GAIA: Local AI Agents for AMD Ryzen AI
GAIA is AMD's open-source agent factory. It uses coding-agent skills, evals and an auto-fix loop to improve and build agent harnesses that run locally by default on AMD Ryzen AI hardware. Local inference keeps your data private, avoids cloud usage fees, and supports air-gapped deployment with hardware-accelerated performance.
Anyone can build an agent harness with GAIA. The GAIA agent is the one we built to show what it can do: conversation, documents, data, web, memory and skills in one agent.
The terminal UI also supports optional Fireworks AI and AMD LLM Gateway chat through Lemonade. Use /provider to connect and choose a model; cloud chat sends conversation history to the selected provider. See AI provider setup.
Download
One download for users: on Windows, one installer offers the desktop app, the terminal, or both. See Install GAIA for every platform, and its For developers section for script, pip/uv and source installs.
Note (Email agent): the Email agent installs with gaia hub install email, which fetches a binary sidecar into your GAIA agents directory — no PyPI wheel and no Node.js toolchain required. An npm client is also published for embedding the agent in a JS/TS app.
Why GAIA?
| Feature | Description |
|---|
| Local Inference | Run models on your machine for sensitive workloads and air-gapped deployments |
| No Cloud Inference Fees | Local models require no API subscription; optional cloud providers have their own pricing |
| Privacy-First | HIPAA-compliant, GDPR-friendly—ideal for healthcare, finance, and enterprise |
| Ryzen AI Optimized | Hardware-accelerated inference using NPU + iGPU on AMD Ryzen AI processors |
Build with GAIA
The SDK is the base layer under the GAIA agent. A minimal harness is a class with a prompt and tools:
from gaia.agents.base.agent import Agent
from gaia.agents.base.tools import tool
class MyAgent(Agent):
"""A simple agent with custom tools."""
def _get_system_prompt(self) -> str:
return "You are a helpful assistant."
def _register_tools(self):
@tool
def get_weather(city: str) -> dict:
"""Get weather for a city."""
return {"city": city, "temperature": 72, "conditions": "Sunny"}
agent = MyAgent()
result = agent.process_query("What's the weather in Austin?")
print(result)
See the full quickstart guide →
Key Capabilities
- Agent Factory — Skills, an eval benchmark, and
gaia eval agent --fix, which patches failing scenarios and re-runs them (guide)
- Agent SDK — Base class with tool orchestration, state management, and error recovery
- Agent UI — Privacy-first desktop app with chat, file browser, document indexing, and tool execution
- RAG System — Document indexing and semantic search for Q&A over 50+ file formats
- Voice Integration — Whisper ASR + Kokoro TTS for speech interaction (P0 enabling technology)
- Vision Models — Extract text from images with Qwen3-VL-4B
- MCP Integration — Connect to any MCP server for external tool access
- Skills — Extend the GAIA agent with a
SKILL.md, no code required (guide)
C++ Framework
A C++17 port of the GAIA base agent framework is available under cpp/. It implements the same agent loop, tool registry, and MCP client interface without any Python dependency — suitable for embedding in native applications or resource-constrained environments.
#include <gaia/agent.h>
class MyAgent : public gaia::Agent {
protected:
std::string getSystemPrompt() const override {
return "You are a helpful assistant.";
}
};
C++ build and usage instructions →
Quick Install
pip install amd-gaia
For complete setup instructions including Lemonade Server, see the Quickstart Guide.
System Requirements
| Requirement | Minimum | Recommended |
|---|
| Processor | AMD Ryzen AI 300-series | AMD Ryzen AI Max+ 395 |
| OS | Windows 11, Linux | - |
| RAM | 16GB | 64GB |
Documentation
- Quickstart — Install GAIA and build a first harness in 10 minutes
- SDK Reference — Complete API documentation
- Guides — Chat, Voice, RAG, and more
- FAQ — Frequently asked questions
Releases
See the full Release Notes on the documentation site, or browse GitHub Releases.
Release Process
To publish a new release (e.g. v0.17.0), create a release PR that updates these 3 files:
| # | File | What to change |
|---|
| 1 | src/gaia/version.py | Set __version__ = "0.17.0" |
| 2 | docs/releases/v0.17.0.mdx | Create release notes (see format guide) |
| 3 | docs/docs.json | Add "releases/v0.17.0" to the Releases tab pages array |
Then run python util/release_versions.py --stamp. It writes every version field that follows version.py, including the docs.json navbar label, the Agent UI package, and the Terminal UI's hub manifest; lint fails if one drifts. Commit what it changed with the PR, then merge and tag:
git tag v0.17.0 && git push origin v0.17.0
CI validates all three files are consistent with the tag before publishing to GitHub Releases and PyPI.
Contributing
We welcome contributions! See our Contributing Guide for details.
- Build agents in your own repository using GAIA as a dependency
- Improve the framework — check GitHub Issues for open tasks
- Add documentation — examples, tutorials, and guides
Contact
License
MIT License
Copyright(C) 2024-2026 Advanced Micro Devices, Inc. All rights reserved.
SPDX-License-Identifier: MIT
