Large Language Maid

Throw files against LLMs.

llmaid is a command-line tool designed to automate the process of AI supported file changes using large language models. It reads source code files, sends them to Ollama, LM Studio, or any OpenAI-compatible API, and writes back the models answers. The tool is highly configurable and supports every kind of text-based input file.

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💬 But there is GitHub Copilot, Cursor, Claude Code, OpenCode and so many more. Isn't llmaid outdated?

Yes, very. But it serves a slightly different use-case.

These partly autonomous agents are amazing but they cannot be used to fix typos or add documentation in every single code file of a repository. These tools work in a different way and need to minimize their context while navigating through your codebase.

llmaid is different: Every file is a new conversation. While there is no autonomous intelligence, it can review or edit every file in total based on your instructions. This is handy to find things in your codebase you could not search with RegEx, for example. The feature of writing the LLM response back also enables batch-processing of every single file in the codebase, like "fix all typos".

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Note

1. Paid services such as ChatGPT can cause high API costs if they are used with many files. Double check your config.
2. You may get lower quality when using local models with Ollama or LM Studio, but it's completely free and your files will never leave your computer.

What can it do?

llmaid will run through every file in a path you specify and rewrite, analyse or summarize it. Pretty much everything you can come up with, as long as you can write a good system prompt.

This repository provides a few profile files, for example:

Documenting code

With this prompt, llmaid will scan and rewrite each code file and generate missing summaries, fix typos, remove wrong comments and much more:

Finding unprofessional slang

This prompt will output one json code block for each file. There it lists findings such as insults, cringe comments, and much more including a severity rating and a description what it thinks about the things it found:

Prerequisites

• .NET 10.0 SDK
• An Ollama instance, LM Studio, or an OpenAI-compatible API including api key

Installation

git clone https://github.com/awaescher/llmaid
cd llmaid

# edit ./llmaid/appsettings.json for provider settings
# edit or create a profile in ./profiles/ for task-specific settings

dotnet run --project llmaid

Configuration

llmaid uses a layered configuration system where each layer can override the previous:

1. appsettings.json – Connection settings only (provider, URI, API key)
2. Profile file (.yaml) – Complete task configuration (model, paths, files, system prompt)
3. Command line arguments – Runtime overrides (highest priority)

This means you can prepare self-contained profiles for different tasks and still override individual settings via CLI.

appsettings.json (Connection Settings)

This file only contains your LLM provider connection settings:

{
  "Provider": "lmstudio",
  "Uri": "http://localhost:1234/v1",
  "ApiKey": "",
  "WriteResponseToConsole": true,
  "CooldownSeconds": 0,
  "MaxFileTokens": 102400,
  "OllamaMinNumCtx": 24000 // Minimum context length for the Ollama provider to prevent unnecessary model reloads (default 20480)
}

Profile Files (.yaml)

Profiles are self-contained task definitions that include everything needed to run a specific job: model, target path, file patterns, and system prompt.

# profiles/code-documenter.yaml

model: deepseek-coder-v2:16b
targetPath: ./src
temperature: 0.25
applyCodeblock: true
maxRetries: 1

files:
  include:
    - "**/*.{cs,vb,js,ts}"
  exclude:
    - bin/
    - obj/

systemPrompt: |
  You are an AI documentation assistant.
  The user will provide a code snippet. Review and improve its documentation:
  
  - Add missing summaries for public members
  - Fix typos in comments
  - Do NOT change the executable code
  
  Return the entire file in a markdown code block.

Command Line Arguments

All settings can be overridden via CLI:

# Use a specific profile with dotnet run
dotnet run --project llmaid -- --profile ./profiles/code-documenter.yaml

# Use a specific profile (when running the compiled binary)
llmaid --profile ./profiles/sql-injection-changer.yaml

# Run without profile (all settings via CLI)
llmaid --provider openai --model gpt-4o --targetPath ./src --systemPrompt "..."

# Dry run to see which files would be processed
llmaid -- --profile ./profiles/code-documenter.yaml --dryRun

# Verbose output with detailed token and timing information
llmaid -- --profile ./profiles/code-documenter.yaml --verbose

Available arguments:

• --provider – ollama, openai, lmstudio, openai-compatible
• --uri – API endpoint URL
• --apiKey – API key (if required)
• --model – Model identifier
• --profile – Path to YAML profile
• --targetPath – Directory with files to process, or a single file (when specifying a file, glob patterns are ignored)
• --applyCodeblock – true extracts codeblock and overwrites file, false outputs response to console
• --temperature – Model temperature (0-2)
• --systemPrompt – System prompt text
• --dryRun – Simulate without changes
• --maxRetries – Retry count on failures
• --verbose – Show detailed output (tokens, timing, settings)
• --cooldownSeconds – Cooldown time after processing each file to prevent overheating (default: 0)
• --maxFileTokens – Maximum tokens a file may contain before it is skipped (default: 102400)
• --resumeAt – Resume processing from a specific file (skips all files until a filename containing this pattern is found)
• --preserveWhitespace – Preserve original leading and trailing whitespace when writing files to avoid diff noise (default: false)

Supported Providers

Provider	URI	API Key Required
ollama	http://localhost:11434	No
lmstudio	http://localhost:1234/v1	No (use empty string or any placeholder)
openai	https://api.openai.com/v1	Yes
openai-compatible	Your server's URL	Depends on server

FAQ

Can I continue where I left off earlier?

Yes! Use the --resumeAt parameter with a pattern matching the filename where you want to resume. All files before that match will be skipped (like a dry run).

Example: If you interrupted llmaid after hundreds of files while it was processing ~/Developer/MyApp/UserService.cs, you can continue like this`:

llmaid --profile ./profiles/code-documenter.yaml --resumeAt UserService

The pattern is case-insensitive and matches any part of the file path, so you don't need to specify the full path.

I get an 404 (Not Found)

It is very likely that Ollama returns this 404 because it doesn't know the model that's specified in the appsettings.json. Make sure to specify a model you have downloaded.

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❤ Made with Spectre.Console and OllamaSharp.