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microsoft/TokenEconomizer

Experience-Driven Black-Box Cost Control for Coding Agents

Latest metrics (snapshot 2026-11-01)

stars3238
forks190
issues9
pushed_at2026-10-31T22:53:30Z

History

History chart: 1 daily snapshot so far (archive started 2026-10-14). The line fills in as the daily archive grows; full series via the API.

DateGitHub stars
2026-11-013238

ADC Momentum: 77/100

ADC Momentum 77 Leaderboard

Growth velocity93
Consistency74
Recency99
Agent demand0
Basisbootstrap

ADC Momentum (0-100) = 45% growth velocity (stars/likes gained per day in our daily archive, relative and absolute, percentile-ranked within the category) + 20% consistency (share of recent days with growth) + 20% recency (latest push/update/release, decaying) + 15% agent demand (how often AI agents looked it up via our API/MCP this week). MCP servers use release cadence; LLMs use price cuts, context and recency. Recomputed daily; scores marked "bootstrap" use lifetime rates until the archive has enough days. Method

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Details

descriptionExperience-Driven Black-Box Cost Control for Coding Agents
urlhttps://github.com/microsoft/TokenEconomizer
languagePython
licenseApache-2.0
topicsagentic-ai
agentic-coding
ai-coding
ai-coding-assistant
anthropic
artificial-intelligence
claude-code
codex
coding-agent
coding-agents
cost-optimization
cost-reduction
created_at2026-10-13T04:16:50Z

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