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License: MIT Any use incl. commercial
Local-run terms: Install and run via dotnet tool; full source available under MIT; use with local Ollama models or optional cloud.

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MandoCode

FreeOpen SourceSelf-HostedAgentic

Pricing

Model
Free

Summary

Most CLI coding agents demand an OpenAI key, a cloud subscription, or both — and then leak your codebase through a third-party API you didn't fully audit. MandoCode skips all of that by running entirely against a local or self-hosted Ollama instance, no account required.

MandoCode is a .NET CLI agent that reads your project, proposes diffs, and applies changes across files — the full plan-search-edit loop, entirely on your machine. It is built on Semantic Kernel and RazorConsole, which renders a Spectre.Console terminal UI using Razor components and a virtual DOM. The agent is designed around C# and .NET codebases, so the file understanding and diff proposals are tuned for that ecosystem. Web search is available without a key but the vendor states a free Tavily key improves reliability. The ceiling appears when you push outside .NET: community reports on the GitHub page are thin, and the tool's own framing is explicit about its target audience.

Bottom line: The right pick for a .NET team that refuses to route source code through external APIs — and the wrong pick the moment your project mixes Python services or you need a model that Ollama does not run well on your hardware.

Community Performance Report Card

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Best For: .NET and C# developers, Users preferring terminal/CLI workflows, Local Ollama-based AI coding without API keys, Project-aware code editing and updates

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  • Runs against local or self-hosted Ollama with no API keys required for core functionality, so your source code never leaves infrastructure you control — which means you skip the legal and security review that external AI coding tools typically trigger.
  • Single `dotnet tool install` command gets the agent running, so you are not wrestling with Python virtual environments or Node version conflicts before writing a line of code.
  • Project-aware planning loop — the agent reads across files, builds a plan, and proposes diffs before writing — so you review the full change set rather than discovering side effects after the fact.
  • RazorConsole terminal UI renders structured, navigable output in the console without a browser or IDE dependency, which means the tool works cleanly over SSH and in headless CI environments where other agent UIs break.
  • MIT-licensed and open-source, so you can audit exactly what the agent sends to the model and fork it when the default behavior does not match your workflow.
  • The agent's file understanding, diff proposals, and documented use cases target .NET and C# explicitly. Teams with Python, Go, or TypeScript services in the same repository will find the agent has not been tested or tuned for those languages — at which point they are evaluating a different tool rather than configuring this one.
  • Model quality and context window are entirely determined by what Ollama can run on the available hardware. On a developer laptop with a mid-range GPU, large refactoring tasks that require holding the full context of a multi-file module will start failing silently or producing partial diffs — the agent cannot compensate for a model that cannot fit the prompt.
  • There is no API surface documented in the source page, which means MandoCode cannot be embedded in a larger automation pipeline or triggered by an external system. Teams that want the agent to run as a step in a CI workflow rather than interactively will need to build that wrapper themselves or switch to an agent that exposes a programmatic interface.

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About

Platforms
.NET 8, CLI/terminal, Ollama (local or cloud)
API Available
No
Self-Hosted
Yes
Last Updated
2026-06-12T15:07:39.548Z

Best For

Who it's for

  • .NET and C# developers
  • Users preferring terminal/CLI workflows
  • Local Ollama-based AI coding without API keys
  • Project-aware code editing and updates

What it does well

  • Refactoring codebases in the terminal
  • Proposing and applying code diffs
  • Searching and planning across entire projects
  • Running local or cloud Ollama models for coding tasks

Integrations

OllamaSemantic KernelRazorConsoleoptional Tavily

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Frequently Asked Questions

Is MandoCode free?
Yes — MandoCode is fully free to use. There is no paid tier.
Is MandoCode open source?
Yes. MandoCode is open source.
Can I self-host MandoCode?
Yes. MandoCode supports self-hosting on your own infrastructure.
What platforms does MandoCode support?
MandoCode is available on: .NET 8, CLI/terminal, Ollama (local or cloud).

Hours Saved & ROI Stories Community

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MandoCode

Most coding agents treat your codebase as input to a remote API call. MandoCode treats it as a local workspace: the agent reads files, builds a plan, proposes diffs, and writes changes back — all inside your terminal, against a model running in Ollama on your own hardware or a self-hosted Ollama cloud instance. The install path is a single `dotnet tool install` command. No API keys are provisioned for core functionality; an optional Tavily key unlocks more reliable web search, but the vendor documents it as optional.

The terminal interface is a distinguishing technical choice. Rather than a web dashboard or an IDE plugin, MandoCode uses RazorConsole — a library that runs Razor components with a virtual DOM and renders through Spectre.Console. The result is a structured, navigable terminal UI that goes beyond raw text scrolling without requiring a browser or Electron shell. For teams working over SSH or in locked-down CI environments, that architecture matters.

MandoCode fits .NET and C# teams who want Claude-Code-style project awareness — planning across files, proposing changes, applying diffs — without sending source to a third-party API. It fits that scenario well. It fits poorly when your stack is polyglot: the tooling, framing, and community are squarely .NET-oriented, and there is no documented evidence of comparable depth for other ecosystems. Teams running large monorepos will also hit a practical ceiling determined by the context window of whatever Ollama model they provision — the agent cannot outrun the model underneath it.