# DLLM: Minimal, clean coding agent built directly on llama.cpp without overhead

> Source: <https://github.com/DannyArends/DLLM>
> Published: 2026-08-12 22:34:15+00:00

A minimal, clean [D language](https://dlang.org/) agent built directly on [llama.cpp](https://github.com/ggerganov/llama.cpp) via
[importC](https://dlang.org/spec/importc.html). No Python, no bindings, no overhead. Runs a three-model
pipeline (*agent*, *summary*, *embed*) with full [CUDA](https://developer.nvidia.com/cuda/toolkit)
offloading, multimodal vision via mtmd, RAG, KV-cache condensation, thinking budget, and an extensible tool
system (auto-registered via user-defined attribute *@Tool("Description")* on functions). Tools included
cover: file I/O, web search, date & time, text encoding, Docker sandboxed code execution, and audio playback.

Compilation guide for dependencies are found in `deps/README.md`

**D Compiler**: Use either DMD, LDC, or GDC to compile the agent.** llama.cpp**: Bundled with[llama.cpp](https://github.com/ggerganov/llama.cpp)** Cuda Toolkit**: Built with[CUDA](https://developer.nvidia.com/cuda/toolkit)for GPU acceleration support.** SearxNG**:[SearxNG](https://github.com/searxng/searxng)is used to provide unlimited web searching by the webSearch() tool.** Docker**:[Docker](https://www.docker.com/)provides a code execution sandbox used by the runCode() tool.

Tested with the following [HuggingFace](https://huggingface.co/) models:

**Agent**:[Qwen3.5-4B](https://huggingface.co/unsloth/Qwen3.5-4B-GGUF/resolve/main/Qwen3.5-4B-Q5_K_M.gguf)&[mmproj-F16](https://huggingface.co/unsloth/Qwen3.5-4B-GGUF/resolve/main/mmproj-F16.gguf)(vision).**Summary**:[Qwen2.5-0.5b instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF/resolve/main/qwen2.5-0.5b-instruct-q4_k_m.gguf).**Embed**:[Nomic embed text v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5-GGUF/resolve/main/nomic-embed-text-v1.5.Q4_K_M.gguf).

Model paths are configured in [ src/main.d](/DannyArends/DLLM/blob/master/src/main.d) and with the above model setup require 8GB VRAM.

Execute with prompt to OneShot:

```
  dub -- "What is your name?"
  dub -- "Download the image at https://picsum.photos/400, load it, and write a poem about it"
  dub -- "What date is it today ? and which day of the week was 2 days ago ?"
  dub -- "Generate an 8-second audio file of a 440hz sine wave that fades out, then play it"
  dub -- "Generate a spoken 16-bit PCM WAV of 'Hello World' and play it"
  dub -- "Ingest the file README.md into RAG, tell me what DLLM is in one sentence"
  dub -- "Read the file dub.json, and summarize in a single line what the file is about."
  dub -- "How is the weather in Newcastle upon Tyne (UK) ?"
  dub -- "Fetch the Bitcoin price history online, plot it as a chart and save to workspace"
  dub -- "Think about then tell me, a story about math, 4 lines of text and be creative!"
```

Or start an interactive session:

```
  dub
```

Want to contribute? Great! Contribute to this repo by starring ⭐ or forking 🍴, and feel free to start an issue first to discuss idea's before sending a pull request. You're also welcome to post comments on commits.

Written by Danny Arends and released as [GPLv3](/DannyArends/DLLM/blob/master/LICENSE.txt)