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ax-python-agent-observability

Diagnose Python Ax agent observability issues in axllm workflows.

2.9k|186|Updated Feb 23, 2023
Search Tags:#python#replay#runtime diagnostics#agent tracing#axllm#usage accounting#action logs
One-click install
npx skills add https://github.com/ax-llm/ax --skill ax-python-agent-observability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ax-python-agent-observability
Source: https://github.com/ax-llm/ax/tree/main/website/static/python/.well-known/agent-skills/ax-python-agent-observability
Command: npx skills add https://github.com/ax-llm/ax --skill ax-python-agent-observability

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you inspect and debug Python Ax agents by making traces, usage accounting, action logs, runtime envelopes, and replay data easier to understand and use.

Core Features & Use Cases

  • Trace Inspection: Review agent execution details, model calls, tool activity, and runtime progress in axllm workflows.
  • Usage and Diagnostics: Analyze usage accounting, envelope state, and callback-driven runtime signals for production debugging.
  • Replay and Recovery: Reconstruct action logs and compare runs to find where an agent loop diverged or failed.
  • Use Case: A developer can use this Skill to troubleshoot a multi-step agent that is looping, misusing tools, or producing unexpected output in a Python application.

Quick Start

Use the ax-python-agent-observability skill to inspect traces, usage, and action logs for a Python axllm agent.

Frequently Asked Questions about ax-python-agent-observability

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I debug a Python Ax agent that is looping or misusing tools?▼

To debug a looping Python Ax agent, inspect agent traces, usage accounting, and action logs to review execution details and tool activity. Reconstructing replay data helps identify where the agent loop diverged or failed during runtime.

What is agent tracing and how does it help with production debugging?▼

Agent tracing records execution details, model calls, and tool activity within axllm workflows. Tracing helps production debugging by providing runtime envelopes and callback-driven signals to analyze agent behavior and pinpoint failures.

How do I inspect usage accounting and callback signals in axllm workflows?▼

Inspect usage accounting and callback signals by analyzing the runtime envelope state within axllm workflows. This diagnostic process exposes runtime signals needed to troubleshoot unexpected agent output in Python applications.

Can I replay action logs to find where an agent loop failed?▼

Yes, you can reconstruct action logs and compare runs to replay agent execution. Replaying action logs allows you to find exactly where an agent loop diverged or failed, enabling fast recovery and debugging.

Do I need specific runtime profiles to diagnose Python Ax agent observability issues?▼

Yes, diagnosing Python Ax agent observability issues requires runtime profile support for javascript-quickjs and python-pyodide. You also need the generated Python package API, capability manifest, and runnable examples.

Why does my Python Ax agent produce unexpected output in production?▼

Unexpected output from a Python Ax agent often stems from misused tools or divergent loops. Analyzing trace details, usage accounting, and runtime envelopes helps identify the exact execution step causing the anomaly.

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