{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-langsmith","slug":"pypi-langsmith","name":"langsmith","type":"framework","url":"https://pypi.org/project/langsmith/","page_url":"https://unfragile.ai/pypi-langsmith","categories":["observability"],"tags":["evaluation","langchain","langsmith","language","llm","nlp","platform","tracing","translation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-langsmith__cap_0","uri":"capability://automation.workflow.decorator.based.function.tracing.with.traceable","name":"decorator-based function tracing with @traceable","description":"Automatically instruments Python functions and async coroutines with distributed tracing via the @traceable decorator, which wraps function execution to capture inputs, outputs, latency, and errors as hierarchical run records sent to LangSmith. The decorator uses Python's functools.wraps and async context managers to maintain execution context without modifying function signatures, supporting both sync and async functions with automatic parent-child run linking via context variables.","intents":["I want to trace all LLM calls and custom functions in my application without manually creating run objects","I need to see the full execution tree of my multi-step LLM pipeline with timing and error information","I want to automatically capture function inputs and outputs for debugging without boilerplate code"],"best_for":["Python developers building LLM applications who want zero-instrumentation tracing","Teams migrating from print debugging to structured observability","LangChain users who want native integration with existing @chain decorators"],"limitations":["Decorator approach requires function definition modification — cannot retroactively trace third-party library calls without wrapper functions","Context variable propagation may break in certain async contexts (e.g., thread pools, multiprocessing) requiring manual RunTree management","Large input/output payloads are serialized to JSON, adding latency and storage overhead for verbose function arguments"],"requires":["Python 3.9+","langsmith package installed via pip","LANGSMITH_API_KEY environment variable or explicit Client initialization","LangSmith platform account with valid project"],"input_types":["Python function (sync or async)","Function arguments (any JSON-serializable type)","Optional metadata dict for custom tags"],"output_types":["Run record (structured trace with id, name, inputs, outputs, latency, status)","Hierarchical run tree visible in LangSmith UI","Feedback-ready run IDs for post-hoc evaluation"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_1","uri":"capability://automation.workflow.manual.run.tree.construction.and.management.via.runtree","name":"manual run tree construction and management via runtree","description":"Provides a RunTree class for explicit, hierarchical tracing of execution flows where developers manually create parent and child run nodes, set inputs/outputs, and manage run lifecycle (create, update, end). RunTree supports both sync and async contexts, handles batched persistence to LangSmith via background threads, and enables fine-grained control over run metadata, tags, and custom fields for complex workflows that don't fit decorator patterns.","intents":["I need to trace non-function workflows like data pipelines, agent loops, or custom orchestration logic","I want to manually control when runs are created, updated, and finalized for complex branching logic","I need to attach custom metadata, tags, and feedback to specific execution nodes"],"best_for":["Developers building custom LLM agents or orchestration frameworks","Teams with complex, non-standard execution patterns (e.g., dynamic branching, conditional sub-runs)","Advanced users who need fine-grained control over run hierarchy and metadata"],"limitations":["Requires explicit run creation and finalization — developers must manage run lifecycle, risking incomplete traces if exceptions occur before run.end() is called","No automatic parent-child linking — developers must manually pass parent run IDs to child constructors, increasing boilerplate","Background batching adds ~100-500ms latency before runs appear in LangSmith UI, with no guarantee of delivery if process crashes before flush"],"requires":["Python 3.9+","langsmith package with RunTree class","LANGSMITH_API_KEY and valid project","Understanding of run lifecycle (create → update → end)"],"input_types":["Run name (string)","Run type (e.g., 'llm', 'chain', 'tool')","Inputs dict (JSON-serializable)","Optional parent run ID for hierarchy"],"output_types":["RunTree object with run ID","Persisted run record in LangSmith","Child run references for nested execution"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_10","uri":"capability://tool.use.integration.opentelemetry.integration.for.standards.based.observability","name":"opentelemetry integration for standards-based observability","description":"Provides optional OpenTelemetry (OTEL) integration that exports LangSmith traces to OTEL-compatible backends (Jaeger, Datadog, New Relic), enabling LLM traces to be correlated with infrastructure metrics and logs. Integration is opt-in via environment variables (OTEL_EXPORTER_OTLP_ENDPOINT) and automatically bridges LangSmith run metadata to OTEL span attributes, supporting both Python and JavaScript SDKs.","intents":["I want to correlate LLM traces with infrastructure metrics and logs in my observability platform","I need to export LangSmith traces to Datadog or Jaeger for centralized monitoring","I want to use standard OTEL tooling for trace analysis and visualization"],"best_for":["Teams using OTEL-compatible observability platforms (Datadog, Jaeger, New Relic)","Organizations with existing OTEL infrastructure who want to integrate LLM tracing","DevOps teams requiring standards-based observability"],"limitations":["OTEL integration is optional and requires explicit configuration — not enabled by default, requiring environment variable setup","Span attribute mapping is lossy — complex LangSmith metadata (nested objects, arrays) may not translate cleanly to OTEL span attributes","OTEL exporter performance depends on backend — slow exporters may block application code if not configured with async batching"],"requires":["Python 3.9+ or Node.js 16+","langsmith with OTEL support","OTEL SDK and exporter package (e.g., opentelemetry-exporter-otlp)","OTEL_EXPORTER_OTLP_ENDPOINT environment variable pointing to OTEL collector"],"input_types":["LangSmith runs (automatic conversion)","OTEL span context (automatic propagation)"],"output_types":["OTEL spans exported to configured backend","Correlated with infrastructure metrics and logs"],"categories":["tool-use-integration","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_11","uri":"capability://memory.knowledge.prompt.management.and.versioning.via.client.api","name":"prompt management and versioning via client api","description":"Provides Client methods (create_prompt, get_prompt, list_prompts) to store, version, and retrieve prompt templates in LangSmith, enabling teams to manage prompts as first-class artifacts with version history and metadata. Prompts are stored server-side with optional tags and descriptions, supporting retrieval by name or ID, enabling prompt experimentation and A/B testing without code changes.","intents":["I want to version and manage prompt templates without hardcoding them in my application","I need to A/B test different prompts without redeploying my application","I want to track which prompt version was used for each LLM call"],"best_for":["Teams iterating on prompt engineering","Applications requiring prompt versioning and rollback","Researchers comparing prompt variants"],"limitations":["Prompt management is basic — no built-in templating language or variable substitution, requiring application code to handle prompt formatting","No automatic prompt-to-run linking — teams must manually track which prompt version was used for each execution","Prompt retrieval is by name or ID — no semantic search or similarity-based retrieval"],"requires":["Python 3.9+","langsmith Client","LANGSMITH_API_KEY","Valid LangSmith project"],"input_types":["Prompt name (string)","Prompt template (string)","Optional metadata and tags"],"output_types":["Prompt object with version ID","Stored in LangSmith with history","Retrievable by name or ID"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_2","uri":"capability://tool.use.integration.automatic.llm.provider.wrapping.openai.anthropic","name":"automatic llm provider wrapping (openai, anthropic)","description":"Provides pre-built wrapper functions (wrap_openai, wrap_anthropic) that intercept API calls to popular LLM providers, automatically capturing request/response payloads, token counts, and model metadata as LangSmith runs without modifying application code. Wrappers patch the provider's client classes at runtime, extracting structured data from API responses and linking runs to parent execution context via context variables.","intents":["I want to automatically trace all OpenAI/Anthropic API calls without manually wrapping each call","I need to capture token usage, model names, and latency for cost tracking and performance analysis","I want LLM calls to automatically appear as child runs under my application's execution tree"],"best_for":["Teams using OpenAI or Anthropic APIs who want zero-code tracing integration","Applications that need automatic token counting and cost attribution","Developers who want LLM calls to automatically link to parent runs without explicit run ID passing"],"limitations":["Wrappers only support specific provider APIs (OpenAI, Anthropic) — custom LLM providers or older API versions require manual RunTree instrumentation","Runtime patching of provider clients can conflict with other instrumentation libraries or mocking frameworks used in testing","Wrapper captures full request/response payloads, which may include sensitive data (API keys, user prompts) — requires careful environment variable management and log filtering"],"requires":["Python 3.9+","openai or anthropic package installed (version-specific compatibility)","LANGSMITH_API_KEY environment variable","Valid OpenAI or Anthropic API key in application environment"],"input_types":["Initialized OpenAI.Client or Anthropic.Client instance","No changes to existing API call code required"],"output_types":["Automatically created LangSmith runs for each LLM API call","Captured metadata: model name, token counts, latency, temperature, max_tokens","Structured run records linked to parent execution context"],"categories":["tool-use-integration","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_3","uri":"capability://data.processing.analysis.dataset.creation.and.example.management","name":"dataset creation and example management","description":"Provides Client methods (create_dataset, create_example, list_examples) to programmatically build and manage test datasets in LangSmith, storing input-output pairs with optional metadata and tags. Datasets are versioned collections of examples that serve as ground truth for evaluation runs, supporting batch example creation via list operations and lazy-loaded pagination for large datasets.","intents":["I want to create a test dataset of input-output pairs to benchmark my LLM application","I need to upload existing evaluation data (CSV, JSON) as LangSmith datasets for reuse across multiple evaluation runs","I want to version and manage multiple datasets for A/B testing different model versions"],"best_for":["Teams building evaluation pipelines who need centralized dataset management","Researchers comparing model performance across multiple datasets","DevOps teams automating evaluation as part of CI/CD workflows"],"limitations":["No built-in CSV/JSON import — datasets must be created programmatically via API calls, requiring custom ETL code for bulk data loading","Dataset versioning is implicit (new dataset creation) rather than explicit branching — no native diff or merge operations","Large datasets (>10k examples) require pagination and careful memory management when iterating, as list_examples returns paginated results"],"requires":["Python 3.9+","langsmith Client initialized with API key","LANGSMITH_API_KEY environment variable or explicit credentials","Valid LangSmith project"],"input_types":["Dataset name (string)","Example inputs (dict or any JSON-serializable type)","Example outputs (dict or any JSON-serializable type)","Optional metadata and tags"],"output_types":["Dataset object with UUID","Example objects with IDs","Paginated example lists for querying"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_4","uri":"capability://planning.reasoning.evaluation.framework.with.runevaluator.and.experimentmanager","name":"evaluation framework with runevaluator and experimentmanager","description":"Provides an evaluation system where RunEvaluator classes score LLM outputs against ground truth examples, and ExperimentManager orchestrates batch evaluation runs across datasets. Evaluators implement a standard interface (evaluate method) that accepts run data and returns structured scores, supporting both synchronous and asynchronous evaluation logic. The framework batches evaluations, tracks results per example, and aggregates metrics for comparison across model versions.","intents":["I want to automatically score my LLM outputs against a test dataset using custom evaluation metrics","I need to compare performance across multiple model versions or prompt variations using consistent evaluation criteria","I want to track evaluation results over time and identify regressions in model quality"],"best_for":["ML teams building evaluation pipelines for LLM applications","Researchers comparing model variants using standardized metrics","DevOps teams automating quality gates in CI/CD before model deployment"],"limitations":["Evaluators must be implemented as custom Python classes — no built-in evaluators for common metrics (BLEU, ROUGE, semantic similarity), requiring integration with external libraries","Evaluation results are stored in LangSmith but not automatically compared across runs — teams must manually query and aggregate results for trend analysis","Async evaluators may timeout on slow external APIs (e.g., LLM-as-judge), with no built-in retry logic or circuit breaker pattern"],"requires":["Python 3.9+","langsmith with evaluation module","LANGSMITH_API_KEY and valid project","Custom RunEvaluator subclass implementation","Dataset with ground truth examples"],"input_types":["Run object (from tracing)","Example object (from dataset)","Optional reference outputs"],"output_types":["Score dict with metric names and numeric values","Aggregated metrics across all examples","Experiment results stored in LangSmith"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_5","uri":"capability://automation.workflow.asynchronous.client.with.concurrent.batch.operations","name":"asynchronous client with concurrent batch operations","description":"Provides AsyncClient class that implements all Client operations (create_run, update_run, list_runs, create_dataset, etc.) as async/await coroutines, enabling concurrent execution of multiple API calls without blocking. Uses Python's asyncio library with connection pooling (httpx.AsyncClient) to efficiently handle high-throughput tracing and evaluation workloads, with automatic retry logic and exponential backoff for transient failures.","intents":["I want to trace multiple concurrent LLM requests without blocking my application","I need to batch-upload large numbers of runs or examples efficiently","I want to query LangSmith data concurrently without sequential API call overhead"],"best_for":["High-throughput LLM applications (e.g., batch inference, multi-user systems)","Async Python frameworks (FastAPI, aiohttp, asyncio-based agents)","Teams processing large datasets with concurrent evaluation"],"limitations":["AsyncClient requires async/await syntax — cannot be used in synchronous code without event loop management (asyncio.run), adding complexity","Connection pooling is per-AsyncClient instance — creating multiple AsyncClient instances defeats pooling benefits, requiring careful lifecycle management","Retry logic uses exponential backoff with jitter, but no circuit breaker pattern — sustained API failures may cause cascading delays across all concurrent operations"],"requires":["Python 3.9+","langsmith with AsyncClient","asyncio event loop (native in async frameworks like FastAPI)","LANGSMITH_API_KEY environment variable"],"input_types":["Same as Client (run data, dataset data, etc.)","Coroutines for async execution"],"output_types":["Awaitable results (same as Client)","Concurrent execution without blocking"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_6","uri":"capability://data.processing.analysis.run.feedback.and.annotation.system","name":"run feedback and annotation system","description":"Provides Client methods (create_feedback, update_feedback, delete_feedback) to attach post-hoc feedback, scores, and annotations to existing runs after execution. Feedback is stored as separate records linked to run IDs, supporting multiple feedback types (numeric scores, categorical labels, text comments) and enabling human-in-the-loop evaluation where evaluators review and score runs after the fact.","intents":["I want to collect human feedback on LLM outputs after they're generated","I need to attach ground truth labels or corrections to runs for model fine-tuning","I want to track user satisfaction scores or error reports linked to specific executions"],"best_for":["Teams collecting human feedback for model improvement","Applications with user-facing feedback mechanisms (thumbs up/down, ratings)","Researchers building datasets from human annotations"],"limitations":["Feedback is append-only — updates create new feedback records rather than modifying existing ones, requiring careful deduplication logic","No built-in conflict resolution for multiple feedback sources — teams must implement their own logic for handling disagreements between annotators","Feedback queries require run ID knowledge — no bulk feedback retrieval by date range or tag, limiting analytics capabilities"],"requires":["Python 3.9+","langsmith Client","LANGSMITH_API_KEY","Valid run ID (from prior tracing)"],"input_types":["Run ID (UUID)","Feedback type (e.g., 'correctness', 'relevance')","Score or label value","Optional comment text"],"output_types":["Feedback record with ID","Linked to run in LangSmith UI","Queryable for evaluation analysis"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_7","uri":"capability://search.retrieval.run.querying.and.filtering.with.list.runs","name":"run querying and filtering with list_runs","description":"Provides Client.list_runs() method to query and filter execution traces using flexible criteria (project name, run type, status, tags, date range, metadata), returning paginated run records with full execution details. Supports both exact matching and regex patterns for filtering, enabling developers to slice trace data for analysis, debugging, and evaluation without exporting to external tools.","intents":["I want to find all failed runs in my LLM application to debug errors","I need to query runs by tag or metadata to analyze specific experiment variants","I want to retrieve runs from a specific time window for performance analysis"],"best_for":["Developers debugging LLM application failures","Teams analyzing trace data for performance optimization","Researchers comparing runs across experiments"],"limitations":["Filtering is performed server-side but pagination is required for large result sets — no built-in aggregation or grouping, requiring client-side post-processing","Query performance degrades with large date ranges or broad filters — no index hints or query optimization guidance","Regex filtering is limited to string fields — no full-text search or semantic similarity queries"],"requires":["Python 3.9+","langsmith Client","LANGSMITH_API_KEY","Valid project name"],"input_types":["Project name (string)","Optional filters: run_type, status, tags, created_at range, metadata","Pagination parameters (limit, offset)"],"output_types":["Paginated list of Run objects","Full run details: inputs, outputs, latency, errors, metadata"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_8","uri":"capability://automation.workflow.background.batching.and.persistence.with.configurable.flush.intervals","name":"background batching and persistence with configurable flush intervals","description":"Implements an internal background thread (Python) or microtask queue (JavaScript) that batches run updates and feedback operations before sending to LangSmith, reducing API call overhead and network latency. Batching is configurable via environment variables (LANGSMITH_BATCH_SIZE, LANGSMITH_BATCH_TIMEOUT_MS) and automatically flushes on process exit or explicit client.flush() calls, enabling high-throughput tracing without blocking application code.","intents":["I want to trace thousands of function calls without overwhelming the LangSmith API","I need to minimize latency impact of tracing on my application's response time","I want to ensure all traces are persisted even if my application crashes unexpectedly"],"best_for":["High-throughput applications with frequent tracing (>100 runs/sec)","Latency-sensitive applications where blocking I/O is unacceptable","Batch processing pipelines that generate large numbers of runs"],"limitations":["Batching introduces ~100-500ms latency before runs appear in LangSmith UI — not suitable for real-time debugging workflows","Background thread may not flush if process crashes abruptly (SIGKILL) — graceful shutdown (SIGTERM) is required for guaranteed persistence","Batch size and timeout are global per Client instance — no per-operation control, requiring careful tuning for mixed workloads"],"requires":["Python 3.9+","langsmith Client with background batching enabled (default)","LANGSMITH_API_KEY","Graceful shutdown handling in application"],"input_types":["Run updates, feedback operations, dataset operations","Configurable batch size and timeout"],"output_types":["Batched API requests to LangSmith","Reduced network overhead and latency impact"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-langsmith__cap_9","uri":"capability://automation.workflow.javascript.typescript.sdk.with.traceable.function.and.async.support","name":"javascript/typescript sdk with traceable() function and async support","description":"Provides a parallel JavaScript/TypeScript implementation of the LangSmith SDK with traceable() function for decorator-like tracing (using higher-order functions), Client class for API operations, and RunTree for manual instrumentation. Uses AsyncLocalStorage for context propagation across async boundaries, Promises for async/await support, and TypeScript types for compile-time safety, enabling LLM tracing in Node.js and browser environments.","intents":["I want to trace LLM calls in my Node.js or TypeScript application with the same API as the Python SDK","I need to instrument async functions and Promise chains without callback hell","I want type-safe tracing with TypeScript definitions"],"best_for":["Node.js developers building LLM applications","TypeScript projects requiring compile-time type safety","Teams using JavaScript frameworks (Next.js, Express, Fastify) with LLM integrations"],"limitations":["AsyncLocalStorage is Node.js-only — browser environments require manual context passing or alternative storage mechanisms","traceable() function uses higher-order functions instead of decorators — less ergonomic than Python @traceable syntax, requiring explicit function wrapping","No built-in OpenAI wrapper for Node.js (only Vercel AI SDK) — teams using openai npm package must manually instrument calls"],"requires":["Node.js 16+","langsmith npm package","LANGSMITH_API_KEY environment variable","TypeScript 4.5+ (optional, for type definitions)"],"input_types":["JavaScript/TypeScript function (sync or async)","Function arguments (JSON-serializable)","Optional metadata"],"output_types":["Run record in LangSmith","Hierarchical run tree","Feedback-ready run IDs"],"categories":["automation-workflow","observability"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","langsmith package installed via pip","LANGSMITH_API_KEY environment variable or explicit Client initialization","LangSmith platform account with valid project","langsmith package with RunTree class","LANGSMITH_API_KEY and valid project","Understanding of run lifecycle (create → update → end)","Python 3.9+ or Node.js 16+","langsmith with OTEL support","OTEL SDK and exporter package (e.g., opentelemetry-exporter-otlp)"],"failure_modes":["Decorator approach requires function definition modification — cannot retroactively trace third-party library calls without wrapper functions","Context variable propagation may break in certain async contexts (e.g., thread pools, multiprocessing) requiring manual RunTree management","Large input/output payloads are serialized to JSON, adding latency and storage overhead for verbose function arguments","Requires explicit run creation and finalization — developers must manage run lifecycle, risking incomplete traces if exceptions occur before run.end() is called","No automatic parent-child linking — developers must manually pass parent run IDs to child constructors, increasing boilerplate","Background batching adds ~100-500ms latency before runs appear in LangSmith UI, with no guarantee of delivery if process crashes before flush","OTEL integration is optional and requires explicit configuration — not enabled by default, requiring environment variable setup","Span attribute mapping is lossy — complex LangSmith metadata (nested objects, arrays) may not translate cleanly to OTEL span attributes","OTEL exporter performance depends on backend — slow exporters may block application code if not configured with async batching","Prompt management is basic — no built-in templating language or variable substitution, requiring application code to handle prompt formatting","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:11.786Z","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=pypi-langsmith","compare_url":"https://unfragile.ai/compare?artifact=pypi-langsmith"}},"signature":"o2hFwc2E30tXjBIzHj1as0obZbVbsaeZR21AMmB6js6l1A7HiJti5budSvHxmRQR9o8NYWwYzcNXI6ec36rpCQ==","signedAt":"2026-06-22T13:21:33.063Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-langsmith","artifact":"https://unfragile.ai/pypi-langsmith","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}