GitHub - Tura-AI/tura: Across 348 long-horizon benchmark sessions, Tura used up

AI UNDERDOGSDAILY PICK

AI UNDERDOGS

AI 跑 1 小时， token 烧光

AI agents burn cash

Tura-AI/tura

让 AI 帮你干活

你猜它一小时能烧掉多少 token？

这个项目说：能省七成

Let an AI agent work for an hour

Guess how much token cost it burns. This

project says: you can save 70%

348 次测试，省 70%

70% savings, 348 runs

Tura 是一个 Python 库

专门优化 AI Agent 跑长时间任务时的 token 成本

它在 348 次基准测试里

平均把 token 消耗砍掉了 70%

而且任务完成率没掉

Tura is a Python library that optimizes token

costs for AI agents on long-horizon tasks. Across

348 benchmark sessions, it cut token usage by

70% on average, without dropping task completion rates

★ SIGNAL 1

不只省 token，还管上下文

More than saving tokens

光缓存回复不够

Tura 还会管理 Agent 的上下文窗口

它把重复的思考步骤合并

让模型不用每次从头推理

这是「省token」和「让Agent不犯蠢」的双重收益

Just caching responses isn't enough. Tura also manages

the agent's context window, merging repeated reasoning steps

so the model doesn't start from scratch each

time. It's a dual win: saving tokens and

preventing stupid mistakes

★ SIGNAL 2

给开发者挖的坑都填了

Handles the nasty edge cases

真下功夫的地方在于它处理了缓存系统最难的三个坑：多 Agent 并发时的状态一致

缓存何时自动失效

还有怎么判断上下文变了该重新推理

这些是大多数「省 token 方案」里你踩了才发现的雷

The real craft is how it tackles the

three hardest problems in caching: state consistency across

concurrent agents, knowing when to invalidate the cache

and detecting when context has changed enough to

require re-reasoning. These are landmines most 'save-token' solutions

leave for you to step on

作者没写什么「颠覆」文案

README 里就是 348 次基准测试的详细数据表格

哪种任务省多少、完成率变化多少

全列出来了

这种老实做技术的态度

在 AI 开源项目里不多见

The author didn't write any 'disruptive' marketing copy

The README just has detailed data from 348

benchmark runs. How much saved per task type

how completion rates changed—it's all there. This kind

of honest technical work is rare in AI

open source

AI UNDERDOGS

让 Agent 跑任务，别烧钱

Let agents work, stop burning cash

Tura-AI/tura

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github.com/Tura-AI/tura