llm-orchestration-agent-opentelemetry-api

Connects AI assistants to OpenTelemetry trace backends such as Jaeger, Tempo, Traceloop, and Datadog, enabling natural-language searching of traces and spans, error discovery, and service listing. It also provides LLM-specific analytics, including token usage aggregation, model performance comparison, and identification of the most expensive or slowest traces.

An MCP server that instruments Cursor AI agent interactions with OpenTelemetry traces and logs to monitor agent turns and performance. It enables tracking of user queries, assistant responses, and tool usage through GenAI-compliant telemetry spans.

Enables natural language querying and analysis of OpenTelemetry traces, metrics, and logs stored in Elasticsearch/OpenSearch, allowing AI assistants to investigate performance issues, find root causes, and explore system behavior.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.