Golden Agent
Your local AI agent. No cloud. No API keys. No excuses.
A terminal coding agent that runs entirely on your machine — auto-installs its own GPU-accelerated inference stack, auto-downloads models, and gets to work on basically any post 2000 hardware.
Why Golden Agent?
Every AI coding assistant wants your code uploaded to someone else's server. Golden Agent flips that: the model, the tools, and your data never leave your machine.
- 100% local — inference happens on-device via llama.cpp. Nothing is sent anywhere (except when you ask it to search or fetch the web).
- Zero-config setup — one
pip install, one command. Golden Agent detects your GPU and downloads the matching prebuilt llama.cpp backend automatically (Vulkan on Windows/Linux, Metal on macOS, CUDA where available, CPU otherwise), then pulls the model weights from Hugging Face with resumable, retry-hardened downloads. - Runs on basically any hardware — Vulkan means one code path for NVIDIA, AMD, and Intel GPUs. Full offload, flash attention, and Q5_0 KV-cache quantization squeeze maximum performance out of modest VRAM. Small models start at ~1.5 GB — a laptop iGPU can run it.
- A real agent, not a chatbot — reads and edits files, runs shell commands, searches the web, fetches pages, and loops on results until the job is done. Writes outside your project ask first.
- Models for everyone — three sizes from 1.5 GB to 8.2 GB, switchable with a numbered picker on startup.
Quickstart
pip install golden-agent
golden-agent setup
golden-agent
That's it. First launch:
- Golden Agent sets up the llama.cpp wheel for your platform.
- Pick a model
- The model downloads on first run (~1.5–9 GB, resumable if interrupted).
- You get a prompt:
❯— start asking it to do things.
❯ fix the failing test in tests/test_agent.py
❯ search for how uv handles lockfiles and summarize
❯ write a script that renames all photos by EXIF date
Models
| Key | Model | Size | Context | Good for |
|---|---|---|---|---|
tiny |
LFM2.5-2.6B | ~1.5 GB | 128K | Low-end hardware, laptops, iGPUs |
lite (default) |
Ornith-1.5-9B | ~5.2 GB | 153K | Best balance of speed and capability |
pro |
Qwen3.8-27B | ~8.2 GB | 153K | Heavy reasoning when you can afford it |
Built-in tools
Golden Agent ships with six tools the model uses autonomously:
| Tool | What it does |
|---|---|
read_file |
Reads files with automatic paging for large ones |
write_file |
Creates/overwrites files |
edit_file |
Exact-match string replacement (with replace_all) |
bash |
Runs real shell commands — actual bash even on Windows |
web_search |
DuckDuckGo search |
web_fetch |
Fetches a URL and extracts readable text |
Guardrails: file writes outside the directory you launched from trigger an
interactive permission prompt (once / always this session / deny). Tool output
is capped so runaway commands can't flood the context.
What you'll see
- Live streaming markdown as the model writes.
- Visible reasoning —
<think>blocks stream inline, dimmed, so you can watch it think. - Tool activity panels — file diffs and command output render with syntax highlighting; reads and searches show as quiet status lines.
- Automatic context compaction — past 140K tokens, older turns are summarized by the model itself so long sessions keep going.
Slash commands
/tools list available tools
/clear wipe conversation history
/help show help
/exit quit
Requirements
- Python 3.10+
- A GPU (NVIDIA, AMD, Intel) — or it still works on CPU, just slower.
- Internet once, for the initial setup + model download. After that: fully offline.
Golden Agent downloads a prebuilt llama.cpp binary for your detected backend, so no compilation is required. If you want to build a custom backend yourself, the Vulkan SDK is only needed for that manual path.
Under the hood
- Inference: the prebuilt
llama-serverwith full GPU offload (-ngl 99), flash attention, and Q5_0-quantized KV cache. - Downloads: direct-from-HuggingFace GGUF fetching with HTTP range resume, byte-exact validation, and exponential-backoff retries.
- Tool calling: hand-rolled parsers for both Qwen-style XML calls and LFM2 pythonic call syntax — robust to streaming chunk boundaries, unclosed tags, and parallel calls.
- Model cache:
%LOCALAPPDATA%\golden-agent\models(Windows),~/Library/Caches/golden-agent/models(macOS),$XDG_CACHE_HOME/golden-agent/models(Linux).
Debugging
Set GOLDEN_AGENT_RAW_LOG to a file path to capture every raw model response — invaluable
when a tool call goes sideways:
GOLDEN_AGENT_RAW_LOG=./raw.log golden-agent
Development
git clone <repo> && cd golden-agent
pip install -e .[dev]
pytest
ruff check src tests
Run without installing:
pip install -e .
python -m golden_agent
License
MIT — see LICENSE.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file golden_agent-0.1.0.tar.gz.
File metadata
- Download URL: golden_agent-0.1.0.tar.gz
- Upload date:
- Size: 43.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.11.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
cd561ebd09e1bb9f70f75a0384637c097c1028a8b0a47f1be2726533501441d8
|
|
| MD5 |
919d2ca9309d82effbd22abf22aed70f
|
|
| BLAKE2b-256 |
0e437a7afa273af013a74a979fa8dc08a11d4f6cb89e1044ba503fdb8f912383
|
File details
Details for the file golden_agent-0.1.0-py3-none-any.whl.
File metadata
- Download URL: golden_agent-0.1.0-py3-none-any.whl
- Upload date:
- Size: 40.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/7.0.0 CPython/3.11.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f89befc2653f9dd17e654f67d9c1f761e3fa089423299779342c40fb6ed154a8
|
|
| MD5 |
260182c8b4b729a27208c3c0f1e59a21
|
|
| BLAKE2b-256 |
29172aaa672d2fa50a5894ee866f946b472de3e99f2cecd6485fe485b3191f50
|