# Fiddler Documentation

> Unified observability for trustworthy AI across traditional ML, LLM applications, autonomous agents, and self-hosted deployments.

## Docs

- [Compatibility Matrix](https://handbook.fiddler.ai/changelog/compatibility-matrix.md): Compatibility guidance between the Fiddler platform and Python client. Find the recommended Python client version to use with your Fiddler platform version.
- [Fiddler Evals SDK](https://handbook.fiddler.ai/changelog/evals-sdk.md): Fiddler's Evals SDK release history. Contains Evals SDK release highlights, SDK deprecation notices, and more.
- [Changelog](https://handbook.fiddler.ai/changelog/index.md): Release notes for the Fiddler AI Observability Platform and supported SDKs.
- [Fiddler LangChain SDK](https://handbook.fiddler.ai/changelog/langchain-sdk.md): Fiddler's LangChain SDK release history. Contains LangChain SDK release highlights, SDK deprecation notices, and more.
- [Fiddler LangGraph SDK](https://handbook.fiddler.ai/changelog/langgraph-sdk.md): Fiddler's LangGraph SDK release history. Contains LangGraph SDK release highlights, SDK deprecation notices, and more.
- [Fiddler OTel SDK](https://handbook.fiddler.ai/changelog/otel-sdk.md): Fiddler's OTel SDK release history. Contains OTel SDK release highlights, SDK deprecation notices, and more.
- [Product Releases](https://handbook.fiddler.ai/changelog/product-releases.md): Discover the latest updates to Fiddler's AI observability platform - new features for ML, LLM, GenAI, and agentic observability.
- [Python Client SDK](https://handbook.fiddler.ai/changelog/python-sdk.md): Fiddler's Python client history reference. Contains Python client release highlights, client deprecation notices, and more.
- [Fiddler Strands Agent SDK](https://handbook.fiddler.ai/changelog/strands-sdk.md): Fiddler's Strands Agent SDK release history. Contains Strands Agent SDK release highlights, SDK deprecation notices, and more.
- [RAG Health Diagnostics](https://handbook.fiddler.ai/concepts/rag-health-diagnostics.md): Understand how RAG Health Metrics diagnose Retrieval-Augmented Generation pipeline failures using Answer Relevance, Context Relevance, and RAG Faithfulness evaluators.
- [Model Onboarding](https://handbook.fiddler.ai/developers/client-library-reference/model-onboarding.md)
- [Publishing Production Data](https://handbook.fiddler.ai/developers/client-library-reference/publishing-production-data.md): Navigate our client guide to publishing production data. Learn how to provide event data to Fiddler, update it, and retrieve it efficiently.
- [Monitoring Agentic Content Generation](https://handbook.fiddler.ai/developers/cookbooks/agentic-content-generation.md): Monitor agentic content generation for quality, safety, and brand compliance using built-in evaluators and custom LLM-as-a-Judge scoring.
- [Agentic Document Extraction](https://handbook.fiddler.ai/developers/cookbooks/agentic-document-extraction.md): Build observable, measurable document extraction pipelines using Fiddler's agentic tracing, custom evaluators, and experiments.
- [Overview](https://handbook.fiddler.ai/developers/cookbooks/cookbooks.md): Use-case oriented guides for solving real-world AI evaluation and monitoring problems with Fiddler.
- [Building Custom Judge Evaluators](https://handbook.fiddler.ai/developers/cookbooks/custom-judge-evaluators.md): Build domain-specific LLM-as-a-Judge evaluators using CustomJudge with prompt templates, structured output fields, and iterative prompt improvement.
- [Detecting Hallucinations in RAG](https://handbook.fiddler.ai/developers/cookbooks/hallucination-detection-pipeline.md): Build a complete hallucination detection pipeline combining Evals SDK evaluation with LLM Observability enrichments for continuous RAG monitoring.
- [Multimodal Evaluators](https://handbook.fiddler.ai/developers/cookbooks/multimodal-evaluators.md): Build evaluators for document processing pipelines using CustomJudge with vision-capable models. Verify extraction accuracy and summarization faithfulness.
- [RAG Evaluation Fundamentals](https://handbook.fiddler.ai/developers/cookbooks/rag-evaluation-fundamentals.md): Evaluate RAG application quality using Fiddler's built-in evaluators with direct scoring for rapid iteration on retrieval and generation quality.
- [Running RAG Experiments at Scale](https://handbook.fiddler.ai/developers/cookbooks/rag-experiments-at-scale.md): Run structured RAG experiments with Datasets, golden label validation, and side-by-side comparison of pipeline configurations.
- [Overview](https://handbook.fiddler.ai/developers/index.md)
- [Alerts with Fiddler Client](https://handbook.fiddler.ai/developers/python-client-guides/alerts-with-fiddler-client.md): Discover our guide to alerts with Fiddler Client. Learn to set up alert rules to add, delete, and list all alerts, including triggered alerts.
- [Installation and Setup](https://handbook.fiddler.ai/developers/python-client-guides/installation-and-setup.md): Explore our installation guide to set up Fiddler’s Python SDK client. Learn how to connect, install, import, authorize, and set log levels in your environment.
- [Create a Project and Model](https://handbook.fiddler.ai/developers/python-client-guides/model-onboarding/create-a-project-and-model.md): Explore our guide to creating a project and onboarding a model for observation. Learn how projects organize models and define a ModelSpec and Model Task.
- [Customizing Your Model Schema](https://handbook.fiddler.ai/developers/python-client-guides/model-onboarding/customizing-your-model-schema.md): Delve into our guide to customize your Model Schema with Fiddler. Learn how to adjust a column’s value range, possible values, and data type to match your model.
- [Custom Missing Values](https://handbook.fiddler.ai/developers/python-client-guides/model-onboarding/specifying-custom-missing-value-representations.md): Learn how you can customize a model column to assign values to be treated as missing or null data in order to handle a value or token that is inserted in place of null.
- [Task Types](https://handbook.fiddler.ai/developers/python-client-guides/model-onboarding/task-types.md): Explore our guide to selecting a model task type when onboarding your ML models and LLM applications.
- [Updating Model Schema](https://handbook.fiddler.ai/developers/python-client-guides/model-onboarding/updating-model-schema.md): Learn how to modify your model's schema after initial creation by adding new columns using the Python client's add\_column() method. Add features, metadata, and tracking columns to existing models.
- [Naming Convention Guidelines](https://handbook.fiddler.ai/developers/python-client-guides/naming-convention-guidelines.md): Learn Fiddler's naming requirements: start with lowercase letters, use only a-z, 0-9, and underscores afterwards. See examples and best practices.
- [Creating a Baseline Dataset](https://handbook.fiddler.ai/developers/python-client-guides/publishing-production-data/creating-a-baseline-dataset.md): Learn to create a baseline dataset and detect data drift in production. Explore baseline types in Fiddler and start building one that fits your model best.
- [Deleting Events](https://handbook.fiddler.ai/developers/python-client-guides/publishing-production-data/deleting-events.md): Dive into our guide on deleting existing events from your data whether due to regulatory compliance, custom data retention policies, or publishing mistakes.
- [Publishing Batches of Events](https://handbook.fiddler.ai/developers/python-client-guides/publishing-production-data/publishing-batches-of-events.md): Dive into our guide on publishing batches of events. Learn how Fiddler supports multiple source formats when publishing batches of events.
- [Ranking Events](https://handbook.fiddler.ai/developers/python-client-guides/publishing-production-data/ranking-events.md): Explore our guide to publishing production data. Learn how to publish and update ranking events in a grouped format with a detailed example.
- [Streaming Live Events](https://handbook.fiddler.ai/developers/python-client-guides/publishing-production-data/streaming-live-events.md): Learn how to stream your ML model's inference event data using the Fiddler Python client.
- [Updating Events](https://handbook.fiddler.ai/developers/python-client-guides/publishing-production-data/updating-events.md): Dive into our guide on updating inference events. Learn how to update your ground truth labels and metadata using the Fiddler Python client.
- [Experiments Quick Start](https://handbook.fiddler.ai/developers/quick-starts/experiments-quick-start.md): Quick start guide for running experiments on LLM applications using the Fiddler Evals SDK
- [Get Started in <10 Minutes](https://handbook.fiddler.ai/developers/quick-starts/get-started-in-less-than-10-minutes.md): Choose your integration path and get started with Fiddler in under 10 minutes
- [Guardrails Quick Start](https://handbook.fiddler.ai/developers/quick-starts/guardrails-quick-start.md): Get started with Fiddler Guardrails to protect your LLM applications from harmful content, PII leaks, and hallucinations
- [LangGraph SDK Quick Start](https://handbook.fiddler.ai/developers/quick-starts/langgraph-sdk-quick-start.md): Monitor AI agent behavior in LangGraph and LangChain applications. Fiddler LangGraph SDK provides real-time observability for GenAI workflows and debugging.
- [OpenTelemetry Quick Start](https://handbook.fiddler.ai/developers/quick-starts/opentelemetry-quick-start.md): Integrate custom AI agents and agentic frameworks with Fiddler using OpenTelemetry for comprehensive observability and monitoring in multi-framework environments.
- [Simple LLM Monitoring](https://handbook.fiddler.ai/developers/quick-starts/simple-llm-monitoring.md): Learn the basic onboarding steps to use Fiddler for monitoring LLM applications. Access Google Colab or download the notebook directly from GitHub.
- [Simple ML Monitoring](https://handbook.fiddler.ai/developers/quick-starts/simple-ml-monitoring.md): This document provides a guide for using Fiddler for model monitoring using sample data provided by Fiddler.
- [Strands Agent SDK Quick Start](https://handbook.fiddler.ai/developers/quick-starts/strands-agent-quick-start.md): Learn how to integrate Strands agents with Fiddler using the Fiddler Strands SDK for automatic instrumentation and comprehensive observability of your AI agent workflows.
- [Experiments](https://handbook.fiddler.ai/developers/tutorials/experiments.md): Master LLM and AI application experiments with comprehensive tutorials covering the Fiddler Evals SDK, custom evaluators, model comparison, and custom experiment creation.
- [Evals SDK Advanced Guide](https://handbook.fiddler.ai/developers/tutorials/experiments/evals-sdk-advanced.md): Advanced experiment patterns for production LLM applications including multi-score evaluators, complex parameter mapping, and comprehensive experiment analysis.
- [RAG Health Metrics Tutorial](https://handbook.fiddler.ai/developers/tutorials/experiments/rag-health-metrics-tutorial.md): Step-by-step guide to evaluating RAG applications using the RAG Health Metrics diagnostic triad: Answer Relevance, Context Relevance, and RAG Faithfulness.
- [Guardrails](https://handbook.fiddler.ai/developers/tutorials/guardrails.md)
- [Faithfulness](https://handbook.fiddler.ai/developers/tutorials/guardrails/guardrails-faithfulness.md): This Quick Start notebook introduces Fiddler Guardrails, an enterprise solution that safeguards LLM applications from risks like hallucinations, toxicity, and jailbreaking attempts.
- [PII](https://handbook.fiddler.ai/developers/tutorials/guardrails/guardrails-pii.md): Learn to detect and protect PII, PHI, and sensitive data in text using Fiddler's fast PII guardrail for comprehensive privacy compliance.
- [Safety](https://handbook.fiddler.ai/developers/tutorials/guardrails/guardrails-safety.md): This Quick Start Notebook introduces Fiddler Guardrails' Safety Detection capabilities, an essential component of our enterprise solution for protecting LLM applications.
- [Agentic & LLM Monitoring](https://handbook.fiddler.ai/developers/tutorials/llm-monitoring.md)
- [LangGraph SDK Advanced](https://handbook.fiddler.ai/developers/tutorials/llm-monitoring/langgraph-sdk-advanced.md): Advanced observability patterns for LangGraph applications including multi-agent workflows, conversation tracking, and production configuration.
- [Advanced Prompt Specs](https://handbook.fiddler.ai/developers/tutorials/llm-monitoring/prompt-specs-advanced.md): Advanced guide to Fiddler's LLM-as-a-Judge capabilities, including custom prompting, model selection, performance optimization, and enterprise deployment patterns.
- [ML Monitoring](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring.md)
- [Class Imbalance](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring/class-imbalance-monitoring-example.md): Discover how Fiddler uses class weighting to address class imbalance. Compare two identical models–with and without weighting–to detect drift signals.
- [CV Inputs](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring/cv-monitoring.md): Explore our guide to using Fiddler’s monitoring for computer vision models. Learn to detect drift in image data with our unique Vector Monitoring approach.
- [Model Versions](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring/ml-monitoring-model-versions.md): Explore our guide to using Fiddler’s sample data to set up and manage multiple versions of a model with the powerful Model Versions feature.
- [Regression](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring/ml-monitoring-regression.md): Check out our guide on using Fiddler to evaluate regression models. See examples of detecting issues using data drift and performance metrics like MAE.
- [Ranking Models](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring/ranking-model.md): Explore our notebook to see how Fiddler monitors ranking models using a public dataset organized around “search result impressions” from Expedia hotel searches.
- [NLP Inputs](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring/simple-nlp-monitoring-quick-start.md): Dive into our guide on using Fiddler to monitor NLP models. Learn how a multi-class classifier is applied to the dataset and monitored with Vector Monitoring.
- [Feature Impact](https://handbook.fiddler.ai/developers/tutorials/ml-monitoring/user-defined-feature-impact.md): Leverage this guide on using Fiddler's feature impact upload API to supply your own feature impact values for your Fiddler model.
- [Evals SDK Quick Start](https://handbook.fiddler.ai/evaluate-and-test/evals-sdk-quick-start.md): Learn how to evaluate Large Language Model (LLM) applications, RAG systems, and AI agents using the Fiddler Evals SDK with built-in and custom evaluators.
- [Evaluator Rules](https://handbook.fiddler.ai/evaluate-and-test/evaluator-rules.md): Configure automated evaluations for your GenAI application spans using Evaluator Rules. Learn to map evaluators to span data, define application rules, and manage backfill configuration.
- [Compare LLM Outputs](https://handbook.fiddler.ai/evaluate-and-test/llm-evaluation-example.md): Learn how to systematically compare outputs from different LLM models (GPT-3.5, Claude, etc.) using Fiddler's pre-production evaluation environment to make data-driven model selection decisions.
- [Overview](https://handbook.fiddler.ai/evaluate-and-test/overview.md): Hands-on quick start guides for evaluating LLM applications, testing with custom LLM-as-a-Judge metrics, and comparing model outputs using Fiddler Experiments.
- [Prompt Specs Quick Start](https://handbook.fiddler.ai/evaluate-and-test/prompt-specs-quick-start.md): Get started with Fiddler's LLM-as-a-Judge evaluation using Prompt Specs in minutes. Learn to create custom evaluations, test them, and deploy to production monitoring.
- [Agentic Observability](https://handbook.fiddler.ai/getting-started/agentic-monitoring.md): Comprehensive monitoring, tracing, and analysis of AI agent systems that provides hierarchical visibility into agent reasoning, coordination, and decision-making across multi-agent applications
- [AWS SageMaker Partner AI App](https://handbook.fiddler.ai/getting-started/aws-sagemaker-partner-ai-app.md): Get started with Fiddler's Partner AI App on AWS SageMaker. Monitor, explain, and analyze your ML models and GenAI apps in your own AWS environment.
- [Experiments](https://handbook.fiddler.ai/getting-started/experiments.md): Systematically evaluate and compare your LLM and agentic applications with confidence using Fiddler Experiments - comprehensive evaluation framework with built-in metrics (faithfulness, relevancy, saf
- [Onboard Your GenAI Application](https://handbook.fiddler.ai/getting-started/genai-application-onboarding.md): Set up your first GenAI project and application in Fiddler. Learn how to create projects, configure applications, and start monitoring your GenAI interactions.
- [Guardrails](https://handbook.fiddler.ai/getting-started/guardrails.md): Experience the industry's fastest guardrail with the Fiddler Free Guardrails offering, protecting LLMs against hallucinations, safety risks, and jailbreaks.
- [LLM Monitoring](https://handbook.fiddler.ai/getting-started/llm-monitoring.md)
- [ML Observability](https://handbook.fiddler.ai/getting-started/ml-observability.md)
- [Agentic Observability](https://handbook.fiddler.ai/glossary/agentic-observability.md): Comprehensive monitoring, tracing, and analysis of AI agent systems that provides hierarchical visibility into agent reasoning, coordination, and decision-making across distributed multi-agent applica
- [Baseline](https://handbook.fiddler.ai/glossary/baseline.md): Reference datasets in Fiddler that serve as comparison points for detecting data drift, evaluating model performance, and identifying when production data deviates from expected patterns.
- [Custom Metric](https://handbook.fiddler.ai/glossary/custom-metrics.md): User-defined calculations in Fiddler that extend monitoring beyond standard metrics, allowing teams to track business-specific KPIs and specialized measurements for their AI applications.
- [Data Drift](https://handbook.fiddler.ai/glossary/data-drift.md): The statistical change in data distributions over time that can impact model performance. Fiddler detects drift by comparing production data against baselines to identify degradation causes.
- [Embedding Visualization](https://handbook.fiddler.ai/glossary/embedding-visualization.md): Interactive visualizations in Fiddler AI that transform complex embedding vectors into 3D displays, revealing semantic patterns, clusters, and outliers in LLM data.
- [Enrichment](https://handbook.fiddler.ai/glossary/enrichment.md): Comprehensive overview of enrichments in AI monitoring and evaluation. Learn how Fiddler's enrichment framework transforms raw LLM data into actionable insights through specialized metrics and custom
- [Experiments](https://handbook.fiddler.ai/glossary/experiments.md): Systematic assessment of LLM application quality through structured testing with datasets, evaluators, and experiments that enable data-driven decision-making for prompt optimization, model selection,
- [Fiddler Guardrails](https://handbook.fiddler.ai/glossary/guardrails.md): Explore the Fiddler's administrative features and settings available to Org Admins.
- [Glossary](https://handbook.fiddler.ai/glossary/index.md): Review product concepts and terminology for the Fiddler platform to help get up to speed quickly when adopting Fiddler for your ML and GenAI monitoring.
- [LLM Observability](https://handbook.fiddler.ai/glossary/llm-observability.md): Comprehensive monitoring of LLM applications that evaluates safety, quality, and performance metrics to detect issues like hallucinations, toxicity, and drift in generative AI systems.
- [Metric](https://handbook.fiddler.ai/glossary/metric.md): Metrics in Fiddler AI are quantitative measurements that evaluate model behavior, data quality, and performance over time, enabling proactive monitoring and issue detection.
- [ML Observability](https://handbook.fiddler.ai/glossary/ml-observability.md): A comprehensive approach to monitoring AI systems that goes beyond performance metrics to provide insights into model behavior, data quality, and root causes of issues throughout the ML lifecycle.
- [Model Drift](https://handbook.fiddler.ai/glossary/model-drift.md): Changes in model performance over time due to shifting data patterns, concept evolution, or system degradation. Fiddler detects and diagnoses model drift to maintain AI reliability.
- [Model Performance](https://handbook.fiddler.ai/glossary/model-performance.md): Quantitative evaluation of AI model accuracy and effectiveness in production. Fiddler tracks performance metrics over time to detect degradation and identify opportunities for improvement.
- [Trust Score](https://handbook.fiddler.ai/glossary/trust-score.md): Quantitative scores generated by Fiddler's enrichment processes that measure LLM output quality and safety. These numerical metrics enable monitoring, alerting, and real-time decision-making for AI go
- [Fiddler Trust Service](https://handbook.fiddler.ai/glossary/trust-service.md): A specialized infrastructure that hosts purpose-built LLMs for evaluation, powering both monitoring metrics and real-time guardrails with higher efficiency than general-purpose models.
- [Introduction to Fiddler](https://handbook.fiddler.ai/index.md): Fiddler helps AI teams evaluate, monitor, and protect traditional ML models, LLM applications, and agentic systems in production.
- [Agentic AI Overview](https://handbook.fiddler.ai/integrations/agentic-ai-and-llm-frameworks/agentic-ai.md): Native SDKs and framework integrations for agentic AI and LLM applications
- [Fiddler Evals SDK](https://handbook.fiddler.ai/integrations/agentic-ai/evals-sdk.md): LLM experiments framework with pre-built evaluators and custom metrics
- [Fiddler OTel SDK](https://handbook.fiddler.ai/integrations/agentic-ai/fiddler-otel-sdk.md): Instrument any Python AI agent or LLM application with Fiddler's core OpenTelemetry SDK
- [Fiddler LangChain SDK](https://handbook.fiddler.ai/integrations/agentic-ai/langchain-sdk.md): Instrument LangChain V1 agents with Fiddler observability
- [Fiddler LangGraph SDK](https://handbook.fiddler.ai/integrations/agentic-ai/langgraph-sdk.md): Instrument LangGraph agents and custom AI applications with Fiddler's native SDK
- [LiteLLM Integration](https://handbook.fiddler.ai/integrations/agentic-ai/litellm-integration.md): Integrate LiteLLM with Fiddler for unified LLM cost tracking, latency monitoring, and LLM call observability — via the LiteLLM SDK or the LiteLLM proxy gateway.
- [OpenTelemetry Integration](https://handbook.fiddler.ai/integrations/agentic-ai/opentelemetry-integration.md): Connect custom AI agents and multi-framework agentic applications to Fiddler using OpenTelemetry's OTLP protocol for comprehensive observability.
- [Exporting OTel Traces to Fiddler](https://handbook.fiddler.ai/integrations/agentic-ai/otel-trace-export.md): Map pre-existing OpenTelemetry span attributes to Fiddler's schema and export them to the v1/traces protobuf endpoint from your own storage or pipeline.
- [S3 Trace Ingestion](https://handbook.fiddler.ai/integrations/agentic-ai/s3-trace-ingestion.md): Ingest pre-generated OTLP trace files from Amazon S3 into Fiddler without modifying your application code. Ideal for ECS Fargate, air-gapped environments, or any architecture where direct SDK integration is not possible.
- [Fiddler Strands SDK](https://handbook.fiddler.ai/integrations/agentic-ai/strands-sdk.md): Native monitoring for Strands Agents with Strands Agents SDK
- [Admin Guide](https://handbook.fiddler.ai/integrations/aws-sagemaker/partner-ai-app-admin-guide.md): Admin guide to deploy the Fiddler Partner AI App on AWS SageMaker. Configure IAM roles, permissions, and subscriptions for secure AI systems observability.
- [Quick Setup Script](https://handbook.fiddler.ai/integrations/aws-sagemaker/partner-ai-app-quick-setup-script.md): Quickly set up Fiddler's Partner AI App on SageMaker with our script. Automates IAM roles, permissions, and configuration for fast deployment.
- [User Guide](https://handbook.fiddler.ai/integrations/aws-sagemaker/partner-ai-app-user-guide.md): User guide for Fiddler on AWS SageMaker. Learn to use the Fiddler UI and Python SDK to monitor, explain, and analyze your models and GenAI apps.
- [AWS SageMaker Partner AI App](https://handbook.fiddler.ai/integrations/cloud-platforms-and-deployment/aws-sagemaker.md): Run Fiddler AI Observability platform within Amazon SageMaker as a Partner AI App. Procure, provision, and securely operate Fiddler seamlessly all within your existing AWS account.
- [Cloud Platforms Overview](https://handbook.fiddler.ai/integrations/cloud-platforms-and-deployment/cloud-platforms.md): Deploy and operate Fiddler natively on leading cloud platforms
- [Data Platforms Overview](https://handbook.fiddler.ai/integrations/data-platforms-and-pipelines/data-platforms.md): Connect Fiddler to data warehouses, streaming platforms, and ML pipelines
- [Apache Airflow](https://handbook.fiddler.ai/integrations/data-platforms/airflow-integration.md): Discover how to integrate Fiddler with an Airflow DAG for your ML pipeline, enabling you to train, manage, onboard models, and monitor performance seamlessly.
- [BigQuery](https://handbook.fiddler.ai/integrations/data-platforms/bigquery-integration.md): Discover BigQuery integration with Fiddler. Learn how to load ML data from BigQuery tables and use it for tasks like publishing production data to Fiddler.
- [Amazon S3](https://handbook.fiddler.ai/integrations/data-platforms/integration-with-s3.md): Effortlessly extract AWS S3 data for model onboarding and inference publishing to Fiddler for monitoring.
- [Apache Kafka](https://handbook.fiddler.ai/integrations/data-platforms/kafka-integration.md): Dive into Fiddler’s Kafka connector services. Learn about prerequisites, installation, and limitations to manage production events and publish them to Fiddler.
- [SageMaker Pipelines](https://handbook.fiddler.ai/integrations/data-platforms/sagemaker-integration.md): Learn how integrating SageMaker with Fiddler simplifies model monitoring. Explore our guide on using AWS Lambda with the Fiddler Python client.
- [Snowflake](https://handbook.fiddler.ai/integrations/data-platforms/snowflake-integration.md): Learn how to extract baseline or production data from Snowflake for model onboarding and publishing production data to Fiddler for ML and LLM monitoring.
- [Integrations](https://handbook.fiddler.ai/integrations/index.md)
- [ML Platforms Overview](https://handbook.fiddler.ai/integrations/ml-platforms-and-tools/ml-platforms.md): Integrate Fiddler with MLOps platforms, experiment tracking tools, and ML frameworks
- [Databricks](https://handbook.fiddler.ai/integrations/ml-platforms/databricks-integration.md): Discover how Fiddler helps monitor, explain, and analyze models in Databricks Workspace. Integrate with MLFlow and Spark to manage, validate, and monitor models.
- [MLflow](https://handbook.fiddler.ai/integrations/ml-platforms/ml-flow-integration.md): Explore how Fiddler helps your team onboard, monitor, explain, and analyze models with MLFlow. Learn to ingest model metadata and artifacts for observability.
- [Datadog](https://handbook.fiddler.ai/integrations/monitoring-alerting/datadog-integration.md): Learn about Fiddler’s Datadog integration to bring AI Observability metrics into your dashboards. Follow steps to centralize ML model and application monitoring.
- [PagerDuty](https://handbook.fiddler.ai/integrations/monitoring-alerting/pagerduty.md): Discover how customer churn prediction works with Fiddler’s AI observability platform. Follow our example to detect and diagnose issues step by step.
- [Monitoring & Alerting Overview](https://handbook.fiddler.ai/integrations/monitoring-and-alerting/monitoring-alerting.md): Connect Fiddler alerts to incident management, observability, and communication tools
- [Agentic Observability](https://handbook.fiddler.ai/observability/agentic/index.md): Monitor AI agents and multi-step workflows with specialized dashboards, metrics, and trace visualization
- [Events Table in RCA](https://handbook.fiddler.ai/observability/analytics/data-table-in-rca.md): Learn how to use Fiddler's root cause analysis features to quickly hone in on the data issues adversely impacting your ML models and LLM applications.
- [Feature Analytics](https://handbook.fiddler.ai/observability/analytics/feature-analytics-chart.md): Dive into our guide on creating feature analytics charts and visualizations for important features in your ML Models and LLM applications.
- [Analytics](https://handbook.fiddler.ai/observability/analytics/index.md): Explore our UI guide to Fiddler analytics. Learn about interfaces for various analytics charts and root cause analysis to better understand your models.
- [Metric Card](https://handbook.fiddler.ai/observability/analytics/metric-card.md): Dive into our guide for metric card creation. Follow step-by-step instructions to create metric cards, use custom metrics, right-side controls, and save charts.
- [Performance Charts Creation](https://handbook.fiddler.ai/observability/analytics/performance-charts-creation.md): Discover our guide to creating performance charts. Learn key steps to select charts, use right-side and in-chart controls, and save your customized charts.
- [Performance Charts Visualization](https://handbook.fiddler.ai/observability/analytics/performance-charts-visualization.md): Dive into our guide on performance charts and visualizations used to monitor the behavior and performance of your ML models.
- [Dashboard Interactions](https://handbook.fiddler.ai/observability/dashboards/dashboard-interactions.md): Explore our guide to dashboard interactions. Learn to remove, edit, zoom into charts, switch between bar and line views, and undo toolbar changes.
- [Dashboard Utilities](https://handbook.fiddler.ai/observability/dashboards/dashboard-utilities.md): Discover dashboard utilities on Fiddler’s platform. Learn to rename, save, share, copy links, or delete dashboards to manage your collection effortlessly.
- [Creating Dashboards](https://handbook.fiddler.ai/observability/dashboards/dashboards-creating.md): Navigate our guide for the Dashboard page. Learn how to select new or existing dashboards and access them to monitor performance, drift, integrity, and traffic.
- [Dashboards](https://handbook.fiddler.ai/observability/dashboards/index.md): Explore our guide to Fiddler’s dashboards for centralized monitoring. Discover key features like filters, utilities, default dashboards, and performance.
- [Fairness](https://handbook.fiddler.ai/observability/fairness.md): Explore our walkthrough of ML model fairness and bias. Review the sample calculations you can customize to your data and use with Fiddler's custom metrics.
- [Embedding Visualizations](https://handbook.fiddler.ai/observability/llm/embedding-visualization-with-umap.md): Explore our guide on embedding visualization to enhance LLM monitoring. Discover UMAP techniques, analyze high-dimensional data, and uncover patterns with ease.
- [Enrichments](https://handbook.fiddler.ai/observability/llm/enrichments.md): Explore our guide on how Fiddler can enrich your LLM application's data to help analyze and evaluate application behavior and performance.
- [LLM Monitoring](https://handbook.fiddler.ai/observability/llm/index.md): Explore our guide to LLM application monitoring. Learn how Fiddler generates enrichments using trust and safety metrics for alerting, analysis, and debugging.
- [LLM-Based Metrics](https://handbook.fiddler.ai/observability/llm/llm-based-metrics.md): Explore our guide on LLM-specific metrics useful for evaluating AI-generated content for use cases like chatbots, writing assistants, or content creation tools.
- [LLM Evaluation Prompt Specs](https://handbook.fiddler.ai/observability/llm/llm-evaluation-prompt-specs.md): Prompt specs is a framework Fiddler provides for leveraging a general-purpose LLM to quickly create custom scoring functions without the need to manually tune an evaluation prompt.
- [Selecting Enrichments](https://handbook.fiddler.ai/observability/llm/selecting-enrichments.md): Learn about Fiddler’s enrichments and monitor key aspects of LLM applications. Discover the different factors to analyze for your specific use case.
- [Model UI](https://handbook.fiddler.ai/observability/model-ui/index.md): Learn about Fiddler's no-code Model Editor for streamlined ML model onboarding, featuring draft mode for iterative development and team collaboration.
- [Model Editor](https://handbook.fiddler.ai/observability/model-ui/model-editor.md): Step-by-step instructions for onboarding ML models using Fiddler's UI-based editor, from dataset upload to schema validation and publication.
- [Model Schema Editing](https://handbook.fiddler.ai/observability/model-ui/model-schema-editing.md): Learn how to modify numeric ranges, edit categorical features, and add metadata columns to keep your model schema aligned with evolving production data.
- [Overview](https://handbook.fiddler.ai/observability/monitoring.md): Monitor production models in real-time with comprehensive observability
- [Alerts](https://handbook.fiddler.ai/observability/platform/alerts-platform.md): Discover how to enhance monitoring with Alerts. Learn about alert types and how to set up and view them using the alerts tab in the navigation bar.
- [Class Imbalanced Data](https://handbook.fiddler.ai/observability/platform/class-imbalanced-data.md): Explore how Fiddler uses weighting to help improve drift detection when class distribution is highly imbalanced.
- [Custom Metrics](https://handbook.fiddler.ai/observability/platform/custom-metrics.md): Dive into our guide to enhancing ML and LLM insights with custom metrics. Learn to define, add, access, modify, and delete custom metrics in charts and alerts.
- [Data Drift](https://handbook.fiddler.ai/observability/platform/data-drift-platform.md): Learn about data drift and how Fiddler can monitor your ML model data for drift to provide early detection of issues that could impact model performance.
- [Data Integrity](https://handbook.fiddler.ai/observability/platform/data-integrity-platform.md): Dive into our guide on ensuring data integrity in ML models and LLMs. Learn to monitor violations with Fiddler’s auto-generated charts and alerts.
- [Embedding Visualization](https://handbook.fiddler.ai/observability/platform/embedding-visualization-with-umap.md): Dive into our guide on embedding visualization with UMAP in Fiddler. Learn to create charts, select parameters, and interact with visualizations.
- [Fiddler Query Language](https://handbook.fiddler.ai/observability/platform/fiddler-query-language.md): Explore our guide on using Fiddler Query Language to build custom metrics to drive additional business value in dashboards and extra capability in alerting.
- [Monitoring Platform](https://handbook.fiddler.ai/observability/platform/index.md): Dive into our guide to optimizing ML models and LLM applications with Fiddler’s monitoring tools. Learn key metrics to track data drift, performance, and more.
- [Model Versions](https://handbook.fiddler.ai/observability/platform/model-versions.md): Discover model versions in Fiddler. Learn structured approaches to managing related models, their use cases, capabilities, and how to create a model version.
- [Monitoring Charts](https://handbook.fiddler.ai/observability/platform/monitoring-charts-platform.md): Explore our guide to the monitoring charts UI. Learn how to create charts, explore functions, customize tabs, and track LLM metrics effectively.
- [Performance Tracking](https://handbook.fiddler.ai/observability/platform/performance-tracking-platform.md): Learn to track performance with Fiddler. Discover why performance metrics matter and the steps to take when your model isn’t performing as expected.
- [Segments](https://handbook.fiddler.ai/observability/platform/segments.md): Learn to use model segments for monitoring diverse dimensions. Define, add, and modify segments to gain valuable insights into specific cohorts and dimensions.
- [Statistics](https://handbook.fiddler.ai/observability/platform/statistics.md): Discover Fiddler’s statistical metrics guide to monitor column aggregations. Learn what’s tracked, how to monitor metrics, and how to set up alerts.
- [Template-Based Alerts](https://handbook.fiddler.ai/observability/platform/template-based-alerts.md): Learn how to create and deploy template-based alerts in Fiddler using Google Sheets and YAML configurations for efficient model monitoring.
- [Traffic](https://handbook.fiddler.ai/observability/platform/traffic-platform.md): Learn how Fiddler tracks your ML and GenAI models' traffic patterns and when to take action when traffic patterns deviate from normal.
- [Vector Monitoring](https://handbook.fiddler.ai/observability/platform/vector-monitoring-platform.md): Dive into our vector monitoring guide to learn about model inputs represented as vectors and how to use Fiddler's custom features to monitor and detect drift.
- [Guardrails](https://handbook.fiddler.ai/protection/guardrails.md): Fiddler Guardrails is a powerful solution designed to serve as the first-line of defense to protect enterprises from costly GenAI and LLM risks in real-time environments.
- [Guardrails FAQ](https://handbook.fiddler.ai/protection/guardrails-faq.md): Find answers to common questions about Fiddler Free Guardrails, including setup, implementation, and general information for protecting your LLM applications.
- [Guardrails Quick Start](https://handbook.fiddler.ai/protection/guardrails-quick-start.md): Get started with Fiddler Free Guardrails to protect LLM applications. Explore implementation examples in your preferred language and framework.
- [Overview](https://handbook.fiddler.ai/protection/index.md): Ensure AI safety and compliance with guardrails and monitoring
- [Authentication Management](https://handbook.fiddler.ai/reference/access-control/authn-authentication-management-console.md)
- [Email Login](https://handbook.fiddler.ai/reference/access-control/email-login.md): This page documents the details of Fiddler's native email-based authentication including user account creation and password policy.
- [Google SSO](https://handbook.fiddler.ai/reference/access-control/google-integration.md): Learn how to configure Fiddler with Google for seamless Single Sign-On (SSO) authentication.
- [Access Control](https://handbook.fiddler.ai/reference/access-control/index.md): Explore our guides on authentication options with leading IDPs like Okta and Ping. Dive deep into authorization topics using the Fiddler UI.
- [Mapping IdP Groups to Teams](https://handbook.fiddler.ai/reference/access-control/mapping-ad-groups-to-fiddler-teams.md): This document describes the naming convention and rules for mapping internal AD groups to Fiddler Teams automatically.
- [Okta Integration](https://handbook.fiddler.ai/reference/access-control/okta-integration.md): Learn how to configure Fiddler with Okta for seamless Single Sign-On (SSO) authentication.
- [Okta SAML](https://handbook.fiddler.ai/reference/access-control/okta-integration-saml.md): Learn how to configure Fiddler with Okta using SAML for seamless Single Sign-On (SSO) authentication.
- [Ping Identity SAML](https://handbook.fiddler.ai/reference/access-control/ping-identity-saml.md): Learn how to configure Fiddler with Ping for seamless Single Sign-On (SSO) authentication.
- [Role-Based Access Control](https://handbook.fiddler.ai/reference/access-control/role-based-access.md): Learn how Fiddler uses role-based access control with resources and roles. Discover how to manage access with resources, roles, and permissions in your company.
- [Microsoft Entra ID OIDC](https://handbook.fiddler.ai/reference/access-control/single-sign-on-with-azure-ad.md): Learn to integrate Fiddler and Microsoft Entra ID, formerly known as Azure AD, for seamless Single Sign-On (SS0).
- [SSO Authentication Guide](https://handbook.fiddler.ai/reference/access-control/sso-authentication-guide.md): Configure Single Sign-On authentication for Fiddler with Okta, Azure AD, Google, Ping, and others. Complete setup guide with troubleshooting tips.
- [AWS VPC Endpoint Setup](https://handbook.fiddler.ai/reference/administration/aws-vpc-endpoint-setup.md): Automated script to create AWS VPC endpoints for secure communication with Fiddler Cloud using AWS Virtual PrivateLink.
- [AWS Virtual PrivateLink Setup](https://handbook.fiddler.ai/reference/administration/aws-vpl-setup.md): Step-by-step guide to configure AWS Virtual PrivateLink for secure communication between your AWS VPC and Fiddler Cloud.
- [LLM Gateway](https://handbook.fiddler.ai/reference/administration/llm-gateway.md): Configure LLM provider credentials to enable AI-powered features in Fiddler using your own API keys from OpenAI, Anthropic, Gemini, and other providers.
- [Administration](https://handbook.fiddler.ai/reference/administration/settings.md): Dive into our guide to application settings in Fiddler. Learn to use the settings page to manage team setup, permissions, and credentials.
- [Supported Browsers](https://handbook.fiddler.ai/reference/administration/supported-browsers.md): Discover our product guide on supported web browsers for accessing Fiddler, including Google Chrome, Firefox, Safari, and Microsoft Edge.
- [Feature Maturity Definitions](https://handbook.fiddler.ai/reference/feature-maturity-definitions.md): Review Fiddler's release and support policies for product features at different stages of maturity.
- [LLM Observability Metrics Reference](https://handbook.fiddler.ai/reference/llm-observability-metrics.md): Complete reference of all LLM observability metrics and enrichments supported by the Fiddler monitoring platform.
- [ML Metrics Reference](https://handbook.fiddler.ai/reference/ml-metrics-reference.md): Complete reference of all built-in ML metrics supported by the Fiddler monitoring platform, organized by category and model task type.
- [AnswerRelevance](https://handbook.fiddler.ai/sdk-api/evals/answer-relevance.md): AnswerRelevance
- [Application](https://handbook.fiddler.ai/sdk-api/evals/application.md): Application
- [Coherence](https://handbook.fiddler.ai/sdk-api/evals/coherence.md): Coherence
- [Conciseness](https://handbook.fiddler.ai/sdk-api/evals/conciseness.md): Conciseness
- [Connection](https://handbook.fiddler.ai/sdk-api/evals/connection.md)
- [ContextRelevance](https://handbook.fiddler.ai/sdk-api/evals/context-relevance.md): ContextRelevance
- [CustomJudge](https://handbook.fiddler.ai/sdk-api/evals/custom-judge.md): CustomJudge
- [Dataset](https://handbook.fiddler.ai/sdk-api/evals/dataset.md): Dataset
- [DatasetItem](https://handbook.fiddler.ai/sdk-api/evals/dataset-item.md): DatasetItem
- [Entities](https://handbook.fiddler.ai/sdk-api/evals/entities.md)
- [EvalFn](https://handbook.fiddler.ai/sdk-api/evals/eval-fn.md): EvalFn
- [evaluate](https://handbook.fiddler.ai/sdk-api/evals/evaluate.md): evaluate
- [Evaluator](https://handbook.fiddler.ai/sdk-api/evals/evaluator.md): Evaluator
- [Evaluators](https://handbook.fiddler.ai/sdk-api/evals/evaluators.md)
- [Experiment](https://handbook.fiddler.ai/sdk-api/evals/experiment.md): Experiment
- [ExperimentItemStatus](https://handbook.fiddler.ai/sdk-api/evals/experiment-item-status.md): ExperimentItemStatus
- [ExperimentStatus](https://handbook.fiddler.ai/sdk-api/evals/experiment-status.md): ExperimentStatus
- [FTLPromptSafety](https://handbook.fiddler.ai/sdk-api/evals/ftl-prompt-safety.md): FTLPromptSafety
- [FTLResponseFaithfulness](https://handbook.fiddler.ai/sdk-api/evals/ftl-response-faithfulness.md): FTLResponseFaithfulness
- [init](https://handbook.fiddler.ai/sdk-api/evals/init.md): init
- [NewDatasetItem](https://handbook.fiddler.ai/sdk-api/evals/new-dataset-item.md): NewDatasetItem
- [Project](https://handbook.fiddler.ai/sdk-api/evals/project.md): Project
- [Pydantic Models](https://handbook.fiddler.ai/sdk-api/evals/pydantic-models.md)
- [RAGFaithfulness](https://handbook.fiddler.ai/sdk-api/evals/rag-faithfulness.md): RAGFaithfulness
- [RegexMatch](https://handbook.fiddler.ai/sdk-api/evals/regex-match.md): RegexMatch
- [RegexSearch](https://handbook.fiddler.ai/sdk-api/evals/regex-search.md): RegexSearch
- [Score](https://handbook.fiddler.ai/sdk-api/evals/score.md): Score
- [ScoreStatus](https://handbook.fiddler.ai/sdk-api/evals/score-status.md): ScoreStatus
- [Sentiment](https://handbook.fiddler.ai/sdk-api/evals/sentiment.md): Sentiment
- [TopicClassification](https://handbook.fiddler.ai/sdk-api/evals/topic-classification.md): TopicClassification
- [Overview](https://handbook.fiddler.ai/sdk-api/index.md)
- [add\_session\_attributes](https://handbook.fiddler.ai/sdk-api/langgraph/add-session-attributes.md): add\_session\_attributes
- [add\_span\_attributes](https://handbook.fiddler.ai/sdk-api/langgraph/add-span-attributes.md): add\_span\_attributes
- [FiddlerChain](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-chain.md): FiddlerChain
- [FiddlerClient](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-client.md): FiddlerClient
- [FiddlerGeneration](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-generation.md): FiddlerGeneration
- [FiddlerResourceAttributes](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-resource-attributes.md): FiddlerResourceAttributes
- [FiddlerSpan](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-span.md): FiddlerSpan
- [FiddlerSpanAttributes](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-span-attributes.md): FiddlerSpanAttributes
- [FiddlerSpanProcessor](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-span-processor.md): FiddlerSpanProcessor
- [FiddlerTool](https://handbook.fiddler.ai/sdk-api/langgraph/fiddler-tool.md): FiddlerTool
- [get\_client](https://handbook.fiddler.ai/sdk-api/langgraph/get-client.md): get\_client
- [get\_current\_span](https://handbook.fiddler.ai/sdk-api/langgraph/get-current-span.md): get\_current\_span
- [initialize\_jsonl\_capture](https://handbook.fiddler.ai/sdk-api/langgraph/initialize-jsonl-capture.md): initialize\_jsonl\_capture
- [is\_fiddler\_span](https://handbook.fiddler.ai/sdk-api/langgraph/is-fiddler-span.md): is\_fiddler\_span
- [JSONLSpanCapture](https://handbook.fiddler.ai/sdk-api/langgraph/jsonl-span-capture.md): JSONLSpanCapture
- [JSONLSpanExporter](https://handbook.fiddler.ai/sdk-api/langgraph/jsonl-span-exporter.md): JSONLSpanExporter
- [LangGraphInstrumentor](https://handbook.fiddler.ai/sdk-api/langgraph/lang-graph-instrumentor.md): LangGraphInstrumentor
- [set\_conversation\_id](https://handbook.fiddler.ai/sdk-api/langgraph/set-conversation-id.md): set\_conversation\_id
- [set\_llm\_context](https://handbook.fiddler.ai/sdk-api/langgraph/set-llm-context.md): set\_llm\_context
- [SpanType](https://handbook.fiddler.ai/sdk-api/langgraph/span-type.md): SpanType
- [trace](https://handbook.fiddler.ai/sdk-api/langgraph/trace.md): trace
- [AlertCondition](https://handbook.fiddler.ai/sdk-api/python-client/alert-condition.md): AlertCondition
- [AlertRecord](https://handbook.fiddler.ai/sdk-api/python-client/alert-record.md): AlertRecord
- [AlertRule](https://handbook.fiddler.ai/sdk-api/python-client/alert-rule.md): AlertRule
- [AlertThresholdAlgo](https://handbook.fiddler.ai/sdk-api/python-client/alert-threshold-algo.md): AlertThresholdAlgo
- [ApiError](https://handbook.fiddler.ai/sdk-api/python-client/api-error.md): ApiError
- [ArtifactStatus](https://handbook.fiddler.ai/sdk-api/python-client/artifact-status.md): ArtifactStatus
- [ArtifactType](https://handbook.fiddler.ai/sdk-api/python-client/artifact-type.md): ArtifactType
- [AsyncJobFailed](https://handbook.fiddler.ai/sdk-api/python-client/async-job-failed.md): AsyncJobFailed
- [Baseline](https://handbook.fiddler.ai/sdk-api/python-client/baseline.md): Baseline
- [BaselineCompact](https://handbook.fiddler.ai/sdk-api/python-client/baseline-compact.md): BaselineCompact
- [BaselineType](https://handbook.fiddler.ai/sdk-api/python-client/baseline-type.md): BaselineType
- [BinSize](https://handbook.fiddler.ai/sdk-api/python-client/bin-size.md): BinSize
- [Column](https://handbook.fiddler.ai/sdk-api/python-client/column.md): Column
- [CompareTo](https://handbook.fiddler.ai/sdk-api/python-client/compare-to.md): CompareTo
- [Conflict](https://handbook.fiddler.ai/sdk-api/python-client/conflict.md): Conflict
- [ConnError](https://handbook.fiddler.ai/sdk-api/python-client/conn-error.md): ConnError
- [ConnTimeout](https://handbook.fiddler.ai/sdk-api/python-client/conn-timeout.md): ConnTimeout
- [Connection](https://handbook.fiddler.ai/sdk-api/python-client/connection.md)
- [ConnectionMixin](https://handbook.fiddler.ai/sdk-api/python-client/connection-mixin.md): ConnectionMixin
- [create\_columns\_from\_df](https://handbook.fiddler.ai/sdk-api/python-client/create-columns-from-df.md): create\_columns\_from\_df
- [CustomFeature](https://handbook.fiddler.ai/sdk-api/python-client/custom-feature.md): CustomFeature
- [CustomFeatureType](https://handbook.fiddler.ai/sdk-api/python-client/custom-feature-type.md): CustomFeatureType
- [CustomMetric](https://handbook.fiddler.ai/sdk-api/python-client/custom-metric.md): CustomMetric
- [DataType](https://handbook.fiddler.ai/sdk-api/python-client/data-type.md): DataType
- [Dataset](https://handbook.fiddler.ai/sdk-api/python-client/dataset.md): Dataset
- [DatasetCompact](https://handbook.fiddler.ai/sdk-api/python-client/dataset-compact.md): DatasetCompact
- [DatasetDataSource](https://handbook.fiddler.ai/sdk-api/python-client/dataset-data-source.md): DatasetDataSource
- [DeploymentParams](https://handbook.fiddler.ai/sdk-api/python-client/deployment-params.md): DeploymentParams
- [DeploymentType](https://handbook.fiddler.ai/sdk-api/python-client/deployment-type.md): DeploymentType
- [DownloadFormat](https://handbook.fiddler.ai/sdk-api/python-client/download-format.md): DownloadFormat
- [Enrichment](https://handbook.fiddler.ai/sdk-api/python-client/enrichment.md): Enrichment
- [EnvType](https://handbook.fiddler.ai/sdk-api/python-client/env-type.md): EnvType
- [EventIdDataSource](https://handbook.fiddler.ai/sdk-api/python-client/event-id-data-source.md): EventIdDataSource
- [ExplainMethod](https://handbook.fiddler.ai/sdk-api/python-client/explain-method.md): ExplainMethod
- [File](https://handbook.fiddler.ai/sdk-api/python-client/file.md): File
- [group\_by](https://handbook.fiddler.ai/sdk-api/python-client/group-by.md): group\_by
- [HttpError](https://handbook.fiddler.ai/sdk-api/python-client/http-error.md): HttpError
- [ImageEmbedding](https://handbook.fiddler.ai/sdk-api/python-client/image-embedding.md): ImageEmbedding
- [IncompatibleClient](https://handbook.fiddler.ai/sdk-api/python-client/incompatible-client.md): IncompatibleClient
- [Job](https://handbook.fiddler.ai/sdk-api/python-client/job.md): Job
- [JobStatus](https://handbook.fiddler.ai/sdk-api/python-client/job-status.md): JobStatus
- [Model](https://handbook.fiddler.ai/sdk-api/python-client/model.md): Model
- [ModelCompact](https://handbook.fiddler.ai/sdk-api/python-client/model-compact.md): ModelCompact
- [ModelDeployment](https://handbook.fiddler.ai/sdk-api/python-client/model-deployment.md): ModelDeployment
- [ModelInputType](https://handbook.fiddler.ai/sdk-api/python-client/model-input-type.md): ModelInputType
- [ModelSchema](https://handbook.fiddler.ai/sdk-api/python-client/model-schema.md): ModelSchema
- [ModelSpec](https://handbook.fiddler.ai/sdk-api/python-client/model-spec.md): ModelSpec
- [ModelTask](https://handbook.fiddler.ai/sdk-api/python-client/model-task.md): ModelTask
- [ModelTaskParams](https://handbook.fiddler.ai/sdk-api/python-client/model-task-params.md): ModelTaskParams
- [Multivariate](https://handbook.fiddler.ai/sdk-api/python-client/multivariate.md): Multivariate
- [NotFound](https://handbook.fiddler.ai/sdk-api/python-client/not-found.md): NotFound
- [Priority](https://handbook.fiddler.ai/sdk-api/python-client/priority.md): Priority
- [Project](https://handbook.fiddler.ai/sdk-api/python-client/project.md): Project
- [ProjectCompact](https://handbook.fiddler.ai/sdk-api/python-client/project-compact.md): ProjectCompact
- [RowDataSource](https://handbook.fiddler.ai/sdk-api/python-client/row-data-source.md): RowDataSource
- [Segment](https://handbook.fiddler.ai/sdk-api/python-client/segment.md): Segment
- [TextEmbedding](https://handbook.fiddler.ai/sdk-api/python-client/text-embedding.md): TextEmbedding
- [Unsupported](https://handbook.fiddler.ai/sdk-api/python-client/unsupported.md): Unsupported
- [VectorFeature](https://handbook.fiddler.ai/sdk-api/python-client/vector-feature.md): VectorFeature
- [Webhook](https://handbook.fiddler.ai/sdk-api/python-client/webhook.md): Webhook
- [WindowBinSize](https://handbook.fiddler.ai/sdk-api/python-client/window-bin-size.md): WindowBinSize
- [XaiParams](https://handbook.fiddler.ai/sdk-api/python-client/xai-params.md): XaiParams
- [Creates Alert Rules](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/create-alert.md): Creates Alert Rules
- [createNotificationForAlertRule](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/create-notification-for-alert-rule.md): Create Notification for an Alert Rule
- [Deletes an Alert Rule](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/delete-alert-rule.md): Deletes an Alert Rule
- [List latest alert record for each time bucket during specified time_bucket_start and time_bucket_end for the given alert rule](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/get-alert-record-history.md): List latest alert record for each time bucket during specified time_bucket_start and time_bucket_end for the given alert rule
- [Returns Alert Rule for the given id](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/get-alert-rule.md): Returns Alert Rule for the given id
- [List of all alert records during specified time_bucket_start and time_bucket_end for the given alert rule](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/get-alert-rule-records.md): List of all alert records during specified time_bucket_start and time_bucket_end for the given alert rule
- [Lists all alert rules configured for a model.](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/get-alert-rules.md): Lists all alert rules configured for a model.
- [List of all alert rule summary in the given time_bucket_start and time_bucket_end](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/get-alert-rules-summary.md): List of all alert rule summary in the given time_bucket_start and time_bucket_end
- [Get thresholds that were used to evaluate the given time bin using the given alert rule.](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/get-alert-thresholds-for-time-bin.md): Get thresholds that were used to evaluate the given time bin using the given alert rule.
- [Returns notification for the given Alert Rule id](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/get-notification-for-alert-rule.md): Returns notification for the given Alert Rule id
- [Alert Rules REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/index.md): Discover Fiddler’s Alert Rules REST API guide. Learn to list alerts by time, create alert rules, send notifications for alerts, and more.
- [Send a test notification for the given Alert Rule](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/test-notification-for-alert-rule.md): Send a test notification for the given Alert Rule to verify notification configuration
- [Update by id](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/update-alert-rule.md): Update Alert Rule by id
- [Update by id](https://handbook.fiddler.ai/sdk-api/rest-api/alert-rules/update-notification-for-alert-rule.md): Update Notification by Alert Rule id
- [Get Application](https://handbook.fiddler.ai/sdk-api/rest-api/applications/get-application.md): Get application by ID
- [Get Application Metrics Metadata](https://handbook.fiddler.ai/sdk-api/rest-api/applications/get-application-metrics.md): Retrieves available metrics and aggregations for a GenAI application
- [Applications](https://handbook.fiddler.ai/sdk-api/rest-api/applications/index.md): REST API endpoints for managing applications in Fiddler Platform.
- [List attributes](https://handbook.fiddler.ai/sdk-api/rest-api/attributes/get-attributes.md): Get list of attributes
- [Attributes](https://handbook.fiddler.ai/sdk-api/rest-api/attributes/index.md): REST API endpoints for managing attributes in Fiddler Platform.
- [add baseline to a model](https://handbook.fiddler.ai/sdk-api/rest-api/baseline/add-baseline.md): Adds a baseline to a model
- [Delete baseline from a model](https://handbook.fiddler.ai/sdk-api/rest-api/baseline/delete-baseline.md): Delete baseline from a model
- [Get baseline details](https://handbook.fiddler.ai/sdk-api/rest-api/baseline/get-baseline.md): Get baseline details
- [List of baselines based on user permissions and filters](https://handbook.fiddler.ai/sdk-api/rest-api/baseline/get-baselines.md): List of baselines based on user permissions and filters
- [List of baselines of a model](https://handbook.fiddler.ai/sdk-api/rest-api/baseline/get-model-baselines.md): List of baselines of a model
- [Baselines REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/baseline/index.md): Discover Fiddler’s Baseline feature. Learn how to add, retrieve, delete, and list baselines for a model based on user permissions and filters.
- [Create new custom metric](https://handbook.fiddler.ai/sdk-api/rest-api/custom-metrics/create-custom-metric.md): Create new custom metric
- [Delete custom metric by uuid](https://handbook.fiddler.ai/sdk-api/rest-api/custom-metrics/delete-custom-metric.md): Delete custom metric by uuid
- [Detail info of a custom metric](https://handbook.fiddler.ai/sdk-api/rest-api/custom-metrics/get-custom-metric.md): Detail info of a custom metric
- [List all custom metrics](https://handbook.fiddler.ai/sdk-api/rest-api/custom-metrics/get-custom-metrics.md): List all custom metrics
- [List all custom metrics for a model](https://handbook.fiddler.ai/sdk-api/rest-api/custom-metrics/get-model-custom-metrics.md): List all custom metrics for a model
- [Custom Metrics REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/custom-metrics/index.md): Discover Fiddler’s Custom Metric feature. Learn how to add, retrieve, and delete custom metrics from you Fiddler models.
- [Get environment of a model](https://handbook.fiddler.ai/sdk-api/rest-api/environment/get-environment.md): Retrieve details of a specific environment associated with a model.
- [List Environments](https://handbook.fiddler.ai/sdk-api/rest-api/environment/get-environments.md): Retrieve a list of environments from authorized projects.
- [List of environments of a model](https://handbook.fiddler.ai/sdk-api/rest-api/environment/get-model-environments.md): Retrieve a list of environments associated with a specific model.
- [Environments REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/environment/index.md): Learn about Fiddler’s environments API . Learn how to list pre-production and production environment datasets.
- [Add new Evals dataset items](https://handbook.fiddler.ai/sdk-api/rest-api/evals/add-evals-dataset-items.md)
- [Create an Evals dataset](https://handbook.fiddler.ai/sdk-api/rest-api/evals/create-evals-dataset.md)
- [Delete a dataset](https://handbook.fiddler.ai/sdk-api/rest-api/evals/delete-evals-dataset.md)
- [Get a dataset by id](https://handbook.fiddler.ai/sdk-api/rest-api/evals/get-evals-dataset-by-id.md)
- [List dataset items](https://handbook.fiddler.ai/sdk-api/rest-api/evals/get-evals-dataset-items.md): Get list of items for a dataset
- [Evals](https://handbook.fiddler.ai/sdk-api/rest-api/evals/index.md): REST API endpoints for managing evals in Fiddler Platform.
- [List Evals datasets](https://handbook.fiddler.ai/sdk-api/rest-api/evals/list-evals-datasets.md): Retrieve datasets with pagination, search, ordering, and filters.
- [Update a dataset](https://handbook.fiddler.ai/sdk-api/rest-api/evals/update-evals-dataset.md)
- [Evaluation](https://handbook.fiddler.ai/sdk-api/rest-api/evaluation/index.md): REST API endpoints for managing evaluation in Fiddler Platform.
- [Score inputs using an evaluator](https://handbook.fiddler.ai/sdk-api/rest-api/evaluation/score-inputs.md): Score inputs using an evaluator
- [Delete events in Fiddler Platform](https://handbook.fiddler.ai/sdk-api/rest-api/events/delete-events.md): Delete events in Fiddler Platform
- [Events](https://handbook.fiddler.ai/sdk-api/rest-api/events/index.md): REST API endpoints for managing events in Fiddler Platform.
- [Publish events to Fiddler Platform](https://handbook.fiddler.ai/sdk-api/rest-api/events/publish-events.md): Publish events to Fiddler Platform
- [Publish update events to Fiddler Platform](https://handbook.fiddler.ai/sdk-api/rest-api/events/publish-events-update.md): Publish update events to Fiddler Platform
- [Create New Experiment](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/create-experiment.md): Create a new experiment
- [Delete Experiment](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/delete-experiment.md): Delete an experiment
- [getEvalScores](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/get-eval-scores.md): Get list of evaluation scores for an experiment
- [Get Experiment](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/get-experiment.md): Get experiment by ID
- [Get Experiment Metrics](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/get-experiment-metrics.md): Get per-evaluator aggregate metrics for an experiment. Auto-detects each evaluator's chart type based on score cardinality: numeric_range (>10 distinct numeric values, histogram), numeric_categorical (<=10 distinct numeric values, distribution bars), or categorical (label-only scores, distribution w…
- [Get Experiment Row Metrics](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/get-experiment-metrics-rows.md): Get top and bottom performing rows via percentile-based outlier detection. Numeric evaluators use P10/P90 thresholds; categorical evaluators flag labels that differ from the mode. Rows are ranked by how many evaluators flagged them as outliers.
- [List experiment results](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/get-experiment-results.md): Get list of results for an experiment
- [List experiments](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/get-experiments.md): Get list of experiments for provided query and filters
- [Experiments](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/index.md): REST API endpoints for managing experiments in Fiddler Platform.
- [Update Experiment](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/update-experiment.md): Update an experiment
- [Upload new experiment items](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/upload-experiment-items.md): Upload new experiment items
- [Upload experiment results](https://handbook.fiddler.ai/sdk-api/rest-api/experiments/upload-experiment-results.md): Upload new experiment results, each with an item and its associated scores.
- [Get feature impact](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/feature-impact.md): Get feature impact for the given environment or a slice query
- [Get feature importance](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/feature-importance.md): Get feature importance for the given environment or a slice query
- [Fetch slice query](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/fetch-slice-query.md): Fetch slice query data
- [Get precomputed feature impact](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/get-precomputed-feature-impact.md): Get precomputed feature impact for a model
- [Get precomputed feature importance](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/get-precomputed-feature-importance.md): Get precomputed feature importance for a model
- [Explainability REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/index.md): Uncover Fiddler’s explainability features. Learn how to fetch slice queries, get feature impact, update precomputed impact, and optimize model operations.
- [Fetch slice query](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/parse-slice-query.md): Parse slice query data
- [Precompute feature impact](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/precompute-feature-impact.md): Compute and cache feature impact on an environment
- [Precompute feature importance](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/precompute-feature-importance.md): Compute and cache feature importance on an environment
- [predictV3](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/predict.md): Get predictions from a model
- [Get model scores](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/scores.md): Get model scores for the given environment or a slice query
- [Update precomputed feature impact](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/update-precompute-feature-impact.md): Compute and update cached feature impact on an environment
- [Update precomputed feature importance](https://handbook.fiddler.ai/sdk-api/rest-api/explainability/update-precompute-feature-importance.md): Compute and update cached feature importance on an environment
- [Fiddler Trust Service REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/fiddler-trust-service-guide.md): Discover Fiddler’s Trust Service models. Documentation for these endpoints will be published when they are added to the public OpenAPI spec.
- [Complete multi-part upload](https://handbook.fiddler.ai/sdk-api/rest-api/file-upload/complete-multi-part-upload.md): Completes the multi-part upload process for a large file in Fiddler.
- [Upload file in a single part](https://handbook.fiddler.ai/sdk-api/rest-api/file-upload/file-upload.md): Uploads a file in a single part to Fiddler.
- [File Upload REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/file-upload/index.md): Explore API integration with Fiddler’s platform. Learn about file uploads, from single-part to multi-part options, and optimize your workflow effortlessly.
- [Initiate multi-part upload](https://handbook.fiddler.ai/sdk-api/rest-api/file-upload/initiate-multi-part-upload.md): Initiates a multi-part upload process for a large file to Fiddler.
- [Upload file a part of the file](https://handbook.fiddler.ai/sdk-api/rest-api/file-upload/upload-part.md): Uploads a part of a large file to Fiddler as part of a multi-part upload process.
- [FQL Expressions](https://handbook.fiddler.ai/sdk-api/rest-api/fql-expressions/index.md): REST API endpoints for managing fql expressions in Fiddler Platform.
- [List available FQL functions](https://handbook.fiddler.ai/sdk-api/rest-api/fql-expressions/list-fql-expressions.md): Returns the list of FQL functions available for GenAI custom metrics, including function metadata, parameter definitions, and return types. The response is used by the frontend for autocomplete suggestions, signature hints, and documentation in the FQL editor.
- [Create GenAI Alert Rule](https://handbook.fiddler.ai/sdk-api/rest-api/genai-alert-rules/create-gen-ai-alert-rule.md): Creates a new GenAI Alert Rule
- [Deletes a GenAI Alert Rule](https://handbook.fiddler.ai/sdk-api/rest-api/genai-alert-rules/delete-gen-ai-alert-rule.md): Deletes a GenAI Alert Rule
- [Get GenAI Alert Rule](https://handbook.fiddler.ai/sdk-api/rest-api/genai-alert-rules/get-gen-ai-alert-rule.md): Retrieves a specific GenAI Alert Rule by its ID
- [GenAI Alert Rules](https://handbook.fiddler.ai/sdk-api/rest-api/genai-alert-rules/index.md): REST API endpoints for managing genai alert rules in Fiddler Platform.
- [List GenAI Alert Rules](https://handbook.fiddler.ai/sdk-api/rest-api/genai-alert-rules/list-gen-ai-alert-rules.md): Lists all GenAI Alert Rules with pagination
- [Send Test Notification](https://handbook.fiddler.ai/sdk-api/rest-api/genai-alert-rules/test-gen-ai-alert-rule-notification.md): Sends a test notification for a GenAI Alert Rule using the configured notification settings. The notification uses the actual alert email template with placeholder breach values so recipients can preview what a real alert notification will look like.
- [REST API](https://handbook.fiddler.ai/sdk-api/rest-api/index.md): Reference for the Fiddler REST API. Every operation in the public OpenAPI specification is documented here, grouped by resource.
- [Get async job details for a job id](https://handbook.fiddler.ai/sdk-api/rest-api/jobs/get-job.md): Get async job details for a job id
- [Get details of all background/async jobs](https://handbook.fiddler.ai/sdk-api/rest-api/jobs/get-jobs.md): Get details of all background/async jobs
- [Jobs REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/jobs/index.md): Learn how to create requests and responses to retrieve async job details by job ID and view all background/async jobs with the Fiddler Platform.
- [Create new LLM provider](https://handbook.fiddler.ai/sdk-api/rest-api/llm-gateway/create-llm-provider.md): Create a new LLM provider with credentials and models
- [Delete LLM provider](https://handbook.fiddler.ai/sdk-api/rest-api/llm-gateway/delete-llm-provider.md): Delete an LLM provider by name
- [Get available provider options](https://handbook.fiddler.ai/sdk-api/rest-api/llm-gateway/get-llm-provider-options.md): Get available models for each supported LLM provider
- [List LLM providers](https://handbook.fiddler.ai/sdk-api/rest-api/llm-gateway/get-llm-providers.md): Get list of LLM providers with pagination support
- [LLM Gateway](https://handbook.fiddler.ai/sdk-api/rest-api/llm-gateway/index.md): REST API endpoints for managing llm gateway in Fiddler Platform.
- [Test LLM connection](https://handbook.fiddler.ai/sdk-api/rest-api/llm-gateway/test-llm-connection.md): Test that a registered model + credential combination works by sending a minimal LLM completion call. Returns HTTP 200 with success/failure for LLM-level results. Returns 4xx for invalid inputs (unknown model, unknown credential, malformed request) before any LLM call is made.
- [Update LLM provider](https://handbook.fiddler.ai/sdk-api/rest-api/llm-gateway/update-llm-provider.md): Update an existing LLM provider's models and credentials. This is a replace operation - pass the complete desired state. For credentials: include existing ones (name/uuid) to keep them, add new ones (name/credential_config) to create them, omit any to remove them. For models: pass the complete list…
- [Add a new model under a project](https://handbook.fiddler.ai/sdk-api/rest-api/model/add-model.md): Add a new model under a project
- [Delete a model](https://handbook.fiddler.ai/sdk-api/rest-api/model/delete-model.md): Delete a model
- [Upload artifacts associated with the model](https://handbook.fiddler.ai/sdk-api/rest-api/model/deploy-model-artifact.md): Upload artifacts associated with the model
- [Update artifacts associated with the model](https://handbook.fiddler.ai/sdk-api/rest-api/model/deploy-model-artifact-update.md): Update artifacts associated with the model
- [Deploy a surrogate model](https://handbook.fiddler.ai/sdk-api/rest-api/model/deploy-surrogate.md): Deploy a surrogate model
- [Update a surrogate model](https://handbook.fiddler.ai/sdk-api/rest-api/model/deploy-surrogate-update.md): Update a surrogate model
- [Get details of a model](https://handbook.fiddler.ai/sdk-api/rest-api/model/get-model.md): Details of a model for given model id
- [Details of all columns](https://handbook.fiddler.ai/sdk-api/rest-api/model/get-model-all-columns.md): Details of all columns for a model
- [getModelColumnV3](https://handbook.fiddler.ai/sdk-api/rest-api/model/get-model-column.md): Details of a specific column for a model
- [List models](https://handbook.fiddler.ai/sdk-api/rest-api/model/get-models.md): Get list of model for provided query and filters
- [Model REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/model/index.md): Dive into our guide to the Model REST API. Learn to list models, add models to a project, get details, update fields, generate models from samples, and more.
- [Generate model from the given data sample](https://handbook.fiddler.ai/sdk-api/rest-api/model/model-factory.md): Generate model from the given data sample
- [Update the fields of a model](https://handbook.fiddler.ai/sdk-api/rest-api/model/update-model.md): Update the fields of a model
- [Create new project](https://handbook.fiddler.ai/sdk-api/rest-api/projects/create-project.md): Create new project
- [Delete project by id](https://handbook.fiddler.ai/sdk-api/rest-api/projects/delete-project.md): Delete project by id
- [Detail info of a project for the specified project id](https://handbook.fiddler.ai/sdk-api/rest-api/projects/get-project.md): Detail info of a project
- [List of projects](https://handbook.fiddler.ai/sdk-api/rest-api/projects/get-projects.md): List of projects
- [Projects REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/projects/index.md): Learn about REST API projects on the Fiddler platform. Explore how to list projects, create a new project, and get detailed info for a specified project ID.
- [API to fetch metrics used to plot monitoring charts](https://handbook.fiddler.ai/sdk-api/rest-api/queries/get-queries.md): API to fetch metrics used to plot monitoring charts
- [Queries](https://handbook.fiddler.ai/sdk-api/rest-api/queries/index.md): REST API endpoints for managing queries in Fiddler Platform.
- [Create new segment](https://handbook.fiddler.ai/sdk-api/rest-api/segments/create-segment.md): Create new segment
- [Delete segment by uuid](https://handbook.fiddler.ai/sdk-api/rest-api/segments/delete-segment.md): Delete segment by uuid
- [List all segments for a model](https://handbook.fiddler.ai/sdk-api/rest-api/segments/get-model-segments.md): List all segments for a model
- [Detail info of a segment](https://handbook.fiddler.ai/sdk-api/rest-api/segments/get-segment.md): Detail info of a segment
- [List all segments](https://handbook.fiddler.ai/sdk-api/rest-api/segments/get-segments.md): List all segments
- [Segments REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/segments/index.md): Discover Fiddler’s Segments REST API guide. Learn to list, create, view details, delete segments by UUID, and list all segments for a model.
- [Get server info](https://handbook.fiddler.ai/sdk-api/rest-api/server-info/get-server-info.md): Get detailed information about the Fiddler server.
- [Server Info REST API Guide](https://handbook.fiddler.ai/sdk-api/rest-api/server-info/index.md): Learn API guidelines for retrieving server info with Fiddler’s platform. Discover how to create requests, receive responses, and maximize our solutions.
- [Spans](https://handbook.fiddler.ai/sdk-api/rest-api/spans/index.md): REST API endpoints for managing spans in Fiddler Platform.
- [Query spans](https://handbook.fiddler.ai/sdk-api/rest-api/spans/query-spans.md): Search, filter, and paginate through spans with advanced criteria
- [This API is used to get identity and access related information of a user. It also provides details into the last successful login of the user.](https://handbook.fiddler.ai/sdk-api/rest-api/users/get-users.md): List users
- [Users](https://handbook.fiddler.ai/sdk-api/rest-api/users/index.md): REST API endpoints for managing users in Fiddler Platform.
- [FiddlerInstrumentationHook](https://handbook.fiddler.ai/sdk-api/strands/fiddler-instrumentation-hook.md): FiddlerInstrumentationHook
- [FiddlerSpanProcessor](https://handbook.fiddler.ai/sdk-api/strands/fiddler-span-processor.md): FiddlerSpanProcessor
- [get\_conversation\_id](https://handbook.fiddler.ai/sdk-api/strands/get-conversation-id.md): get\_conversation\_id
- [get\_llm\_context](https://handbook.fiddler.ai/sdk-api/strands/get-llm-context.md): get\_llm\_context
- [get\_session\_attributes](https://handbook.fiddler.ai/sdk-api/strands/get-session-attributes.md): get\_session\_attributes
- [get\_span\_attributes](https://handbook.fiddler.ai/sdk-api/strands/get-span-attributes.md): get\_span\_attributes
- [set\_conversation\_id](https://handbook.fiddler.ai/sdk-api/strands/set-conversation-id.md): set\_conversation\_id
- [set\_llm\_context](https://handbook.fiddler.ai/sdk-api/strands/set-llm-context.md): set\_llm\_context
- [set\_session\_attributes](https://handbook.fiddler.ai/sdk-api/strands/set-session-attributes.md): set\_session\_attributes
- [set\_span\_attributes](https://handbook.fiddler.ai/sdk-api/strands/set-span-attributes.md): set\_span\_attributes
- [StrandsAgentInstrumentor](https://handbook.fiddler.ai/sdk-api/strands/strands-agent-instrumentor.md): StrandsAgentInstrumentor