LLM observability
Track prompts, traces, errors, runs, and related metadata so teams can inspect how LLM apps behave in production.
Lunary is an observability and prompt management platform for LLM-based applications. It is built to help teams monitor, improve, and secure AI chatbots and other LLM workflows as they move from development into production.
The product combines logs, traces, prompt templates, human review, analytics, and guardrails in one place. The site also emphasizes self-hosting options, enterprise controls, and SDK-based integration so teams can add monitoring without placing Lunary directly in the request path.
Track prompts, traces, errors, runs, and related metadata so teams can inspect how LLM apps behave in production.
Create prompt templates, collaborate with teammates, and use versioning and A/B testing to iterate on prompt changes.
Review responses, label data, and export logs to JSONL for downstream fine-tuning workflows.
Search, filter, and analyze usage, costs, topics, and satisfaction with analytics and custom dashboards.
Mask personal information, manage roles and access, and support SSO/SAML for controlled data access.
Connect through SDKs, an HTTP API, and integrations across OpenAI, LangChain, LiteLLM, Flowise, and other tools.
Use Lunary to inspect production LLM traffic, review traces and errors, and understand how real users interact with a chatbot or agent.
Create templates, compare prompt versions, and run A/B tests when iterating on system prompts or chat flows.
Label logs, review responses, and export data to JSONL when preparing datasets for model fine-tuning or analysis.
Apply PII masking, access controls, and self-hosting when handling sensitive user data or compliance-sensitive workloads.
Share projects, invite teammates, and use human reviews and dashboards to coordinate feedback across technical and non-technical collaborators.
Lunary is a platform for monitoring, improving, and securing AI chatbots. Its FAQ describes it as covering observability, prompt management, evaluations, and LLM guardrails.
According to the FAQ, a run can be types such as llm, trace, chain, embedding, or chat. For LLMs, a run is typically one API call to an inference API or a chat message in a thread.
No. The FAQ says Lunary SDKs run asynchronously rather than sitting between your code and the inference API, so they should not affect request latency.
The FAQ says free-plan data is available for 30 days, paid-plan data is kept indefinitely, and self-hosted data stays with you indefinitely.
Yes. The FAQ says you can self-host for free with the Community Edition, while the Enterprise Edition supports Docker, Kubernetes, and more and is paid.
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