What is Opik by Comet?
Opik by Comet is an open-source observability and evaluation platform for large language model (LLM) applications and AI agents. It captures agent traces, evaluates application behavior, helps teams investigate recurring failures, and monitors production quality and cost.
Key Capabilities
- LLM observability: Records agent activity across context retrieval, tool selection, prompts, model calls, and user feedback.
- Trace diagnostics: Detects and groups recurring issues in traces, including failures that do not generate explicit error messages.
- Evaluation and testing: Creates test suites, golden datasets, and evaluations using built-in LLM-as-a-judge metrics.
- Agent optimization: Uses trace and evaluation findings to identify improvements to prompts, tool calls, retrieval steps, and agent workflows.
- Production monitoring: Provides dashboards and alerts for production behavior, model cost, performance, and governance-related oversight.
- Human review: Supports annotations from expert reviewers as an input to evaluation and optimization workflows.
Audience & Use Cases
- Audience: AI engineers, application developers, ML engineers, and platform teams building generative AI applications and agents.
- Use cases: Debugging agent workflows, evaluating application changes, investigating silent failures, maintaining regression tests, monitoring production agents, and analyzing coding-agent spend.
Technical Specifications
- Deployment options: Cloud-hosted, self-hosted open-source deployment, and custom deployment options.
- Integrations: Supports more than 60 integrations, including common LLM application frameworks and an MCP server for coding-agent access.
- Related platform: Comet separately provides MLOps capabilities for experiment tracking, model versioning, dataset management, and predictive-model monitoring.
Categories & Use Cases
Technical Details
| Mobile Application | No |
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FAQs
What is Opik by Comet?
Opik by Comet is an open-source observability and evaluation platform for large language model (LLM) applications and AI agents. It captures agent traces, evaluates application behavior, helps teams investigate recurring failures, and monitors production quality and cost.