Show HN: MandoCode – local-first AI coding agent (.NET and Ollama)

MandoCode, a new open-source AI coding agent built on .NET and Ollama, launched today as a local-first alternative to cloud-based assistants. The tool, which requires no API keys and runs entirely in the terminal, can read, write, search, and plan across entire codebases while supporting any file type. Its release addresses developer concerns about privacy and cost by offering a fully local AI coding experience that can optionally connect to cloud models for more powerful capabilities.

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 https://github.com/RazorConsole/RazorConsole , powered by Semantic Kernel https://github.com/microsoft/semantic-kernel and Ollama https://ollama.ai . 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 an optional free Tavily https://www.tavily.com/ key upgrades search reliability . 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. .NET 8 SDK — dotnet.microsoft.com/download/dotnet/8.0 https://dotnet.microsoft.com/en-us/download/dotnet/8.0 SDK includes the runtime — install only the SDK Ollama — ollama.com/download https://ollama.com/download MandoCode walks you through setup on first run dotnet tool install -g MandoCode mandocode First run launches a guided wizard: it detects Ollama, offers to start it, walks you through cloud sign-in if you'd like more powerful models, and auto-pulls a sensible default. You can re-run it any time with /setup . mandocode --doctor Prints your runtime version, Ollama status, models pulled, and cloud sign-in state. Using cloud models Cloud model context is managed on Ollama's servers and set to the model's maximum by default — nothing on your machine affects it, including the desktop app's slider. :cloud tags ? Skip this section. If you use local models and see responses cut off, the model "forgetting" earlier conversation, edits failing repeatedly on files it just wrote, or this message: ⚠ Response was cut off because the model's CONTEXT WINDOW filled … …your Ollama context window is almost certainly too small. The context window is how much conversation + code the model can see at once — and Ollama defaults it to ~4k tokens , which an agentic session fills almost immediately. When it overflows, the oldest content including the system prompt — the model's instructions is silently dropped. If you use the Ollama desktop app the tray icon , the app's Settings → Context length slider controls this — and it overrides everything else, including MandoCode's config: There's no universally right slider position — it's a trade between how much the model can see and fitting in your GPU's memory every 8k of window costs roughly 0.5–1.5 GB of VRAM depending on the model : Too low the 4k default : the symptoms above — the model's own instructions silently fall out of the window and it stops behaving. Too high for your GPU : the model spills into system RAM, tokens/sec craters, and turns crawl or look hung. Starting points : 16k for most GPUs, 32k with 8 GB+ VRAM. Only raise it if you're seeing the symptoms above; step back down a notch if generation slows badly after raising it. If you run ollama serve yourself no desktop app , MandoCode handles it: it sets OLLAMA CONTEXT LENGTH from your contextLength config when it starts the daemon, and auto-sizes it to the hardware tier of the model you pick in /setup or /model . Tune it manually with: mandocode --config set contextLength 16384 Verify what your daemon is actually using with ollama ps look at the CONTEXT column . Run /learn inside MandoCode for a friendly explainer. The context window's evil twin — and unlike the slider above, this one applies to every model, cloud included . If the model announces work and then just stops — "I'll create the game…" and the turn ends with no plan, no files, and no error — your maxTokens is too low. It caps a single reply NumPredict , and reasoning models spend output tokens thinking before they emit a tool call, so a low cap cuts them off before they ever act. Fresh installs default to 32k and never notice it. But if your config predates v0.11, or you once lowered maxTokens thinking it was the context window they're different knobs — this caps what the model says , the context window caps what it sees , check it: mandocode --config show look at "Max Tokens" mandocode --config set maxTokens 32768 The telltale sign: token tracking shows output pinned at exactly your cap, turn after turn e.g. 2k out every time . Note that a running session keeps the config it loaded at startup — restart MandoCode or use /config set in-app for the change to take effect. 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 | Every file write and delete is intercepted with a color-coded diff. You approve, deny, or redirect — nothing touches disk without your say-so. | Type | | Complex requests are automatically broken into step-by-step plans. Review the plan, then watch each step execute with progress tracking. | The AI can search the web and read webpages to find documentation, tutorials, or answers — no API keys needed. Optionally add a free | | Lofi and synthwave tracks bundled right in. A waveform visualizer runs in the corner while you code. Because vibes matter. | If Ollama isn't running, MandoCode shows setup guidance inline instead of a bare error. Use | | Feature | Description | | |---|---|---| AI | Project-aware assistant | Reads, writes, deletes, and searches your entire codebase | AI | Web search & fetch | Web search and webpage reading — keyless via DuckDuckGo, or Tavily with a free API key | AI | MCP server support | Connect to any Model Context Protocol server stdio or remote HTTP — Claude-Desktop-compatible config | 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 | Type / to see the autocomplete dropdown, or to run a shell command. | Command | What it does | |---|---| /help | Show commands and usage examples | /setup | Guided wizard — reconnect to Ollama, install/sign in, or pick a different model | /model | Quick switch — pick a different model context window auto-sized for local tiers | /config | Adjust settings — guided wizard | /config set <key <value | Set one setting inline without leaving the session e.g. /config set modelResponseTimeout 300 ; no args lists all keys + current values | /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 | /mcp | List configured MCP servers with status and tool counts | /mcp add | Interactively add a new MCP server to config | /mcp remove <name | Remove an MCP server from config | /mcp tools <server | List tools exposed by connected MCP servers server optional | /mcp-reload | Restart all MCP servers and re-register their tools | /clear | Clear conversation history | /exit | Exit MandoCode | <cmd | Shell escape e.g., git status | cd <path | Change project root directory | — first-run wizard, guided. Detects Ollama, offers to install it, walks you through cloud sign-in, picks a model with hardware-aware tiers, auto-pulls a sensible default. Use when something's broken or you're a newcomer. /setup — quick switch. Pick a model from your pulled list and go — local picks get a context window sized to their hardware tier automatically. Use when you just want to swap models. /model — adjust settings. Full configuration form covering temperature, timeouts, ignore dirs, etc. Use when you know exactly what knob you want to turn. /config mandocode --doctor preflight check: .NET runtime, Ollama status, models, sign-in mandocode --config show print current config mandocode --config init create a default config file mandocode --config set <key <value set a single value e.g. set model qwen3.5:9b mandocode --config path show config file location Run mandocode --doctor any time chat is misbehaving — exits 0 if everything's green, 1 if anything's missing, with a clear summary of what's wrong. 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 Tavily or DuckDuckGo, webpage fetching — works without any API key . All file operations are locked to your project root — path traversal is blocked. Models with tool/function calling support work best with MandoCode. The first-run wizard offers exactly the models below — auto-pulls the cloud default, or lets you pick a local tier matched to your hardware. Cloud no GPU required — runs on Ollama's servers, free with ollama signin : | Model | Notes | |---|---| minimax-m2.7:cloud | Default — auto-pulled by /setup when you pick Cloud | Local fully offline, runs on your hardware : | Model | Size | Hardware | |---|---|---| qwen3.5:0.8b | ~1.0 GB | CPU-only / integrated GPU — fast on any laptop, light reasoning | qwen3.5:2b | ~2.7 GB | Modern CPU or 4 GB+ GPU — quick Q&A, simple code edits | qwen3.5:4b | ~3.4 GB | Mid-range GPU 4-6 GB VRAM or 16 GB RAM — balanced day-to-day use | qwen3.5:9b | ~6.6 GB | Dedicated GPU 8+ GB VRAM — best local quality, multi-file refactors | MandoCode validates model compatibility on startup. Run /learn for a detailed guide on model sizes and hardware requirements, or /setup to switch between tiers any time. Located at ~/.mandocode/config.json { "ollamaEndpoint": "http://localhost:11434", "modelName": "minimax-m2.7: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 } } | Key | Default | Description | |---|---|---| ollamaEndpoint | http://localhost:11434 | Ollama server URL | modelName | minimax-m2.7: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 | 32768 | Cap on a single reply NumPredict — a runaway-generation safety ceiling, not the context window. If the model announces work then stops without acting, this is too low see Troubleshooting | contextLength | 8192 | Context window num ctx / KV-cache size for local models, set via OLLAMA CONTEXT LENGTH when MandoCode starts the Ollama daemon. 0 = leave Ollama's default ~4k . Bigger window = more VRAM. Cloud models manage context server-side | 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 | 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 | Variable | Overrides | |---|---| OLLAMA ENDPOINT | ollamaEndpoint in config | OLLAMA MODEL | modelName in config | When the AI writes or deletes a file, MandoCode intercepts the operation and shows a color-coded diff before applying changes. 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 | 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. File deletions show all existing content as red removals with a deletion warning. The same approval options apply. mandocode config set diffApprovals false 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. - Type your prompt and hit @ — a file dropdown appears - Type a partial name to filter e.g., Conf — matches narrow down - Use arrow keys to navigate, Tab or Enter to select - The selected path is inserted e.g., @src/MandoCode/Models/MandoCodeConfig.cs - Continue typing and press Enter to submit - MandoCode reads the referenced file s and injects the content as context for the AI 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. | 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 @ | MandoCode automatically detects complex requests and offers to break them into a step-by-step plan before execution. 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. Detection — heuristics identify complex requests Plan generation — AI creates numbered steps User approval — review the plan table, then choose: execute, skip planning, or cancel Step-by-step execution — each step runs with progress tracking Error handling — skip failed steps or cancel the entire plan See Task Planner Documentation /DevMando/MandoCode/blob/main/src/MandoCode/docs/TaskPlanner.md for full technical details. 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 | What are Open-Weight LLMs? — Free, private, offline models vs. cloud AI Model Sizes & Hardware — Parameters, quantization, VRAM requirements Cloud vs Local Models — Ollama cloud models no GPU vs local models Recommended Models — Table of cloud and local options Getting Started — Step-by-step setup instructions 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. MandoCode speaks the Model Context Protocol https://modelcontextprotocol.io as a client, which means you can plug in any published MCP server — filesystem, database, GitHub, Linear, Slack, whatever — and its tools show up to the model alongside MandoCode's built-in plugins. Two ways: inside MandoCode — an interactive wizard that prompts through name, transport, URL/command, and optional headers/env vars, previews the JSON, and saves + reloads automatically. /mcp add Hand-edit — useful when copy-pasting a ~/.mandocode/config.json mcpServers block from a server's README. Run /mcp-reload after saving. The mcpServers block mirrors Claude Desktop's schema https://modelcontextprotocol.io/docs/develop/connect-local-servers , so you can copy-paste any server's README installation snippet directly into ~/.mandocode/config.json : { "enableMcp": true, "mcpServers": { "filesystem": { "command": "npx", "args": "-y", "@modelcontextprotocol/server-filesystem", "/path/to/allow" }, "solana": { "url": "https://mcp.solana.com/mcp", "transport": "http" }, "github": { "url": "https://api.githubcopilot.com/mcp/", "headers": { "Authorization": "Bearer ghp your token here" }, "autoApprove": "list issues", "get pr" } } } stdio — for local servers. Populate command + args + optional env . Works with any server published as an npm/pip/go binary. HTTP / SSE — for remote servers. Populate url ; the client auto-detects Streamable HTTP or SSE. Custom headers go in headers — most commonly Authorization: Bearer … for servers that accept static tokens. No. MandoCode itself is pure .NET. But individual servers may need whatever runtime their command points at — Node for npx , Python for uvx , or nothing extra for standalone binaries. Same situation as Claude Desktop, Cursor, and VS Code. Native OAuth is not in this release. For servers that require an OAuth flow some hosted connectors like Google Drive , wrap them in stdio via the community mcp-remote https://www.npmjs.com/package/mcp-remote proxy, which handles the browser dance itself: "gdrive": { "command": "npx", "args": "mcp-remote", "https://example.com/mcp" } MandoCode cannot tell a read-only MCP tool from a destructive one by inspecting arguments, so the first call of each server, tool pair prompts you with Approve / Approve for session / Deny . Pre-trusted tools can be listed under autoApprove in a server's config entry to skip the prompt entirely. /mcp — shows each configured server with its transport, connection status, and live tool count /mcp add — interactive wizard for adding a new server without hand-editing JSON /mcp remove <name — remove a server from config with confirm /mcp tools <server — list every tool exposed by connected servers with descriptions server arg optional — omit to list all /mcp-reload — tears down every MCP client, restarts them, and re-registers their tools on the kernel useful when you edit the config mid-session mandocode --config set mcp false disable all MCP integration Individual servers can be muted without deleting them — set "disabled": true on any entry in mcpServers . 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, startLine?, endLine? | Reads file content with line count — large files page via startLine / endLine , and truncated output names the exact line to resume from | 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. The AI can search the web and fetch page content — no API keys required. | Function | Description | |---|---| search web query, maxResults | Searches the web 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 | Out of the box, search uses DuckDuckGo's free HTML endpoint — which rate-limits and temporarily blocks IPs under heavy agentic use, so searches can randomly fail. For reliable, AI-optimized search, add a free Tavily https://www.tavily.com/ API key free tier ~1,000 searches/month : /config set tavilyKey tvly-... in-app — verifies the key live against Tavily mandocode --config set tavilyKey tvly-... or from the CLI With a key set, search web prefers Tavily and keeps DuckDuckGo as the fallback; clear it anytime with /config set tavilyKey clear . The key is stored locally in ~/.mandocode/config.json and only ever sent to Tavily — set the TAVILY API KEY environment variable instead if you'd rather keep it out of the file. Fetched pages are cleaned of scripts, nav, and non-content elements via HtmlAgilityPack. Transient errors HTTP failures, timeouts, socket errors are retried with exponential backoff: php Attempt 1 - fail - wait 500ms Attempt 2 - fail - wait 1000ms Attempt 3 - fail - throw | Operation | Window | Matching | |---|---|---| | Read operations | 2 seconds | Function name + arguments | | Write operations | 5 seconds configurable | Function name + path + content hash SHA256 | 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": {...}}} } 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. 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 Function executions use semaphore-based signaling, ensuring each task plan step fully completes before the next begins. 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 | Package | Purpose | |---|---| | Ollama Connector https://github.com/microsoft/semantic-kernel 1.72.0-alpha RazorConsole.Core https://github.com/RazorConsole/RazorConsole 0.5.0-alpha Markdig https://github.com/xoofx/markdig 1.0.0 NAudio https://github.com/naudio/NAudio 2.2.1 HtmlAgilityPack https://html-agility-pack.net/ 1.11.72 FileSystemGlobbing https://www.nuget.org/packages/Microsoft.Extensions.FileSystemGlobbing 10.0.3Most AI coding agents in the wild are built with Python, Rust, or TypeScript. .NET rarely gets mentioned — but it should. Semantic Kernel https://github.com/microsoft/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.