Glassray - 2026 Pricing, Features, Reviews & Alternatives

Glassray is a cloud-based SDK and evaluation layer that creates a self-improvement loop for AI agents running in production. It connects to agent traces via a native SDK, OpenTelemetry, or existing observability providers such as Langfuse, LangSmith, PostHog, and Braintrust, then reads the agent's code to derive a plain-language behavioral spec for each reasoning step. An AI judge evaluates every step in every production trace against that spec using a combination of deterministic checks and LLM-based evaluation, detecting silent failures without requiring ground-truth labels or a labeling pipeline. Recurring misbehavior patterns are clustered into deviations tracked over time, with Slack and webhook alerts firing only when a pattern reaches significance. Glassray targets engineering teams building multi-step pipelines, tool-calling agents, retrieval chains, and multi-agent systems at startups and scaling companies that need systematic agent quality monitoring without maintaining custom evaluation tooling in-house.

When a confirmed deviation is identified, Glassray generates code-level fix proposals as diffs with the failing trace attached, posted directly to the repository. Before any fix is merged, a sandbox replay validates it against historical trace history, surfacing an accuracy delta and regression count. This closes the loop from detection to resolution without requiring manual trace review. The trace explorer supports full-text search with one-year retention, and unlimited seats are included on all plans. Pricing includes a free observability tier covering up to 150,000 traces per month, with monitoring charges applied only on confirmed deviations.

The enterprise tier of Glassray extends the platform with model post-training on production traces, custom reinforcement learning environments, SSO/SAML, and audit logs. Access is available via MCP, API, and CLI in addition to the web interface, and the framework-agnostic architecture requires no changes to an existing LLM stack.