Powerful tracing & observability, purpose-built for GenAI

Analyze performance and user feedback from your application in production to detect anomalies, address issues, and drive continuous improvement.

Monitoring

The path to improving performance starts by measuring it.

Online evaluators

Compute integrity and performance metrics across your data to detect LLM failures in production.

User feedback & actions

Capture user feedback to track performance and user experience across your LLM apps.

RAG and agent analytics

Create your own queries to monitor the performance of specific components in your RAG or agent pipelines.

Custom charts and dashboard

Save custom charts to your team workspace for quick access to insights that matter to you the most.

Filters and groups

Slice and dice your data across segments and get detailed insights into application performance.

Async logging

Log application data synchronously and asynchronously, depending on your specific needs. No proxy required.

Get deep visibility into performance and failures

LLMs often lead to unexpected failures in production. HoneyHive allows you to monitor your LLM apps with quantitative rigor and get actionable insights to continuously improve your app.

Log LLM application data with just a few lines of code

Enrich logs with user feedback, metadata, and user properties

Query logs and save custom charts in your team dashboard

Trace and debug errors in your multi-step pipelines

LLM apps fail due to issues in either the prompt, model, or your data retrieval pipeline. With full visibility into the entire chain of events, you can quickly pinpoint errors and iterate with confidence.

Debug chains, agents, tools and RAG pipelines

Root cause errors with AI-assisted RCA

Integrates with leading orchestration frameworks

Automatically curate datasets for fine-tuning and evals

HoneyHive enables you to filter, curate, and label datasets from production logs for fine-tuning and evaluation.

Filter and add underperforming test cases from production

Invite domain experts to annotate and provide ground truth labels

Manage and version fine-tuning datasets across workspaces

Run online evaluations to catch LLM failures as they happen

Run online evaluators on your live production data to catch LLM failures automatically.

Evaluate faithfulness and context relevance across RAG pipelines

Write assertions to validate JSON structures or SQL schemas

Implement moderation filters to detect PII leakage and unsafe responses

Semantically analyze text for topic, tone, and sentiment

Calculate NLP metrics such as ROUGE-L or METEOR

Any model. Any framework. Any use-case.

OpenTelemetry native. Our tracers use OTLP protocol, allowing seamless interoperability across your DevOps stack.

SDKs and APIs. Allow you to deeply integrate with your application logic and build custom automations using your logs.

Auto-instrumentation. Our tracers automatically instrument popular model providers and tools like OpenAI, Anthropic, Pinecone, and more.

Continuously improve your LLM-powered products.