{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-comet-ml--opik","slug":"comet-ml--opik","name":"opik","type":"agent","url":"https://www.comet.com/docs/opik/","page_url":"https://unfragile.ai/comet-ml--opik","categories":["observability","rag-knowledge","deployment-infra"],"tags":["evaluation","hacktoberfest","hacktoberfest2025","langchain","llama-index","llm","llm-evaluation","llm-observability","llmops","open-source","openai","playground","prompt-engineering"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-comet-ml--opik__cap_0","uri":"capability://memory.knowledge.distributed.trace.collection.with.multi.framework.sdk.integration","name":"distributed trace collection with multi-framework sdk integration","description":"Captures execution traces across LLM applications using language-specific SDKs (Python, TypeScript) that instrument framework-native hooks for LangChain, LlamaIndex, Claude SDK, Pydantic AI, and others. The SDK batches trace events and sends them asynchronously via HTTP to the backend, which persists them in a relational database with Redis Streams for async processing, enabling full visibility into multi-step agent and RAG workflows without code modification.","intents":["I need to see exactly what my LLM application is doing at each step without rewriting my code","I want to trace execution across multiple frameworks (LangChain, LlamaIndex, etc.) in a single unified view","I need to capture token counts and costs for every LLM call automatically"],"best_for":["teams building LLM agents and RAG systems who need production observability","developers migrating between frameworks and needing consistent tracing","organizations tracking LLM costs across multiple models and providers"],"limitations":["SDK batching adds ~50-200ms latency per trace batch depending on batch size configuration","Framework integrations require explicit SDK initialization; auto-instrumentation not available for all frameworks","Trace storage scales linearly with application volume; no built-in sampling or trace filtering at collection time"],"requires":["Python 3.9+ or Node.js 18+ depending on SDK choice","Opik backend running (self-hosted or cloud)","API key or authentication token for backend access","Framework-specific SDK package (e.g., opik[langchain] for LangChain integration)"],"input_types":["LLM framework execution events (function calls, model invocations, tool usage)","Custom span metadata (tags, scores, user feedback)"],"output_types":["Structured trace objects with hierarchical span relationships","Cost and token count aggregations per trace"],"categories":["memory-knowledge","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_1","uri":"capability://data.processing.analysis.automated.llm.evaluation.with.multi.provider.model.support","name":"automated llm evaluation with multi-provider model support","description":"Executes evaluation metrics against trace data using a pluggable evaluation framework that supports LiteLLM for multi-provider LLM access (OpenAI, Anthropic, Ollama, etc.) and custom Python evaluators. The system runs evaluations asynchronously via a Python backend service, storing results as feedback scores linked to traces, enabling comparison of model outputs against ground truth or custom criteria without manual annotation.","intents":["I want to automatically score my LLM outputs against quality metrics without manual review","I need to run the same evaluation across different LLM providers to compare their performance","I want to define custom evaluation logic in Python and apply it to all my traces"],"best_for":["teams running A/B tests on LLM prompts and models","organizations building evaluation pipelines for RAG and agent systems","developers who want to integrate evaluation into CI/CD workflows"],"limitations":["Evaluation latency depends on LLM provider response times; no built-in caching of evaluation results across identical inputs","Custom evaluators must be Python functions; no support for external evaluation services or webhooks","Evaluation results are stored as feedback scores; no native support for multi-dimensional scoring or confidence intervals"],"requires":["Python 3.9+","API keys for LLM providers used in evaluations (OpenAI, Anthropic, etc.)","Opik backend with Python backend service running","LiteLLM library (included in opik[eval] extra)"],"input_types":["Trace data (inputs, outputs, metadata)","Ground truth labels or reference outputs","Custom evaluation function definitions"],"output_types":["Numeric scores (0-1 range typical)","Feedback annotations linked to traces","Evaluation result aggregations per experiment"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_10","uri":"capability://text.generation.language.interactive.llm.playground.with.multi.provider.support","name":"interactive llm playground with multi-provider support","description":"Provides a web-based playground in the frontend that allows users to test prompts and model configurations against LLM providers (OpenAI, Anthropic, Ollama, etc.) in real-time. The playground supports variable substitution, message history, and cost estimation, with results automatically captured as traces for later analysis. Users can iterate on prompts without leaving the browser and save successful configurations as reusable prompts.","intents":["I want to test a prompt against different models and see which performs best","I need to quickly prototype a prompt before integrating it into my application","I want to estimate the cost of a prompt before using it in production"],"best_for":["prompt engineers prototyping and testing prompts interactively","teams comparing model performance on specific tasks","developers exploring LLM behavior before implementation"],"limitations":["Playground is limited to single-turn conversations; no multi-turn conversation history management","Variable substitution is basic string replacement; no support for complex templating","Results are not automatically saved as traces; manual save required"],"requires":["Web browser with ES2020+ support","Opik backend running","API keys for LLM providers to test"],"input_types":["Prompt text with optional variables","Model selection (OpenAI, Anthropic, etc.)","Model parameters (temperature, top_p, max_tokens)"],"output_types":["LLM response text","Token counts and cost estimation","Execution time"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_11","uri":"capability://safety.moderation.guardrails.backend.for.content.filtering.and.safety.checks","name":"guardrails backend for content filtering and safety checks","description":"Provides a separate Python backend service that runs safety and content filtering checks on LLM inputs and outputs using configurable rules and external safety APIs. Guardrails can be applied at trace collection time or as a post-processing step, with results stored as feedback scores. The system supports custom guardrail definitions and integrates with popular safety frameworks.","intents":["I want to automatically filter harmful content from my LLM application","I need to check that my LLM outputs comply with specific safety policies","I want to flag traces that violate safety guidelines for manual review"],"best_for":["organizations with strict safety and compliance requirements","teams building customer-facing LLM applications","enterprises deploying LLMs in regulated industries"],"limitations":["Guardrail evaluation adds latency to trace processing; no built-in caching of guardrail results","Custom guardrails require Python code; no visual rule builder","Guardrail effectiveness depends on external safety APIs; no guarantees on false positive/negative rates"],"requires":["Python 3.9+","Opik backend with guardrails service running","API keys for external safety services (if using third-party guardrails)"],"input_types":["LLM inputs and outputs from traces","Guardrail rule definitions"],"output_types":["Safety check results (pass/fail)","Feedback scores linked to traces","Flagged traces for manual review"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_12","uri":"capability://automation.workflow.asynchronous.trace.processing.with.redis.streams","name":"asynchronous trace processing with redis streams","description":"Uses Redis Streams as a message queue for asynchronous processing of trace events, enabling decoupling of trace collection from persistence and evaluation. Trace events are published to Redis Streams, consumed by background workers, and processed (persisted, evaluated, guardrails checked) without blocking the SDK. This architecture supports high-throughput trace collection and enables scaling of evaluation and guardrails processing independently.","intents":["I want to collect traces without blocking my application","I need to scale trace processing independently from trace collection","I want to ensure traces are not lost even if the backend is temporarily unavailable"],"best_for":["high-throughput LLM applications with strict latency requirements","teams running Opik at scale with thousands of traces per second","organizations with complex evaluation and guardrails pipelines"],"limitations":["Redis Streams requires separate Redis infrastructure; adds operational complexity","At-least-once delivery semantics; duplicate trace processing possible if workers crash","No built-in dead-letter queue for failed trace processing; manual intervention required for recovery"],"requires":["Redis 5.0+ running and accessible","Opik backend configured with Redis connection","Background worker processes running"],"input_types":["Trace events from SDKs"],"output_types":["Persisted traces in database","Evaluation results","Guardrails check results"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_2","uri":"capability://data.processing.analysis.experiment.tracking.with.dataset.based.comparison","name":"experiment tracking with dataset-based comparison","description":"Manages datasets (collections of input-output pairs) and experiments (runs of an application against a dataset) with automatic comparison of results across runs. The system stores datasets in the relational database, executes applications against them, and computes aggregate metrics (accuracy, latency, cost) across experiment runs, enabling side-by-side comparison of different prompts, models, or configurations without manual result aggregation.","intents":["I want to test my prompt changes against a fixed dataset and see how metrics changed","I need to compare performance across multiple model versions using the same test cases","I want to track how my application's latency and cost evolve over time"],"best_for":["prompt engineers iterating on LLM prompts with quantitative feedback","teams running systematic A/B tests on LLM applications","organizations building regression test suites for LLM systems"],"limitations":["Datasets are immutable once created; versioning requires creating new dataset objects","Experiment execution is sequential by default; no built-in parallelization across dataset items","Metric computation is limited to built-in aggregations; custom metrics require post-processing"],"requires":["Python 3.9+ or TypeScript SDK","Opik backend running","Pre-created dataset with input-output pairs"],"input_types":["Dataset objects (list of input-output pairs)","Application code or API endpoint to test","Evaluation metrics or custom scoring functions"],"output_types":["Experiment run records with trace data","Aggregate metrics per experiment (accuracy, latency, cost)","Comparison matrices across multiple runs"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_3","uri":"capability://memory.knowledge.real.time.trace.visualization.and.interactive.debugging","name":"real-time trace visualization and interactive debugging","description":"Provides a web-based frontend (React/TypeScript) that renders traces as interactive trees showing span relationships, inputs, outputs, and metadata. The frontend queries the REST API to fetch trace data, renders message content with syntax highlighting for code and JSON, and allows filtering/searching traces by project, tags, and metadata. Users can drill down into individual spans to inspect LLM calls, tool invocations, and intermediate results without leaving the browser.","intents":["I want to visually inspect what my LLM application did on a specific request","I need to find traces matching specific criteria (e.g., traces with errors, traces from a specific user)","I want to understand the execution flow of my agent by seeing the tree of function calls"],"best_for":["developers debugging LLM application behavior in real-time","teams reviewing production traces to understand failures","non-technical stakeholders reviewing application behavior"],"limitations":["Trace rendering performance degrades with very deep spans (>50 levels); no automatic tree collapsing","Search and filtering operate on indexed fields only; full-text search across all span content not available","Real-time trace updates require polling; no WebSocket-based live trace streaming"],"requires":["Web browser with ES2020+ support","Opik backend running and accessible","Authentication token or API key for backend access"],"input_types":["Trace IDs or project names","Filter criteria (tags, metadata, date ranges)"],"output_types":["Interactive HTML/SVG trace trees","Rendered message content (code, JSON, plain text)","Span metadata and timing information"],"categories":["memory-knowledge","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_4","uri":"capability://data.processing.analysis.llm.cost.tracking.and.aggregation","name":"llm cost tracking and aggregation","description":"Automatically extracts token counts from LLM provider responses (OpenAI, Anthropic, etc.) and computes costs using a pricing database that syncs daily with provider pricing data. The system aggregates costs at multiple levels (per trace, per project, per experiment) and stores them alongside trace data, enabling cost analysis without requiring manual token counting or external billing APIs.","intents":["I want to know how much each LLM call costs without manually tracking tokens","I need to compare the cost-effectiveness of different models or prompts","I want to track total spending across my LLM application over time"],"best_for":["teams optimizing LLM application costs","organizations with strict budgeting requirements","developers comparing cost-quality tradeoffs across models"],"limitations":["Pricing data is updated daily; real-time pricing changes from providers are not reflected immediately","Cost calculation depends on accurate token count reporting from LLM providers; some providers may report approximate counts","Custom pricing (e.g., volume discounts) not supported; only public provider pricing available"],"requires":["LLM provider API keys that return token count information","Opik backend with pricing sync service running","Support for provider (OpenAI, Anthropic, Ollama, etc.)"],"input_types":["LLM API responses with token count metadata"],"output_types":["Cost per trace (in USD or other currency)","Aggregate cost metrics per project/experiment","Cost breakdowns by model and provider"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_5","uri":"capability://data.processing.analysis.feedback.annotation.and.scoring.system","name":"feedback annotation and scoring system","description":"Allows users to attach feedback scores and annotations to traces via the UI or API, supporting numeric scores (0-1 range), categorical labels, and free-form text comments. Feedback is stored in the database linked to specific traces and can be used as ground truth for evaluation, as training data for prompt optimization, or for manual quality assessment. The system supports batch feedback operations for bulk annotation of experiment results.","intents":["I want to manually rate LLM outputs and use those ratings to evaluate my system","I need to collect human feedback on application behavior for model training","I want to mark traces as correct/incorrect for regression testing"],"best_for":["teams collecting human feedback for LLM system evaluation","organizations building feedback loops for continuous improvement","developers creating ground truth datasets from production traces"],"limitations":["Feedback is immutable once created; corrections require creating new feedback entries","No built-in workflow for multi-reviewer consensus or inter-rater agreement metrics","Batch feedback operations are synchronous; large-scale annotation (>10k traces) may timeout"],"requires":["Opik backend running","Trace data already collected","User authentication for audit trail"],"input_types":["Numeric scores (0-1 range)","Categorical labels","Free-form text comments"],"output_types":["Feedback records linked to traces","Feedback aggregations per experiment","Feedback history with timestamps"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_6","uri":"capability://memory.knowledge.prompt.management.and.versioning","name":"prompt management and versioning","description":"Stores and versions LLM prompts in a centralized registry with support for variables, metadata, and deployment tracking. Prompts can be retrieved by name and version, used in experiments to test prompt variations, and linked to traces for audit trails. The system supports semantic versioning and allows rollback to previous prompt versions without code changes.","intents":["I want to version my prompts and track which version was used in each trace","I need to test multiple prompt variations against the same dataset","I want to manage prompt templates with variables without hardcoding them in code"],"best_for":["prompt engineers iterating on LLM prompts with version control","teams managing prompts across multiple environments (dev, staging, prod)","organizations auditing which prompts were used in production"],"limitations":["Prompt storage is limited to text; no support for binary or structured prompt formats","Variable substitution is basic string replacement; no support for conditional logic or complex templating","No built-in diff visualization for prompt versions; comparison requires manual inspection"],"requires":["Opik backend running","Python SDK or REST API access"],"input_types":["Prompt text with optional variables","Metadata (tags, description, version info)"],"output_types":["Versioned prompt objects","Prompt retrieval by name and version","Prompt usage audit trail"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_7","uri":"capability://safety.moderation.multi.tenant.project.isolation.with.rbac","name":"multi-tenant project isolation with rbac","description":"Implements multi-tenancy at the database and API levels, with projects as the primary isolation boundary. Each project has its own traces, datasets, and experiments, with role-based access control (RBAC) supporting admin, editor, and viewer roles. Authentication is handled via API keys or OAuth, with audit logging of all data access and modifications for compliance.","intents":["I want to isolate traces and data for different teams or customers","I need to grant different permissions to team members (read-only vs edit)","I want to audit who accessed or modified my LLM application data"],"best_for":["organizations with multiple teams or customers using shared Opik infrastructure","enterprises with compliance requirements for data isolation and audit trails","SaaS platforms offering Opik as a managed service"],"limitations":["RBAC is project-level only; no support for fine-grained permissions (e.g., per-trace access control)","API key rotation requires manual intervention; no automatic key expiration","Audit logs are stored in the same database as operational data; no separate audit log storage"],"requires":["Opik backend configured with authentication enabled","API keys or OAuth provider setup","Database with multi-tenant schema support"],"input_types":["User credentials (API key or OAuth token)","Project identifiers","Role assignments"],"output_types":["Authenticated API responses scoped to user's projects","Audit log entries with user, action, and timestamp"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_8","uri":"capability://planning.reasoning.agent.optimization.with.hyperparameter.tuning","name":"agent optimization with hyperparameter tuning","description":"Provides a BaseOptimizer framework that supports multiple optimization algorithms (e.g., Bayesian optimization, genetic algorithms) to automatically tune agent hyperparameters (temperature, top_p, system prompts, etc.) based on evaluation metrics. The system runs experiments with different hyperparameter combinations, evaluates results, and suggests optimal configurations without manual trial-and-error.","intents":["I want to automatically find the best temperature and top_p settings for my LLM","I need to optimize my system prompt to maximize a specific metric","I want to run a systematic hyperparameter search without manually testing each combination"],"best_for":["teams optimizing LLM agent performance with limited manual tuning time","researchers exploring hyperparameter sensitivity","organizations maximizing quality metrics within cost constraints"],"limitations":["Optimization algorithms require multiple experiment runs; total time scales with search space size","No support for multi-objective optimization (e.g., maximizing quality while minimizing cost)","Optimization results are specific to the dataset and evaluation metrics used; generalization to new data not guaranteed"],"requires":["Python 3.9+","Opik backend running","Dataset and evaluation metrics defined","Hyperparameter search space specification"],"input_types":["Hyperparameter search space (ranges, discrete values)","Evaluation metric to optimize","Dataset for testing"],"output_types":["Optimal hyperparameter configuration","Optimization history with metrics per iteration","Suggested next hyperparameters to test"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-comet-ml--opik__cap_9","uri":"capability://tool.use.integration.rest.api.with.openapi.specification.and.sdk.generation","name":"rest api with openapi specification and sdk generation","description":"Exposes all Opik functionality via a REST API with a complete OpenAPI 3.0 specification, enabling automatic SDK generation for Python and TypeScript. The API supports CRUD operations on traces, datasets, experiments, prompts, and feedback, with pagination, filtering, and sorting built-in. The OpenAPI spec is versioned and published, allowing clients to generate type-safe SDKs automatically.","intents":["I want to integrate Opik into my custom application without using the provided SDKs","I need to generate a type-safe SDK for my language of choice","I want to build a custom UI or tool that queries Opik data"],"best_for":["developers building custom integrations with Opik","teams using languages not officially supported by Opik SDKs","organizations building internal tools on top of Opik"],"limitations":["API rate limiting is not enforced; high-volume clients may impact backend performance","Pagination is cursor-based; no support for offset-based pagination","Filtering syntax is custom (not GraphQL or standard query language); learning curve for complex queries"],"requires":["HTTP client library","API key for authentication","OpenAPI client generator (e.g., openapi-generator, swagger-codegen)"],"input_types":["HTTP requests with JSON payloads","Query parameters for filtering and pagination"],"output_types":["JSON responses with structured data","OpenAPI specification (YAML/JSON)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ or Node.js 18+ depending on SDK choice","Opik backend running (self-hosted or cloud)","API key or authentication token for backend access","Framework-specific SDK package (e.g., opik[langchain] for LangChain integration)","Python 3.9+","API keys for LLM providers used in evaluations (OpenAI, Anthropic, etc.)","Opik backend with Python backend service running","LiteLLM library (included in opik[eval] extra)","Web browser with ES2020+ support","Opik backend running"],"failure_modes":["SDK batching adds ~50-200ms latency per trace batch depending on batch size configuration","Framework integrations require explicit SDK initialization; auto-instrumentation not available for all frameworks","Trace storage scales linearly with application volume; no built-in sampling or trace filtering at collection time","Evaluation latency depends on LLM provider response times; no built-in caching of evaluation results across identical inputs","Custom evaluators must be Python functions; no support for external evaluation services or webhooks","Evaluation results are stored as feedback scores; no native support for multi-dimensional scoring or confidence intervals","Playground is limited to single-turn conversations; no multi-turn conversation history management","Variable substitution is basic string replacement; no support for complex templating","Results are not automatically saved as traces; manual save required","Guardrail evaluation adds latency to trace processing; no built-in caching of guardrail results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7192006750371539,"quality":0.5,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-05-03T10:30:01Z"},"community":{"stars":19170,"forks":1462,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=comet-ml--opik","compare_url":"https://unfragile.ai/compare?artifact=comet-ml--opik"}},"signature":"z0ShIVuo5RaYQ/ffZyf16ZVLGBn/qibW2H7vMeQlMc/O+T2a0LnvA8Te+F792ehP5O2goFcAi/YqpgEzVl8KDw==","signedAt":"2026-06-20T15:14:57.496Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/comet-ml--opik","artifact":"https://unfragile.ai/comet-ml--opik","verify":"https://unfragile.ai/api/v1/verify?slug=comet-ml--opik","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"}}