Aither ADK — Build AI Agent Fleets

Docs · 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 65 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, Ollama, 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

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

# 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
• 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 / Ollama / 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