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CLI Agents

Command-line coding agents run in your terminal rather than inside an editor. They read and write files, run commands, and call tools on their own, driven by a model you point them at. Because mittwald AI Hosting speaks the OpenAI API, any agent that accepts a custom OpenAI-compatible endpoint works with it — with your code and prompts staying on infrastructure in Germany.

For agents that live inside an editor instead, see IDE Integration.

What an agent needs from the endpoint​

A coding agent needs three things beyond plain chat, and mittwald AI Hosting provides all three:

  • An OpenAI-compatible /v1/chat/completions endpoint. This is what the baseURL or base_url setting in each agent points at.
  • Streaming responses, so output appears as it is generated instead of arriving in one block at the end.
  • Tool calling. This is the one that matters most and the one that most often breaks against non-OpenAI endpoints. An agent without tool calling cannot read a file or run a command — it can only talk. Verified working on Qwen3.5-122B-A10B-FP8, Qwen3.8-27B-NVFP4, Qwen3.6-35B-A3B-FP8 and gpt-oss-120b.

Examples of supported agents​

These three are documented here because they were verified end to end against mittwald AI Hosting. Any agent that lets you point it at a custom OpenAI-compatible endpoint works the same way.

AgentConfig fileMCP supportSkills
Hermes Agent~/.hermes/config.yamlBuilt in (hermes mcp add)Built in, plus registries
Pi~/.pi/agent/models.jsonVia the pi-mcp-adapter extensionDirectory discovery or --skill
opencode~/.config/opencode/opencode.jsonBuilt in (mcp config block)~/.config/opencode/skills/

Choosing a model​

All models are listed in the AI models documentation. For agentic work:

Use forModelContext
Main driverQwen3.5-122B-A10B-FP8245,760
Faster default choiceQwen3.8-27B-NVFP4256,000
Subagents, cheap turnsQwen3.6-35B-A3B-FP8256,000
Reasoning-heavy workgpt-oss-120b131,072
Routing, classificationQwen3.5-0.8B262,144
Documents and imagesGLM-OCRsee model page

Image understanding (vision) isn't limited to one entry here — Qwen3.5-122B-A10B-FP8, Qwen3.8-27B-NVFP4 and Qwen3.6-35B-A3B-FP8 all accept image input as Base64-encoded data URLs, though Qwen3.8-27B-NVFP4 caps requests at 5 images.

Start with Qwen3.5-122B-A10B-FP8 as the driver. If an agent supports per-subagent model overrides, point the subagents at Qwen3.6-35B-A3B-FP8 — the work they do is mostly search and summarising, and the faster model keeps long sessions responsive.

Quick start​

  1. Get an AI Hosting API key from the AI-Hosting section of your mStudio project — see Gaining access.
  2. Pick an agent from the table above and follow its page.
  3. Configure the endpoint as https://llm.aihosting.mittwald.de/v1.
  4. Verify tool calling works before starting real work. Each guide has a verification step for this.

Keeping context under control​

Large context windows are not a reason to send more. Agents burn context fastest on file contents and command output.

  • Point the agent at specific files and functions rather than whole repositories.
  • Use temperature at or below 0.2 for edits and tool calls, so results are reproducible.
  • Raise max_tokens only when a reply is actually being truncated.
  • Where the agent supports compaction or summarisation, leave it enabled for long sessions.

Disclaimer​

Third-party tools, MCP servers and external links are provided for convenience without endorsement or warranty. Use them at your own risk, review licenses and privacy policies, and scope access conservatively (least privilege). Avoid sending sensitive data to external services unless required and permitted.