CLUSTER OBSERVABILITY-CODING-TOKEN · 6 ENTRIES · EARLIEST ENTRY 2026-04-06

AI agent observability tools

This cluster consists of local observability tools for AI coding agents and AI agents, focusing on visualizing sessions, monitoring token usage, costs, latency, quality, and evals. The dated history is too thin to read a shape from, as only one pre-index entry exists. Traction leaders by activity signal are AutotestHub (20 stars), agent-console (4 stars), and silkscope (1 star).

Entry timeline

ENTRY TIMELINE

APR 2026: 1SEP 2026: 3OCT 2026: 2
APR 2026OCT 2026

Members by activity

Activity signals, not revenue.

Build origin · 1 hand-coded · 5 unlabelled

Operator · 2 hobby · 1 indie · 3 unlabelled

Two labels per entry, never collapsed into one: how it was built, and who stands behind it. Counted over the 6 listed below. Unlabelled means nothing has decided yet — not that the answer is small.

  1. 01AutotestHub21★11 days—hobbyTesting workbench that unifies API, UI, and AI-agent testing with observability and review workflows.
  2. 02agent-console4★26 daysliveLocal observability tool for AI coding agents that shows sessions, token usage, model costs, and delivery evidence in one console.
  3. 03silkscope1★9 days—hobbyLocal live observability tool for coding agents that visualizes sessions, showing files read, commands run, and failed calls.
  4. 04pi-trajectory1★1 mo—A tool that investigates and replays coding-agent sessions to analyze performance, costs, and failure points.
  5. 05agent-ops0★1 moliveObservability platform that replays coding-agent runs and surfaces tool latency, token spend, policy gates, and approvals.
  6. 06farol1★5 molivehand-codedindieObservability tool for AI agents that monitors cost, latency, quality, and evals via a decorator.