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Stores trace metadata in-memory or via optional persistent backends without requiring external observability infrastructure.","intents":["Debug why my LLM chain produced unexpected outputs by inspecting intermediate token counts and API responses","Identify latency bottlenecks in multi-step LLM workflows without leaving my notebook","Visualize the execution flow of agentic systems to understand decision paths and tool calls"],"best_for":["ML engineers prototyping LLM applications in notebooks","researchers debugging chain-of-thought reasoning patterns","teams iterating on prompt engineering without external observability platforms"],"limitations":["In-memory trace storage limits scalability to development/small-scale workloads; production deployments require external persistence layer","Notebook-first design means limited integration with containerized or serverless inference pipelines","Trace capture overhead increases latency per API call (estimated 50-150ms depending on payload size)"],"requires":["Python 3.8+","Jupyter/IPython notebook environment or compatible REPL","API credentials for target LLM provider (OpenAI, Anthropic, etc.)"],"input_types":["LLM API calls (intercepted at runtime)","structured trace metadata (tokens, latency, model name)"],"output_types":["interactive HTML trace visualization","structured trace JSON/dict","aggregated metrics (token counts, latency percentiles)"],"categories":["memory-knowledge","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-phoenix__cap_1","uri":"capability://data.processing.analysis.llm.output.quality.evaluation.and.scoring","name":"llm output quality evaluation and scoring","description":"Provides built-in evaluators and custom scoring functions to assess LLM outputs against user-defined metrics (correctness, relevance, toxicity, hallucination detection) using both rule-based heuristics and LLM-as-judge patterns. Integrates with trace data to correlate output quality with input prompts, model versions, and hyperparameters, enabling systematic comparison of model variants.","intents":["Measure whether my LLM outputs meet quality thresholds before deploying to production","Compare two model versions (e.g., GPT-4 vs Claude) on the same test set and quantify differences","Identify which prompts or input patterns consistently produce low-quality outputs"],"best_for":["ML engineers building evaluation pipelines for LLM applications","product teams A/B testing different model configurations","researchers quantifying model behavior across input distributions"],"limitations":["LLM-as-judge evaluators inherit model biases and may not correlate with human judgment; requires human-in-the-loop validation","Custom evaluators require manual implementation; no pre-built library for domain-specific metrics (e.g., medical accuracy, legal compliance)","Evaluation latency scales linearly with dataset size; batch evaluation of 10k+ examples may require hours"],"requires":["Python 3.8+","Jupyter notebook environment","LLM API credentials if using LLM-as-judge evaluators"],"input_types":["LLM outputs (text, structured data)","reference/ground-truth data (optional)","custom evaluation functions (Python callables)"],"output_types":["numeric scores (0-1 range or custom)","evaluation reports with per-sample scores","aggregated metrics (mean, std dev, percentiles)"],"categories":["data-processing-analysis","evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-phoenix__cap_2","uri":"capability://image.visual.computer.vision.model.output.inspection.and.annotation","name":"computer vision model output inspection and annotation","description":"Captures and visualizes outputs from CV models (object detection, segmentation, classification) with bounding boxes, masks, and confidence scores overlaid on input images. Integrates with trace data to correlate model predictions with input preprocessing steps, model versions, and inference latency, enabling systematic debugging of vision pipelines.","intents":["Visualize bounding boxes and confidence scores from my object detection model to identify false positives","Compare segmentation masks across model versions to assess quality improvements","Trace why a specific image produced unexpected predictions by inspecting preprocessing and model outputs"],"best_for":["computer vision engineers debugging detection/segmentation pipelines","teams iterating on CV model selection and hyperparameter tuning","researchers analyzing model failure modes on edge cases"],"limitations":["Visualization limited to 2D outputs; no built-in support for 3D point clouds or volumetric data","Batch visualization of large image datasets (1000+) may cause notebook performance degradation","No built-in annotation tools for ground-truth labeling; requires external tools for dataset creation"],"requires":["Python 3.8+","Jupyter notebook with image display support","CV model outputs in standard formats (bounding boxes as [x, y, w, h] or [x1, y1, x2, y2], masks as numpy arrays)"],"input_types":["images (PNG, JPEG, numpy arrays)","model predictions (bounding boxes, segmentation masks, class labels, confidence scores)"],"output_types":["annotated images with overlays (HTML/PNG)","structured prediction data (JSON)","performance metrics (mAP, IoU, per-class accuracy)"],"categories":["image-visual","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-phoenix__cap_3","uri":"capability://data.processing.analysis.tabular.data.model.monitoring.and.drift.detection","name":"tabular data model monitoring and drift detection","description":"Monitors feature distributions, prediction outputs, and model performance metrics for tabular/structured data models using statistical tests (Kolmogorov-Smirnov, chi-square) to detect data drift and concept drift. Compares current inference data against training data distributions and tracks performance degradation over time, with results visualized in notebooks.","intents":["Detect when my production tabular model's input feature distributions have shifted from training data","Monitor prediction accuracy on recent data to identify when model retraining is needed","Identify which features are drifting most significantly to prioritize data collection or model updates"],"best_for":["data scientists maintaining production tabular models (credit scoring, churn prediction, etc.)","ML ops teams monitoring model performance without external monitoring platforms","teams building automated retraining pipelines triggered by drift detection"],"limitations":["Statistical drift tests assume feature independence; may produce false positives for correlated features","Requires baseline statistics from training data; no automatic baseline inference from model artifacts","Limited to univariate drift detection; no built-in multivariate or causal drift analysis"],"requires":["Python 3.8+","Jupyter notebook environment","pandas DataFrames or numpy arrays for feature data","baseline statistics (training data distribution) for comparison"],"input_types":["tabular data (pandas DataFrames, numpy arrays, CSV)","model predictions (numeric or categorical)","baseline statistics (mean, std dev, distribution histograms)"],"output_types":["drift detection reports (p-values, test statistics)","distribution comparison visualizations (histograms, KDE plots)","performance metrics (accuracy, precision, recall over time)"],"categories":["data-processing-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-phoenix__cap_4","uri":"capability://memory.knowledge.multi.modal.model.trace.correlation.and.comparison","name":"multi-modal model trace correlation and comparison","description":"Unifies tracing and evaluation across heterogeneous model types (LLM, CV, tabular) within a single observability framework, enabling side-by-side comparison of outputs and metrics across modalities. Stores traces in a common schema that maps LLM tokens to CV predictions to tabular model outputs, facilitating analysis of end-to-end multi-modal pipelines.","intents":["Debug a multi-modal RAG system that combines LLM reasoning with image understanding by inspecting both modalities' outputs","Compare performance of a vision-language model against separate LLM and CV models on the same input","Trace how errors propagate through a pipeline combining tabular feature engineering, CV preprocessing, and LLM inference"],"best_for":["teams building multi-modal AI systems (vision-language models, embodied AI, etc.)","researchers studying interactions between different model types","ML engineers debugging complex pipelines with heterogeneous components"],"limitations":["Unified schema may not capture domain-specific metadata for each modality; requires custom extensions","Visualization of multi-modal traces in notebooks is complex; may require external dashboards for large pipelines","No built-in support for temporal alignment of asynchronous multi-modal outputs (e.g., streaming LLM + batch CV)"],"requires":["Python 3.8+","Jupyter notebook environment","trace data from multiple model types (LLM, CV, tabular) in compatible formats"],"input_types":["heterogeneous trace data (LLM tokens, CV predictions, tabular outputs)","unified trace schema (JSON/dict with common fields)"],"output_types":["correlated trace visualizations (multi-modal execution graphs)","cross-modal comparison metrics","unified evaluation reports"],"categories":["memory-knowledge","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-phoenix__cap_5","uri":"capability://planning.reasoning.interactive.model.debugging.with.hypothesis.testing","name":"interactive model debugging with hypothesis testing","description":"Provides interactive tools to formulate and test hypotheses about model behavior (e.g., 'does model accuracy degrade on images with low contrast?') by filtering traces and predictions based on input/output characteristics and computing conditional metrics. Enables iterative refinement of hypotheses through notebook-based exploration without requiring SQL or data engineering.","intents":["Test whether my model's errors correlate with specific input patterns (e.g., rare classes, edge cases)","Slice model performance by input characteristics to identify subgroups with degraded accuracy","Iteratively refine hypotheses about model failure modes by filtering and re-evaluating subsets of data"],"best_for":["data scientists and ML engineers debugging model behavior through exploratory analysis","product teams investigating user-reported model failures","researchers studying model robustness across input distributions"],"limitations":["Interactive filtering on large datasets (100k+ examples) may cause notebook lag; requires sampling or external data stores","Hypothesis testing without multiple comparison correction may lead to false discoveries; no built-in statistical rigor controls","Limited to post-hoc analysis; no support for prospective hypothesis testing or A/B testing frameworks"],"requires":["Python 3.8+","Jupyter notebook environment","trace data with input/output characteristics (features, predictions, metadata)"],"input_types":["trace data (predictions, inputs, metadata)","filter expressions (Python functions or declarative queries)","custom metrics (Python callables)"],"output_types":["filtered trace subsets","conditional performance metrics (accuracy on filtered data)","hypothesis test results (p-values, effect sizes)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-phoenix__cap_6","uri":"capability://data.processing.analysis.model.version.comparison.and.a.b.testing.framework","name":"model version comparison and a/b testing framework","description":"Enables systematic comparison of multiple model versions (different architectures, hyperparameters, training data) by running them on the same test set and computing comparative metrics (accuracy difference, latency ratio, cost per prediction). Supports statistical significance testing to determine whether observed differences are meaningful, with results visualized in notebooks.","intents":["Compare GPT-4 vs Claude vs open-source LLM on my specific task to decide which to deploy","Measure whether my fine-tuned model outperforms the base model with statistical confidence","Analyze cost-performance tradeoffs across model versions to optimize inference budget"],"best_for":["ML engineers selecting between model candidates for production deployment","teams evaluating fine-tuning or prompt engineering improvements","researchers conducting systematic model comparison studies"],"limitations":["Requires running all model versions on the same test set; no support for online A/B testing or streaming evaluation","Statistical significance testing assumes i.i.d. samples; may be invalid for time-series or sequential data","No built-in support for cost-aware comparison; requires manual integration of pricing data"],"requires":["Python 3.8+","Jupyter notebook environment","API credentials for all model versions being compared","shared test dataset"],"input_types":["model predictions from multiple versions","evaluation metrics (accuracy, latency, cost)","test data with ground truth (optional)"],"output_types":["comparative metrics tables (difference, ratio, p-value)","statistical significance test results","visualization of metric distributions across versions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-phoenix__cap_7","uri":"capability://tool.use.integration.trace.export.and.integration.with.external.ml.platforms","name":"trace export and integration with external ml platforms","description":"Exports captured traces and evaluation results to external ML platforms (Weights & Biases, MLflow, Hugging Face Hub) in standard formats (JSON, Parquet, CSV) for integration with downstream workflows. Supports bidirectional sync to enable logging from notebooks and retrieval of historical traces for analysis.","intents":["Export my notebook-based LLM traces to Weights & Biases for team collaboration and long-term storage","Log model predictions and evaluations to MLflow for integration with production ML pipelines","Share trace data with collaborators via Hugging Face Hub without requiring direct database access"],"best_for":["teams transitioning from notebook-based development to production ML platforms","researchers sharing reproducible traces and evaluations with collaborators","ML engineers integrating Phoenix observability with existing MLOps infrastructure"],"limitations":["Export formats may lose modality-specific metadata (e.g., CV bounding box coordinates) if target platform doesn't support custom schemas","Bidirectional sync requires maintaining schema compatibility; breaking changes in target platforms may require adapter updates","Large trace exports (100k+ examples) may exceed API rate limits or storage quotas on target platforms"],"requires":["Python 3.8+","API credentials for target platform (Weights & Biases, MLflow, etc.)","network connectivity to external platforms"],"input_types":["trace data (JSON, dict)","evaluation results (metrics, scores)","model metadata (version, hyperparameters)"],"output_types":["exported traces in target platform format (JSON, Parquet, CSV)","integration with external dashboards and workflows"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Jupyter/IPython notebook environment or compatible REPL","API credentials for target LLM provider (OpenAI, Anthropic, etc.)","Jupyter notebook environment","LLM API credentials if using LLM-as-judge evaluators","Jupyter notebook with image display support","CV model outputs in standard formats (bounding boxes as [x, y, w, h] or [x1, y1, x2, y2], masks as numpy arrays)","pandas DataFrames or numpy arrays for feature data","baseline statistics (training data distribution) for comparison","trace data from multiple model types (LLM, CV, tabular) in compatible formats"],"failure_modes":["In-memory trace storage limits scalability to development/small-scale workloads; production deployments require external persistence layer","Notebook-first design means limited integration with containerized or serverless inference pipelines","Trace capture overhead increases latency per API call (estimated 50-150ms depending on payload size)","LLM-as-judge evaluators inherit model biases and may not correlate with human judgment; requires human-in-the-loop validation","Custom evaluators require manual implementation; no pre-built library for domain-specific metrics (e.g., medical accuracy, legal compliance)","Evaluation latency scales linearly with dataset size; batch evaluation of 10k+ examples may require hours","Visualization limited to 2D outputs; no built-in support for 3D point clouds or volumetric data","Batch visualization of large image datasets (1000+) may cause notebook performance degradation","No built-in annotation tools for ground-truth labeling; requires external tools for dataset creation","Statistical drift tests assume feature independence; may produce false positives for correlated features","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:04.046Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=phoenix","compare_url":"https://unfragile.ai/compare?artifact=phoenix"}},"signature":"DndlaV0gDUzEaSW2xoePlQ+dEXrJ9U6M5RGVyjGWhIe8V0t7eFa0swdVtHBlbcgSWz0y28iUKibOaMPWs8ygDA==","signedAt":"2026-06-22T09:11:19.257Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/phoenix","artifact":"https://unfragile.ai/phoenix","verify":"https://unfragile.ai/api/v1/verify?slug=phoenix","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"}}