distributed.trace.collection.and.visualization.for.llm.chains","name":"distributed trace collection and visualization for llm chains","description":"Captures hierarchical execution traces across LLM calls, chain steps, and agent actions by instrumenting LangChain runtime via SDK hooks and context propagation. Traces include token counts, latencies, inputs/outputs, and error states, visualized as interactive DAGs showing call dependencies and performance bottlenecks. Uses span-based tracing architecture similar to OpenTelemetry but optimized for LLM-specific metadata (model names, temperature, token usage).","intents":["I need to see exactly what my LLM chain is doing at each step, including which models were called and how long each step took","I want to debug why my agent is making unexpected decisions by inspecting the full execution trace","I need to identify performance bottlenecks in my multi-step LLM pipeline"],"best_for":["LangChain users building production LLM applications","teams debugging complex multi-agent systems","developers optimizing token usage and latency"],"limitations":["Trace collection adds network overhead for each span submission (typically 50-200ms per batch)","Requires LangChain SDK integration — no native support for non-LangChain LLM calls without custom instrumentation","Trace retention limited by plan tier; free tier stores traces for 7 days","Sampling required at scale (>10k traces/day) to manage storage costs"],"requires":["LangChain Python SDK 0.0.200+ or LangChain JS 0.0.100+","Valid LangSmith API key from smith.langchain.com","Network connectivity to api.smith.langchain.com"],"input_types":["LangChain chain/agent execution context","LLM call parameters (model, temperature, max_tokens)","Structured metadata (user_id, session_id, tags)"],"output_types":["interactive trace visualization (DAG)","JSON trace export","performance metrics (latency, token counts, cost)"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_1","uri":"capability://memory.knowledge.prompt.versioning.and.management.hub","name":"prompt versioning and management hub","description":"Centralized registry for storing, versioning, and deploying LLM prompts with git-like commit history, branching, and rollback capabilities. Prompts are stored as immutable versions linked to evaluation results and production deployments. Supports templating with Jinja2 or Handlebars for dynamic variable injection, and integrates with LangChain's LLMChain to pull prompts at runtime via semantic versioning (e.g., 'my-prompt@latest' or 'my-prompt@v2.3').","intents":["I want to version my prompts and track which version was used in production for each trace","I need to A/B test two prompt versions and see which one performs better on my evaluation dataset","I want to roll back a prompt change that degraded performance without redeploying my application"],"best_for":["teams iterating on prompt engineering with multiple stakeholders","production LLM applications requiring audit trails for compliance","organizations running prompt experiments across datasets"],"limitations":["No built-in prompt optimization or auto-tuning — versioning is manual","Templating limited to Jinja2/Handlebars; no support for complex conditional logic or custom filters without workarounds","Prompt hub is LangSmith-specific; exporting prompts to other platforms requires manual JSON export","No native support for multi-language prompts (e.g., English + Spanish variants in single version)"],"requires":["LangSmith account with Prompt Hub access","LangChain SDK to pull prompts at runtime","Basic understanding of semantic versioning"],"input_types":["plain text prompts","templated prompts with variables (Jinja2/Handlebars syntax)","metadata (description, tags, author)"],"output_types":["versioned prompt objects with metadata","prompt snapshots linked to evaluation runs","JSON export for external use"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_10","uri":"capability://automation.workflow.real.time.alerting.and.anomaly.detection.on.trace.metrics","name":"real-time alerting and anomaly detection on trace metrics","description":"Monitors trace metrics (latency, error rate, token usage, cost) in real-time and triggers alerts when metrics exceed thresholds or deviate from baseline patterns. Uses statistical anomaly detection (z-score, moving average) to identify unusual behavior without manual threshold configuration. Supports multiple notification channels (email, Slack, webhooks) and integrates with incident management platforms.","intents":["I want to be alerted if my LLM application's latency suddenly increases","I need to know immediately if my error rate exceeds 5% so I can investigate","I want to detect cost spikes caused by unexpected token usage"],"best_for":["teams operating production LLM applications","organizations requiring SLA compliance and incident response","developers monitoring cost and performance metrics"],"limitations":["Anomaly detection is statistical and may produce false positives/negatives with low-volume traces","Alert rules are configured per metric; no support for complex multi-metric conditions","Notification delivery is not guaranteed; no built-in retry logic for failed webhook deliveries","Alert history is limited by plan tier; free tier stores alerts for 30 days"],"requires":["LangSmith account with Alerting feature","Traces being collected in LangSmith","Notification channel configured (email, Slack webhook, etc.)"],"input_types":["alert rules (metric, threshold, condition)","notification channel configuration"],"output_types":["alert notifications (email, Slack, webhook)","alert history and audit logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_11","uri":"capability://tool.use.integration.api.based.trace.and.evaluation.access.for.programmatic.workflows","name":"api-based trace and evaluation access for programmatic workflows","description":"Exposes REST and GraphQL APIs for querying traces, running evaluations, managing datasets, and accessing evaluation results programmatically. Enables building custom dashboards, integrating with external analysis tools, or automating evaluation workflows. APIs support filtering, pagination, and bulk operations. Authentication via API keys with role-based access control.","intents":["build custom dashboards or reports using LangSmith data in your own BI tool","automate evaluation runs triggered by external events (new model release, code deployment)","export traces and evaluation results to data warehouse for analysis","integrate LangSmith into CI/CD pipelines for automated quality gates"],"best_for":["teams with custom analytics or reporting requirements","organizations integrating LangSmith into existing data pipelines","developers building custom tooling on top of LangSmith"],"limitations":["API rate limits depend on plan tier (free: 100 req/min, paid: 1000+ req/min)","GraphQL API has higher latency than REST for simple queries","no built-in pagination for large result sets — requires manual cursor handling","API responses include full trace context — can be large for complex chains (>1MB per trace)"],"requires":["LangSmith API key with appropriate permissions","HTTP client library (requests, fetch, etc.)","understanding of LangSmith data model (traces, runs, evaluations)"],"input_types":["filter criteria (date range, tags, model name)","pagination parameters (limit, offset/cursor)","evaluation configuration for programmatic runs"],"output_types":["JSON-formatted trace objects","evaluation results with scores and metadata","dataset examples and versions","cost and token usage aggregations"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_2","uri":"capability://data.processing.analysis.dataset.driven.evaluation.with.custom.metrics","name":"dataset-driven evaluation with custom metrics","description":"Manages labeled datasets (inputs, expected outputs, metadata) and runs evaluation jobs that execute chains against dataset examples, computing both built-in metrics (exact match, token overlap, semantic similarity via embeddings) and custom Python-defined metrics. Evaluation results are aggregated into scorecards showing pass rates, latency distributions, and cost breakdowns per model or prompt version. Supports batch evaluation with configurable concurrency and retry logic.","intents":["I want to test my LLM chain against 100 examples and see what percentage pass a custom correctness check","I need to compare two prompt versions on the same dataset and see which one has better latency and lower cost","I want to define a custom metric (e.g., 'response mentions all required entities') and track it across evaluation runs"],"best_for":["teams with labeled test datasets for LLM outputs","organizations requiring quantitative evaluation before production deployment","developers building domain-specific LLM applications with custom success criteria"],"limitations":["Custom metrics require Python code execution in LangSmith's sandboxed environment; no support for external metric services","Semantic similarity metrics depend on embedding model choice (OpenAI, Cohere); different embeddings can produce inconsistent results","Evaluation runs are synchronous and block on slow chains; no native support for async evaluation of high-latency models","Dataset size limited by plan tier; free tier capped at 1000 examples per dataset"],"requires":["LangSmith account with Evaluation feature access","Labeled dataset uploaded to LangSmith (CSV, JSON, or via API)","LangChain chain or custom callable that accepts dataset inputs","API keys for embedding models if using semantic similarity metrics"],"input_types":["structured datasets (JSON, CSV with input/output/metadata columns)","Python functions for custom metrics","LangChain chains or arbitrary callables"],"output_types":["evaluation scorecards (pass rate, latency, cost metrics)","per-example results with predictions and metric scores","comparison reports between evaluation runs"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_3","uri":"capability://data.processing.analysis.annotation.queue.and.human.feedback.collection","name":"annotation queue and human feedback collection","description":"Provides a web UI for human annotators to review LLM outputs from production traces, assign labels (correct/incorrect, quality ratings, category tags), and add free-form feedback. Annotations are stored as structured records linked to the original trace and can be exported as labeled datasets for fine-tuning or retraining evaluation models. Supports collaborative workflows with role-based access (viewer, annotator, admin) and bulk operations for labeling multiple examples.","intents":["I want my team to review a sample of production outputs and label them as correct or incorrect to build a ground-truth dataset","I need to collect human feedback on which LLM responses are most helpful to improve my evaluation metrics","I want to identify failure patterns by having annotators categorize errors and then filter traces by error type"],"best_for":["teams with domain expertise to label LLM outputs","organizations building fine-tuning datasets from production data","projects requiring human-in-the-loop evaluation before scaling"],"limitations":["No built-in inter-annotator agreement metrics (Cohen's kappa, Fleiss' kappa); requires external analysis","Annotation UI is web-only; no mobile app or offline annotation capability","No native integration with external annotation platforms (Mechanical Turk, Scale AI); requires manual export/import","Annotations are stored in LangSmith only; exporting to external labeling tools requires custom scripts"],"requires":["LangSmith account with Annotation feature access","Production traces in LangSmith (from trace collection capability)","Team members with LangSmith accounts for annotation access","Defined annotation schema (label types, categories)"],"input_types":["production traces (LLM inputs, outputs, metadata)","custom annotation schemas (label types, categories, rating scales)"],"output_types":["labeled datasets (JSON/CSV with trace ID, annotation, feedback)","annotation statistics (inter-annotator agreement, label distribution)","filtered trace subsets by annotation label"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_4","uri":"capability://data.processing.analysis.cost.and.token.usage.tracking.across.models.and.providers","name":"cost and token usage tracking across models and providers","description":"Automatically extracts and aggregates token counts and API costs from LLM calls across multiple providers (OpenAI, Anthropic, Cohere, Azure, local models) by parsing model names and pricing tables. Provides dashboards showing cost per trace, per user, per prompt version, and per model, with drill-down capabilities to identify expensive chains. Supports custom pricing rules for self-hosted or fine-tuned models. Costs are calculated in real-time during trace collection and stored with each span.","intents":["I want to see how much my LLM application costs to run per user or per feature","I need to identify which prompt versions or models are most expensive and optimize them","I want to set up alerts when daily costs exceed a budget threshold"],"best_for":["teams operating LLM applications at scale with cost sensitivity","organizations comparing multiple models or providers for cost-effectiveness","developers optimizing token usage to reduce API bills"],"limitations":["Pricing data is static and updated periodically; real-time price changes from providers are not reflected immediately","Custom pricing rules require manual configuration; no automatic detection of fine-tuned model pricing","Cost tracking is approximate for streaming responses; actual token counts may differ from estimates","No native cost forecasting or budget optimization recommendations"],"requires":["LangSmith account with cost tracking enabled","LangChain SDK that reports token counts (requires model to support token counting)","API keys for LLM providers (OpenAI, Anthropic, etc.) to enable token reporting"],"input_types":["LLM call metadata (model name, token counts, provider)","custom pricing rules (JSON format with model/provider mappings)"],"output_types":["cost dashboards (cost per trace, user, model, prompt version)","cost breakdowns by provider and model","cost trend reports over time"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_5","uri":"capability://data.processing.analysis.session.and.user.level.trace.aggregation","name":"session and user-level trace aggregation","description":"Groups traces by user ID, session ID, or custom tags to enable conversation-level and user-level analysis. Provides session timelines showing all traces for a user in chronological order, with filtering by date range, model, or trace status. Supports session-level metrics (total cost, total tokens, conversation length) and enables bulk operations (e.g., export all traces for a user, delete traces for a user). Session data is indexed for fast retrieval and supports multi-tenant isolation.","intents":["I want to see all interactions a specific user had with my LLM chatbot, including the full conversation history","I need to analyze patterns across user sessions to identify common failure modes or user intents","I want to export all traces for a user to comply with data deletion requests (GDPR)"],"best_for":["teams building conversational AI applications with user sessions","organizations requiring user-level audit trails for compliance","developers analyzing user behavior and conversation patterns"],"limitations":["Session grouping is based on user-provided IDs; no automatic session detection from conversation flow","Session timelines are read-only; no native support for editing or redacting individual traces within a session","Bulk operations (export, delete) are asynchronous and can take minutes for large sessions","No built-in session analytics (e.g., average session length, user retention); requires external analysis"],"requires":["LangSmith account","LangChain SDK with user_id and session_id metadata passed to traces","Consistent user ID scheme across application"],"input_types":["trace metadata (user_id, session_id, custom tags)","date range filters, model filters"],"output_types":["session timelines (chronological list of traces)","session-level metrics (cost, tokens, trace count)","exported trace datasets per user/session"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_6","uri":"capability://data.processing.analysis.llm.specific.performance.benchmarking.and.comparison","name":"llm-specific performance benchmarking and comparison","description":"Provides built-in benchmarking workflows to compare models, prompt versions, or configurations on the same dataset with statistical significance testing. Generates comparison reports showing latency distributions, token efficiency, cost per output, and custom metric scores with confidence intervals. Supports A/B testing with automatic traffic splitting and statistical power analysis to determine required sample size for significance.","intents":["I want to run an A/B test comparing GPT-4 vs Claude on my evaluation dataset and see which is faster and cheaper","I need to determine if my prompt improvement is statistically significant or just noise","I want to benchmark my fine-tuned model against the base model on the same examples"],"best_for":["teams making model selection decisions with quantitative data","organizations running production A/B tests on LLM applications","researchers comparing prompt engineering techniques"],"limitations":["Statistical testing assumes independent samples; no built-in support for paired testing or within-subject designs","A/B testing requires manual traffic splitting configuration; no automatic traffic allocation based on performance","Benchmarking is limited to models available in LangChain; custom or proprietary models require wrapper implementation","No support for multi-armed bandit algorithms or adaptive testing strategies"],"requires":["LangSmith account with Benchmarking feature","Labeled evaluation dataset","Multiple models or prompt versions to compare","Sufficient sample size for statistical significance (typically 30+ examples per variant)"],"input_types":["evaluation dataset","model/prompt variants to compare","custom metrics (optional)"],"output_types":["comparison reports with latency, cost, and metric distributions","statistical significance tests (p-values, confidence intervals)","A/B test results with traffic split and winner determination"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_7","uri":"capability://tool.use.integration.sdk.based.runtime.instrumentation.with.minimal.code.changes","name":"sdk-based runtime instrumentation with minimal code changes","description":"Provides language-specific SDKs (Python, JavaScript/TypeScript) that automatically instrument LangChain chains and agents with minimal code changes. Uses context variables and decorators to capture execution context without modifying chain logic. Supports both synchronous and asynchronous execution, with automatic error handling and retry logic. Traces are batched and sent asynchronously to avoid blocking application execution.","intents":["I want to add observability to my LangChain application without rewriting my chain code","I need to trace both synchronous and asynchronous chain execution in my FastAPI application","I want to automatically capture errors and exceptions in my LLM chains without try-catch blocks"],"best_for":["LangChain users wanting zero-boilerplate observability","teams with existing LangChain codebases avoiding refactoring","developers building async LLM applications"],"limitations":["Instrumentation is LangChain-specific; non-LangChain LLM calls require manual tracing via SDK methods","Async instrumentation adds overhead for high-concurrency applications (>1000 concurrent traces)","Context propagation across thread/process boundaries requires manual configuration","SDK updates may lag behind LangChain releases, causing compatibility issues"],"requires":["Python 3.8+ or Node.js 14+","LangChain SDK installed","LangSmith API key set as environment variable","Network connectivity to api.smith.langchain.com"],"input_types":["LangChain chain/agent objects","execution context (user_id, session_id, metadata)"],"output_types":["traces sent to LangSmith backend","local trace objects (optional, for debugging)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_8","uri":"capability://tool.use.integration.multi.provider.llm.integration.with.unified.interface","name":"multi-provider llm integration with unified interface","description":"Abstracts LLM provider differences (OpenAI, Anthropic, Cohere, Azure, local models) through a unified tracing interface that captures provider-specific metadata (model name, temperature, top_p, token limits) consistently. Automatically maps provider-specific response formats to a standard trace schema, enabling cross-provider comparison and cost tracking. Supports streaming responses with token-by-token tracing.","intents":["I want to compare OpenAI and Anthropic models on the same traces without rewriting my tracing code","I need to track token usage consistently across different LLM providers","I want to switch from OpenAI to a local model without changing my observability setup"],"best_for":["teams evaluating multiple LLM providers","organizations migrating between providers","developers building provider-agnostic LLM applications"],"limitations":["Provider-specific features (e.g., OpenAI's function calling, Anthropic's tool use) are normalized to a common schema, losing nuance","Streaming token counts are approximate; actual token counts may differ from estimates","Custom provider parameters not in the standard schema are dropped during tracing","Local model support requires manual configuration of token counting logic"],"requires":["LangSmith SDK","API keys for LLM providers being used","LangChain SDK with provider support"],"input_types":["LLM calls from any supported provider","provider-specific parameters (temperature, top_p, etc.)"],"output_types":["unified trace schema with provider metadata","cross-provider comparison reports"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__cap_9","uri":"capability://data.processing.analysis.feedback.loop.integration.for.continuous.model.improvement","name":"feedback loop integration for continuous model improvement","description":"Enables feedback collection from production traces (thumbs up/down, ratings, free-form comments) and automatically exports labeled examples to create fine-tuning datasets. Integrates with evaluation runs to track how model performance changes over time as new feedback is collected. Supports feedback aggregation by user, model, or prompt version to identify improvement opportunities.","intents":["I want to collect user feedback on LLM outputs in production and use it to improve my model","I need to track how my model's performance changes as I collect more user feedback","I want to identify which prompt versions receive the most positive feedback from users"],"best_for":["teams building production LLM applications with user feedback","organizations creating fine-tuning datasets from production data","projects requiring continuous model improvement loops"],"limitations":["Feedback collection requires application-level integration; no automatic feedback capture from user interactions","Feedback is unstructured by default; requires custom schemas for structured feedback","No built-in feedback quality filtering; biased or spam feedback can skew datasets","Fine-tuning dataset export requires external fine-tuning infrastructure (OpenAI, Anthropic, etc.)"],"requires":["LangSmith account with Feedback feature","Application-level feedback collection (e.g., thumbs up/down buttons)","LangSmith SDK to submit feedback linked to traces"],"input_types":["user feedback (ratings, comments, labels)","trace IDs to link feedback to outputs"],"output_types":["labeled datasets for fine-tuning","feedback aggregation reports","performance trend analysis over time"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"langsmith__headline","uri":"capability://data.processing.analysis.llmops.observability.and.evaluation.platform","name":"llmops observability and evaluation platform","description":"LangSmith is the leading platform for observability and evaluation in LLMOps, enabling users to trace LLM calls, manage datasets, and evaluate models effectively.","intents":["best LLMOps platform","LLMOps observability for model evaluation","top tools for tracing LLM calls","evaluation platform for language models","dataset management solutions for LLMs"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["LangChain Python SDK 0.0.200+ or LangChain JS 0.0.100+","Valid LangSmith API key from smith.langchain.com","Network connectivity to api.smith.langchain.com","LangSmith account with Prompt Hub access","LangChain SDK to pull prompts at runtime","Basic understanding of semantic versioning","LangSmith account with Alerting feature","Traces being collected in LangSmith","Notification channel configured (email, Slack webhook, etc.)","LangSmith API key with appropriate permissions"],"failure_modes":["Trace collection adds network overhead for each span submission (typically 50-200ms per batch)","Requires LangChain SDK integration — no native support for non-LangChain LLM calls without custom instrumentation","Trace retention limited by plan tier; free tier stores traces for 7 days","Sampling required at scale (>10k traces/day) to manage storage costs","No built-in prompt optimization or auto-tuning — versioning is manual","Templating limited to Jinja2/Handlebars; no support for complex conditional logic or custom filters without workarounds","Prompt hub is LangSmith-specific; exporting prompts to other platforms requires manual JSON export","No native support for multi-language prompts (e.g., English + Spanish variants in single version)","Anomaly detection is statistical and may produce false positives/negatives with low-volume traces","Alert rules are configured per metric; no support for complex multi-metric conditions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=langsmith","compare_url":"https://unfragile.ai/compare?artifact=langsmith"}},"signature":"MYiVcV1vPRT98l7F/FlYTp34eKJw0PiMn5ZQ0fUHCaAonWSiqeWKmKB9KteLvkmPh9cUG6/8DgzL+jC6DyeZDQ==","signedAt":"2026-06-20T04:51:54.495Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/langsmith","artifact":"https://unfragile.ai/langsmith","verify":"https://unfragile.ai/api/v1/verify?slug=langsmith","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}