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Coding agents on open models and isolated compute

Claude Code, Codex, OpenCode and five more, on open models in the EU with no proxy. Each agent gets a sandbox of its own, forked from a disk that already holds your toolchain.

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Your harness, open models, and a machine to run on

Coding agents are the workload that uses an API hardest: long context, many tool calls, hours of it, and a shell that has to exist somewhere.

Munito AI serves the models and the machines. You keep the harness you already like.

Eight harnesses, each with a guide

Claude Code, Codex, OpenCode, Hermes Agent, pi, OpenClaw, DeepSeek Harness and Prime Agent each have a page in the documentation. See the agent guides.

Claude Code needs no proxy. The API answers the Anthropic Messages API on the same key as the OpenAI one, so you set a base URL and nothing else changes. Most providers make you run a translation layer; see Compatibility.

export ANTHROPIC_BASE_URL="https://api.eu.munito.ai/inference"
export ANTHROPIC_AUTH_TOKEN="munitoai_..."
claude

Give the agent its own computer

An agent that edits a repository, installs packages and runs a test suite needs a machine, and it should not be yours. A sandbox is isolated, starts in about a second, and goes away when the work is done.

SizevCPUsMemoryPer hour
small11 GiB$0.0081
default24 GiB$0.018
large48 GiB$0.036

Billed per second. A shell, a browser, SSH, open ports and a desktop come with it. See Run an agent on a sandbox.

Build the disk once, fork it for each run

The slow part of an agent run is rarely the model. It is npm install, the Docker pull, the dependency tree.

Build a sandbox with your toolchain, pause it, and fork it for every run. A fork starts with everything already installed, and no cache crosses the network. The same mechanism runs your CI. See Persistence and CI on sandboxes.

The context is the cost

A coding agent re-sends the repository map, the instructions and the conversation on every turn. That prefix is most of the bill.

Cached input on DeepSeek-V4.1-Flash-1017 costs $0.0060 per million tokens against $0.30 uncached, and the model reads a million tokens of context. GLM-5.3-Flash caches at $0.030 against $0.15. Price your agent on the cached rate, because that is what a long session actually pays.

Your source code is the sensitive part

A coding agent reads the whole repository. That is the first thing your security team will say.

Send to https://api.eu.munito.ai and the request runs in the EU, with the region in the munito-region header. We keep no prompts and no outputs by default, and train on nothing you send. The DPA applies with no signature, at legal.munito.ai/dpa.

If the code may not leave your network at all, run the platform inside your own cloud account or on-premises. See Enterprise.

What we do not do

We do not fine-tune a model on your codebase, and we host no closed model. If your team wants Claude or GPT weights, this is the wrong platform; if it wants the open models that now sit close behind them, at a posted price in a named region, it is the right one.

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