MandoCode

Your AI coding assistant — run locally or in the cloud with Ollama.
No API keys required. Just you and your code.

MandoCode is an AI coding assistant built on RazorConsole, powered by Semantic Kernel and Ollama. RazorConsole makes the entire terminal UI possible — Razor components, a virtual DOM, and Spectre.Console rendering all running in the console.

Run locally or connect to Ollama cloud — no API keys required for anything, including web search. It gives you Claude-Code-style project awareness — reading, writing, searching, planning, and web browsing across your entire codebase — without ever leaving your terminal. It understands any file type: C#, JavaScript, TypeScript, Python, CSS, HTML, JSON, config files, and more.

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Install

# Prerequisites: .NET 8.0 SDK + Ollama installed and running

dotnet tool install -g MandoCode
mandocode

Or build from source

git clone https://github.com/DevMando/MandoCode.git
cd MandoCode
dotnet build src/MandoCode/MandoCode.csproj
dotnet run --project src/MandoCode/MandoCode.csproj -- /path/to/your/project

On first run, MandoCode uses minimax-m2.5:cloud by default. Run /config inside the app or configure from the command line:

mandocode config set model qwen2.5-coder:14b
mandocode config set endpoint http://localhost:11434

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What Makes MandoCode Different

Safe File Editing with Diff Approvals Every file write and delete is intercepted with a color-coded diff. You approve, deny, or redirect — nothing touches disk without your say-so.	@ File References Type @ to autocomplete any project file and attach it as context. The AI sees the full file content alongside your prompt. Reference multiple files in a single message.
Task Planner Complex requests are automatically broken into step-by-step plans. Review the plan, then watch each step execute with progress tracking.	Web Search & Fetch The AI can search DuckDuckGo and read webpages to find documentation, tutorials, or answers — no API keys needed.
Built-in Music Player Lofi and synthwave tracks bundled right in. A waveform visualizer runs in the corner while you code. Because vibes matter.	Offline-Friendly Startup If Ollama isn't running, MandoCode shows setup guidance inline instead of a bare error. Use /retry to reconnect without restarting.

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Features at a Glance

	Feature	Description
AI	Project-aware assistant	Reads, writes, deletes, and searches your entire codebase
AI	Web search & fetch	DuckDuckGo search and webpage reading — no API keys needed
AI	Streaming responses	Real-time output with animated spinners
AI	Task planner	Auto-detects complex requests and breaks them into steps
AI	Fallback function parsing	Handles models that output tool calls as raw JSON
UI	Diff approvals	Color-coded diffs with approve / deny / redirect
UI	Markdown rendering	Rich terminal output — headers, tables, code blocks, quotes
UI	Syntax highlighting	C#, Python, JavaScript/TypeScript, Bash
UI	Clickable file links	OSC 8 hyperlinks for file paths
UI	Terminal theme detection	Auto-adapts colors for light and dark terminals
UI	Taskbar progress	Windows Terminal integration during task execution
Input	/ command autocomplete	Slash commands with dropdown navigation
Input	@ file references	Attach file content to any prompt
Input	! shell escape	Run shell commands inline (!git status, !ls)
Input	/copy and /copy-code	Copy responses or code blocks to clipboard
Music	Lofi + synthwave	Bundled tracks with volume, genre switching, waveform visualizer
Config	Configuration wizard	Guided setup with model selection and connection testing
Config	Config validation	Auto-clamps invalid settings to safe ranges
Reliability	Retry + deduplication	Exponential backoff and duplicate call prevention
Education	/learn command	LLM education guide with optional AI educator chat

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Commands

Type / to see the autocomplete dropdown, or ! to run a shell command.

Command	What it does
/help	Show commands and usage examples
/config	Open configuration (wizard or view settings)
/retry	Retry Ollama connection
/learn	Interactive guide to LLMs and local AI
/copy	Copy last AI response to clipboard
/copy-code	Copy code blocks from last response
/command <cmd>	Run a shell command
/music	Start playing music
/music-stop	Stop playback
/music-pause	Pause / resume
/music-next	Next track
/music-vol <0-100>	Set volume
/music-lofi	Switch to lofi
/music-synthwave	Switch to synthwave
/music-list	List available tracks
/clear	Clear conversation history
/exit	Exit MandoCode
!<cmd>	Shell escape (e.g., !git status)
!cd <path>	Change project root directory

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How It Works

  You type a prompt
        |
  MandoCode adds project context (@files, system prompt)
        |
  Semantic Kernel sends to Ollama (local or cloud model)
        |
  AI responds with text + function calls
        |
  File operations go through diff approval
  Web searches and fetches run directly
        |
  Rich markdown rendered in your terminal

The AI has sandboxed access to your project through a FileSystemPlugin (9 functions: list files, glob search, read, write, delete files/folders, text search, path resolution) and a WebSearchPlugin (web search via DuckDuckGo, webpage fetching — no API keys required). All file operations are locked to your project root — path traversal is blocked.

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Recommended Models

Models with tool/function calling support work best with MandoCode.

Cloud models (no GPU required — run remotely via Ollama):

Model	Notes
minimax-m2.5:cloud	Default — excellent tool support
kimi-k2.5:cloud	Strong general-purpose
qwen3-coder:480b-cloud	Code-focused

Local models (fully offline, runs on your hardware):

Model	VRAM	Notes
qwen3:8b	~5-6 GB	Recommended — good speed/quality balance
qwen2.5-coder:7b	~5-6 GB	Code-focused
qwen2.5-coder:14b	~10-12 GB	Stronger coding model
mistral	~5 GB	General purpose
llama3.1	~5-6 GB	Meta's model

MandoCode validates model compatibility on startup. Run /learn for a detailed guide on model sizes and hardware requirements.

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Configuration Reference

Config File

Located at ~/.mandocode/config.json

{
  "ollamaEndpoint": "http://localhost:11434",
  "modelName": "minimax-m2.5:cloud",
  "modelPath": null,
  "temperature": 0.7,
  "maxTokens": 4096,
  "ignoreDirectories": [],
  "enableDiffApprovals": true,
  "enableTaskPlanning": true,
  "enableTokenTracking": true,
  "enableThemeCustomization": true,
  "enableFallbackFunctionParsing": true,
  "functionDeduplicationWindowSeconds": 5,
  "maxRetryAttempts": 2,
  "music": {
    "volume": 0.5,
    "genre": "lofi",
    "autoPlay": false
  }
}

All Options

Key	Default	Description
ollamaEndpoint	http://localhost:11434	Ollama server URL
modelName	minimax-m2.5:cloud	Model to use
modelPath	null	Optional path to a local GGUF model file
temperature	0.7	Response creativity (0.0 = focused, 1.0 = creative)
maxTokens	4096	Maximum response token length
ignoreDirectories	[]	Additional directories to exclude from file scanning
enableDiffApprovals	true	Show diffs and prompt for approval before file writes/deletes
enableTaskPlanning	true	Enable automatic task planning for complex requests
enableTokenTracking	true	Show session token totals and per-response token costs
enableThemeCustomization	true	Detect terminal theme and apply a curated ANSI palette
enableFallbackFunctionParsing	true	Parse function calls from text output
functionDeduplicationWindowSeconds	5	Time window to prevent duplicate function calls
maxRetryAttempts	2	Max retry attempts for transient errors
music.volume	0.5	Music volume (0.0 - 1.0)
music.genre	lofi	Default genre (lofi or synthwave)
music.autoPlay	false	Auto-start music on launch

CLI Config Commands

mandocode config show              # Display current configuration
mandocode config init              # Create default configuration file
mandocode config set <key> <value> # Set a configuration value
mandocode config path              # Show configuration file location
mandocode config --help            # Show help

Environment Variables

Variable	Overrides
OLLAMA_ENDPOINT	ollamaEndpoint in config
OLLAMA_MODEL	modelName in config

Diff Approvals — Deep Dive

When the AI writes or deletes a file, MandoCode intercepts the operation and shows a color-coded diff before applying changes.

What You See

• Red lines — content being removed
• Light blue lines — content being added
• Dim lines — unchanged context (3 lines around each change)
• Long unchanged sections are collapsed with a summary

Approval Options

Option	Behavior
Approve	Apply this change
Approve - Don't ask again	Auto-approve future changes to this file (per-file), or all files (global)
Deny	Reject the change, the AI is told it was denied
Provide new instructions	Redirect the AI with custom feedback

For new files, "don't ask again" sets a global bypass — all future writes and deletes are auto-approved for the session. For existing files, the bypass is per-file.

Even when auto-approved, diffs are still rendered so you can follow along.

Delete Approvals

File deletions show all existing content as red removals with a deletion warning. The same approval options apply.

Toggle

mandocode config set diffApprovals false

@ File References — Deep Dive

Type @ anywhere in your input (after a space or at position 0) to trigger file autocomplete. A dropdown appears showing your project files, filtered as you type.

How It Works

1. Type your prompt and hit @ — a file dropdown appears
2. Type a partial name to filter (e.g., Conf) — matches narrow down
3. Use arrow keys to navigate, Tab or Enter to select
4. The selected path is inserted (e.g., @src/MandoCode/Models/MandoCodeConfig.cs)
5. Continue typing and press Enter to submit
6. MandoCode reads the referenced file(s) and injects the content as context for the AI

Examples

explain @src/MandoCode/Services/AIService.cs to me
what does the ProcessFileReferences method do in @src/MandoCode/Components/App.razor
refactor @src/MandoCode/Models/LoadingMessages.cs to use fewer spinners

Multiple @ references in one prompt are supported. Files over 10,000 characters are automatically truncated.

Controls

Key	Action
@	Open file dropdown
Type	Filter files by name
Up/Down	Navigate dropdown
Tab/Enter	Insert selected file path (does not submit)
Escape	Close dropdown, keep text
Backspace	Re-filter, or close if you delete past @

Task Planner — Deep Dive

MandoCode automatically detects complex requests and offers to break them into a step-by-step plan before execution.

Triggers

The planner activates for requests like:

• Create a REST API service with authentication and rate limiting for the user module (12+ words with imperative verb and scope indicator)
• Build an application that handles user registration and sends email confirmations
• Numbered lists with 3+ items
• Requests over 400 characters

Simple questions, short prompts, and single-action operations (delete, remove, read, show, list, find, search, rename) bypass planning automatically.

Workflow

1. Detection — heuristics identify complex requests
2. Plan generation — AI creates numbered steps
3. User approval — review the plan table, then choose: execute, skip planning, or cancel
4. Step-by-step execution — each step runs with progress tracking
5. Error handling — skip failed steps or cancel the entire plan

See Task Planner Documentation for full technical details.

/learn — LLM Education

The /learn command helps new users understand local LLMs and get set up.

Scenario	What happens
Startup, no Ollama detected	Automatically displays the educational guide instead of a bare error
/learn typed, no model running	Displays the static educational guide
/learn typed, model is running	Shows the guide, then offers to enter AI educator chat mode

Educational Content

1. What are Open-Weight LLMs? — Free, private, offline models vs. cloud AI
2. Model Sizes & Hardware — Parameters, quantization, VRAM requirements
3. Cloud vs Local Models — Ollama cloud models (no GPU) vs local models
4. Recommended Models — Table of cloud and local options
5. Getting Started — Step-by-step setup instructions

AI Educator Chat Mode

When Ollama is running, /learn offers an interactive chat mode where the AI explains LLM concepts using beginner-friendly language. Type /clear to return to normal mode.

AI Plugins

FileSystemPlugin

The AI has sandboxed access to your project directory through these functions:

Function	Description
list_all_project_files()	Recursively lists all project files, excluding ignored directories
list_files_match_glob_pattern(pattern)	Lists files matching a glob pattern (*.cs, src/**/*.ts)
read_file_contents(relativePath)	Reads complete file content with line count
write_file(relativePath, content)	Writes/creates a file (creates directories as needed)
delete_file(relativePath)	Deletes a file
create_folder(relativePath)	Creates a new directory
delete_folder(relativePath)	Deletes a directory and all its contents
search_text_in_files(pattern, searchText)	Searches file contents for text, returns paths and line numbers
get_absolute_path(relativePath)	Converts a relative path to absolute

Security: All operations are sandboxed to the project root. Path traversal is blocked with a separator-boundary check.

Ignored directories: .git, node_modules, bin, obj, .vs, .vscode, packages, dist, build, __pycache__, .idea — plus any custom directories from your config.

WebSearchPlugin

The AI can search the web and fetch page content — no API keys required.

Function	Description
search_web(query, maxResults)	Searches DuckDuckGo and returns titles, URLs, and snippets (1–10 results)
fetch_webpage(url, maxCharacters)	Fetches a URL and extracts readable text content (500–15,000 chars)

Web search uses DuckDuckGo's HTML endpoint. Fetched pages are cleaned of scripts, nav, and non-content elements via HtmlAgilityPack.

Reliability & Internals

Retry Policy

Transient errors (HTTP failures, timeouts, socket errors) are retried with exponential backoff:

Attempt 1 -> fail -> wait 500ms
Attempt 2 -> fail -> wait 1000ms
Attempt 3 -> fail -> throw

Function Deduplication

Operation	Window	Matching
Read operations	2 seconds	Function name + arguments
Write operations	5 seconds (configurable)	Function name + path + content hash (SHA256)

Fallback Function Parsing

Some local models output function calls as JSON text instead of proper tool calls. MandoCode detects and parses:

• Standard: {"name": "func", "parameters": {...}}
• OpenAI-style: {"function_call": {"name": "func", "arguments": {...}}}
• Tool calls: {"tool_calls": [{"function": {"name": "func", "arguments": {...}}}]}

Markdown Rendering

AI responses are rendered as rich terminal output:

Markdown	Rendered as
**bold**	Bold text
*italic*	Italic text
`code`	Cyan highlighted
Fenced code blocks	Bordered panels with syntax highlighting
Tables	Spectre.Console table widgets
# Headers	Bold yellow with horizontal rules
- lists	Indented bullet points
> quotes	Grey-bordered block quotes
URLs	Clickable OSC 8 hyperlinks

Syntax highlighting supports C#, Python, JavaScript/TypeScript, and Bash with language-specific keyword coloring.

Token Tracking

• Per-response: [~1.2k in, 847 out] after each AI response
• Session total: Total [4.2k tokens] above the prompt
• File estimates: @file attachments show estimated token cost (chars/4)

Event-Based Completion Tracking

Function executions use semaphore-based signaling, ensuring each task plan step fully completes before the next begins.

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Architecture

src/MandoCode/
  Components/        Razor UI (App, Banner, HelpDisplay, ConfigMenu, Prompt)
  Services/          Core logic (AI, markdown, syntax, tokens, music, diffs, input state machine)
  Models/            Data models, config, system prompts, educational content
  Plugins/           Semantic Kernel plugins (FileSystem, WebSearch)
  Audio/             Bundled lofi and synthwave MP3 tracks
  docs/              Feature and architecture documentation
  Program.cs         Entry point and DI registration

Dependencies

Package	Purpose
Microsoft.SemanticKernel 1.72.0	LLM orchestration and plugin system
Ollama Connector 1.72.0-alpha	Ollama model integration
RazorConsole.Core 0.5.0-alpha	Terminal UI with Razor components
Markdig 1.0.0	Markdown parsing
NAudio 2.2.1	Audio playback
HtmlAgilityPack 1.11.72	HTML parsing for web search
FileSystemGlobbing 10.0.3	Glob pattern matching

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Why .NET?

Most AI coding agents in the wild are built with Python, Rust, or TypeScript. .NET rarely gets mentioned — but it should.

Semantic Kernel is Microsoft's open-source SDK for building AI agents, and it's one of the most capable orchestration frameworks available: native plugin systems, function calling, structured planning, and first-class support for local models through connectors like Ollama. It runs cross-platform on Windows, Linux, and macOS.

MandoCode exists partly to prove the point: you can build a full-featured, agentic CLI tool on .NET and Semantic Kernel that stands alongside anything built in other ecosystems. The tooling is there. It's open source. It just doesn't get the attention it deserves.

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MIT License