TraceRoot.AI

Most agent observability tools stop at the panel: here is your trace, here is the latency, here is the cost. TraceRoot AI starts where those stop. The Tracing layer captures every model call, tool call, and agent step over OpenTelemetry-based instrumentation, and the Timeline view (added April 2026) plus per-span token and cost breakdowns make it clear where a run got expensive or slow. But the product's real claim is the loop after that. Detectors run online evaluations with LLM judges on live production traffic to define what good behavior looks like and flag runs that miss it. Since October 7, 2026, those judge findings are clustered into Signals — one per recurring problem, each with a single root cause analysis, which is a materially more usable unit than a pile of flagged traces. A background coding agent then investigates the signal against connected GitHub code inside a Daytona sandbox and recommends a change down to the commit, file, and line. You can point that investigation at your own model and provider, or power detectors with your own TypeSafe AI Jev key via BYOK. Slack alerts fire when detectors trigger. You apply the change with your own coding agent and verify it against versioned datasets with a baseline comparison, so a fix that breaks something else shows up before merge. The CLI is the piece that makes this practical for teams already living in a terminal. npm install -g traceroot-cli followed by traceroot login exposes Home, traces, dashboards, detectors, evaluations, datasets, signals, and alerts to Claude Code, Codex, or pi-mono — bidirectional except traces, which flow toward the agent. Stack coverage is broad: native Python and TypeScript SDKs plus LangGraph, Vercel AI SDK, Mastra, CrewAI, Agno, Google ADK, Pydantic AI, OpenAI Agents SDK, Claude Agent SDK, Microsoft Agent Framework, AutoGen, DSPy, LangChain, LlamaIndex, Mistral, LiteLLM, and OpenRouter via the OpenAI-compatible SDK. Where it thins out: it is early-stage and developer-first. Documentation is improving but still patchy in places, and writing custom detectors or tuning the investigation agent assumes real engineering skill. Self-hosting under Apache 2.0 (outside ee directories) via Docker takes setup work. Generated fix PRs depend on your repo structure and test coverage, so they need human review rather than blind merges. Compliance is the bigger blocker for regulated buyers: SOC 2 Type II, HIPAA, and ISO 27001 are all still listed as In Progress on the site. And usage is metered — Starter at $30/mo includes 150k events with $4 per additional 50k, 100 chat and 100 RCA runs per month with $10 per additional 100 of each, and hosted-LLM overage billed at token cost × 1.05 — so heavy debugging volume can push spend above the headline price. The free tier's 15-day retention and 30 RCA runs/month are enough to evaluate the loop, not to run a busy agent on indefinitely. If you only need traces and eval scores across many app types, Langfuse or LangSmith fit