spanview

// observability + auto-diagnosis for AI agents

Your agent is broken.
You don't know why.

spanview ingests OpenTelemetry traces from any agent framework, runs an LLM evaluator over every step, and hands you a plain-English verdict — which step failed, why, and what changed since the run that worked.

Get started Read the docs

works with LangGraph·OpenAI Agents SDK·Claude Agent SDK

run · research-agent 6 spans
  1. 01AGENTresearch-assistant2.6s
  2. 02LLMplan840ms
  3. 03TOOLsearch120ms
  4. 04LLManswer1.1s
error trace_report

Stale tool result at step 3

The search tool returned a cached hit from a prior query; the agent answered from it without re-checking, so the final response is wrong.

  • step 02 · LLM plausible plan, no validation
  • step 03 · TOOL stale cache hit

// the problem

Agents fail quietly. You find out from a user.

01

Silent failures

A tool returns wrong data. The model invents a step. The output looks plausible and ships. Nothing throws.

02

Unreadable traces

Raw OTel spans tell you what happened, not why it's wrong. Forty spans a run, read by hand, doesn't scale.

03

Invisible regressions

You change a prompt, a tool, a model. Something breaks. Which change? Which step degraded? No idea.

// how it works

Instrument once. Read the verdict.

  1. 01

    Instrument once

    Standard OpenTelemetry plus your framework’s auto-instrumentor. A few lines of setup, no manual spans.

    from openinference.instrumentation.langchain import LangChainInstrumentor
LangChainInstrumentor().instrument()
  2. 02

    Traces flow in

    Spans land over OTLP in real time. spanview normalizes OpenInference, OTel GenAI, and OpenLLMetry — no adapter.

  3. 03

    Evaluator reads every step

    An LLM walks the trace and leaves a note on each span: what it expected, what it got, whether the step is sound.

  4. 04

    Verdict & diff

    Open the run — the diagnosis lands within minutes. Diff two runs to pin the regression.

// what you get

Everything points at the diagnosis.

Per-step diagnosis

Every span gets an LLM note — not pass/fail, a sentence on what happened and why it matters. Severity-ranked.

Run comparison

Diff any two traces: inputs, outputs, tool calls, tokens, step order. Regressions highlighted automatically.

Any framework

OpenInference, OTel GenAI, OpenLLMetry — all normalized into one model. Bring whatever stack you run.

Web UI · HTTP API

Read verdicts in the browser, or pull runs and diagnoses over the HTTP API. A CLI and an MCP server for Claude are coming soon.

Zero data retention

The evaluator runs on Claude Haiku via AWS Bedrock and Google Cloud with zero data retention — nothing stored, nothing used for training. Your traces stay in your account.

Secret redaction

Built-in patterns strip API keys, tokens, and credentials from spans before anything is written to disk.