alibaba/anolisa
<source media="(prefers-color-scheme: dark)" srcset="docs/images/brand/anolisa-lockup-dark.svg" > <source 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.
安装Install
dsh plugin --profile web add github:alibaba/anolisa把 alibaba/anolisa 加入你的 DSH 配置（web profile）即可启用。
READMEREADME
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.
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.
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.
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.
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