AitherOS ADK

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

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.

Contents

• 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:

1. Agent — AitherAgent("aither"). One object: await agent.chat("...") is the whole API. It has a persona, tools, and memory.
2. Backend — where inference runs. Local (vLLM / llama.cpp / Bonsai) or cloud (Anthropic / OpenAI / DeepSeek / Aitherium gateway). Switchable at runtime, mid-session.
3. 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.
4. Memory — a local SQLite knowledge graph that auto-ingests entities and relations from every conversation. Hybrid keyword + semantic search. No external services.
5. 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

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:

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