DSH Plugins Marketplace

DSH Plugins

Plugins

/

Security & Audit

/

ANOLISA

a

ANOLISA

Discovered★ 644

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

ANOLISA

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.

Chinese Version · Website · Quick Start · User Guide · Contributing

License Platform AARM Aligned OWASP Agentic Top 10: 7 Full, 3 Partial


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 entryContext efficiencyRuntime & 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 responsesTool schemasFull pipeline
65.8% fewer Tokens47.3% fewer Tokens62.9% fewer Tokens
ResponseCompressor · 46.85 µsSchemaCompressor · 11.44 µs198.91 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 →

Standards & Compliance

Standard / FrameworkStatus and details
AARMAligned · Registry entry: AgentSecCore (ANOLISA)
OWASP Agentic Top 10 (2026)All 10 ASI risk categories mapped to security controls

Documentation

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

Community

ANOLISA DingTalk community QR code

Scan with DingTalk to join the ANOLISA community.

License

ANOLISA is released under the Apache License 2.0.

Comments

Loading…

Similar plugins

dsh-riskproof

by onlyqzq

Risk-aware approval layer for high-risk AI Agent tool calls

Security & AuditManifest valid

★ 8

↓ 583/wk

Apache-2.0

TypeScript

Oct 27, 2026

dsh plugin --profile web add dsh-riskproof

by GanyuanRan

Make AI coding agents architecture-aware: baseline-first, evidence-verified, drift-checked, and safe across long tasks.

Agent skillsManifest valid

★ 1.4k

↓ 35/wk

MIT

Python

Oct 25, 2026

dsh plugin --profile agent add aegis

by mokuyoaxis

Make destructive AI-agent actions reversible by default — quarantine + audit + human escalation for rm/git destructive operations. Reliability infrastructure, not a sandbox.

Security & AuditManifest valid

★ 25

MIT

Python

Oct 22, 2026

dsh plugin --profile agent add agent-guard-dsh

by julescules

Windows agent safety for DeepSeek Harness: preview risky calls, protect workspace boundaries, and explain audit decisions.

Security & AuditManifest valid

★ 0

MIT

JavaScript

Sep 5, 2026

dsh plugin --profile web add dsh-windows-workspace-guard

by derekhu0002

An architecture-graph driven framework for Agentic Engineering.

Manifest valid

★ 3

↓ 830/wk

Apache-2.0

JavaScript

Oct 4, 2026

dsh plugin --profile web add archgraph-argo

A project-agnostic multi-agent collaboration protocol for the DeepSeek Harness: loop guards that stop runaway agent loops, a six-field handoff format, risk-tiered review routing, and a fixed verify→co

Workflow & AutomationManifest valid

★ 0

dsh plugin --profile web add agent-loop-workflow