cost-xray

Most usage tools read local logs. That shows the total cost of a call or session, but it misses the request-time context assembled before the model is invoked: system prompts, tool schemas, MCP blocks, tool results, cache reads/writes, and previous thinking blocks.

cost-xray captures the actual local API traffic for Claude Code and Codex, then attributes tokens and dollars back to the sources inside the request. It shows not just how much a turn cost, but why it cost that much.

Requirements

• A supported coding agent — Claude Code or Codex
• macOS or Linux
• No API keys, no account, no config changes to your agent — capture is a transparent local hop

Install

curl -fsSL https://raw.githubusercontent.com/tigerless-labs/cost-xray/master/install.sh | bash

The installer asks which agent(s) to capture — Claude Code, Codex, or both — and prompts even under curl … | bash.

• Skip the prompt (e.g. CI): set COST_XRAY_AGENTS=claude|codex|all.
• Already cloned the repo? Run ./install.sh.

Then open a new terminal and run claude / codex exactly as before — capture is automatic, no flags and no base-URL change. It's forward-only: runs started in that new shell are captured, not past history. Open the live TUI from anywhere:

cx

Capture runs as a background service (auto-start on boot, self-healing, port-adaptive) and doesn't change what your agent does, its results, or its cost — pause anytime with cx stop. See docs/install.md for systemd details, GUI agents (Cursor base-URL setup), manual (no-systemd) mode, and troubleshooting.

Usage

cx                  # open the live cost-xray TUI (from any directory)
cx status           # services' state, live ports, sessions captured
cx stop             # stop monitoring — proxies down; agents run direct (uncaptured)
cx start            # resume monitoring
cx restart          # restart the proxies (after a config change)
cx install          # (re)install — authoritative: installs the chosen agents, removes the rest
cx uninstall        # remove services + shell wrappers (keeps captured data)

cx is the whole CLI and works from any directory. Change port/upstream by editing ~/.cost-xray/env, then cx restart. (./run.sh <cmd> from the repo still works too — cx just calls it for you from anywhere.)

In the TUI, drill from the top down: agent → project → session → category → MCP server → tool → per-turn call → the real output. Every cell carries its cache split (read / write / fresh / output $).

Supported Agents

The wire is decoded by a thin per-agent adapter — the only place code forks by agent (docs/architecture.md; per-agent capture + tokenizer notes under docs/providers/). Adding an agent is one small module; anything speaking the Anthropic or OpenAI-Responses wire shape is close to drop-in.

Features

Cost attribution, below the tool

cost-xray traces every token's cost — split into fresh / cache-read / cache-write / output — to the source that caused it, and down to the individual call: the cost of this Read invocation and its output, not a session sum. Everyone else aggregates — a request- or session-level total, at most grouped by tool type ("Read cost $X this session"). As far as we've found, nothing else prices below the tool, call by call.

Window occupancy

What is taking space in the context window right now — system prompt, every tool schema, MCP servers, messages, and generated output — decomposed into source-level rows. Prompt caching makes a stable 40k-token schema block cheap on cache read, but it still crowds out the code and conversation that matter; cost-xray shows you the occupancy, not just the bill.

Unused MCP waste

Configured servers and tools that are injected into every request's prefix but never actually called. They pay their tool-schema overhead on every turn — cost-xray flags the dead weight.

Tokenization accuracy

Claude's tokenizer is private — no official or open-source tokenizer exists — and calling Anthropic's count_tokens API for everything would add load and hit its limits. So for Claude, cost-xray uses an estimator plus proportional calibration: tiktoken sizes each part, the parts it mis-sizes most (thinking, tool schemas) get targeted corrections — pinned with count_tokens when you're logged in, a fixed ratio otherwise — and the rest is scaled so the calibrated total matches the provider's own usage. The total, and therefore the bill, is exact; only the split between sources in the same request is approximate. We benchmark those residuals continuously (CONTRIBUTING.md) and they're small enough for attribution work.

Coming soon: an opt-in exact mode — every part sized by full count_tokens differencing — manually enabled, for users who need maximum per-source precision.

Self-healing capture

The wrappers are per-command and self-healing: if the proxy is down, the wrapper restarts it and routes through; if it can't, the agent runs direct — never broken. Stop monitoring anytime with cx stop (agents then run direct).

Capture everything, store almost nothing

cost-xray keeps the complete raw API traffic — but a long session re-sends its whole history every turn, so the capture is hugely repetitive. We deduplicate it: each unique block (message, schema, tool result) is stored once, with a tiny per-turn delta. Full per-turn bytes rebuild on demand. Disk stays small even across million-token sessions.

Why not just read the logs?

Log-based tools (ccusage, codeburn, and similar) are excellent at local-first session analytics: they read local transcripts, classify turns by tool usage, and price the session by model, day, or task. That answers how much did I spend. cost-xray answers the lower-level question: what bytes did the model actually receive, and which source owns those tokens?

The difference is the data source. Log readers see the transcript after the agent has run — but the system prompt, injected tool schemas, MCP schemas, reminders, and provider-added blocks are assembled at request time and never written to the transcript. In real coding-agent requests, that invisible prefix can be roughly half the context or more. cost-xray reads the raw API request, so it can compute source-level tokens and attribute cost to schemas, MCP servers, tools, and message buckets.

Reading the dashboard

cost-xray surfaces the data; you read the story. A few patterns worth knowing:

These are starting points, not verdicts. One experimental session looking odd is fine; the same pattern across weeks of work is a config issue.

How it works

cost-xray runs mitmproxy as a local capture hop and keeps all analysis off the request path.

agent ──HTTP──▶ mitmproxy ──HTTPS──▶ model API
                     │
                     └── redacted raw request/response → ~/.cost-xr