AI Engines

Cockpit talks to 5 AI engines out of the box (plus a Claude 2 entry, so 6 picker options total). Each Agent tab picks one engine; you can mix and match across tabs without restarting — pick by what's running locally, what billing account you're on, or which model is best at the task in front of you.

Everything runs locally.

Overview

At a glance

The engine picker in each tab also has a Claude 2 entry — that's the same engine as Claude, just pointed at a second config directory (~/.claude2) so it uses a different Anthropic account. See the Claude section for setup.

How engine selection works

Each Agent tab has an engine picker in its header. When you create a new tab, the engine defaults to Claude. Switching the engine for an existing tab starts a fresh session — Claude history doesn't carry over into a Codex tab, since each engine has its own conversation format.

You can have, say, five tabs open simultaneously:

• Tab 1: Claude on ~/code/backend
• Tab 2: DeepSeek on the same project for a cheaper second opinion
• Tab 3: Codex on a different project
• Tab 4: Kimi on a notebook with a long PDF attached
• Tab 5: Ollama running a local model for an offline draft

Cockpit's Session Browser (grid icon at the top of the sidebar) shows all of them.

What each engine can do

Image support is engine-level. Kimi and Ollama tabs silently drop image attachments (no error, but the AI doesn't see them).

Setting up each engine

Per-engine sections below cover the specifics. Quick pointers:

• Claude — run claude once on your terminal and follow its login prompt. Cockpit reuses your Claude login automatically.
• Codex — install OpenAI's codex CLI and log in with it once. Cockpit reuses that login.
• DeepSeek — get a key from platform.deepseek.com, then paste it in the DeepSeek picker in the Agent tab header (not in the global Cockpit Settings). Pick a model variant in the same picker.
• Kimi — install Moonshot's kimi CLI and log in with it once. Cockpit reuses that login.
• Ollama — install Ollama and pull at least one model (ollama pull llama3.1). When you create an Ollama tab, the model picker lists what you've pulled.

Claude

Claude is Cockpit's default engine — when you start the app and open a new tab, you're talking to Claude unless you pick something else. Cockpit doesn't manage your Claude login; it reuses the claude CLI from Anthropic, so anything you've done there (subscriptions, project settings, MCP servers) is available in Cockpit too.

Setup

You need the Anthropic claude CLI installed and logged in.

1. Install Claude Code if you haven't already:

npm install -g @anthropic-ai/claude-code

2. Log in:

claude

The claude command walks you through the browser-based login. After it's done, Cockpit picks up your credentials automatically — there's nothing to paste into Cockpit.

That's it. Open Cockpit, create a new Agent tab, start chatting.

What you get

• The latest Claude model Anthropic recommends, served through the Claude Agent SDK.
• Image attachments — paste an image into chat (Cmd+V) and Claude can see it. PNG / JPEG / WEBP / GIF up to 5 MB each; you can attach several at once.
• Tool use — Claude can read your files, run shell commands, edit code, hit URLs, use MCP tools.
• Streaming — replies appear word-by-word as Claude thinks.
• Cost visible in the UI — every message shows tokens used and the running USD total per session.

Use a second Claude account: "Claude 2"

If you have two Anthropic accounts — say, one personal and one billed to your company — Cockpit lets you use both at once. The engine picker has two entries: Claude and Claude 2. They're the exact same engine; "Claude 2" just points CLAUDE_CONFIG_DIR at ~/.claude2 so the two tabs don't share billing.

Setup for the second account:

1. Open a fresh terminal and tell claude to use the second config folder:

CLAUDE_CONFIG_DIR=~/.claude2 claude

2. Log in with your second Anthropic account when prompted.

3. Back in Cockpit, open a tab and pick Claude 2 in the engine menu. It now talks to that second account.

You can keep one tab on Claude (personal) and another on Claude 2 (work), side by side. Cockpit tracks tokens and cost separately.

The path has to be exactly ~/.claude2 — it's hard-coded in Cockpit. Any other path won't be found. If you only have one Claude account, ignore "Claude 2" entirely.

Switching models

Cockpit always uses Anthropic's current recommended Claude model. There is no model picker — you get the latest the service offers. Watch Anthropic's announcements to know which model is current; Cockpit picks it up automatically when the official SDK updates.

Common issues

• "Not logged in" / immediate error on first message — run claude in a terminal and make sure the login completed. Cockpit can only use a login that already works for claude on its own.
• Switching accounts mid-day — using Claude 2 is far simpler than logging out and back in.

Codex

If you have a Codex / ChatGPT subscription, you can drive it from inside Cockpit using the same login. Cockpit doesn't speak the OpenAI API directly here — it spawns OpenAI's codex CLI under the hood and shows you its output.

Setup

1. Install the codex CLI from OpenAI (see OpenAI's docs for the current install command — usually a one-liner).

2. Log in:

codex

Follow the prompt to sign in with your OpenAI account.

3. Open Cockpit, create a new Agent tab, pick Codex in the engine menu. The tab uses your Codex login.

Nothing to paste inside Cockpit — it reuses whatever codex is already configured with on your machine.

What you get

• The Codex model that ships with your CLI (no in-app picker — whatever your codex install gives you).
• Image attachments — Cockpit writes pasted images to a temp file and passes them to codex via the --image flag. PNG / JPEG / WEBP / GIF all work.
• Streaming replies.
• Tool use — Codex can read your files, run shell commands, and edit code.
• Multi-tab sessions — open as many Codex tabs as you need, each independent.

What you don't get

• No running cost display. Cockpit can't read pricing back from the codex CLI, so the token bar stays empty for Codex tabs (total_cost_usd: 0). Track usage on your OpenAI dashboard instead.
• No model picker. Whichever model your codex CLI uses is what runs.

Common issues

• "codex not found" / nothing happens on send — the codex CLI isn't on your PATH. Verify with codex --version in a terminal; if that fails, reinstall.
• Login expired — re-run codex in a terminal and complete the login flow again. Cockpit doesn't manage the login itself.
• Outdated CLI — OpenAI updates codex periodically. If something behaves oddly, upgrade.

DeepSeek

DeepSeek is the cheapest cloud engine in Cockpit and the only one besides Claude where you pick a model variant per tab. Unlike Claude / Codex / Kimi (which reuse a CLI's login), DeepSeek is API-key-only — paste a key in the per-tab DeepSeek picker and you're done.

Under the hood it goes through DeepSeek's Anthropic-compatible endpoint routed via the Claude Agent SDK, so tool use, streaming, and context management all work like Claude.

Setup

1. Get an API key from platform.deepseek.com. It looks like sk-....

2. Open a new tab in Cockpit, pick DeepSeek in the engine menu, then click the DeepSeek picker icon in the tab header → paste the key → save. (The key lives in ~/.cockpit/settings.json locally — not in the global Cockpit Settings modal.)

3. Pick a model variant in the same picker.

Done. The key only ever stays on your machine.

Pick a model variant

The Claude Agent SDK also uses deepseek-v4-flash for background subtasks (title generation, compaction, etc.) regardless of your variant choice.

What you get

• flash or pro picked per tab in the dropdown.
• Image attachments — paste images (Cmd+V) and DeepSeek can see them (via the Anthropic-compatible API).
• Streaming replies.
• Tool use — DeepSeek can read your files, run shell commands, edit code.
• Token counts visible in the UI. Note: the dollar amount in the token bar is an estimate using Cockpit's default per-token prices — it's useful as a relative indicator across sessions; for your actual DeepSeek bill check your DeepSeek dashboard.

Common issues

• "DeepSeek API key is not configured" — you haven't pasted a key in the picker. The key goes in the per-tab DeepSeek picker in the header, not in the global Cockpit Settings modal.
• "401 / Unauthorized" — bad or expired key; paste it again in the picker and watch for stray whitespace.
• Slow / hanging replies — pro is genuinely slower than flash; if you don't actually need the reasoning, switch the tab to flash.
• Estimated costs climbing fast — pro is several times more expensive than flash. Look at the per-session cost in the token bar to spot accidental pro usage.

Kimi

Kimi is a Chinese-market AI from Moonshot, known for long context windows. Cockpit drives it via Moonshot's kimi CLI — install the CLI once, log in, and Cockpit reuses your session.

Setup

1. Install Moonshot's kimi CLI (follow Moonshot's official install instructions for your platform).

2. Log in:

kimi

Follow the prompt to sign in with your Kimi / Moonshot account.

3. Open Cockpit, create a new Agent tab, pick Kimi in the engine menu. The tab uses your Kimi login.

Nothing to paste inside Cockpit — it reuses whatever kimi is already configured with on your machine.

What you get

• The Kimi model that ships with the CLI.
• Streaming replies, with the model's "thinking" steps folded into a <details> block before the final answer.
• Tool use — Kimi can read your files, run shell commands, edit code.
• Multi-tab sessions, each independent.

What you don't get

• No image attachments. Kimi tabs don't accept image input; pasted images are silently dropped.
• No running cost display. Cockpit can't read pricing back from the kimi CLI. Check your Moonshot dashboard for billing.
• No model picker. Whichever model your kimi CLI ships with is what runs.

Common issues

• "kimi not found" / nothing happens on send — the kimi CLI isn't on your PATH. Verify with kimi --version in a terminal; if that fails, install it.
• Login expired — re-run kimi in a terminal and complete the login flow again.
• Outdated CLI — Moonshot updates kimi periodically. Upgrade per Moonshot's docs if behaviour gets strange.

Ollama

Ollama is the only engine in Cockpit that runs entirely on your own machine. No API key, no cloud, no per-token cost. Install Ollama, pull the models you want, and Cockpit lists them in the model picker.

Reach for this engine when:

• You're on a plane or otherwise offline.
• You're working with sensitive code that shouldn't leave your laptop.
• You have a workstation with a beefy GPU and want to use it.
• You're experimenting with custom or fine-tuned models.

Setup

1. Install Ollama from ollama.com.

2. Pull at least one model:

ollama pull llama3.1

You can pull more later: ollama pull qwen3.5, ollama pull deepseek-coder, etc. See the Ollama model library for the full list.

3. In Cockpit, create a new Agent tab and pick Ollama in the engine menu. If the Ollama service isn't running, Cockpit auto-spawns ollama serve and waits up to 8 seconds for it to be ready.

4. Click the model dropdown in the tab header — Cockpit asks the Ollama API for the list of models you've pulled.

What you get

• Any model you've pulled, picked per tab.
• Streaming replies.
• Tool use (depends on the model — coding-tuned models support tool use, generic chat models often don't).
• Completely offline. No outbound network calls.
• Zero per-message cost.

What you don't get

• No image attachments. Cockpit's Ollama tab is text-only for now, even if you pull a vision-capable model.
• No "best practice" model picker. Ollama gives you exactly what you pulled — Cockpit has no opinion. (There's a fallback default in code, but you should pick one yourself.) Start with a known-good coding model like qwen3.5-coder or deepseek-coder if you're unsure.

Choosing a model

Rough sizing guidance — actual performance varies by GPU:

For coding work specifically, look at qwen3-coder, deepseek-coder, and codellama families. They're more useful than a generic chat model of the same size.

Common issues

• "No models found" in the dropdown — you haven't pulled anything yet. Open a terminal and run ollama pull <name> for at least one model.
• Replies are extremely slow — the model is bigger than your GPU can comfortably handle. Try a smaller one.
• Auto-start didn't work — run ollama serve in a terminal manually and try again.