xtraceAI Observability Service

Collect, store, and query traces, spans, and metrics across LLM and agent workflows. Built in Rust for speed and reliability.

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Traces & Spans

Full request-chain visibility with nested span trees. Debug P3 cycles (Plan → Execute → Reflect) and multi-step agent workflows.

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Ingest and query GPU utilization, KV cache, token usage, and custom metrics with label-based filtering and downsampling.

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Axum + Tokio async runtime, batched ingestion via mpsc channels, PostgreSQL storage. Sub-millisecond overhead on the write path.

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OTLP/HTTP ingestion endpoint with JSON and Protobuf support. Drop-in replacement for Langfuse-compatible instrumentation.

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Attach session_id, model_name, agent_role, and arbitrary labels to metrics. Filter and group by any dimension.

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Query p50/p90/p99 latency distributions. Compare performance across versions, models, and agent configurations.