ai-agents-metrics 0.1.0

CLI for tracking AI agent task metrics: token cost, retry pressure, and outcome quality.

ai-agents-metrics — track AI agent token cost and retry pressure

Measure the real cost and effectiveness of AI-assisted engineering work.

ai-agents-metrics is a local CLI tool that records goals, attempts, token spend, and retry patterns for every AI coding session — so you can see which workflows are productive and which are burning tokens on rework.

Why

AI coding agents (Claude Code, Codex, and similar) generate real costs and vary widely in effectiveness. Common questions without this tool:

• "How much did my Claude Code session cost?"
• "How do I track AI agent retries across tasks?"
• "What is my token spend per task?"
• "Did this workflow change actually improve anything?"
• "Which model is more cost-effective for my work?"

ai-agents-metrics gives you a lightweight, local ledger to answer all of these from real data.

When to use this

• You use Claude Code, Codex, or another AI coding agent and want to know what each task actually cost
• You suspect certain types of tasks require too many correction passes and want the numbers to confirm it
• You changed a prompt strategy or workflow and want to verify it improved outcome quality or reduced cost
• You run AI agents as part of a paid engineering workflow and need to track whether AI cost is eating into project margins
• You want an AI agent to analyze your workflow history and recommend what to change next

What It Tracks

• Goals and attempts — what you asked the agent to do, how many tries it took
• Token cost — input, output, and cached-input tokens per session, mapped to USD
• Retry pressure — how often attempts fail or require correction
• Model usage — which model ran each session and what it cost
• History analysis — parse conversation transcripts to reconstruct past sessions

Example output

$ ai-agents-metrics show

Codex Metrics Summary

Operational summary:
Closed goals:                    8
Successes:                       8
Fails:                           0
Total attempts:                  8
Success Rate:                    100.00%
Attempts per Closed Goal:        1.00

Known total cost (USD):          9.27
Known total tokens:              26,337,605
  input:                         260
  cached:                        26,088,225
  output:                        44,883

Known Cost per Success (USD):    1.32
Known Cost per Success (Tokens): 3,762,515

Model coverage: 7/8 closed goals with an unambiguous model
By model:
  claude-sonnet-4-6: 7 closed, 7 successes, 0 fails

Closed entries:     8
Entry successes:    8
Entry fails:        0
Entry Success Rate: 100.00%

Install

python -m pip install -e .

Or install the standalone binary:

make package-standalone
./dist/standalone/codex-metrics install-self

Quick Start

Bootstrap a project:

ai-agents-metrics bootstrap

Start tracking a goal:

ai-agents-metrics start-task --title "implement login endpoint" --task-type product

Record another attempt if the agent needed a correction:

ai-agents-metrics continue-task --task-id 2026-04-08-001 --failure-reason wrong_scope

Close it when done:

ai-agents-metrics finish-task --task-id 2026-04-08-001 --outcome success --result-fit exact_fit

Show current metrics:

ai-agents-metrics show

Verify Your Install

make verify

Runs lint, security scan, typecheck, tests, and the public boundary check.

Public Boundary

This repository contains the public-safe core only. Private retrospectives, internal audits, and local metrics history are kept in a separate private overlay. The boundary is enforced automatically:

make verify-public-boundary

Repository

github.com/sg4tech/codex-metrics-public

Contributing

Read CONTRIBUTING.md. In short: keep changes public-safe, run make verify, include tests for behavior changes.

Security

See SECURITY.md for how to report potential private-data leaks or security issues.

Changelog

Notable public changes are tracked in CHANGELOG.md.