# HoneyHive AI Docs

## Docs

- [Create a new configuration](https://docs.honeyhive.ai/api-reference/configurations/create-a-new-configuration.md)
- [Delete a configuration](https://docs.honeyhive.ai/api-reference/configurations/delete-a-configuration.md)
- [Retrieve a list of configurations](https://docs.honeyhive.ai/api-reference/configurations/retrieve-a-list-of-configurations.md)
- [Update an existing configuration](https://docs.honeyhive.ai/api-reference/configurations/update-an-existing-configuration.md)
- [Create a new datapoint](https://docs.honeyhive.ai/api-reference/datapoints/create-a-new-datapoint.md)
- [Delete a specific datapoint](https://docs.honeyhive.ai/api-reference/datapoints/delete-a-specific-datapoint.md)
- [Retrieve a list of datapoints](https://docs.honeyhive.ai/api-reference/datapoints/retrieve-a-list-of-datapoints.md)
- [Retrieve a specific datapoint](https://docs.honeyhive.ai/api-reference/datapoints/retrieve-a-specific-datapoint.md)
- [Update a specific datapoint](https://docs.honeyhive.ai/api-reference/datapoints/update-a-specific-datapoint.md)
- [Add datapoints to a dataset](https://docs.honeyhive.ai/api-reference/datasets/add-datapoints-to-a-dataset.md)
- [Create a dataset](https://docs.honeyhive.ai/api-reference/datasets/create-a-dataset.md)
- [Delete a dataset](https://docs.honeyhive.ai/api-reference/datasets/delete-a-dataset.md)
- [Get datasets](https://docs.honeyhive.ai/api-reference/datasets/get-datasets.md)
- [Update a dataset](https://docs.honeyhive.ai/api-reference/datasets/update-a-dataset.md)
- [Create a batch of events](https://docs.honeyhive.ai/api-reference/events/create-a-batch-of-events.md): Please refer to our instrumentation guide for detailed information
- [Create a batch of model events](https://docs.honeyhive.ai/api-reference/events/create-a-batch-of-model-events.md): Please refer to our instrumentation guide for detailed information
- [Create a new event](https://docs.honeyhive.ai/api-reference/events/create-a-new-event.md): Create a new event (span) within a session trace. The request body wraps the event in a JSON-encoded string under the `event` key. **Required properties** within the JSON-encoded event string: - `event_type` (string) — Must be one of: `chain`, `model`, `tool`, `session`. - `inputs` (object) — Input…
- [Create a new model event](https://docs.honeyhive.ai/api-reference/events/create-a-new-model-event.md): Please refer to our instrumentation guide for detailed information
- [Retrieve events based on filters](https://docs.honeyhive.ai/api-reference/events/retrieve-events-based-on-filters.md)
- [Update an event](https://docs.honeyhive.ai/api-reference/events/update-an-event.md)
- [Create a new evaluation run](https://docs.honeyhive.ai/api-reference/experiments/create-a-new-evaluation-run.md)
- [Delete an evaluation run](https://docs.honeyhive.ai/api-reference/experiments/delete-an-evaluation-run.md)
- [Get a list of evaluation runs](https://docs.honeyhive.ai/api-reference/experiments/get-a-list-of-evaluation-runs.md)
- [Get details of an evaluation run](https://docs.honeyhive.ai/api-reference/experiments/get-details-of-an-evaluation-run.md)
- [Retrieve experiment comparison](https://docs.honeyhive.ai/api-reference/experiments/retrieve-experiment-comparison.md)
- [Retrieve experiment result](https://docs.honeyhive.ai/api-reference/experiments/retrieve-experiment-result.md)
- [Update an evaluation run](https://docs.honeyhive.ai/api-reference/experiments/update-an-evaluation-run.md)
- [Create a new metric](https://docs.honeyhive.ai/api-reference/metrics/create-a-new-metric.md): Add a new metric
- [Delete a metric](https://docs.honeyhive.ai/api-reference/metrics/delete-a-metric.md): Remove a metric
- [Get all metrics](https://docs.honeyhive.ai/api-reference/metrics/get-all-metrics.md): Retrieve a list of all metrics
- [Update an existing metric](https://docs.honeyhive.ai/api-reference/metrics/update-an-existing-metric.md): Edit a metric
- [Create a new project](https://docs.honeyhive.ai/api-reference/projects/create-a-new-project.md)
- [Delete a project](https://docs.honeyhive.ai/api-reference/projects/delete-a-project.md)
- [Get a list of projects](https://docs.honeyhive.ai/api-reference/projects/get-a-list-of-projects.md)
- [Update an existing project](https://docs.honeyhive.ai/api-reference/projects/update-an-existing-project.md)
- [Retrieve a session](https://docs.honeyhive.ai/api-reference/session/retrieve-a-session.md)
- [Start a new session](https://docs.honeyhive.ai/api-reference/session/start-a-new-session.md): Start a new session. The session field should contain a JSON-encoded session object as a string.
- [Product Updates](https://docs.honeyhive.ai/changelog/changelog.md): New updates and improvements to our core platform and SDKs.
- [Key Concepts](https://docs.honeyhive.ai/concepts.md): The key concepts behind the HoneyHive Platform
- [Curate from traces](https://docs.honeyhive.ai/datasets/dataset-curation.md): Curating a dataset of inputs & outputs from your traces
- [Export datasets via SDK](https://docs.honeyhive.ai/datasets/export.md): How to programmatically export datasets in HoneyHive.
- [Import from Hugging Face](https://docs.honeyhive.ai/datasets/hf-datasets.md): How to import datasets from HuggingFace Datasets to HoneyHive.
- [Upload datasets](https://docs.honeyhive.ai/datasets/import.md): How to upload a dataset in HoneyHive
- [Introduction](https://docs.honeyhive.ai/datasets/introduction.md): An overview of HoneyHive datasets and their role in the AI application lifecycle.
- [Annotation Queues](https://docs.honeyhive.ai/evaluation/annotation-queues.md): Learn how to create and manage annotation queues for human review and labeling.
- [Comparing Experiments](https://docs.honeyhive.ai/evaluation/comparing_evals.md): Learn how to compare multiple experiments in HoneyHive to spot improvements and regressions.
- [Evaluating External Logs](https://docs.honeyhive.ai/evaluation/external_logs.md): Upload and evaluate existing logs from external sources like spreadsheets or databases.
- [Introduction](https://docs.honeyhive.ai/evaluation/introduction.md): Get started with running experiments with HoneyHive
- [Using Datasets in UI](https://docs.honeyhive.ai/evaluation/managed_datasets.md): Run experiments using datasets stored and managed in HoneyHive UI.
- [Multi-Step Experiments](https://docs.honeyhive.ai/evaluation/multi_step_evals.md): Learn to evaluate multi-step LLM applications with component-level metrics
- [Quickstart](https://docs.honeyhive.ai/evaluation/quickstart.md): Get started with running experiments with HoneyHive
- [Using Server-Side Evaluators](https://docs.honeyhive.ai/evaluation/server_side_evaluators.md): Run experiments using server-side HoneyHive evaluators
- [Client-Side Evaluators](https://docs.honeyhive.ai/evaluators/client_side.md): Learn how to use client-side evaluators for both tracing and experiments
- [Composite Evaluators](https://docs.honeyhive.ai/evaluators/composites.md): Technical documentation for creating and managing composite evaluators in HoneyHive
- [Evaluator Template List](https://docs.honeyhive.ai/evaluators/evaluator-templates.md): A list of HoneyHive's server-side evaluator templates.
- [Human Annotation](https://docs.honeyhive.ai/evaluators/human.md): Technical documentation for creating custom human evaluator fields in HoneyHive
- [Introduction](https://docs.honeyhive.ai/evaluators/introduction.md): An overview of HoneyHive evaluators
- [LLM Evaluators](https://docs.honeyhive.ai/evaluators/llm.md): Technical documentation for creating custom LLM evaluators in HoneyHive
- [Python Evaluators](https://docs.honeyhive.ai/evaluators/python.md): Technical documentation for creating custom Python evaluators in HoneyHive
- [Version Control](https://docs.honeyhive.ai/evaluators/versioning.md): How to manage and version control your custom evaluators in HoneyHive
- [Anthropic](https://docs.honeyhive.ai/integrations/anthropic.md): Learn how to integrate Anthropic with HoneyHive
- [AWS Bedrock](https://docs.honeyhive.ai/integrations/aws_bedrock.md): Learn how to integrate AWS Bedrock with HoneyHive
- [Azure OpenAI](https://docs.honeyhive.ai/integrations/azure_openai.md): Learn how to integrate Azure OpenAI with HoneyHive
- [Chroma](https://docs.honeyhive.ai/integrations/chromadb.md): Learn how to integrate Chroma with HoneyHive for vector database monitoring, tracing, and retrieval evaluations.
- [Cohere](https://docs.honeyhive.ai/integrations/cohere.md): Learn how to integrate Cohere with HoneyHive
- [CrewAI](https://docs.honeyhive.ai/integrations/crewai.md): This guide explains how to integrate HoneyHive with CrewAI for tracing and monitoring your AI agent workflows.
- [Gemini](https://docs.honeyhive.ai/integrations/gemini.md): Learn how to integrate Gemini with HoneyHive
- [Groq](https://docs.honeyhive.ai/integrations/groq.md): Learn how to integrate Groq with HoneyHive
- [IBM watsonx](https://docs.honeyhive.ai/integrations/ibmwatsonx.md): Learn how to integrate IBM watsonx with HoneyHive
- [LanceDB](https://docs.honeyhive.ai/integrations/lancedb.md): Learn how to integrate LanceDB with HoneyHive for vector database monitoring, tracing, and retrieval evaluations.
- [LangChain](https://docs.honeyhive.ai/integrations/langchain.md): This guide explains how to integrate HoneyHive with LangChain for both Python and TypeScript implementations.
- [LangGraph](https://docs.honeyhive.ai/integrations/langgraph.md): This guide explains how to integrate HoneyHive with LangChain for Python implementations.
- [LiteLLM](https://docs.honeyhive.ai/integrations/litellm.md): Learn how to integrate HoneyHive tracing with LiteLLM for monitoring and optimizing LLM calls
- [LlamaIndex](https://docs.honeyhive.ai/integrations/llamaindex.md): This guide explains how to integrate HoneyHive with LlamaIndex for Python implementations.
- [Marqo](https://docs.honeyhive.ai/integrations/marqo.md): Learn how to integrate HoneyHive tracing with Marqo vector database for RAG applications
- [Mistral AI](https://docs.honeyhive.ai/integrations/mistral.md): Learn how to integrate Mistral AI with HoneyHive
- [NVIDIA NeMo](https://docs.honeyhive.ai/integrations/nvidia.md): Learn how to integrate NVIDIA NeMo Models with HoneyHive
- [Ollama](https://docs.honeyhive.ai/integrations/ollama.md): Learn how to integrate Ollama with HoneyHive
- [OpenAI](https://docs.honeyhive.ai/integrations/openai.md): Learn how to integrate OpenAI with HoneyHive
- [Pinecone](https://docs.honeyhive.ai/integrations/pinecone.md): Learn how to integrate Pinecone with HoneyHive for vector database monitoring, tracing, and retrieval evaluations.
- [Qdrant](https://docs.honeyhive.ai/integrations/qdrant.md): Qdrant RAG with HoneyHive Tracing
- [Vercel AI SDK](https://docs.honeyhive.ai/integrations/vercel.md): This guide explains how to integrate HoneyHive with the Vercel AI SDK for TypeScript implementations.
- [Zilliz/Milvus](https://docs.honeyhive.ai/integrations/zilliz.md): Learn how to integrate Zilliz/Milvus with HoneyHive for vector database monitoring, tracing, and retrieval evaluations.
- [Quickstart](https://docs.honeyhive.ai/introduction/quickstart.md): Get started with tracing in HoneyHive
- [Troubleshooting & FAQs](https://docs.honeyhive.ai/introduction/troubleshooting.md): Troubleshooting common issues with the tracer
- [HoneyHive Overview](https://docs.honeyhive.ai/introduction/what-is-hhai.md): Modern AI Observability and Evaluation
- [Creating Alerts](https://docs.honeyhive.ai/monitoring/alerts/alerts.md): Alerts help you detect critical issues and catch metric drift before it impacts users. Proactively monitor eval scores, guardrail violations, user feedback, latency, cost, or any custom metric.
- [Overview](https://docs.honeyhive.ai/monitoring/alerts/alerts_overview.md)
- [Creating Custom Charts](https://docs.honeyhive.ai/monitoring/charts.md): How to use HoneyHive's query builder interface to monitor performance and drive systematic improvements at scale.
- [Online Evaluations](https://docs.honeyhive.ai/monitoring/onlineevals.md): How to configure online evaluations to monitor your application.
- [Overview](https://docs.honeyhive.ai/monitoring/overview.md): Connect your application to HoneyHive and start monitoring your application performance in production.
- [Platform Architecture](https://docs.honeyhive.ai/platform-architecture.md): HoneyHive Platform Architecture (AWS)
- [Using Prompts in Code](https://docs.honeyhive.ai/prompts/deploy.md): How to deploy prompts to specific environments and export them for use in your application.
- [Managing Prompts](https://docs.honeyhive.ai/prompts/overview.md): Test, version and manage your prompts in the Studio.
- [Schema Overview](https://docs.honeyhive.ai/schema-overview.md): An overview of our data model for logging traces and events
- [Setup and Authentication](https://docs.honeyhive.ai/sdk-reference/authentication.md): Authenticating your requests to the SDK
- [LangChain JS](https://docs.honeyhive.ai/sdk-reference/langchain-ts-tracer-ref.md): Reference documentation for the HoneyHiveLangChainTracer class in JS
- [Manual Evaluation](https://docs.honeyhive.ai/sdk-reference/manual-eval-instrumentation.md): Logging your application execution to HoneyHive without using the tracers
- [Manual Instrumentation](https://docs.honeyhive.ai/sdk-reference/manual-instrumentation.md): Logging your application execution to HoneyHive without using the tracers
- [Python](https://docs.honeyhive.ai/sdk-reference/python-experiments-ref.md): Reference documentation for the evaluate function
- [Python](https://docs.honeyhive.ai/sdk-reference/python-logger-ref.md): Reference documentation for the HoneyHive Logger SDK functions: `start`, `log`, and `update`.
- [Python SDK](https://docs.honeyhive.ai/sdk-reference/python-sdk-ref.md)
- [Python](https://docs.honeyhive.ai/sdk-reference/python-tracer-ref.md): Reference documentation for the HoneyHiveTracer and @trace decorator
- [TypeScript](https://docs.honeyhive.ai/sdk-reference/typescript-experiments-ref.md): Reference documentation for the evaluate function
- [TypeScript](https://docs.honeyhive.ai/sdk-reference/typescript-logger-ref.md): Reference documentation for the HoneyHive Logger
- [TypeScript SDK](https://docs.honeyhive.ai/sdk-reference/typescript-sdk-ref.md)
- [TypeScript](https://docs.honeyhive.ai/sdk-reference/typescript-tracer-ref.md): Reference documentation for the HoneyHiveTracer class
- [Dedicated Cloud](https://docs.honeyhive.ai/setup/dedicated.md): Getting started on HoneyHive's single-tenant dedicated cloud.
- [Infrastructure Requirements](https://docs.honeyhive.ai/setup/infrastructure-requirements.md): Supported dependency versions for self-hosted HoneyHive deployments.
- [SaaS Cloud](https://docs.honeyhive.ai/setup/managed.md): Getting started on HoneyHive's multi-tenant SaaS cloud.
- [Self-Hosted (BYOC)](https://docs.honeyhive.ai/setup/self-hosted.md): Getting started with HoneyHive in your cloud environment.
- [Security Architecture](https://docs.honeyhive.ai/setup/self-hosted/security.md): Security controls, encryption, and compliance for HoneyHive self-hosted deployments
- [Session Aggregations](https://docs.honeyhive.ai/tracing/aggregation-logic.md): Understand user behavior and application performance with session-level aggregations.
- [Client-Side Evaluations](https://docs.honeyhive.ai/tracing/client-side-evals.md): Learn how to log external evaluation results (metrics) with your trace.
- [Custom Spans](https://docs.honeyhive.ai/tracing/custom-spans.md): How to trace custom spans with HoneyHive.
- [Distributed Tracing](https://docs.honeyhive.ai/tracing/distributed-tracing.md): How to trace application execution across multiple services.
- [Overview](https://docs.honeyhive.ai/tracing/enrich-traces.md): How to enrich your traces and spans with additional context
- [Introduction](https://docs.honeyhive.ai/tracing/introduction.md): Getting started with tracing in HoneyHive.
- [Multi-Modal Tracing](https://docs.honeyhive.ai/tracing/multi-modal.md): How to instrument multi-modal pipelines in HoneyHive
- [Multithreading in Python](https://docs.honeyhive.ai/tracing/multithreading.md): How to trace multi-threaded applications in Python with HoneyHive.
- [Online Experiments & A/B Tests](https://docs.honeyhive.ai/tracing/online-experimentation.md): Learn how to A-B test anything online with HoneyHive
- [Export Traces](https://docs.honeyhive.ai/tracing/query-data.md): Understand how to query your LLM application logs using HoneyHive's Data Schema Language.
- [Configurations](https://docs.honeyhive.ai/tracing/setting-config.md): Learn how to track configurations, prompt templates, and other LLM configs in your traces
- [Tags and Metadata](https://docs.honeyhive.ai/tracing/setting-metadata.md): Learn how to set metadata on your traces
- [User Feedback](https://docs.honeyhive.ai/tracing/setting-user-feedback.md): Learn how to track user feedback on your traces
- [User Properties](https://docs.honeyhive.ai/tracing/setting-user-properties.md): Learn how to set user properties in your traces
- [Explore Traces in UI](https://docs.honeyhive.ai/tracing/ui-flows.md): Learn how to use HoneyHive to utilize your traces
- [Observability Tutorial - RAG](https://docs.honeyhive.ai/tutorials/observability-tutorial.md): Instrumenting a RAG application with HoneyHive
- [Create a new configuration](https://docs.honeyhive.ai/v2/api-reference-autogen/configurations/create-a-new-configuration.md)
- [Delete a configuration](https://docs.honeyhive.ai/v2/api-reference-autogen/configurations/delete-a-configuration.md)
- [Retrieve a list of configurations](https://docs.honeyhive.ai/v2/api-reference-autogen/configurations/retrieve-a-list-of-configurations.md)
- [Update an existing configuration](https://docs.honeyhive.ai/v2/api-reference-autogen/configurations/update-an-existing-configuration.md)
- [Create a new datapoint](https://docs.honeyhive.ai/v2/api-reference-autogen/datapoints/create-a-new-datapoint.md)
- [Create multiple datapoints in batch](https://docs.honeyhive.ai/v2/api-reference-autogen/datapoints/create-multiple-datapoints-in-batch.md)
- [Delete a specific datapoint](https://docs.honeyhive.ai/v2/api-reference-autogen/datapoints/delete-a-specific-datapoint.md)
- [Retrieve a list of datapoints](https://docs.honeyhive.ai/v2/api-reference-autogen/datapoints/retrieve-a-list-of-datapoints.md)
- [Retrieve a specific datapoint](https://docs.honeyhive.ai/v2/api-reference-autogen/datapoints/retrieve-a-specific-datapoint.md)
- [Update a specific datapoint](https://docs.honeyhive.ai/v2/api-reference-autogen/datapoints/update-a-specific-datapoint.md)
- [Add datapoints to a dataset](https://docs.honeyhive.ai/v2/api-reference-autogen/datasets/add-datapoints-to-a-dataset.md)
- [Create a dataset](https://docs.honeyhive.ai/v2/api-reference-autogen/datasets/create-a-dataset.md)
- [Delete a dataset](https://docs.honeyhive.ai/v2/api-reference-autogen/datasets/delete-a-dataset.md)
- [Get datasets](https://docs.honeyhive.ai/v2/api-reference-autogen/datasets/get-datasets.md)
- [Remove a datapoint from a dataset](https://docs.honeyhive.ai/v2/api-reference-autogen/datasets/remove-a-datapoint-from-a-dataset.md)
- [Update a dataset](https://docs.honeyhive.ai/v2/api-reference-autogen/datasets/update-a-dataset.md)
- [Create a batch of events](https://docs.honeyhive.ai/v2/api-reference-autogen/events/create-a-batch-of-events.md): Please refer to our instrumentation guide for detailed information
- [Create a batch of model events](https://docs.honeyhive.ai/v2/api-reference-autogen/events/create-a-batch-of-model-events.md): Please refer to our instrumentation guide for detailed information
- [Create a new event](https://docs.honeyhive.ai/v2/api-reference-autogen/events/create-a-new-event.md): Create a new event (span) within a session trace. The request body wraps the event in a JSON-encoded string under the `event` key. **Required properties** within the JSON-encoded event string: - `event_type` (string) — Must be one of: `chain`, `model`, `tool`, `session`. - `inputs` (object) — Input…
- [Create a new model event](https://docs.honeyhive.ai/v2/api-reference-autogen/events/create-a-new-model-event.md): Please refer to our instrumentation guide for detailed information
- [Delete an event](https://docs.honeyhive.ai/v2/api-reference-autogen/events/delete-an-event.md): Delete a specific event by event ID. The `id` parameter is interpreted as an event_id for this operation.
- [Get charting data for events](https://docs.honeyhive.ai/v2/api-reference-autogen/events/get-charting-data-for-events.md): Retrieve aggregated chart data for events with optional grouping and bucketing
- [Get nested events for a session](https://docs.honeyhive.ai/v2/api-reference-autogen/events/get-nested-events-for-a-session.md): Retrieve all nested events for a specific session ID. The `id` parameter is interpreted as a session_id for this operation.
- [Query events with filters and projections](https://docs.honeyhive.ai/v2/api-reference-autogen/events/query-events-with-filters-and-projections.md): Retrieve events with optional filtering, projections, and pagination
- [Retrieve events based on filters](https://docs.honeyhive.ai/v2/api-reference-autogen/events/retrieve-events-based-on-filters.md)
- [Update an event](https://docs.honeyhive.ai/v2/api-reference-autogen/events/update-an-event.md)
- [Compare events between two experiment runs](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/compare-events-between-two-experiment-runs.md): Retrieve and compare events between two experiment runs for detailed analysis
- [Create a new evaluation run](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/create-a-new-evaluation-run.md)
- [Delete an evaluation run](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/delete-an-evaluation-run.md)
- [Get a list of evaluation runs](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/get-a-list-of-evaluation-runs.md)
- [Get details of an evaluation run](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/get-details-of-an-evaluation-run.md)
- [Get event metrics for an experiment run](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/get-event-metrics-for-an-experiment-run.md): Retrieve event metrics from ClickHouse for a specific experiment run
- [Get experiment runs schema](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/get-experiment-runs-schema.md): Retrieve the schema and metadata for experiment runs
- [Retrieve experiment comparison](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/retrieve-experiment-comparison.md): Compare metrics and results between two experiment runs
- [Retrieve experiment result](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/retrieve-experiment-result.md): Compute evaluation summary for an experiment run including pass/fail status, metrics, and datapoints
- [Update an evaluation run](https://docs.honeyhive.ai/v2/api-reference-autogen/experiments/update-an-evaluation-run.md)
- [Create a new metric](https://docs.honeyhive.ai/v2/api-reference-autogen/metrics/create-a-new-metric.md): Add a new metric
- [Delete a metric](https://docs.honeyhive.ai/v2/api-reference-autogen/metrics/delete-a-metric.md): Remove a metric
- [Get all metrics](https://docs.honeyhive.ai/v2/api-reference-autogen/metrics/get-all-metrics.md): Retrieve a list of all metrics
- [Run a metric evaluation](https://docs.honeyhive.ai/v2/api-reference-autogen/metrics/run-a-metric-evaluation.md): Execute a metric on a specific event
- [Update an existing metric](https://docs.honeyhive.ai/v2/api-reference-autogen/metrics/update-an-existing-metric.md): Edit a metric
- [Create a new project](https://docs.honeyhive.ai/v2/api-reference-autogen/projects/create-a-new-project.md)
- [Delete a project](https://docs.honeyhive.ai/v2/api-reference-autogen/projects/delete-a-project.md)
- [Get a list of projects](https://docs.honeyhive.ai/v2/api-reference-autogen/projects/get-a-list-of-projects.md)
- [Update an existing project](https://docs.honeyhive.ai/v2/api-reference-autogen/projects/update-an-existing-project.md)
- [Start a new session](https://docs.honeyhive.ai/v2/api-reference-autogen/session/start-a-new-session.md): Start a new session. The session field should contain a JSON-encoded session object as a string.
- [Delete all events for a session](https://docs.honeyhive.ai/v2/api-reference-autogen/sessions/delete-all-events-for-a-session.md): Delete all events associated with the given session ID from both events and aggregates tables
- [Get session tree by session ID](https://docs.honeyhive.ai/v2/api-reference-autogen/sessions/get-session-tree-by-session-id.md): Retrieve a complete session event tree including all nested events and metadata
- [Product](https://docs.honeyhive.ai/v2/changelog/product.md): New updates and improvements to our core platform and UI.
- [Python SDK](https://docs.honeyhive.ai/v2/changelog/python-sdk.md)
- [Self-Hosting](https://docs.honeyhive.ai/v2/changelog/self-hosting.md): Updates to HoneyHive Helm charts for self-hosted deployments.
- [Overview](https://docs.honeyhive.ai/v2/concepts.md): The key concepts behind the HoneyHive Platform
- [Curate from Traces](https://docs.honeyhive.ai/v2/datasets/dataset-curation.md): Build datasets from production traces in HoneyHive
- [Export Datasets](https://docs.honeyhive.ai/v2/datasets/export.md): Programmatically export datasets from HoneyHive
- [Import from Hugging Face](https://docs.honeyhive.ai/v2/datasets/hf-datasets.md): Import datasets from Hugging Face Datasets to HoneyHive
- [Upload Datasets](https://docs.honeyhive.ai/v2/datasets/import.md): How to upload datasets to HoneyHive via UI or SDK
- [Introduction](https://docs.honeyhive.ai/v2/datasets/introduction.md): An overview of HoneyHive datasets and their role in the AI application lifecycle.
- [Sync from External Sources](https://docs.honeyhive.ai/v2/datasets/sync.md): Keep a HoneyHive dataset synced with S3, databases, or other external sources
- [Annotation Queues](https://docs.honeyhive.ai/v2/evaluation/annotation-queues.md): Learn how to create and manage annotation queues for human review and labeling.
- [CI Regression Detection](https://docs.honeyhive.ai/v2/evaluation/ci-regression-detection.md): Automatically catch quality regressions on every pull request using HoneyHive experiment comparison
- [Comparing Experiments](https://docs.honeyhive.ai/v2/evaluation/comparing_evals.md): Compare experiment runs to identify improvements and regressions across prompts, models, or configurations.
- [Concepts](https://docs.honeyhive.ai/v2/evaluation/concepts.md): How experiments work in HoneyHive
- [Introduction](https://docs.honeyhive.ai/v2/evaluation/introduction.md): Systematically test and improve your AI applications with experiments
- [Experiments via API](https://docs.honeyhive.ai/v2/evaluation/via-api.md): Run experiments using the REST API for non-Python runtimes, custom pipelines, or CI systems
- [Client-Side Evaluators](https://docs.honeyhive.ai/v2/evaluators/client_side.md): Run evaluation logic in your application code
- [Composite Evaluators](https://docs.honeyhive.ai/v2/evaluators/composites.md): Combine multiple evaluators into a single aggregated score
- [Evaluator Template List](https://docs.honeyhive.ai/v2/evaluators/evaluator-templates.md): A list of HoneyHive's server-side evaluator templates.
- [Human Evaluators](https://docs.honeyhive.ai/v2/evaluators/human.md): Create human evaluator fields for manual review and annotation of AI outputs
- [Introduction](https://docs.honeyhive.ai/v2/evaluators/introduction.md): An overview of HoneyHive evaluators
- [LLM Evaluators](https://docs.honeyhive.ai/v2/evaluators/llm.md): Create LLM-powered evaluators to evaluate AI outputs using custom prompts
- [Python Evaluators](https://docs.honeyhive.ai/v2/evaluators/python.md): Create custom server-side evaluators using Python code to assess AI outputs
- [Version Control](https://docs.honeyhive.ai/v2/evaluators/versioning.md): Track changes and roll back evaluators to previous versions
- [Anthropic](https://docs.honeyhive.ai/v2/integrations/anthropic.md): Add HoneyHive observability to your Anthropic Claude applications
- [AutoGen](https://docs.honeyhive.ai/v2/integrations/autogen.md): Add HoneyHive observability to your AutoGen AgentChat applications
- [AWS Bedrock](https://docs.honeyhive.ai/v2/integrations/aws_bedrock.md): Add HoneyHive observability to your AWS Bedrock applications
- [Azure OpenAI](https://docs.honeyhive.ai/v2/integrations/azure_openai.md): Add HoneyHive observability to your Azure OpenAI applications
- [Claude Agent SDK](https://docs.honeyhive.ai/v2/integrations/claude-agent-sdk.md): Add HoneyHive observability to your Claude Agent SDK applications
- [Claude Code](https://docs.honeyhive.ai/v2/integrations/claude-code.md): Export Claude Code sessions into HoneyHive for observability and evaluation
- [CrewAI](https://docs.honeyhive.ai/v2/integrations/crewai.md): Add HoneyHive observability to your CrewAI applications
- [HoneyHive Docs MCP](https://docs.honeyhive.ai/v2/integrations/docs-mcp.md): Give your AI agent access to HoneyHive documentation via Model Context Protocol
- [DSPy](https://docs.honeyhive.ai/v2/integrations/dspy.md): Add HoneyHive observability to your DSPy applications
- [Gemini](https://docs.honeyhive.ai/v2/integrations/gemini.md): Add HoneyHive observability to your Google Gemini applications
- [Google ADK](https://docs.honeyhive.ai/v2/integrations/google-adk.md): Add HoneyHive observability to your Google Agent Development Kit applications
- [LangChain](https://docs.honeyhive.ai/v2/integrations/langchain.md): Add HoneyHive observability to your LangChain applications
- [LangGraph](https://docs.honeyhive.ai/v2/integrations/langgraph.md): Add HoneyHive observability to your LangGraph state graphs and workflows
- [LiteLLM](https://docs.honeyhive.ai/v2/integrations/litellm.md): Add HoneyHive observability to your LiteLLM applications
- [OpenAI](https://docs.honeyhive.ai/v2/integrations/openai.md): Add HoneyHive observability to your OpenAI applications
- [OpenAI Agents SDK](https://docs.honeyhive.ai/v2/integrations/openai-agents.md): Add HoneyHive observability to your OpenAI Agents SDK applications
- [Pydantic AI](https://docs.honeyhive.ai/v2/integrations/pydantic-ai.md): Add HoneyHive observability to your PydanticAI agent applications
- [Microsoft Semantic Kernel](https://docs.honeyhive.ai/v2/integrations/semantic-kernel.md): Add HoneyHive observability to your Semantic Kernel applications
- [AWS Strands Agents](https://docs.honeyhive.ai/v2/integrations/strands.md): Add HoneyHive observability to your Strands Agents applications
- [Use HoneyHive with Coding Agents](https://docs.honeyhive.ai/v2/introduction/ai-coding-agents.md): Connect HoneyHive documentation to your AI tools and coding agents via MCP, llms.txt, or direct markdown copy.
- [Experiments Quickstart](https://docs.honeyhive.ai/v2/introduction/experiments-quickstart.md): Run your first experiment with HoneyHive in 5 minutes
- [Tracing Quickstart](https://docs.honeyhive.ai/v2/introduction/tracing-quickstart.md): Trace your first AI application in 5 minutes
- [Troubleshooting & FAQs](https://docs.honeyhive.ai/v2/introduction/troubleshooting.md): Common issues, solutions, and frequently asked questions
- [HoneyHive Overview](https://docs.honeyhive.ai/v2/introduction/what-is-hhai.md): Modern AI Observability and Evaluation
- [Creating Alerts](https://docs.honeyhive.ai/v2/monitoring/alerts/alerts.md): Step-by-step guide to creating alerts that monitor your AI application's performance, quality, and cost metrics.
- [Overview](https://docs.honeyhive.ai/v2/monitoring/alerts/alerts_overview.md): Learn about HoneyHive alerts - types, states, and how they help you monitor LLM application performance.
- [Custom Charts](https://docs.honeyhive.ai/v2/monitoring/charts.md): How to use HoneyHive's Discover interface to measure performance and discover interesting trends.
- [Online Evaluations](https://docs.honeyhive.ai/v2/monitoring/onlineevals.md): Run evaluators automatically on ingested traces to continuously monitor quality.
- [Dashboard](https://docs.honeyhive.ai/v2/monitoring/overview.md): Analyze cost, latency, and quality metrics from your AI application in HoneyHive's monitoring dashboard.
- [Platform Architecture](https://docs.honeyhive.ai/v2/platform-architecture.md): How HoneyHive's Management Plane and Data Plane architecture works.
- [Using Prompts in Code](https://docs.honeyhive.ai/v2/prompts/deploy.md): Fetch deployed prompts from HoneyHive and use them in your application.
- [Managing Prompts](https://docs.honeyhive.ai/v2/prompts/overview.md): Create, test, and manage prompts in the HoneyHive Playground.
- [Environment Variables](https://docs.honeyhive.ai/v2/sdk-reference/environment-variables.md): Configuration reference for HoneyHive Python SDK environment variables
- [OpenAPI Client (Any Language)](https://docs.honeyhive.ai/v2/sdk-reference/openapi-sdks.md): Generate lightweight API clients in any language using HoneyHive's public OpenAPI spec
- [SDK Overview](https://docs.honeyhive.ai/v2/sdk-reference/overview.md): Choose the right HoneyHive SDK for your use case
- [Python SDK](https://docs.honeyhive.ai/v2/sdk-reference/python-sdk-ref.md): Release history and installation on PyPI
- [Framework Attribute Mapping](https://docs.honeyhive.ai/v2/sdk-reference/semconv-alignment.md): How attributes from OTel GenAI, OpenInference, and Traceloop map to HoneyHive canonical schema keys
- [Semantic Convention Reference](https://docs.honeyhive.ai/v2/sdk-reference/semconv-reference.md): Canonical HoneyHive attribute names, special attribute behaviors, and UI sideview rendering reference
- [TypeScript SDK](https://docs.honeyhive.ai/v2/sdk-reference/typescript-sdk-ref.md): Type-safe TypeScript client for the HoneyHive REST API
- [Dedicated Cloud](https://docs.honeyhive.ai/v2/setup/dedicated.md): Getting started on HoneyHive's dedicated cloud with isolated infrastructure.
- [Infrastructure Requirements](https://docs.honeyhive.ai/v2/setup/infrastructure-requirements.md): Supported dependency versions for self-hosted HoneyHive deployments.
- [Multi-Tenant SaaS](https://docs.honeyhive.ai/v2/setup/managed.md): Getting started on HoneyHive's multi-tenant SaaS cloud.
- [Security](https://docs.honeyhive.ai/v2/setup/security.md): How HoneyHive protects your data, infrastructure, and AI applications.
- [Self-Hosting Overview](https://docs.honeyhive.ai/v2/setup/self-hosted.md): Deploy HoneyHive in your private cloud or on-premise environment.
- [Data Flow & Residency](https://docs.honeyhive.ai/v2/setup/self-hosted/data-flow.md): Data classification, flow boundaries, and residency controls for HoneyHive self-hosted deployments
- [Operations Guide](https://docs.honeyhive.ai/v2/setup/self-hosted/operations.md): Day 2 operations: upgrades, monitoring, backup, scaling, and incident response for self-hosted HoneyHive
- [Security Architecture](https://docs.honeyhive.ai/v2/setup/self-hosted/security.md): Security controls, encryption, and compliance for HoneyHive self-hosted deployments
- [Session Aggregations](https://docs.honeyhive.ai/v2/tracing/aggregation-logic.md): Reserved session metadata fields that HoneyHive automatically calculates
- [Custom Metrics](https://docs.honeyhive.ai/v2/tracing/client-side-evals.md): Log evaluation scores and guardrail results computed in your application
- [Concepts](https://docs.honeyhive.ai/v2/tracing/concepts.md): Core concepts behind HoneyHive's tracing data model, event schema, and OpenTelemetry architecture.
- [Configuration](https://docs.honeyhive.ai/v2/tracing/configuration-details.md): Log prompt templates, model parameters, and other configuration context on your traces
- [Custom Spans](https://docs.honeyhive.ai/v2/tracing/custom-spans.md): Create custom spans for business logic tracing and workflow observability
- [Distributed Tracing](https://docs.honeyhive.ai/v2/tracing/distributed-tracing.md): How to trace application execution across multiple services.
- [Overview](https://docs.honeyhive.ai/v2/tracing/enrich-traces.md): Add custom attributes to your traces for filtering, debugging, and evaluation
- [Schema Reference](https://docs.honeyhive.ai/v2/tracing/enrichment-schema.md): Complete reference for enrichment namespaces, data types, and backend attributes
- [Span Filtering](https://docs.honeyhive.ai/v2/tracing/filtering.md): Filter out noisy or unwanted spans from your traces using prefix-based rules.
- [Graph View](https://docs.honeyhive.ai/v2/tracing/graph-view.md): Visualize execution flow as a directed graph with latency bottlenecks and agent paths
- [Introduction](https://docs.honeyhive.ai/v2/tracing/introduction.md): Getting started with tracing in HoneyHive.
- [Tracing via API](https://docs.honeyhive.ai/v2/tracing/manual-instrumentation.md): Logging your application execution to HoneyHive without using the tracers
- [Multi-Instance Tracing](https://docs.honeyhive.ai/v2/tracing/multi-instance.md): Run multiple tracer instances for multi-tenant, A/B testing, or environment-based routing.
- [Multi-Modal Tracing](https://docs.honeyhive.ai/v2/tracing/multi-modal.md): Trace pipelines that process images, audio, video, and other media
- [Multi-Provider Tracing](https://docs.honeyhive.ai/v2/tracing/multi-provider.md): Trace applications that call more than one LLM provider in the same workflow.
- [Multi-Threading (Python)](https://docs.honeyhive.ai/v2/tracing/multithreading.md): How to trace multi-threaded applications in Python with HoneyHive.
- [Online Experiments](https://docs.honeyhive.ai/v2/tracing/online-experimentation.md): Tag traces with experiment IDs and variants to analyze A/B test results in HoneyHive
- [Export Data](https://docs.honeyhive.ai/v2/tracing/query-data.md): Programmatically query and export trace data from HoneyHive.
- [Sampling](https://docs.honeyhive.ai/v2/tracing/sampling.md): Control which requests get traced in high-volume applications.
- [User Feedback](https://docs.honeyhive.ai/v2/tracing/setting-user-feedback.md): Capture user ratings, comments, and implicit signals on your traces
- [User Properties](https://docs.honeyhive.ai/v2/tracing/setting-user-properties.md): Add user context to your traces for user-centric filtering and analysis
- [Thread View](https://docs.honeyhive.ai/v2/tracing/thread-view.md): View conversational traces as a chronological message thread
- [Timeline View](https://docs.honeyhive.ai/v2/tracing/timeline-view.md): Visualize span durations as a Gantt chart to identify latency bottlenecks
- [Tracer Initialization](https://docs.honeyhive.ai/v2/tracing/tracer-initialization.md): Where to initialize the tracer for scripts, evaluate(), Lambda, and web servers.
- [Trajectory View](https://docs.honeyhive.ai/v2/tracing/trajectory-view.md): Visualize agent behavior patterns as a bubble chart across execution steps
- [Tree View](https://docs.honeyhive.ai/v2/tracing/tree-view.md): Inspect the hierarchical span tree showing parent-child relationships in your traces
- [Explore in UI](https://docs.honeyhive.ai/v2/tracing/ui-flows.md): Learn how to navigate, debug, and curate data from your traces in the Traces page
- [Add Tracing to Existing Apps](https://docs.honeyhive.ai/v2/tutorials/add-tracing-5min.md): Add HoneyHive tracing to your existing LLM application with just 5 lines of code
- [Trace Distributed Systems](https://docs.honeyhive.ai/v2/tutorials/distributed-tracing.md): Trace requests across service boundaries with context propagation
- [Enrich Your Traces](https://docs.honeyhive.ai/v2/tutorials/enriching-traces.md): Add user IDs and custom metadata to make traces more useful
- [End-to-End: Multi-Agent Tracing and Evaluation](https://docs.honeyhive.ai/v2/tutorials/multi-agent-cookbook.md): Build a multi-agent customer support bot with Google ADK, add HoneyHive observability, and evaluate agent quality
- [Deploy Tracing to Production](https://docs.honeyhive.ai/v2/tutorials/production-deployment.md): Configure HoneyHive tracing for production environments
- [Inviting Teammates](https://docs.honeyhive.ai/v2/workspace/inviting-teammates.md): How to invite teammates to your HoneyHive organization, workspaces, and projects.
- [Organization Hierarchy](https://docs.honeyhive.ai/v2/workspace/organization-hierarchy.md): How organizations, workspaces, and projects structure your HoneyHive account.
- [Managing Projects](https://docs.honeyhive.ai/v2/workspace/projects.md): How to organize projects in HoneyHive for your AI applications.
- [Provider Keys](https://docs.honeyhive.ai/v2/workspace/provider-keys.md): Configure AI provider API keys for LLM evaluators and the Playground.
- [Role Based Access Control](https://docs.honeyhive.ai/v2/workspace/roles.md): Manage user permissions across your organization, workspaces, and projects.
- [Templates](https://docs.honeyhive.ai/v2/workspace/templates.md): Configure standard evaluators and monitoring charts across every project.
- [Usage](https://docs.honeyhive.ai/v2/workspace/usage.md): View event counts, enrichment metrics, and export usage reports from Organization Settings.
- [Inviting Teammates](https://docs.honeyhive.ai/workspace/inviting-teammates.md): How to invite teammates to your HoneyHive account.
- [Managing Projects](https://docs.honeyhive.ai/workspace/projects.md): Guide to projects in HoneyHive
- [Role Based Access Control](https://docs.honeyhive.ai/workspace/roles.md): Guide to user access control on organization and project levels

## OpenAPI Specs

- [openapi](https://raw.githubusercontent.com/honeyhiveai/honeyhive-openapi/main/openapi.yaml)
- [output](https://docs.honeyhive.ai/output.yaml)
- [package](https://docs.honeyhive.ai/package.json)
- [codeSamples](https://docs.honeyhive.ai/codeSamples.yaml)