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CodeBurn

See where your AI coding tokens actually go

freeopen sourceupdated Apr 21, 2026

Open-source TUI dashboard and CLI that shows where your AI coding tokens actually go, broken down by task type, tool, model, MCP server, and project. CodeBurn reads local session data directly from Claude Code, Codex, Cursor, OpenCode, Pi, and GitHub Copilot — no wrapper, proxy, or API keys — and layers on one-shot success rates so you can see whether the AI nails work first try or burns budget on edit/test/fix retries. Ships with a macOS menu bar widget and CSV/JSON export.

CodeBurn is an open-source cost and token observability tool for AI coding agents built by AgentSeal. It ships as a Node CLI that opens an interactive TUI dashboard showing exactly where your AI coding tokens go: broken down by task type, tool, model, MCP server, and project. Instead of a wrapper, proxy, or SDK, CodeBurn reads session data directly off disk from each supported agent — Claude Code (`~/.claude/projects/`), Codex (`~/.codex/sessions/`), Cursor, OpenCode, Pi, and GitHub Copilot — so there are no API keys to configure and nothing to install in your IDE.

The dashboard is built around gradient charts, responsive panels, and keyboard navigation, with a provider plugin system so new agents can be dropped in without touching the core. Beyond raw token and dollar totals, CodeBurn tracks the one-shot success rate per activity type, surfacing the gap between tasks where the AI gets it right first try and tasks where it burns tokens cycling through edit/test/fix loops. Pricing is pulled from LiteLLM and auto-cached, covering every supported model, with configurable currency and a macOS menu bar widget via SwiftBar for always-on visibility.

CodeBurn is MIT licensed and installs with `npm install -g codeburn` or runs directly with `npx codeburn` on Node 20+. Data never leaves your machine; the tool simply parses local session files and produces CSV/JSON exports when you need to pipe numbers into a spreadsheet or team dashboard. For solo developers it answers "am I getting value from my $20/month plan?" and for engineering managers it quantifies which agents, projects, and task types are quietly becoming the most expensive — a growing need as AI coding spend moves from hobby experiment to real line-item on the cloud bill.

Pricing

Free and open source (MIT) — no paid tier; install with `npm install -g codeburn` or run via `npx codeburn`

Platforms

Terminal TUI (Node.js 20+, macOS/Linux/Windows), plus optional macOS menu bar widget via SwiftBar

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Comparisons

CodeBurn vs Helicone: Local Agent Costs or LLM Observability?

CodeBurn and Helicone address different layers of AI cost visibility. CodeBurn reads local coding-agent session files to explain spend across tools such as Claude Code, Codex, and Cursor without changing the request path. Helicone is an LLM gateway and observability platform for application traffic, with logs, cost analytics, caching, fallbacks, prompts, scores, and team controls. For the coding-agent FinOps job represented by this page, CodeBurn is the stronger default because it sees local developer sessions with no proxy or prompt egress. Helicone is the better architecture when the workload is a production LLM application that already needs centralized request telemetry.

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CodeBurn vs Tokscale: Which AI Coding Cost Tracker Wins?

CodeBurn and Tokscale are local-first, open-source tools for understanding token use and cost across AI coding agents. Both read data produced by tools such as Claude Code, Codex, Cursor, Gemini, and OpenCode, then apply model pricing without forcing requests through a proxy. CodeBurn is the stronger overall choice for buyers who want cost diagnostics tied to projects, tasks, retries, cache behavior, and shipped work. Tokscale is attractive when a fast Rust TUI, a very broad client matrix, contribution-style visualizations, and optional social leaderboards are the priority.

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