Skip to main content

Agent Usage Atlas

Turn your local AI coding agent logs into a rich, interactive analytics dashboard — zero dependencies, fully offline, one command.

一个从本地 AI 编程 Agent 日志生成可视化仪表盘的工具。支持 Codex CLI / Claude Code / Cursor 三大 Agent 栈,25+ 交互式图表,纯 Python 标准库实现。

Hero Overview

Why Agent Usage Atlas?

You're burning tokens across multiple AI coding agents every day — but how much are you actually spending? Which model is the most cost-effective? When are you most productive? Are your caches saving you money?

Agent Usage Atlas reads your local log files (~/.codex/, ~/.claude/, ~/.cursor/), crunches the numbers, and generates a single self-contained HTML dashboard with 25+ interactive charts. No API keys, no cloud uploads, no dependencies beyond Python itself.

Supported Agents

Agent Token Tracking Cost Estimation Tool Call Tracking Session Meta
Codex CLI (GPT-5 family)
Claude Code (Claude 3–4.6)
Cursor Activity only

Pricing covers GPT-5.x, Claude 3/3.5/4.x (Haiku/Sonnet/Opus), and MiniMax-M2 out of the box.

Features

  • 25+ interactive ECharts visualizations — cost trends, token breakdowns, Sankey flows, chord diagrams, heatmaps, radar charts, calendar views, burn rate projections
  • Multi-agent unified view — Claude Code + Codex CLI + Cursor in one dashboard, with per-source drill-down
  • Cost analytics — per-model, per-day, per-session cost estimation with 30-day burn rate forecasting
  • Tool call intelligence — ranking, frequency density, bigram sequences, command success rates
  • Cache efficiency tracking — hit rate, savings estimation, cache vs. uncached token split
  • Working pattern heatmap — hour × weekday activity distribution to find your flow states
  • Session deep-dive — duration histogram, complexity scatter, median session cost
  • Live dashboard mode — SSE-powered auto-refresh server with date range tabs (All / 7 days / Today)
  • Bilingual narratives — auto-generated story summaries in both Chinese and English
  • Animated data updates — stock-style green/red flash on number changes in live mode
  • Single self-contained HTML — one file, works offline, shareable, archivable
  • Zero dependencies — pure Python standard library, no npm/Node/Rust/Docker required
  • Fully local — all data stays on your machine, nothing sent anywhere

Screenshots

Cost Analysis
Daily cost trends, cost breakdown by token type, model cost ranking, Sankey flow
Token & Activity
Daily token trends, source radar, narrative summary, rose chart
Cost Analysis Token Charts
Heatmap & Sessions
Activity heatmap, source radar, token calendar, session bubble
Tool Intelligence
Tool ranking, bigram chord diagram, top commands, efficiency metrics
Heatmap & Sessions Tool Intelligence

Installation

pip (recommended)

pip install agent-usage-atlas

Homebrew

brew install heggria/tap/agent-usage-atlas

From source

git clone https://github.com/heggria/agent-usage-atlas.git
cd agent-usage-atlas
pip install .

Usage

# Default: last 30 days, output to ./reports/dashboard.html
python -m agent_usage_atlas

# Last 7 days
python -m agent_usage_atlas --days 7

# Custom start date
python -m agent_usage_atlas --since 2026-03-01

# Custom output path and auto-open in browser
python -m agent_usage_atlas --output /tmp/dashboard.html --open

# Start live dashboard with auto-refresh (SSE)
agent-usage-atlas --serve --interval 5 --open

# Live dashboard on custom host/port
agent-usage-atlas --serve --port 8765 --host 127.0.0.1 --interval 5

CLI Options

Flag Description Default
--days N Include the last N days 30
--since YYYY-MM-DD Custom start date (overrides --days)
--output PATH Output HTML file path ./reports/dashboard.html
--open Open in browser after generation off
--serve Start local live dashboard server off
--host Host for --serve mode 127.0.0.1
--port Port for --serve mode 8765
--interval SSE refresh interval in seconds 5

Live Mode Endpoints

Endpoint Description
GET / Interactive HTML dashboard
GET /api/dashboard?days=30 JSON payload
GET /api/dashboard?since=2026-03-01 Custom date range
GET /api/dashboard/stream?interval=5 SSE stream (auto-refresh)
GET /health Health check

How It Works

~/.codex/**/*.jsonl  ─┐
~/.claude/**/*.jsonl ─┼─→  Parse  →  Aggregate  →  Render  →  dashboard.html
~/.cursor/**/*.jsonl ─┘     (parallel)   (rollup)    (ECharts)
  1. Parse — Reads JSONL log files and SQLite databases from each agent's local directory. Codex uses cumulative-delta token counting; Claude deduplicates by message ID; Cursor tracks activity counts.
  2. Aggregate — Computes source rollups, daily rollups, session rollups, tool bigrams, chord diagram data, Sankey flows, burn rate projections, heatmaps, and narrative text.
  3. Render — Injects the aggregated data into a self-contained HTML template with ECharts visualizations. In live mode, an SSE server pushes updates when log files change.

All data stays local — nothing is sent to any server.

Comparison with Alternatives

Agent Usage Atlas ccusage splitrail claudetop Langfuse Helicone
Multi-agent Claude + Codex + Cursor Claude + Codex + others 10+ agents Claude only Any (via SDK) Any (via proxy)
Visualization 25+ interactive ECharts (CN+EN) CLI tables CLI + cloud TUI (7 views) Web dashboard Web dashboard
Self-contained HTML
Zero dependencies ✅ Python stdlib Node.js Rust Node.js Docker + PG Docker + infra
Fully local Cloud optional Self-host Self-host
Live dashboard ✅ SSE
Tool-call analytics Bigram, chord, Sankey Basic Basic Via tracing Via proxy
Cache efficiency Partial
Burn rate projection
Setup python -m agent_usage_atlas npx ccusage Build from source npx claudetop Deploy stack Deploy proxy

Key Differentiators

  1. Unified multi-agent dashboard — the only tool that tracks Claude Code + Codex CLI + Cursor simultaneously from local logs in a single view
  2. Richest visualization suite — 25+ chart types including Sankey flows, chord diagrams, tool-call bigrams, heatmaps, burn rate projections, calendar views
  3. True zero dependencies — Python stdlib only, no npm/Node/Rust/Docker/database required
  4. Single self-contained HTML — one file, works offline, email it to yourself, archive it
  5. Live SSE server — real-time auto-refresh with date range switching (All / 7 days / Today)
  6. Cache efficiency analytics — unique among local tools, tracks savings and hit rates
  7. Animated transitions — smooth number counter animations with stock-style green/red flash on live data refresh
  8. Bilingual narrative — auto-generated story/summary in both Chinese and English

Architecture

src/agent_usage_atlas/
├── cli.py          # CLI entry point, build_dashboard_payload()
├── parsers.py      # Codex / Claude / Cursor log parsers
├── models.py       # UsageEvent, ToolCall, SessionMeta + pricing (GPT-5, Claude 3–4.6, MiniMax)
├── aggregation.py  # Full dashboard payload computation (CN + EN narratives)
├── template.py     # Self-contained HTML/CSS/JS template (number flash animations)
└── server.py       # Live SSE server (stdlib http.server)

License

MIT

Release files for agent-usage-atlas 0.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for agent-usage-atlas 0.0.2
File Size Uploaded
agent_usage_atlas-0.0.2.tar.gz 51.0 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for agent-usage-atlas 0.0.2
File Interpreter ABI Platform
agent_usage_atlas-0.0.2-py3-none-any.whl Python 3 none any Details

Total release size: 100.7 kB

Release files / agent_usage_atlas-0.0.2.tar.gz

Download URL agent_usage_atlas-0.0.2.tar.gz
Size 51.0 kB
Tags Source
SHA-256 checksum
How to use checksums
a33ac1266dcff6912942420b0d92eb0b22289c0b0d454ffcfd735fa8d288ba0d
BLAKE2b-256 checksum
How to use checksums
ab3c983afff3d4200c24ea1d7298143a29d31c407efa5dedc92b333ee52f67f8
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 17, 2026.

Transparency log

Release files / agent_usage_atlas-0.0.2-py3-none-any.whl

Download URL agent_usage_atlas-0.0.2-py3-none-any.whl
Size 49.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a97145e45e173f169da1ef436ab71cf0100f5f748d2cfbb233b1810c19e1b3cc
BLAKE2b-256 checksum
How to use checksums
b1a6321caab84180b9c3f19753b919070ec2fef9a90075c23aa59f5aa8caa7d1
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 17, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.0.2 This release

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page