Local-first / Open source

Optimise your coding agents.

Track token usage, estimated cost, context, and execution across every run.

ClaudeCodexCursorOpenCodeKiroPi
START / 01 Install locally
$
git clone https://github.com/splunk/token-meter.git
./token-meter/scripts/install

Requires Python 3.8+ · Xcode Command Line Tools · curl

Then open http://127.0.0.1:8722 No administrator access required.

Field notes / Findings

Things your bill won't tell you.

Surfaced by Token Meter on one engineer's laptop, estimated at public list prices.

  1. 6% → 55%

    Cache writes were 6% of tokens and 55% of the dollars on Claude Opus 5.5.

  2. 83,907

    Tokens of tool definitions loaded into prompts and never called.

  3. $6.04 → $0.96

    Cost per subagent run, a month after switching to short, narrowly scoped tasks.

Read the five levers

01 / Product signal

Every run leaves a signal.

Read the cost. See the work.

Live runs and historical patterns stay connected to the evidence behind them. Costs and selected token values can be estimates; coverage stays visible.

Token Meter Sessions dashboard showing live cost, token, context, and execution evidence
Current sessionsCost, context pressure, pace, and execution stay together on the run.
Native companion / 04 A fast read without opening the dashboard.

This is the companion rebuilt for the web—not a screenshot. The native hierarchy stays intact while the presentation responds to the page.

02 / Signal ledger

What becomes visible.

Not another score. A legible record for deciding when to continue, intervene, or investigate.

01

Live runs

Cost, context pressure, token shape, timing, and execution in one current-session view.

COST / CONTEXT / TIME
02

History

Compare sessions, runtimes, projects, and calendar days without flattening missing evidence into zero.

RUN / DAY / PROJECT
03

Models

Inspect output per covered dollar, reasoning ratio, and context load with coverage beside the result.

OUTPUT / COVERED $
04

Execution

Understand tool calls, failures, repeats, and skill evidence without publishing the work itself.

TOOLS / REPEATS / SKILLS

03 / Efficiency field

Efficiency, with the evidence beside it.

Compare four signals across covered, comparable work. Token Meter keeps the limits attached, so a missing value never becomes a score.

How Token Meter reads efficiency
EF / 01

Output / covered $

Reported output tokens per covered dollar. Higher across comparable work means more response output for the covered spend.

HigherComparable work
EF / 02

Reasoning ratio

Reported reasoning tokens as a share of output. Lower or stable is usually better for comparable work; difficult work may need more.

ContextDifficulty matters
EF / 03

Context load

Processed input tokens per output token. Lower means less context carried into each response.

LowerUsually better
EF / 04

Cache hit ratio

Cache-read tokens as a share of cache-covered input. Higher means more context is served from the prompt cache.

HigherUsually better
01No universal score

Read movement over time, not a single grade.

02Coverage remains visible

Unavailable evidence stays unavailable.

03Local estimates

Costs and selected token values can be estimates.

Open source from Signals, not verdicts.

04 / Local boundary

The evidence stays where the work happened.

Token Meter reads supported local stores and serves the dashboard on 127.0.0.1. It is not a hosted analytics service.

Inspect the security boundary
01Read-only inputs

Supported local traces and databases are read, never rewritten.

02Bounded output

No prompt or response content appears in the dashboard, native companion, or bounded MCP projections.

03No hosted plane

No API keys for trace analysis. No Token Meter analytics or telemetry leaves your machine.

05 / Start measuring

Your agents are already producing the evidence.

Make it useful. Keep it local.

Install Token Meter