AitherOS ADK

Name: AitherOS ADK
Author: Aitherium

by Aitheriumversion 3.5.0i★ –iRuns on your computer

Registry nameio.github.Aitherium/aither-adk

Install

Overview

Description from the maker (original English)

Agent runtime with typed memory, knowledge and code graphs, plus file and web tools

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: AITHERIUM_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: AITHERIDENTITY_URL, AITHERIUM_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "aither-adk": {
      "command": "uvx",
      "args": [
        "aither-adk"
      ],
      "env": {
        "AITHERIDENTITY_URL": "your-value",
        "AITHERIUM_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add aither-adk --env AITHERIDENTITY_URL=your-value --env AITHERIUM_API_KEY=your-value -- uvx aither-adk

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "aither-adk": {
      "command": "uvx",
      "args": [
        "aither-adk"
      ],
      "env": {
        "AITHERIDENTITY_URL": "your-value",
        "AITHERIUM_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "aither-adk": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "aither-adk"
      ],
      "env": {
        "AITHERIDENTITY_URL": "your-value",
        "AITHERIUM_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

AITHERIDENTITY_URL env
Override the AitherIdentity endpoint used for sign-in. Only needed for a self-hosted node; the public default works out of the box.
AITHERIUM_API_KEY secret env
Optional API key for the hosted gateway. The server runs fully local without it; its file and web tools need no credential.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account Aitherium, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7274 servers in this category
Published to the registry more recently than 31% of the other 7274 servers in this category
Basic checks equal to or better than 100% of the other 7274 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is AitherOS ADK safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: AITHERIUM_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (3.5.0) reached the registry 3 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

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Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

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Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Aither ADK — Build AI Agent Fleets

[Docs](https://aitherium.github.io/awdk/) · Source · pip install awdk · The Aither World

The Aither World is an operating system for agents — a Linux you can hand to one, the runtimes it works in, and the tools it works with. awnix is the Linux underneath it; awdk is one of its 69 bricks — each installs on its own, runs offline, and needs no account.

Start here: Point it at a backend you already pay for and run one agent loop.

3 lines of code. Any backend. Local or cloud. Zero lock-in.

Aither ADK is a Python SDK + CLI for building AI agents that run on your hardware — a single helpful agent or a coordinated fleet that delegates work to each other. Agents get tools, persistent knowledge-graph memory, safety filtering, and effort-based model routing out of the box. Swap the LLM backend at runtime — your GPU, llama.cpp, or any cloud API — same code, same agents.

pip install awdk

adk quickstart # auto-detect hardware, set up inference adk init my-agent && cd my-agent && python agent.py

The package is awdk; the command is adk (awdk is the same command, and python -m adk always works). Windows, "adk is not recognized"? pip put the command in a Scripts folder that is not on PATH — its warning names the folder. Add it once, then open a new terminal:

$s = python -c "import sysconfig; print(sysconfig.get_path('scripts'))"

Get running in 60 seconds — pick your path

You have…	Run this	You get
Nothing — not even Python	one-line installer (below)	isolated env + first-run wizard
No GPU, no API key	`adk bonsai-local`	Bonsai running free, offline, on CPU or GPU — native, no container; picks the size that fits, serves on :8090
A GPU (6 GB+)	`adk quickstart`	auto-detected vLLM/llama.cpp, models pulled, ready to chat
Just an API key	`adk quickstart --cloud`	cloud inference (Anthropic / OpenAI / DeepSeek)
A whole LAN of machines	`adk deploy grid`	multi-machine effort-routed inference

The no-Python one-liner — sets up an isolated environment (via uv) and launches the wizard:

# macOS / Linux

curl -fsSL https://aitherium.com/install.sh | sh

```powershell

Windows

powershell -ExecutionPolicy ByPass -c "irm https://aitherium.com/install.ps1 | iex"

Then, whichever path you took:

adk start          # chat with your agent (zero config)

adk doctor # something wrong? this names it

Using an AI coding agent (Claude Code, Cursor, Copilot)? Paste the Agent Setup Prompt into your session — it walks the agent through install, auth, inference, and the path from zero to fleet. There's also llms.txt / llms-full.txt for tools that ingest those.

New here? The five concepts
Documentation map — every guide, linked
Subagents — drive Claude Code, Codex, and eight more — real binaries, scoped, torn down
Quick Start
Aither Hearth: your agent, on your phone — serve, channels, approvals, receipts
Bonsai: an agent on literally anything
Reasoning capture & code intelligence — external thinking, omp interop, DeepSeek Coder
Setting Up Inference
Building Agents
Agent Fleets
Agents & Packs
CLI Reference
The Aitherium ecosystem
Environment Variables · Examples · License

New here? The five concepts

Everything in the ADK hangs off five ideas:

Agent — AitherAgent("aither"). One object: await agent.chat("...") is the whole API. It has a persona, tools, and memory.
Backend — where inference runs. Local (vLLM / llama.cpp / Bonsai) or cloud (Anthropic / OpenAI / DeepSeek / Aitherium gateway). Switchable at runtime, mid-session.
Effort routing — every call carries a 1–10 effort level; cheap calls go to small fast models, hard calls go to the big reasoning model. Automatically. You never pick a model per call again.
Memory — a local SQLite knowledge graph that auto-ingests entities and relations from every conversation. Hybrid keyword + semantic search. No external services.
Fleet — multiple agents that can call each other via the built-in ask_agent tool. One YAML file, one adk-serve command, and you have an orchestrator delegating to specialists.

If you only remember one thing: `agent.chat()` is the agent. Everything else is configuration.

Documentation map

I want to…	Read this
Drive Claude Code / Codex / OpenCode / Aider from one shell	[AWSH-OMNISHELL-PLAYBOOK.md](AWSH-OMNISHELL-PLAYBOOK.md) — install → detect → daemon → UI, verified end to end
Build a real agent or publish a pack	[docs/AGENT_DEV_GUIDE.md](docs/AGENT_DEV_GUIDE.md) — the golden path + gotcha checklist
Self-host the full managed-agent experience	QUICKSTART_SELF_HOSTED.md — `adk onboard --quick`
Operate a self-hosted node long-term	docs/SELF_HOSTING_RUNBOOK.md
Run inference across several machines	GRID_SETUP.md
Wire up a specific LLM provider	docs/providers/ — DeepSeek, Kimi, OpenAI-compatible, local AitherOS
Give my agent a persistent identity/persona	docs/PERSONA.md · `adk soul import	export`
Understand the world-model layer	docs/WORLD_MODEL.md
Connect agents across machines (relay)	docs/AITHERRELAY_GUIDE.md
Run a private, local-only companion	PRIVATE_COMPANION.md
Reach my own agent from my phone (Aither Hearth)	docs/agent-home.md — `adk home serve`, channels, approvals, receipts
See working code	`examples/` — five runnable scripts
See what changed	CHANGELOG.md
Browse rendered docs	aitherium.github.io/awdk

Interoperability

Aither agents speak three protocols for seamless integration with external systems:

1. ACP (Agent Client Protocol) — IDE Integration

Connect your agent to JetBrains, Zed, VS Code, or any ACP-compatible editor over JSON-RPC 2.0 stdio.

adk acp serve                          # Serve your agent to an editor

Harness ID: acp (registered in adk.harnesses.registry)
Transport: STRUCTURED_BIDI (JSON-RPC 2.0)
Usage: Agents appear as room participants in AitherShell, driven by editors that speak ACP v2

2. A2A (Agent-to-Agent) — Remote Agent Integration

Map remote A2A agents (Google A2A v0.3.0 compatible) as room participants with full task lifecycle visibility.

from adk.a2a_adapter import A2AAdapter

adapter = A2AAdapter(room_id="main", remote_agent_id="foo") adapter.on_task_submitted("task_001", "what is AI?") adapter.on_task_working("task_001", "thinking...") adapter.on_task_completed("task_001", "AI is...")

Module: adk.a2a_adapter.A2AAdapter
Events: Task lifecycle maps to AitherEvents (orchestration + cognition pillars)
Flux codes: a2a.s (submit), a2a.u (update), a2a.d (done)
Actor kind: a2a — remote agents appear with their own identity in rooms

3. MCP-UI — Render Blocks as Resources

Serve agent-generated RenderBlocks (server-driven UI: tables, forms, charts, approval gates) via the MCP resource protocol using ui:// URIs.

from adk.mcp_ui_resources import RenderBlocksMCPServer, create_table_block, create_scores_block

server = RenderBlocksMCPServer() blocks = [ create_table_block(columns=["Issue", "Severity"], rows=[[...], [...]]), create_scores_block({"security": 0.92, "style": 0.78}), ] uri = server.from_agent_response("reviewer", "task_123", blocks)

uri -> "ui://agent/reviewer/task_123"

Module: adk.mcp_ui_resources.RenderBlocksMCPServer
Block types: 24 primitives (markdown, header, table, code, form, approve, slider, file_upload, etc.)
Schema validation: Block schemas are kept at parity with the AitherOS RenderBlocks protocol, so a block emitted here renders identically in any AitherOS surface
MIME type: application/vnd.aitheros.renderblocks+json
Integration: Mount into FastAPI, use in MCP clients that understand ui://

The `aw` packages — three questions adk can ask about a repository

adk is the agent runtime; three small, independent packages give it the facts it would otherwise have to guess at. Each answers a different question, each installs on its own, and none of the three requires the others:

Package	Knows	The question it answers
`awgraph`	what the code is, and what depends on what	Where is this symptom coming from?
`awgit`	what changed, and who is editing it	Is this an in-flight edit someone else owns?
`awrelay`	who found what, and who still needs to hear it	Who do I tell?

pip install awgraph awgit awrelay   # or any one of them, alone

Used together, an agent can find a symptom with awgraph, check whether it is an in-flight edit with awgit, and tell the agent already working that file with awrelay — three questions a solo grep-and-guess loop cannot ask at all. The failure they remove is not "the agent was wrong"; it is two agents editing the same file without knowing, and a finding that died in a transcript nobody read.

Each publishes an aither-manifest.json beside its page, and each page renders the others live from those manifests — a project whose manifest is missing shows as unknown rather than silently disappearing: awgraph · awgit · awrelay.

Subagents — drive Claude Code, Codex, and eight more

Just want it working? → [AWSH-OMNISHELL-PLAYBOOK.md](AWSH-OMNISHELL-PLAYBOOK.md).

Install → detect → start the daemon → use it, with verified output at each step.

The step people miss is that the harness daemon has to be running: without it the

desktop app reports "No harnesses reported by the daemon yet", which reads as a missing

feature rather than a stopped process.

Your agent can delegate a task to another coding agent's real product — not a reimplementation of it against the raw API.

That distinction is the whole design. Rebuilding Claude Code's behaviour yourself means inheriting none of its skills, hooks or account handling, and then chasing a prod

Only the beginning is shown; read the rest in the repo

AitherOS ADK

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Aither ADK — Build AI Agent Fleets

Get running in 60 seconds — pick your path

Windows

Contents

New here? The five concepts

Documentation map

Interoperability

1. ACP (Agent Client Protocol) — IDE Integration

2. A2A (Agent-to-Agent) — Remote Agent Integration

3. MCP-UI — Render Blocks as Resources

uri -> "ui://agent/reviewer/task_123"

The aw packages — three questions adk can ask about a repository

Subagents — drive Claude Code, Codex, and eight more

The `aw` packages — three questions adk can ask about a repository