Rust Agent Development Kit (ADK-Rust): Build AI agents in Rust with modular components for models, tools, memory, realtime voice, and more. ADK-Rust is a flexible framework for developing AI agents with simplicity and power. Model-agnostic, deployment-agnostic, optimized for frontier AI models. Includes support for real-time voice agents.
Give your AI a real, persistent memory. The open-source system plus templates that turn an Obsidian vault into your AI's working memory. No vector database, just markdown.
Specialized AI skills, agents, rules and hooks for modern Rails AI driven-development + Spec-Driven-Development kit + MCP
关键参数与规格
关键参数与规格
参数
值
语言
Rust
类别
AI Engineering
子类别
Give agent tools,Prompt & Scaffold
关键词
Automation, Productivity, Terminal, Unstructured data extraction
星标数
698
分叉数
43
贡献者
2
创建日期
2026-03-06
更新日期
2026-10-16
1 天星标数
2
7 天星标数
5
排名
4,170
上次排名
14,714
排名变化
10,544
7 天星标排名
5,726
上次 7 天星标排名
5,750
7 天星标排名变化
24
总星标排名
14,916
上次总星标排名
14,919
总星标排名变化
3
README 摘录
Distill Distill is a lightweight coding agent harness and TUI, built to **get far more done with far fewer tokens. ** It works with Grok and Codex subscriptions, and with any model that speaks the OpenAI-compatible protocol. md for the full results tables, definitions, methodology, cost bounds, reproduction commands, and source JSON. Install Mac / Linux Windows Upgrade For an installation made with the release installer: Choose your models Open **Model tiers** on the home screen, click **change** beside the current
model, or enter . Owns every session: it plans, specifies, delegates, and reviews the work. Runs every delegated assignment the main model hands it as a precise spec. Without it, the main model does all the work. | or | | Utility model | Handles bounded tasks such as extraction, summaries, and compression. | or | Pick your strongest model as the main model and a cheaper one as the worker: the main model gives the worker as much of the work as it can do well
. Each tier takes an effort level or , which lets Jev pick the effort for every call; is the default. In the Model tiers screen, enter a tier as (for example ). You can supply each selection directly: Always-on behavior Two rule sets are built into every system prompt, for the main model and for its subagents, and neither has an off switch: **Caveman**: the agent writes terse prose and keeps code, paths and error strings exact. changes how hard it
compresses (default ); a stored is ignored. **Ponytail**: the agent works like a lazy senior developer. Before writing code it asks whether the code needs to exist, then reuses what the codebase has, then the standard library, then an installed dependency, and only then writes the minimum that works. Validation, error handling, security and anything you asked for are never cut. Read More: Accounts and local models How Jev routes work UltraCode activation, hierarchy limits and paired comparisons Where the token
savings come from
常见问题
记录了多少星标和分叉？
samuelfaj/distill 有 698 个星标和 43 个分叉。
仓库排名是多少？
samuelfaj/distill 排名第 4,170。
记录的语言和类别是什么？
samuelfaj/distill 有 698 个星标，语言为 Rust，类别为 AI Engineering。
For an installation made with the release installer:
distill update
Choose your models
Open Model tiers on the home screen, click change beside the current
model, or enter /tiers. You can edit each tier in that screen.
Tier
Purpose
Picker
Main model
Required. Owns every session: it plans, specifies, delegates, and reviews the work.
/model or /tiers main
Worker model
Optional. Runs every delegated assignment the main model hands it as a precise spec. Without it, the main model does all the work.
/worker-model or /tiers worker
Utility model
Handles bounded tasks such as extraction, summaries, and compression.
/utility-model or /tiers utility
Pick your strongest model as the main model and a cheaper one as the worker:
the main model gives the worker as much of the work as it can do well. Each
tier takes an effort level or auto, which lets Jev pick the effort for every
call; auto is the default. In the Model tiers screen, enter a tier as
model effort (for example gpt-6-luna auto). You can supply each selection
directly:
/model gpt-6-sol auto
/worker-model gpt-6-luna auto
/utility-model openrouter-qwen37 auto
Always-on behavior
Two rule sets are built into every system prompt, for the main model and for
its subagents, and neither has an off switch:
Caveman: the agent writes terse prose and keeps code, paths and error
strings exact. /caveman lite|full|ultra changes how hard it compresses
(default full); a stored off is ignored.
Ponytail: the agent works like a lazy senior developer. Before writing
code it asks whether the code needs to exist, then reuses what the codebase
has, then the standard library, then an installed dependency, and only then
writes the minimum that works. Validation, error handling, security and
anything you asked for are never cut. Adapted from
ponytail (MIT).
