Vibeleaderboard
← Back to Tools
Index / tool

axonpush

axonpush.xyz
Visit axonpush.xyz
Category
Developer Tools
Rank
No. 2387Tools index

Previous survey · No. 2277 ·

Pricing
Paid
Type
TOOL
Use case
Deployment & Operations · Model & Agent Evaluation
Interfaces
Client SDK
Date

About

axonpush is an observability platform for debugging AI agents in production, tracing model calls, tool invocations, and backend events into a single view to pinpoint failed steps. It also lets teams turn recorded traces into evaluation datasets, compare candidate changes against a baseline on quality, latency and cost, and enforce regression thresholds via CI checks.

What it does

The site describes axonpush as a tool for following one agent request across model calls, tool use, and backend events, then turning real examples into test cases and checking new versions against a baseline before deploying changes.

Stated on the product site

Interfaces
Python and TypeScript SDKs alongside OpenTelemetry and Sentry ingestion
Packaging
Installable via pip and npm
Deployment model
Hosted service or a customer-managed AWS deployment
Pricing model
Trial period before billing is required

Not stated on the site

  • The page does not state which model providers are supported for the optional semantic analysis feature.
  • The page does not give the specific pricing tiers or costs that apply once the trial period ends.
  • The page does not specify which backend languages or frameworks the SDKs support beyond Python and TypeScript.

Written from the product site at axonpush.xyz.

What it can do

  • Trace model calls, tool invocations, and backend events into a single view

    Agent telemetry data → Unified trace view

  • Connect telemetry via SDKs or OpenTelemetry/Sentry

    SDK or OpenTelemetry/Sentry integration → Connected telemetry data

  • Pinpoint failed steps in an agent request

    Traced agent request → Identified failed step

  • Build evaluation datasets from recorded traces

    Recorded traces → Evaluation dataset

  • Compare candidate changes against a baseline on quality, latency, and cost

    Candidate change and baseline → Comparison results

  • Enforce regression thresholds via CI checks

    Regression threshold configuration → CI pass/fail check

Tags

ai-agentsobservabilitydebuggingtracingevaluationopentelemetryllmci-cd

Media

axonpush

Comments (0)

No comments yet

Editorially curated, with community endorsements as a secondary signal. Corrections welcome.