alibaba/anolisa

Name: alibaba/anolisa
Author: alibaba

插件Plugin ⭐ 402 Apache-2.0 cat_label.agent-infrastructurecat_label.agent-infrastructure cat_label.agent-memorycat_label.agent-memory cat_label.agent-runtimecat_label.agent-runtime cat_label.agent-securitycat_label.agent-security cat_label.agentic-aicat_label.agentic-ai cat_label.agentic-oscat_label.agentic-os

media="(prefers-color-scheme: dark)" srcset="docs/images/brand/anolisa-lockup-dark.svg" media="(prefers-color-scheme: light)" srcset="docs/images/brand/anolisa-lockup-light.svg" src="docs/images/brand/anolisa-lockup-light.svg" alt="ANOLISA"

catalog 简介 / catalog descriptioncatalog description：ANOLISA (Agentic Nexus Operating Layer & Interface System Architecture) | Agentic OS with runtime, security, observability, and Tokenless response compression for lower token usage and cost.

项目介绍Project Overview

由知微Agent(zhiwei)编辑Edited by Zhiwei Agent (zhiwei)

ANOLISA 是面向 AI Agent 工作负载的服务端系统层。它通过 cosh-ng 提供终端入口,利用 Token-less、Agent Memory 与 AgentSight 压缩工具输出、追踪 Token 使用,并借助 Agent Sec Core 与 ws-ckpt 为执行提供沙箱与回滚。适用于在终端驱动 Agent、降低上下文开销、隔离风险操作的场景。注意:节省针对进入上下文的工具响应计算,非整次会话账单。

ANOLISA is a server-side operating layer for AI Agent workloads. It provides a terminal entry via cosh-ng, compresses tool outputs and traces token usage through Token-less, Agent Memory, and AgentSight, and adds sandboxing plus checkpoint rollback via Agent Sec Core and ws-ckpt. Use it when running agents from the terminal, reducing context cost, or isolating risky execution. Note: savings apply to tool responses entering the context, not the full session bill.

在 GitHub 查看View on GitHub

发给你的 DSH 安装Send to your DSH

请帮我了解并安装插件：【anolisa】【https://github.com/alibaba/anolisa】

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或使用命令行安装（适合开发者）Or use CLI install (for developers)

命令行安装CLI Install

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READMEREADME

10.0 KB · 缓存于 2026-08-23 03:40:43 · 10.0 KB

_{Agentic Nexus Operating Layer & Interface System Architecture}

The operating system layer for Agent workloads.

Let Agents drive the system straight from your terminal, and strip the tool responses that reach the model before they cost you — while keeping the Shell, Agent framework, and sandbox you already run.

中文版 · Website · Quick Start · User Guide · Contributing

ANOLISA is a server-side operating layer for AI Agent workloads. It addresses three practical constraints of Agent execution: terminal entry, Token cost, and execution environments. Keep the Shell, Agent framework, and sandbox you already use. ANOLISA CLI provides a single installation entry point, while each capability can be enabled independently.

New to ANOLISA? Choose your first outcome in the Quick Start →

Components

Agent entry	Context efficiency	Runtime & security
cosh-ng _{Shell copilot}	Token-less _{Tool-output compression}	ws-ckpt _{Checkpoint and rollback}
OS Skills _{System and DevOps expertise}	AgentSight _{Trace and Token visibility}	SkillFS _{Focused Skill views}
ktuner _{Kernel tuning}	Agent Memory _{Cross-session memory}	Agent Sec Core _{Sandbox and verification}
		Blaze _{Sandbox lifecycle}

What it solves

01 · AGENT INTERFACE

Let the Agent work directly in the terminal

cosh-ng is an AI-native Linux terminal: it keeps familiar Bash/Zsh behavior, then adds an Agent that can understand intent, use tools and Skills, and ask for approval before risky work. Shell commands and natural language share one terminal instead of forcing users into a separate chat application.

Get started with cosh-ng →

02 · CONTEXT EFFICIENCY

See where Tokens go and cut waste before it reaches the model

Token-less removes redundancy from tool schemas and responses before they reach the model. Agent Memory reuses useful context across sessions. SkillFS keeps the current Skill view focused and makes other Skills discoverable when needed. AgentSight shows where Tokens are spent.

See an Agent run from the kernel up

On Linux, AgentSight uses eBPF to observe an Agent without changing its code. Follow user input through model and tool calls, with Token use and sub-agent branches in the same view.

Open the AgentSight guide →

Try Token-less with Claude Code in 3 minutes

Install Token-less and connect it to Claude Code:

curl -fsSL https://get.agentic-os.sh | bash
export PATH="$HOME/.local/bin:$PATH"
anolisa install tokenless
anolisa adapter enable tokenless claude-code

Restart Claude Code, run one tool-heavy task, then inspect the result:

tokenless stats summary
tokenless stats list --limit 5

Open the full Token-less Quick Start → · Read the user manual

_{In one observed coding task, Token-less saved 317K Tokens (40.5%), based
on AgentSight measurements.
Results vary by workload.}

debug and trace are dropped by the field blacklist, metadata as null, and tags / extra as empty values. Compression runs between the Agent and the model, so no Agent framework code changes. Dropped array items stay retrievable through a <<tokenless:KEY>> marker, which keeps the compression reversible.

Tool responses	Tool schemas	Full pipeline
65.8% fewer Tokens	47.3% fewer Tokens	62.9% fewer Tokens
ResponseCompressor · 46.85 µs	SchemaCompressor · 11.44 µs	198.91 µs

Savings apply to the tool responses entering the context, not to the whole session bill. The Token-less user manual explains how to estimate the effect for a given workload.

03 · EXECUTION RUNTIME

Give every Agent execution a boundary and a way back

ANOLISA is building out the Agent execution environment: Agent Sec Core isolates risky operations, and ws-ckpt keeps recovery points for workspace changes.

Catch a changed Skill before it runs

When a signed Skill changes, the Agent reports drifted before using it again. A rescan records blocking findings as deny.

Try the Agent demo → · Skill Ledger guide

Choose a runtime or security starting point → · Start with ANOLISA CLI

Install

ANOLISA CLI is the common installation entry point. cosh-ng is installed in system mode; Token-less and other capabilities can be added independently.

curl -fsSL https://get.agentic-os.sh | bash

sudo anolisa --install-mode system install cosh-ng
anolisa install tokenless

Run cosh to enter the AI-native terminal. Token-less can also optimize tool calls from an existing Agent without changing its framework.

Read the Quick Start →

Documentation

Quick Start · Installation · User Guide · Troubleshooting · Build from Source · Changelog

Community

Scan with DingTalk to join the ANOLISA community.

Open an issue for bugs and feature requests.
Read CONTRIBUTING.md before submitting a pull request.
Report vulnerabilities through the Security Policy.

License

ANOLISA is released under the Apache License 2.0.

渲染基于仓库 alibaba/anolisa 的 README.md。在 GitHub 查看原文 →

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