Codex CLI on Tensormesh

Get stock Codex CLI driving open-weight models against Tensormesh in 5 minutes. Every Tensormesh serverless model supports the full Codex flow today (chat + tool use); MiniMaxAI/MiniMax-M2.5 is the recommended default.

Quick Links

Sign up / Log in

Create your Tensormesh account with Google or GitHub.

Get an API key

Generate an ak-... key under Profile → API keys.

Install Codex CLI

npm install -g @openai/codex — verified against Codex 0.128+.

Supported models

Full matrix of Codex-ready models on Tensormesh serverless.

Get support

Email saas-support@tensormesh.ai for help.

TL;DR

Three steps — install the CLI, configure the provider, set the API key — then run codex. Step 2 has an ordering constraint (top-level keys must precede any [section] header); see Setup below if you have an existing ~/.codex/config.toml.

# Step 1 — install Codex CLI (skip if already installed)
npm install -g @openai/codex

# Step 2 — configure the Tensormesh provider in ~/.codex/config.toml.
#         Safe only if the file is missing or empty. If you already have
#         a config.toml, see Step 2 in the Setup section below.
cat > ~/.codex/config.toml <<'EOF'
# Top-level keys (must come before any [section] header)
model_provider = "tensormesh"
model = "MiniMaxAI/MiniMax-M2.5"
model_reasoning_effort = "medium"

[model_providers.tensormesh]
name = "tensormesh"
base_url = "https://serverless.tensormesh.ai/v1"
wire_api = "responses"
env_key = "TENSORMESH_INFERENCE_API_KEY"
EOF

# Step 3 — set your API key (add to ~/.zshrc or ~/.bashrc to persist)
export TENSORMESH_INFERENCE_API_KEY=ak-...

# Run it
codex

That’s the full setup — no third-party tooling installed, no PATH addition. Setup below walks through the same three steps in detail (and the merge-into-existing-config case).

Prerequisites

Codex CLI installed via npm install -g @openai/codex. Verified against 0.128.0 through 0.131.0 (chat + tool use end-to-end); forward-compatible with later versions barring upstream Codex breaking changes.
A Tensormesh account with an API key (ak-...). Sign up, then visit Profile → API keys.
macOS or Linux, bash or zsh. Windows binaries ship in Codex 0.131+ but aren’t currently tested by Tensormesh.

You do not need to run codex login. The provider’s env_key field tells Codex to read your API key from the environment variable directly; the OAuth login flow is for OpenAI-hosted models only.

You do not need to run any local infrastructure. Tensormesh handles Codex compatibility server-side, so stock codex pointed at serverless.tensormesh.ai (per the TL;DR above) is the complete setup.

Supported models

Model	Codex CLI status	Notes
`MiniMaxAI/MiniMax-M2.5`	✓ chat + tool use	Recommended default; 192K context
`openai/gpt-oss-120b`	✓ chat + tool use	Reasoning + tools, 128K context
`openai/gpt-oss-20b`	✓ chat + tool use	Reasoning + tools, 128K context
`Qwen/Qwen3-235B-A22B`	✓ chat + tool use	Reasoning + tools, 40K context
`Qwen/Qwen3-30B-A3B`	✓ chat + tool use	Reasoning + tools, 40K context
`Qwen/Qwen3-Coder-30B-A3B-Instruct`	✓ chat + tool use	Coding-tuned, 256K context
`Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8`	✓ chat + tool use	Coding-tuned, 256K context
`mistralai/Devstral-2-123B-Instruct-2512`	✓ chat + tool use	Mistral-architecture coder, 32K context
`moonshotai/Kimi-K2.6`	✓ chat + tool use	Reasoning + tools, 32K context

GET /v1/models reflects what’s currently available — Tensormesh hides any model that’s temporarily cold. If you see fewer models than the table lists, retry after 30–60 seconds; the first request to a cold model warms it up.

For non-Codex clients (Python SDK, curl, custom apps), every model in the table works fine via /v1/chat/completions as well. Codex CLI specifically cannot fall back to that wire (see openai/codex#7782 for OpenAI’s rationale), which is why the matrix above is Codex-CLI specific.

Switching models

The Codex TUI’s /model picker only shows OpenAI’s built-in lineup (gpt-5.5, gpt-5.4, …) — Tensormesh model slugs aren’t in it. Likewise, the “GPT-5.5 is now available” tip on launch refers to models you can’t use here. Press Esc to dismiss the picker and switch models via one of the methods below. This is a Codex CLI limitation that affects every third-party provider; it’s not specific to Tensormesh.

Per-invocation (no config edit)

codex -m MiniMaxAI/MiniMax-M2.5                       # MiniMax (recommended default)
codex -m openai/gpt-oss-120b                          # reasoning, large
codex -m openai/gpt-oss-20b                           # reasoning, small
codex -m Qwen/Qwen3-235B-A22B                         # reasoning, 40K
codex -m Qwen/Qwen3-30B-A3B                           # reasoning, 40K
codex -m Qwen/Qwen3-Coder-30B-A3B-Instruct            # coder, 256K
codex -m Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8      # coder, 256K
codex -m mistralai/Devstral-2-123B-Instruct-2512      # Mistral coder
codex -m moonshotai/Kimi-K2.6                         # Kimi reasoning

-m is a per-invocation override; your config.toml default is unchanged. Equivalent long form: codex -c 'model="..."'.

Change the default

Edit ~/.codex/config.toml and update the model = line, or use sed:

sed -i '' 's|^model = ".*"|model = "Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8"|' ~/.codex/config.toml

(Drop the empty-string argument from -i on Linux: sed -i 's|...|' ~/.codex/config.toml.)

Optional: shell aliases for frequent switching

If you switch models often, add aliases to your ~/.zshrc (zsh) or ~/.bashrc (bash):

alias tm-mini='codex -m MiniMaxAI/MiniMax-M2.5'
alias tm-gpt='codex -m openai/gpt-oss-120b'
alias tm-qwen='codex -m Qwen/Qwen3-235B-A22B'
alias tm-coder='codex -m Qwen/Qwen3-Coder-480B-A35B-Instruct-FP8'
alias tm-coder-small='codex -m Qwen/Qwen3-Coder-30B-A3B-Instruct'
alias tm-devstral='codex -m mistralai/Devstral-2-123B-Instruct-2512'
alias tm-kimi='codex -m moonshotai/Kimi-K2.6'

Reload your shell, then run tm-coder from any directory to start a Codex session against that model. Exit codex (/exit or Ctrl-D twice) and run a different alias to switch.

Setup

The three steps from the TL;DR, with explanation.

Install Codex CLI

npm install -g @openai/codex

This installs the codex command to your global node_modules/bin. Verify with codex --version.

Configure the Tensormesh provider

Codex reads provider configuration from ~/.codex/config.toml. Add this content:

# Top-level keys (must come before any [section] header)
model_provider = "tensormesh"
model = "MiniMaxAI/MiniMax-M2.5"
model_reasoning_effort = "medium"

[model_providers.tensormesh]
name = "tensormesh"
base_url = "https://serverless.tensormesh.ai/v1"
wire_api = "responses"
env_key = "TENSORMESH_INFERENCE_API_KEY"

The first three keys make Tensormesh + MiniMax + medium reasoning the default for every codex invocation. The [model_providers.tensormesh] block defines the provider those defaults reference. You can override per-invocation with -c.

TOML ordering matters. The model_provider, model, and model_reasoning_effort keys are top-level — they must appear in the file before any [section] header (TOML doesn’t reset table context on blank lines, so anything below a [header] belongs to that section).

Fresh config (file missing or empty)
Merge into existing config

Just paste the block as-is — the TL;DR’s cat > ~/.codex/config.toml does this.

If you already use Codex with another provider (OpenAI direct, Azure, etc.), open ~/.codex/config.toml in your editor and merge:

Put the three top-level model_* keys at the top of the file, replacing your previous defaults if any (the keys must be unique in TOML — you can’t have two model_provider lines).
Put the [model_providers.tensormesh] section anywhere below the top-level keys; it’s additive and won’t conflict with other [model_providers.<other>] sections.

To switch back to your other provider for a single run, use -c 'model_provider="openai"' (or whichever you’ve configured) — no config edit needed.

Set your API key

export TENSORMESH_INFERENCE_API_KEY=ak-...

Add this to ~/.zshrc (zsh) or ~/.bashrc (bash) to persist across sessions. The API key prefix selects environment: ak-dev-... for dev, ak-staging-... for staging, ak-live-... for production.Don’t have a key yet? Generate one in your profile.

Usage

# Interactive TUI (works in any directory)
codex

# One-shot prompt — note the </dev/null. Without it, codex exec also reads
# from stdin even when the prompt is in argv, and appears to hang waiting
# for EOF (Ctrl-D). Redirecting stdin tells codex "argv is the full input".
codex exec --skip-git-repo-check "say pong" </dev/null

# Interactive, auto-approve commands, writes allowed in workspace
codex -s workspace-write -a never

# Interactive, full network + unrestricted shell (use only on a trusted machine)
codex --dangerously-bypass-approvals-and-sandbox

# Resume the most recent session (see footgun note below)
codex exec resume --last --skip-git-repo-check "follow-up question" </dev/null

Slash commands in TUI

Inside an interactive codex session, type / to bring up the command picker (autocomplete). /help is not a valid command — typing it gives Unrecognized command '/help'. A few that customers reach for most often:

Command	What it does
`/status`	Session config + token usage display
`/compact`	Manually summarize history to free up context
`/init`	Generate an `AGENTS.md` for the current repo
`/diff`	Show git diff in the conversation
`/copy`	Copy the last response as markdown
`/exit`	Quit cleanly (also `Ctrl-D` twice)

For the full list of slash commands available in your installed version, type / and arrow through the picker.

Sandbox / approval modes

Codex defaults to a strict sandbox: read-only, no network, prompts before each shell command. Three modes are available:

Mode	Network	Shell approval	When to use
(no flag)	no	Prompts before each command	Reading code, exploring; safest
`-s workspace-write -a never`	no	Auto-approves; writes allowed inside workdir	Normal coding; the most common daily mode
`--dangerously-bypass-approvals-and-sandbox`	yes	Auto-approves all commands; no sandbox	Only on a trusted machine, for prompts you’ve reviewed

For codex exec (non-interactive), the -a never flag is implicit, so just codex exec -s workspace-write is sufficient. The -s workspace-write and --dangerously-bypass-approvals-and-sandbox configurations are mutually exclusive in effect — workspace-write sandboxes filesystem writes and disables network. If you need network access for a tool, use the bypass flag.

Codex CLI 0.128.0 removed the older --full-auto shortcut. Use -s workspace-write (+ -a never for interactive) instead. If you copy a snippet that uses --full-auto, Codex will error with unexpected argument '--full-auto' found.

Resume gotcha — `--last` orders by file mtime

codex exec resume --last picks the session whose JSONL file was most recently modified, not the one most recently created. So if you just touched an older session (even by reading or resuming it once), that session — not your latest one — wins --last. To target a specific session, pass its UUID explicitly:

codex exec resume <session-uuid> --skip-git-repo-check "follow-up" </dev/null

Session UUIDs are listed in ~/.codex/sessions/YYYY/MM/DD/rollout-*.jsonl filenames.

Recommended settings

model_reasoning_effort = "medium" is the empirical sweet spot for MiniMax-M2.5. The full set Codex accepts is none / minimal / low / medium / high / xhigh. Against MiniMax-M2.5, high does not consistently improve output quality, costs more latency, and occasionally produces self-contradicting reasoning chains in long generations. Use none or minimal only when you specifically want to skip the model’s reasoning step. This recommendation is calibrated only for MiniMax-M2.5. For the other supported models, medium is a reasonable starting point but not validated; experiment if you’re optimizing for a specific workload.
Stay in -s workspace-write for most coding work. Switch to the bypass flag only for tasks that need network access (e.g., fetching docs, hitting an API).

Known cosmetic issues

”Model metadata not found” warning in TUI

When you run interactive codex (TUI), you’ll see this between every user message and the model’s response:

⚠ Model metadata for `<your-model-slug>` not found. Defaulting to
  fallback metadata; this can degrade performance and cause issues.

This is cosmetic. Functionality is unaffected. Codex CLI ships a built-in registry of OpenAI’s own models (gpt-5.5, gpt-5.4, …); when it sees a slug that isn’t in the registry it falls back to default metadata and emits this warning. Tensormesh model slugs (MiniMax, Qwen3, etc.) are not in OpenAI’s registry, so the warning fires for every supported model here. The warning does not appear in codex exec (non-interactive) runs — only in TUI. It’s a Codex CLI behavior, not a Tensormesh behavior; the same applies to every other third-party provider.

Auto-compact threshold

Codex automatically compacts conversation history when it approaches the model’s context window. The exact trigger varies per model, and the threshold is configurable via Codex’s auto_compact_token_limit setting:

Model	Context window
`MiniMaxAI/MiniMax-M2.5`	192K (196,608)
`openai/gpt-oss-120b` / `gpt-oss-20b`	128K (131,072)
`Qwen/Qwen3-235B-A22B` / `Qwen3-30B-A3B`	40K (40,960)
`Qwen/Qwen3-Coder-30B-A3B-Instruct` / `Coder-480B-A35B-Instruct-FP8`	256K (262,144)
`mistralai/Devstral-2-123B-Instruct-2512`	32K (32,768)
`moonshotai/Kimi-K2.6`	32K (32,768)

If you’re using a 40K-window model, expect compaction to fire much earlier in long sessions than on a 192K MiniMax run. What you’ll see when it fires:

In TUI mode: a visible “compacting…” event between turns.
In exec mode: silent — the compact-trigger turn just takes longer (around 30–60 seconds extra) and the next turn proceeds with the compressed context. No explicit message is printed.

What’s preserved vs lost when compaction fires:

✓ Preserved: the topical context of the conversation (documents you’ve fed to Codex, the high-level task you’re working on).
✗ Lost: specific conversational facts you told Codex (“remember the codeword BLUEFISH42”). After compaction, Codex sees a summary of earlier turns, not the verbatim history. If specific recall matters for your workflow, plan for compaction or split into fresh sessions.

To trigger compaction manually before reaching the threshold, type /compact in the TUI. Otherwise, start a fresh session.

Troubleshooting

When a request fails, Codex retries five times before surfacing the cause:

ERROR: Reconnecting... 1/5
ERROR: Reconnecting... 2/5
... (3/5, 4/5, 5/5)
ERROR: <the actual cause — this is the line to read>

The five Reconnecting... lines are noise. The meaningful error is the line immediately after 5/5.

Symptom (substring of the meaningful error line)	Likely cause	Fix
`command not found: codex`	Codex CLI not installed	`npm install -g @openai/codex`
`Missing environment variable: TENSORMESH_INFERENCE_API_KEY`	Variable not exported in the current shell	`export TENSORMESH_INFERENCE_API_KEY=ak-...`; persist in `~/.zshrc` / `~/.bashrc`
`We're currently experiencing high demand, which may cause temporary errors` (after 5 retries)	Most often an invalid or wrong-environment API key (auth failures are reported as 5xx, which Codex retries before surfacing). Less often, a transient capacity event.	Verify your key against your account and confirm the `ak-<env>-...` prefix matches your target environment. Quick check: `curl -sS -H "Authorization: Bearer $TENSORMESH_INFERENCE_API_KEY" https://serverless.tensormesh.ai/v1/models`. If it errors, the key is the problem.
`404 Not Found` / `Model '<slug>' not found` (or) `400 Bad Request` / `The '<slug>' model is not supported when using Codex with a ChatGPT account`	Wrong model slug, or `base_url` typo in `config.toml`. The 404 means Tensormesh doesn’t recognize the slug; the 400 is from Codex CLI itself rejecting the slug before sending it. Both mean “fix the model name.”	Compare your `~/.codex/config.toml` against the snippet in Setup; `curl $base_url/models` to list what’s actually available; or check the Supported models matrix.
`404 Not Found` / `Model '<slug>' not found` but the slug appears in `curl $base_url/models`	The model is temporarily cold (no instance running).	Wait 30–60 seconds and retry — a request will warm the model. After warm-up, subsequent requests are typically under 5 seconds.
`Operation not allowed: read-only` (mid-session)	Codex’s default sandbox is read-only	Restart with `codex -s workspace-write -a never` for normal coding, or `codex --dangerously-bypass-approvals-and-sandbox` for full access.
`error: unexpected argument '--full-auto' found`	Codex 0.128.0 removed `--full-auto`	Use `-s workspace-write -a never` (interactive) or `-s workspace-write` (`codex exec`).
`Maximum context length is 196608 tokens` (or model-specific limit)	Conversation grew past the model’s context window	Type `/compact` in TUI, or start a new session.
`codex exec` appears to hang printing `Reading additional input from stdin...`	Codex’s exec mode reads stdin even when the prompt is in argv	Add `</dev/null` to the command, or press Ctrl-D in the terminal to close stdin.

Still stuck? Email saas-support@tensormesh.ai with the meaningful error line (the one after 5/5) and the output of codex --version.

Rollback / uninstall

To remove Tensormesh from your Codex setup:

Delete the [model_providers.tensormesh] block (and the three top-level model_provider / model / model_reasoning_effort lines if you added them) from ~/.codex/config.toml.
Unset TENSORMESH_INFERENCE_API_KEY and remove from ~/.zshrc / ~/.bashrc.

All actions reverse the setup steps above.

Overview

Get Started

Coding Agents

Operations & Analytics

Billing & Pricing

Account

Troubleshooting & Support

Reference

Quick Links

Sign up / Log in

Get an API key

Install Codex CLI

Supported models

Get support

TL;DR

Prerequisites

Supported models

Switching models

Per-invocation (no config edit)

Change the default

Optional: shell aliases for frequent switching

Setup

Usage

Slash commands in TUI

Sandbox / approval modes

Resume gotcha — `--last` orders by file mtime

Recommended settings

Known cosmetic issues

”Model metadata not found” warning in TUI

Auto-compact threshold

Troubleshooting

Rollback / uninstall

​Quick Links

Sign up / Log in

Get an API key

Install Codex CLI

Supported models

Get support

​TL;DR

​Prerequisites

​Supported models

​Switching models

​Per-invocation (no config edit)

​Change the default

​Optional: shell aliases for frequent switching

​Setup

​Usage

​Slash commands in TUI

​Sandbox / approval modes

​Resume gotcha — --last orders by file mtime

​Recommended settings

​Known cosmetic issues

​”Model metadata not found” warning in TUI

​Auto-compact threshold

​Troubleshooting

​Rollback / uninstall

Quick Links

TL;DR

Prerequisites

Supported models

Switching models

Per-invocation (no config edit)

Change the default

Optional: shell aliases for frequent switching

Setup

Usage

Slash commands in TUI

Sandbox / approval modes

Resume gotcha — `--last` orders by file mtime

Recommended settings

Known cosmetic issues

”Model metadata not found” warning in TUI

Auto-compact threshold

Troubleshooting

Rollback / uninstall