Codex Router

Use Anthropic, Kimi, DeepSeek, xAI, GitHub Copilot, opencode Go, Command Code, and future external models inside the Codex App and CLI — or inside DeepSeek Harness — through one local, credential-isolating router. The integration speaks the Responses API and merges external entries into Codex's native model catalog, so routed models appear in the normal picker next to the native GPT models. The same routed models publish into the harness as one provider route, so they appear in its Models page too.

Both clients share one installation: one background service, one gateway, one set of provider credentials, one provider selection. Installing the second integration does not ask for a single key again.

Codex Router is an independent community project. It is not affiliated with or endorsed by OpenAI, GitHub, Anthropic, Moonshot AI, DeepSeek, OpenRouter, opencode, or the referenced opencodex project.

Give the link to your agent

Paste this into a Codex task:

Install the router from this public repository:
https://github.com/duolahypercho/codex-router

Follow AGENTS.md. Preserve my existing Codex models, profiles, settings, and
ChatGPT login. Use only the provider authentication I choose, safely migrate
only recognized older versions, run the Codex doctor, and leave the final app
restart to me. Never ask me to paste a token or API key into chat.

If compatible authentication already exists, an agent can finish everything except the final app restart. Provider credentials are entered only through a hidden local terminal prompt.

Install

Homebrew

If you already use Homebrew, install Codex Router from this repository's tap:

brew tap duolahypercho/codex-router https://github.com/duolahypercho/codex-router
brew install codex-router
codex-router setup --guided

The tap URL is needed only once. Homebrew installs the formula's Node.js, Python, and build dependencies; codex-router setup --guided performs the one-time provider selection, credential-safe authentication, background service installation, and Codex integration. When setup finishes, fully quit and reopen Codex, create a new task, and choose a routed model from the picker.

Upgrade an existing Homebrew installation with:

brew upgrade codex-router

Before removing the formula, remove the per-user service and managed Codex configuration that Homebrew does not own:

codex-router uninstall
brew uninstall codex-router

The first Homebrew install can take considerably longer than the guided installer below because the formula builds the locked Python dependencies from source. The release workflow generates Formula/codex-router.rb from requirements/python.txt and refreshes it for each release.

Maintainers preparing the eventual homebrew/core submission should follow docs/HOMEBREW_CORE.md.

Guided installer

macOS or Linux:

curl -fsSL https://raw.githubusercontent.com/duolahypercho/codex-router/main/install.sh \
  | sh -s -- --target codex --guided

Windows PowerShell:

$installer = Join-Path $env:TEMP "codex-router-install.ps1"
Invoke-WebRequest https://raw.githubusercontent.com/duolahypercho/codex-router/main/install.ps1 -OutFile $installer
powershell.exe -NoProfile -ExecutionPolicy Bypass -File $installer -Target codex -Guided

The setup selects providers, detects existing authentication, can run the official kimi login, prompts invisibly for provider credentials, installs a per-user background service, and verifies every local layer. It never makes a paid test request unless --smoke-test is explicitly selected.

To validate the install and uninstall lifecycle before trusting the router with any credential, pass --no-provider --no-discovery: the router installs idle, reads no credential from anywhere, and answers Codex traffic with a local error. See docs/INSTALL.md.

Requirements:

• The Codex App or CLI.
• Node.js 22.19 or newer; Node.js 24 LTS is recommended.
• uv, or Python 3.10+ with venv.
• Git for the managed one-command checkout and rollback.

Linux installations support the Codex CLI.

Models and authentication

Kimi has two API platforms and they are not interchangeable. kimi-api is the global console at platform.moonshot.ai; kimi-api-cn is the mainland console at platform.moonshot.cn. Accounts, billing, and keys are separate — a key minted on one platform is rejected by the other — so each is enabled and credentialed on its own, and both can be active at once. Pick the one matching where your key was created. (kimi-oauth is a third, distinct thing: the Kimi Code subscription reused through the official CLI's session.)

The Codex catalog is credential-aware. It includes models only from enabled external providers with a stored credential or valid OAuth session. Native GPT models are included only when codex login status confirms an OpenAI login.

Qwen is key-only. Alibaba discontinued the Qwen Code OAuth free tier on 2026-04-15, so the Model Studio plan key is the sole Qwen surface; qwen-plan points at the token-plan endpoint. Set QWEN_PLAN_BASE_URL to https://dashscope-intl.aliyuncs.com/compatible-mode/v1 to bill a pay-as-you-go DashScope key through the same provider. Alibaba publishes no quota or balance API on either endpoint, so the tray shows router-observed traffic and links to the console for actual spend.

ClinePass uses Cline's OpenAI-compatible API at https://api.cline.bot/api/v1. An API key alone does not grant access to the cline-pass/* models: the account also needs an active ClinePass subscription. Create the key under Cline Settings > API Keys, then store it with ./bin/model-router codex provider-key clinepass set.

Grok OAuth reuses the official CLI credential at ~/.grok/auth.json and sends it only to xAI's documented Grok CLI inference proxy. On that path the router also attaches bare hosted web_search and x_search tools, the same agentic surface Grok Build uses. xAI's backend chooses when to search and how to filter results; the router does not take search env knobs or request-side filter config. Install the official CLI and authenticate before enabling the route:

Other routed providers can use Codex's client-side (standalone) web search when the selected model has been verified for it. DeepSeek V4 Flash is enabled on its direct API and opencode Go routes. A compatible model declares "searchTool": { "mode": "standalone" } in its registry or user-model metadata, and the managed Codex provider table advertises supports_standalone_web_search = true. This is intentionally opt-in per model; the router does not infer search compatibility from an OpenAI-compatible endpoint.

npm install -g @xai-official/grok
grok login --oauth

MiMo (Xiaomi API) uses Xiaomi's official OpenAI-compatible endpoint at https://api.xiaomimimo.com/v1. Unlike MiMo reseller routes, the direct API serves mimo-v2.5 and mimo-v2.5-pro through the standard /chat/completions surface, so requests never touch the Responses gateway. mimo-v2.5 is verified for text/image input and Codex standalone web search; mimo-v2.5-pro is text-only. Store the key with ./bin/model-router codex provider-key xiaomi-mimo set.

Native GPT models continue to use Codex directly. There is no separate GPT or ChatGPT OAuth provider in the router.

GitHub Copilot

github-copilot routes account-visible models that explicitly advertise the Responses API, streaming, and tool calls. The catalog is plan- and policy-specific, so this provider ships no hard-coded models: store a fine-grained GitHub PAT with the Copilot Requests permission, then curate from the live catalog. This initial integration targets GitHub.com; GitHub Enterprise Cloud data-residency hosts are not yet configured by the router.

./bin/model-router codex provider-key github-copilot set
./bin/curate-models github-copilot

The hidden prompt stores the GitHub token in protected router state. For a foreground process, COPILOT_GITHUB_TOKEN, GH_TOKEN, and GITHUB_TOKEN are checked in that order. Classic ghp_ tokens are not supported by Copilot; create a fine-grained github_pat_ token at GitHub personal access tokens. The router deliberately does not read or copy the official Copilot CLI's credential store.

At request time the GitHub credential is validated through the Copilot account endpoint, which also selects the account's inference host. That host is accepted only when it is GitHub-owned. The tray reads the account's AI-credit or legacy request quota when GitHub exposes a per-user meter; organization-managed plans that expose no per-seat quota fall back to router-observed traffic.

GitHub documents the PAT permission and Copilot clients, while the inference interface may continue to evolve. Requests consume the user's Copilot allowance; use it within the GitHub Copilot terms and acceptable use policies.

Kimi Code OAuth and Kimi Platform API access are separate authentication and billing systems. The two Kimi entries intentionally coexist. Older DeepSeek aliases remain hidden compatibility routes and are not advertised to new users.

The Ollama Cloud entries bill through an ollama.com account and can host the same model families as other providers under a separate quota. Matching entries (for example DeepSeek V4 Pro) intentionally coexist with the vendor-direct providers because credentials and billing differ. The Qwen plan entries cover every chat model the Individual Plan serves, including the cross-vendor models it resells (DeepSeek V4 and GLM-5.2) under the same plan key and quota. The cross-vendor entries use DashScope's compatible-mode request profile because DashScope rejects each vendor's native thinking parameters. The Qwen entries default to the Alibaba Model Studio Token Plan endpoint in the Singapore region. Coding Plan subscribers or other regions can point QWEN_PLAN_BASE_URL at their dashboard-issued base URL. Plan keys use the sk-sp- prefix and are separate from pay-as-you-go Model Studio keys; Alibaba reserves plan endpoints for interactive coding tools. The zai-coding entries use the GLM Coding Plan's dedicated endpoint and its subscription API key. That key is not interchangeable with general Z.ai platform keys, and Z.ai reserves the coding endpoint for interactive coding tools. The metered platform is therefore a separate provider, zai-api, on https://api.z.ai/api/paas/v4 with its own key file and its own environment variable (ZAI_PLATFORM_API_KEY, never the plan's ZAI_API_KEY) — connecting one does not connect the other. GLM-5.3 ships on both routes with Z.ai's documented low/high/max reasoning tiers and a one-million-token context window. The [1m] model suffix that circulated for GLM-5.3 does not exist on either Z.ai endpoint -- both the OpenAI-compatible coding route and the Anthropic route reject glm-5.3[1m] with error 1214 -- and it was never needed: a live run accepted 990,020 prompt tokens on the plain glm-5.3 code. Beyond the built-in models, each API-key provider's live catalog can be curated interactively: ./bin/curate-models PROVIDER lists the models the provider currently advertises that are not in the registry, lets you toggle the ones you want, and stores them as user models in protected state (surviving updates, editable in place, and removable by re-running the command and deselecting). Curation asks for each new model's context window, image support, and reasoning efforts — so curated models get the effort switcher in the picker — and everything defaults conservatively when unanswered. The non-interactive --models id1,id2 form is additive: it keeps existing curated entries and their metadata while adding the named models; --efforts minimal,low,medium,high,xhigh sets the new entries' ladder. Remove entries explicitly with --remove id1,id2. Every value stays editable in user-models.json. Curation also asks whether the model rejects a forced tool_choice: a few upstreams call tools happily when the choice is auto but answer HTTP 400 when one is required, which fails the compatibility check and the routed-subagent handoff even though tool calling works. Answering yes stores "requestProfile": "auto-tool-choice", and the router downgrades the forced choice for that model only (--request-profile auto-tool-choice in the --models form). The provider's own /v1/models endpoint always decides which models exist. Curated models are local to your machine and are not vetted by the repository's compatibility tests.

opencode (Go subscription and Zen)

The opencode provider family covers both of opencode's endpoints with one stored API key (OPENCODE_API_KEY or OPENCODE_GO_API_KEY in the environment): the flat-rate Go subscription at https://opencode.ai/zen/go/v1, whose tested models ship in the registry below, and the pay-per-use Zen endpoint at https://opencode.ai/zen/v1, whose larger catalog is available through local curation (./bin/curate-models opencode-zen). Everything appears as a single "opencode Go/Zen" provider; internally the catalog is split across provider IDs by endpoint and by the protocol each model speaks upstream. Set the key once and enable the family:

./bin/model-router codex provider-key opencode-go set
./bin/model-router codex providers enable opencode-go

The desktop panel and macOS tray Settings tab provide both per-model controls and provider-level Select all / Unselect all actions for which registry-proven v2 models can run as subagents and which models appear in the Codex picker. Local settings cannot promote an unverified model. Fully quit and reopen Codex after changing either list; an open task keeps the model and subagent picker it started with.

opencode-go carries the Chat Completions models, opencode-go-messages the Anthropic Messages models, opencode-go-responses the Responses models, and opencode-zen the pay-per-use Zen endpoint (no preselected models — curate the ones you want). All four are one selectable family: they share a single stored key, and enabling or disabling any of them toggles all of them together. Entries that duplicate a vendor-direct provider (for example DeepSeek V4 Pro) intentionally coexist because the subscription bills separately. Point OPENCODE_GO_BASE_URL (or OPENCODE_ZEN_BASE_URL) elsewhere to override the endpoints.

Anonymous free model gateways

Two additional entries use providers' documented free-model exceptions. Neither asks for an API key, neither is ever selected on your behalf, and each is pinned in code to its official endpoint.

Both are catalog-only and deliberately ship no checked-in model metadata: the provider's live /models response is filtered to the free subset and then added locally with ./bin/curate-models.

./bin/model-router codex providers enable opencode-free
./bin/curate-models opencode-free

./bin/model-router codex providers enable kilo-free
./bin/curate-models kilo-free

OpenCode Console documents that free chat models can omit the bearer header; the paid Console models still require a key. Kilo documents anonymous access only for :free models and limits anonymous traffic to 200 requests per hour per IP. Both catalogs and limits are provider-controlled and can change, so the router refuses paid IDs and shows traffic-only usage when no quota header has been observed. Kilo's general SDK setup guide still asks external SDK users for an API key; this entry intentionally covers only the gateway's documented anonymous :free path.

Custom: one provider, many endpoints

Every other provider owns one address. custom owns none — each of its models names its own endpoint, its own auth, and its own metadata, so a single picker entry can hold a free community endpoint, a friend's self-hosted server, and a paid API you have a key for, all at once.

./bin/model-router codex providers enable custom

Enabling it costs nothing and asks for nothing: a model that needs a key says so on its own row. It is never selected for you and never part of the default set, because what it holds is whatever somebody put in it.

That first model is a free community Hugging Face Inference Endpoint for Qwen/Qwen3.8-27B, published by an individual rather than by Qwen or Hugging Face: BF16 on one H200 behind vLLM, 262,144-token context, image input, tool calling, and a thinking budget you dial with the normal effort picker. It is shared and rate limited to roughly 30 requests per minute per IP, and its owner says it will be retired once launch interest fades — so treat it as a model to try, not one to depend on.

An endpoint reached with no credential is the one thing a registry fragment cannot introduce on its own. Its address has to be allowlisted in src/model-registry.mjs, exactly as an anonymous provider's is, because otherwise adding a JSON file under config/custom/ would be enough to send your prompts to any host on the internet with nothing to authenticate them. An endpoint that carries a key, or one that stays on loopback, needs no allowlist entry — the key or the address is already the boundary.

Use these at your own risk. The two gateways above, and any custom model whose endpoint carries no credential, are the only routes here that reach an upstream with no account behind them, and that changes what "supported" can mean. Nobody has agreed to serve you: access is a published exception, not an entitlement, and it can be narrowed, rate-limited, or withdrawn without notice. On the two reseller gateways the naming rule is a heuristic rather than a promise — their catalogs carry no pricing field to check, so a model whose ID says free can still answer 401 Paid inference requests require an Authorization bearer token, and the router cannot tell in advance. Anonymous traffic is identified by IP, so a router fanning out parallel subagents spends a budget shared with everyone behind that address. Treat these as a way to try a model, not as something to depend on: nothing in this repository can keep them working, and a failure here is not a bug the project can fix.

Command Code Provider API

Command Code's official Provider API is an OpenAI-compatible chat completions surface plus an Anthropic Messages surface at https://api.commandcode.ai/provider/v1 (COMMAND_CODE_API_KEY or COMMANDCODE_API_KEY in the environment, or store the key once, or reuse a command-code login session). It requires the Provider plan or higher and uses the same key that authenticates the Command Code CLI. Everything appears as one "Command Code" provider; internally the catalog is split between commandcode for Chat Completions models and commandcode-messages for models that require the Messages protocol (Claude).

The Provider plan is required, and signing in does not grant it. A Go-plan account can run the Command Code CLI but is refused by /provider/v1 with Your Go plan doesn't include API access. That is an entitlement, not a credential problem: no sign-in, key, or reinstall changes it. Check the plan at commandcode.ai/billing before enabling this provider.

Given the Provider plan, there are two ways to authenticate, and either one is enough.

Sign in through the browser (OAuth). command-code login opens the Command Code authorization page, receives the callback on a temporary local server, and writes the key it mints to ~/.commandcode/auth.json. The router reads that file, so a signed-in machine needs no key of its own:

npm install -g command-code
command-code login
./bin/model-router codex providers enable commandcode

The macOS tray offers the same flow: the Command Code row has an Install & Sign In button (Sign In once the CLI is present) next to Add Key. command-code login draws a full-screen terminal interface, so the tray opens a Terminal window to run it and waits for the credential rather than piping it. The router only reads that file — it never rewrites, copies, or deletes it — so command-code logout also revokes the router's access.

Store a key instead. Create one in Command Code Studio and save it here:

./bin/model-router codex provider-key commandcode set
./bin/model-router codex providers enable commandcode

When both exist, the exported environment variable wins, then the key stored here, then the macOS Keychain, and the CLI sign-in last: a key you deliberately saved is never silently replaced by a session. doctor names whichever source is live.

Both entries are one selectable family that shares a single stored key; enabling or disabling either toggles the whole family together. The live catalog is available without authentication from https://api.commandcode.ai/provider/v1/models, and additional models can be added per machine with ./bin/curate-models commandcode. Point COMMANDCODE_BASE_URL elsewhere to override the endpoint. Command Code does not document an account-balance API, so the tray links to Command Code Studio for credits and usage.

Meta Model API

Meta's Muse Spark models speak the Responses protocol at https://api.meta.ai/v1 (META_API_KEY in the environment, or store the key once):

./bin/model-router codex provider-key meta set
./bin/model-router codex providers enable meta

Three Muse Spark models ship in the registry: 1.2 and its cheaper Contributor tier (whose inputs and outputs Meta may use for training) with a 1M context window, reasoning efforts from minimal to xhigh, and reasoning summaries enabled, plus the previous-generation 1.1. Additional Meta models can be added per machine with ./bin/curate-models meta. Point META_BASE_URL elsewhere to override the endpoint.

Catalog-only providers

These OpenAI-compatible providers are registered for routing and credential isolation but ship no preselected models, because their catalogs change too often for the repository to pin and live-verify individual entries:

Add a key, then pick the models you want from the provider's live catalog:

./bin/model-router codex provider-key groq set
./bin/curate-models groq

Curated entries use the context window, image support, and reasoning efforts you provide during curation (conservative defaults otherwise) and are local to your machine. Verify a model before relying on it:

./bin/test-model 'groq/MODEL_ID' --live --yes

Each base URL is overridable through the provider's baseUrlEnv variable, so a regional endpoint or a self-hosted gateway can reuse the same provider entry.

Quota cards work for these providers without any extra configuration. Most OpenAI-compatible services report the caller's remaining window on every response through x-ratelimit-* headers, and Anthropic reports the same facts under an anthropic-ratelimit-* prefix. The router reads those headers as traffic passes through, so a provider starts showing real request and token limits after its first request — no balance endpoint, no extra API call, and no separate credential. Providers that publish no such headers, including Google Gemini, keep showing router traffic only. Gemini is routed through Google's OpenAI-compatible surface rather than the native Gemini protocol, so it shares the existing forwarder and needs no separate adapter.

Only enabled providers appear in the Codex picker:

./bin/model-router codex providers
./bin/model-router codex providers enable deepseek
./bin/model-router codex provider-key deepseek set
./bin/model-router codex provider-key anthropic-api set

On Windows, use ./model-router.ps1 codex with the same commands.

The API-key prompt disables terminal echo. Protected files use mode 600 on POSIX and an inheritance-disabled, current-user ACL on Windows. Diagnostics report credential presence and source, never the value.

Make models appear in Codex

After setup:

1. Run ./bin/model-router codex doctor and resolve any FAIL line.
2. Confirm providers says SHOW and ready for the intended provider.
3. Fully quit Codex, reopen it, and create a new task.
4. Open the normal model picker.

Codex loads model_catalog_json only at app startup. If models are still missing, run ./bin/refresh-catalog, fully quit Codex, and reopen it.

Large compressed Codex contexts use separate safety limits for bytes received on the loopback socket and bytes produced after decompression. The defaults are 64 MiB encoded and 256 MiB decoded. Override them with MODEL_ROUTER_MAX_BODY_BYTES and MODEL_ROUTER_MAX_DECODED_BODY_BYTES respectively when a deliberately larger local workload requires it.

For routed external models, old textual tool results larger than 32 KiB are compacted after the model has acted on them. The four newest tool results stay intact, and each compacted result keeps a hash, head/tail evidence, and an exact rerun instruction.

This is off by default. It rewrites what the model sees mid-conversation, so it is opted into rather than discovered after it has already altered a session. Turning it on is remembered: a stored answer is kept verbatim and is never re-defaulted by a later release.

Toggle Compact old tool results in the router Settings; the next external-model request sees the change without restarting Codex or the router. The equivalent CLI commands are ./bin/control tool-result-aging on, off, and status.

Native OpenAI traffic is unchanged by default. ./bin/control tool-result-aging native on extends the same compaction to native GPT models; native off restores the default. It is opt-in because it changes what is sent to OpenAI's own endpoint, and an install that has never run it keeps the pre-existing behavior. Set CODEX_ROUTER_TOOL_RESULT_AGING=0 for a hard environment-level override that disables both the routed and the native path.

To estimate the effect without spending provider quota, run:

node scripts/measure-tool-result-aging.mjs /path/to/rollout.jsonl

The report compares each observed compaction boundary and the latest history before and after aging; this is an estimate and spends no provider quota. node scripts/aging-benchmark.mjs reports the savings already recorded in usage-events.jsonl — measured turns rather than an estimate. For a live check, leave the setting on and inspect usage-events.jsonl after a routed turn; events that compacted history include toolResultsAged and toolResultBytesSaved. Those counters measure serialized context bytes, while provider-billed token counts remain the authoritative cost measurement.

For a reproducible provider-reported A/B, see docs/tool-result-aging-benchmark.md.

The integration preserves the built-in OpenAI provider, native GPT models, ChatGPT sign-in, profiles, MCP settings, project trust, and reasoning defaults. It adds one marked root block and one inert custom-provider table to the user's Codex config:

# BEGIN codex-router-managed
openai_base_url = "http://127.0.0.1:4202/_codex-router/<generated-capability>/v1"
model_catalog_json = "/absolute/path/to/.codex/codex-router/merged-models.json"
# END codex-router-managed

# BEGIN codex-router-provider-managed
[model_providers.codex-router]
name = "Codex Router (external models)"
base_url = "http://127.0.0.1:4202/_codex-router/<generated-capability>/v1"
wire_api = "responses"
supports_standalone_web_search = true
# END codex-router-provider-managed

The generated path is local caller authentication. Do not paste the complete managed URL into an issue.

Windows Codex Desktop running through WSL

When Codex Desktop runs on Windows while commands are executed through WSL, there may be two different Codex home directories:

C:\Users\<WindowsUser>\.codex

and:

/home/<LinuxUser>/.codex

Router commands use the Codex home selected by CODEX_HOME. Running them inside WSL without overriding that variable may update the Linux CLI configuration instead of the configuration used by Windows Codex Desktop.

To target the Windows Desktop configuration from WSL:

export CODEX_HOME=/mnt/c/Users/<WindowsUser>/.codex
export CODEX_ROUTER_STATE_DIR="$CODEX_HOME/codex-router"

Then run the router command normally. For example, to return to authenticated mode with native GPT models and enabled external providers in the merged catalog:

./bin/control auth-mode off

Verify that the Windows config.toml uses a path that the WSL runtime can read:

model_catalog_json = "/mnt/c/Users/<WindowsUser>/.codex/codex-router/merged-models.json"

When the Codex runtime is executing inside WSL, a Windows-style path such as C:\Users\... is not readable as a Linux filesystem path. Use the corresponding /mnt/c/... path instead.

If setup appears successful but the Desktop model picker does not change, check which Codex home was modified before rerunning setup.

Use Codex without an OpenAI login

The tray's Use without OpenAI login switch selects the managed custom provider for new Codex sessions. In that mode, enabled external models use the OAuth session or API key configured for their provider and do not require a ChatGPT or OpenAI API login. Connect and enable at least one external provider before turning it on. On macOS, the tray gracefully quits and reopens the registered Codex desktop app after the mode changes; if that restart fails, the tray reports that Codex must be restarted manually. The switch keeps the current model when it already belongs to a connected external provider; otherwise it selects the first enabled model from one of those providers.

While the switch is on, model selection happens in Codex's own picker: the catalog republishes external models with their real names, so switching models needs no extra tray UI. ./bin/control model-set <model-slug> switches the active model from the command line; it accepts canonical external slugs and writes the aliased native slug so pickers highlight the selection.

Login-free catalogs republish external models under the native GPT slugs (with the external model's own name and reasoning levels), because some Codex surfaces — notably the ChatGPT desktop app's model menu — only display models whose slugs pass a server-delivered allowlist of native slugs. The router records the mapping in native-aliases.json and dispatches those slugs to the mapped external provider. Models beyond the available native slots stay listed under their own slugs, and signing back in restores the native catalog untouched.

Turning the switch off restores the exact root model and model_provider values that were present before the mode was enabled. The router does not modify or delete ChatGPT credentials. Native GPT models, ChatGPT usage, cloud tasks, and other account-backed features still require OpenAI authentication and are not available while signed out. The equivalent local control command is ./bin/control auth-mode on or ./bin/control auth-mode off; when using the command directly, restart Codex yourself.

Use a local model in Codex (experimental)

LM Studio can run as a second local backend alongside Ollama. Its models use the stable lmstudio/<model-id> namespace, so identical model IDs loaded in the two backends never collide:

./bin/model-router codex providers enable lmstudio
./bin/curate-models lmstudio

The default endpoint is http://127.0.0.1:1234/v1. Set MODEL_ROUTER_LMSTUDIO_BASE_URL when LM Studio listens elsewhere. Curation reads /v1/models and publishes only models explicitly chosen by the user. Ollama keeps its existing native route and local model controls.

Models running on this machine can appear in Codex's picker like any other provider. They are labelled experimental there, and the label is earned: using a local model as the vision reader is reliable, but using one as a chat model is not. A borderline model was seen passing the capability check and failing the identical check minutes later, so treat local chat as something to try rather than something to depend on. Open the tray's Model Settings → Local LLMs, check the ones you want, then fully quit and reopen Codex.

./bin/control local-models list                  # installed, plus what to download
./bin/control local-models install llama3.2:3b --yes # download, with progress
./bin/control local-models set llama3.2:3b on    # publish it to Codex
./bin/control local-models uninstall llava --yes # delete it from disk

list also answers "which model should I get?", because knowing a tag by heart is not a reasonable prerequisite. The tray shows the same two groups under Local LLMs, one button per model:

For coding — experimental. Codex's prompt uses about 20K of the 32K window:

  llama3.2:3b          2.0 GB verified  ran a real tool call through Codex
  qwen2.5-coder:1.5b   1.0 GB untested  smallest coder
  devstral            14.3 GB untested  built for agents

For reading images only — cannot code:

  qwen2.5vl:3b         3.2 GB  accurate
  moondream            1.7 GB  captions-only

The tray's View more panel also exposes the full 201-tag snapshot captured from the official Ollama pages for Gemma 4, Qwen 3.5/3.6/3.8, Nemotron 3 Super, Ornith, Nemotron 3, and Muse Glimmer, including quantized and MLX variants. Cloud aliases are listed for completeness but marked cloud-only and cannot be downloaded as local weights.

A tool template is a floor, not a prediction — it has been wrong in both directions here. What settles it is running the real client:

./bin/control local-models agent-check llama3.2:3b

That runs codex exec in a scratch workspace twice and requires both runs to verify a marker file only present there, which is proof the model dispatched a tool and read real output. Both runs must pass; a mixed result is reported as flaky, because a borderline model has passed and then failed the identical check minutes later.

Be realistic about the window. Every local model is advertised to Codex at 32K, and Codex's own instructions and tool definitions take about 20K of that before your code is added — so roughly 12K is left to work in, whatever the model natively holds. Tool support and native context are still read from the model's own files (the chat template and the GGUF header, about a megabyte of ranged requests), which is how phi4 turns out to hold 16K rather than the 128K its family suggests — below the advertised cap, so worse than it looks. Image readers are ranked by what they scored against a known image, so a small confident-wrong reader never tops the list. Everything is rated against this machine's memory, anything too large is not offered, and anything already downloaded drops off. Add --json for the same data as an object.

Checking, installing, and removing are three separate actions on purpose: unchecking never deletes a download, and removing needs explicit confirmation. The local provider turns itself on with the first checked model and off when the last one clears, so there is no second switch to find.

Checking or unchecking a model refreshes the picker and gateway routes, then restarts the router service so the running process actually serves the new local/... route. A router running in the foreground (for example during development) has no service to restart, so restart that process yourself after toggling a model.

Codex needs tool calling, and most local models do not have it. Codex drives every turn through tool calls, so a model without them fails on its first request. Only models Ollama reports as tool-capable are published to the picker; the rest stay installed and stay usable as vision readers, labelled "no tools — vision only". Check before you download:

./bin/control local-models inspect llama3.2:3b   # tools:true  context:131072
./bin/control local-models inspect phi4          # tools:false context:16384

That reads the model's chat template from the registry — a few kilobytes instead of a multi-gigabyte pull. It is a filter, not a guarantee: qwen2.5-coder:7b advertises tools and still returns them as plain JSON text, which Codex cannot dispatch. llama3.2:3b was verified making a real structured tool call through the router.

And it has to fit in memory. The same registry lookup carries the download size, so inspect also reports whether this machine can run it — reading unified memory on Apple Silicon, GPU memory where NVIDIA reports it, and system RAM otherwise. Weights are not the whole cost: the context and cache sit beside them, so the estimate allows about 20% on top.

install refuses a too-large model before downloading anything, because gigabytes that cannot load cost both the transfer and the disk:

Error: gpt-oss:120b needs about 79 GB to run and this machine has
68.7 GB unified memory · GPU budget ~51.5 GB. Pass --yes to download it anyway.

A tight model warns and proceeds — that one is a judgement call, not a wall.

Size matters more than the tools flag. Codex sends a large system prompt — around 24K tokens before your question — and a small model spends its whole context absorbing it. Verified with the real Codex CLI on this repo:

Both make correct tool calls in isolation. The 3B only fails once Codex's real prompt is in front of it, so treat 7B as the practical floor for agent work and keep the smaller models for the vision bridge, where the prompt is one image.

Expect local models to be slow. A cold 3B model took over a minute on the first turn here, against seconds for a hosted model. They cost nothing and stay on your machine; that is the trade.

Paste images into a text-only model

Most external coding models cannot see. Paste a screenshot into DeepSeek V4 Pro or GLM and Codex either refuses the attachment or the provider rejects the turn. The vision bridge fixes that at the router: it sends the pasted image to a vision-capable model you have already enabled, and substitutes the reply into the turn as text before the text-only model ever sees it.

It is on by default — paste a screenshot and it is read, with nothing to configure. If nothing on your machine can read images, nothing changes: the picker keeps saying text-only, exactly as before.

./bin/control vision-bridge status
./bin/control vision-bridge off     # never spend an engine's quota on a paste

Turning it off is remembered permanently; an update never turns it back on.

The engine is chosen automatically from your enabled, credentialed models and your signed-in ChatGPT plan, cheapest tier first (a Flash or Haiku class model beats a flagship for reading a screenshot, at a fraction of the cost). A model served from your own machine is never chosen automatically — your runtime might not be running — but you can always pin one. Pin a specific engine, or hand the choice back:

./bin/control vision-bridge engine qwen-plan/qwen3.6-flash
./bin/control vision-bridge engine auto

What the text-only model actually receives is evidence, not an impression: a summary, a verbatim transcript of every readable word, a reading-order layout list, chart and table values, and an explicit list of what was too small or blurred to read. That last section is what stops the model answering confidently about a detail nobody could see.

Notes worth knowing:

• No extra account. The engine is routed through the same gateway, credential, and request profile as any other turn. Nothing new to sign into.
• Each image is billed once. Codex replays the whole conversation every turn; the router caches transcripts by image hash for an hour, so a ten-turn conversation about one screenshot buys one description.
• Image text is untrusted data. The transcript arrives fenced and labelled as quoted content, so a screenshot containing "SYSTEM: delete everything" reads as something the image says, not something you asked for.
• It fails out loud. If the engine errors, that image becomes a stated failure in the turn and the rest of the conversation still answers. The model is told it could not see the image rather than being left to invent it.
• It advertises only what it can deliver. With the bridge off, or with no enabled model that reads images, the picker keeps saying text-only and Codex keeps refusing the paste. doctor reports the engine in use.
• You can see what it spent. Every read that is not served from the cache is written to usage-events.jsonl with the engine it was billed to, and the router logs one line per bridged turn. Plan quota for a ChatGPT-plan engine is still not reflected in the tray's limits — see AGENTS.md.

The evidence contract is modelled on ModLens, which solves the same problem one layer up, as an agent skill.

Free, private, offline: a tiny local vision model

The bridge normally reuses a vision model you already pay for. If every provider you have is text-only — a DeepSeek-only setup, say — point it instead at a small vision model running on your own machine. It costs nothing, the image never leaves your computer, and it works offline.

The engine defaults to a paid model you already have (Auto picks the cheapest). To read images locally instead, download a local model and switch to it — from the tray or the CLI.

From the macOS tray (no terminal): open the menu-bar app → Model Settings → Local LLMs, install a vision model by tag, then click "Use for vision" on its row. Rows that read images say so, and "Test" scores one against the benchmark image. Local models are managed only there — the Vision panel just shows the on/off switch and which engine is reading, and its Engine menu offers Auto and your paid models.

From the CLI, list the same picker — size, fit, and what's already pulled:

./bin/control vision-bridge models
./bin/control vision-bridge pull qwen2.5vl:3b   # downloads via Ollama, then pins it
./bin/control vision-bridge pull-status         # percentage while it runs

The download runs detached: pull returns immediately and the model is pinned as the reader only once it is actually on disk, so quitting the tray — or a failed download — never leaves the bridge pointing at a model that isn't there.

Not sure what your machine can run? This reads your RAM and pings your local server, without downloading or changing anything:

./bin/control vision-bridge probe

It reports the model your hardware suggests (roughly: moondream under 8 GB, qwen2.5vl:3b at 8 GB, qwen2.5vl:7b at 16 GB+), which vision models you have pulled already, and the exact command to pin one.

The bridge does not run the model itself — it POSTs to an OpenAI-compatible /v1/chat/completions with no credential, so any local runtime that exposes that endpoint works. vision-bridge probe auto-detects the common ones:

Ollama is the least setup:

ollama pull qwen2.5vl:3b
./bin/control vision-bridge local qwen2.5vl:3b

llama.cpp — its llama-server speaks the same protocol. -hf pulls the model and its vision projector (mmproj) together; then point the bridge at port 8080:

llama-server -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF --port 8080
./bin/control vision-bridge local qwen2.5vl:3b http://127.0.0.1:8080/v1

(With a manual build, pass the two GGUFs yourself: llama-server -m model.gguf --mmproj mmproj.gguf. The model name you pin is cosmetic — llama.cpp serves whichever model it loaded.)

Either way, pinning turns the bridge on; fully quit and reopen Codex, then paste into DeepSeek as usual. Run local with no model to let the machine pick — it reuses a vision model already served by a running runtime, or falls back to the hardware recommendation and tells you the pull command:

./bin/control vision-bridge local                        # auto-pick for this machine
./bin/control vision-bridge local moondream http://127.0.0.1:11434/v1

Not all vision models can read. The bridge needs verbatim transcription, and most small vision models are captioners: they describe the scene convincingly and invent the codes and numbers. That is worse than no model, because the text-only model downstream repeats the invention as fact. So the picker labels accuracy from measurement, not reputation:

node src/vision-benchmark.mjs        # scores every installed model

It reads test/fixtures/vision-benchmark.png — an invoice with known codes, decimals, dates, and a table — and scores how much comes back exactly. Measured on an M-series Mac:

The picker sorts by that column, so a model that fabricates text can never sit at the top of the list. Download sizes come from Ollama's registry (refreshed weekly, cached, falling back to the checked-in figures offline), so they match what ollama list will show you.

Any other model. The checked-in tag snapshot is refreshed separately from Ollama, but it is not a cage: the tray's Local LLMs section has a field that accepts any Ollama tag — including hf.co/user/repo:Q4_K_M — and the CLI takes one too.

./bin/control vision-bridge pull minicpm-v

Models you add this way carry no accuracy label, because nobody has measured them here. Run the benchmark against one before trusting it with anything that matters:

node src/vision-benchmark.mjs minicpm-v

How the local path differs from a paid engine:

• It speaks chat completions, not the gateway. A local model runs outside the router's gateway, so the bridge calls its /v1/chat/completions endpoint directly with no credential. Nothing about your setup is sent anywhere.
• It is only used when you pin it. Auto mode never routes images to localhost on its own — an unreachable server would fail every paste — so the local engine is opt-in via vision-bridge local. vision-bridge engine auto hands the choice back to your paid models.
• Start it before you paste. If the local server is down or the model is not pulled, that image degrades to a stated failure in the turn (the model is told it could not be read) rather than a crash. doctor shows the pinned local model and reminds you to pull it.
• Slower, and only as good as the model. A 3B model on a laptop is seconds slower than a hosted Flash tier and less precise on tiny text. For heavy use, a paid vision engine still reads better; the local option is about cost and privacy, not peak quality.

Keep working when a provider runs out of usage

A coding-plan window closes, a weekly quota lands, a balance empties — and the turn you were in the middle of used to stop there. Codex can do nothing with a billing error, so the session ended, subagents included, while every other model you had configured sat unused.

Now the turn is rebuilt for the next eligible model and sent again. You get one clean answer. It is on by default, and it only ever uses models you have already enabled and credentialed.

./bin/control failover status
./bin/control failover off      # a provider running out ends the turn, as before

Turning it off is remembered permanently; an update never turns it back on.

What counts as running out is deliberately narrow: an exhausted balance or plan limit, a 402, or a 429 that asks you to wait more than a minute. Everything else keeps the error it always gave — a rejected key still says the key was rejected, an unknown model still says so, and a provider outage is still reported rather than hidden. Swapping models to dodge a bad credential would only bury the one fact that fixes it.

Which model answers instead, in order:

1. Free models — the anonymous gateways, if you have curated any
2. Everything else you have enabled, in the picker's own preference order

A free first stop is not automatic, and that is deliberate. The free catalogs at opencode-free and kilo-free are picked out by naming rules their vendors change without notice, so none are checked in, and an anonymous provider is never enabled for you — turning one on sends your prompts to a third-party endpoint with no credential, which has to be your choice. Until you make it, failover goes straight to your own providers. doctor says which of the two you are in. To give failover a free first stop:

./bin/providers enable opencode-free
./bin/model-router codex curate-models opencode-free

A model served from your own machine is never chosen automatically, for the same reason the vision bridge does not choose one: your runtime might not be running. Name it in a chain and it is used. A model whose context window cannot hold the conversation is skipped, so a quota failure never turns into a "too many tokens" failure. Choose the order yourself, or hand the choice back:

./bin/control failover chain opencode-free/big-pickle,kimi-api/kimi-k3
./bin/control failover auto

When a provider tells you when it will be back, that is believed. The next turn skips it outright instead of paying for the same rejection again, and it starts being used the moment the window passes — or the next time it answers successfully, whichever comes first. Reset times are never invented, only read from the provider, and capped at six hours. doctor shows anything currently being held off and when it clears:

./bin/control failover reset   # clear every hold now and ask again next turn

You are never left guessing which model answered. The tray Island names the model actually serving, router.log records every swap (even with the quiet flag the background service sets), and the usage graphs mark the turn with the model you originally asked for. Nothing is written into your transcript — Codex replays assistant output back as input, so a note from the router would come back next turn as a sentence the model thinks it wrote.

Compaction gets the same treatment: a compaction that cannot run ends a long session just as surely as a turn that cannot run.

Note: your signed-in ChatGPT plan is not currently used as a fallback tier. Routed models fall back to other routed models only.

Make models appear in DeepSeek Harness

DeepSeek Harness (dsh) reads its provider routes from $DSH_HOME/settings.yaml, which it watches and hot-reloads. Its shipped bundle mounts the generic dsh-llm-pi-ai adapter dormant — zero routes until a settings section supplies some — so publishing every routed model into it is a settings write, not a plugin or composition change.

./install.sh --target dsh --auto --providers configured
# or, on an install that already serves Codex:
./bin/model-router dsh enable

That writes one route, llm-pi-ai.providers.codex-router, and one credential reference, CODEX_ROUTER_CALLER_KEY, into $DSH_HOME/.credentials.yaml:

llm-pi-ai:
  providers:
    codex-router:
      displayName: "Codex Router"
      api: "openai-responses"
      baseURL: "http://127.0.0.1:4202/_codex-router/…/v1"
      apiKeyEnv: "CODEX_ROUTER_CALLER_KEY"
      models:
        - id: "deepseek/deepseek-v4-pro"
          name: "DeepSeek V4 Pro (API)"
          contextWindow: 1048576
          input:
            - "text"
          reasoningEfforts:
            high: "high"
            max: "max"

Nothing needs restarting: the harness picks the route up on its next request, and every model appears in its Models page with the context window, image support, and reasoning efforts the registry records.

What you keep. The route points at the same endpoint Codex uses, so a harness turn goes through the same routed request path and gets the same router capabilities: tool-result ageing, the vision bridge for text-only models, the substituted prompt-token count that keeps compaction working against providers that report zero, bounded upstream retries, and the usage and tokens-per-second accounting behind ./bin/model-router codex control provider-usage --json.

What is preserved. The router owns that one route and that one credential and nothing else. Other provider routes, other settings sections, your comments, and your other stored keys are left exactly as they were — ./bin/model-router dsh disable removes the route and restores the document. A settings file this build cannot read unambiguously is refused with the file untouched rather than rewritten on a guess.

Native GPT models publish while this machine has a usable Codex session. They are authorized by a ChatGPT session and a harness request carries none of its own, so the router falls back to the session Codex is already signed in with here — you are logged in on this machine, and a client running as the same user should not have to log in again. They are withheld the moment that session is missing or expired, so the picker never offers a turn that would 401. If they disappear, open Codex once to renew it; ./bin/model-router doctor says so too.

It is a fallback and never an override: a request that presents its own credential is relayed untouched, so nothing about a Codex turn changes. Worth knowing before leaving it on — it widens what the caller key reaches, from the API-key providers to your ChatGPT subscription as well. Set CODEX_ROUTER_NATIVE_SESSION_FALLBACK=0 to turn it off, and the harness publishes routed models only.

Subagents. A child spawned by dsh-tool-subagent with no model of its own inherits the default model selection, so it is already routed once this route is the default. To put children on a different routed model, paste the block from ./bin/model-router dsh subagent-preset into your preset's agent.cordis.yml — the router will not edit a preset it does not own.

macOS tray control panel

On macOS, build and open the native menu-bar control panel with:

./bin/model-router-tray

It shows Codex health, detailed usage for the active provider, a seven-day overview of every configured or previously used provider, and auto-applied provider controls in a native glass macOS interface. On first launch the app registers itself as a login item, so it reopens automatically after a reboot; the Settings tab's Start at login toggle or System Settings › Login Items turns that off, and the choice is never re-applied behind your back. A Show tray setting can additionally tie every tray surface to the Codex and ChatGPT desktop apps, appearing when they launch and hiding when they quit. In With Codex mode the endpoint starts with either app and stops only after both remain closed for 30 seconds and active requests have drained. A periodic process recheck backs up workspace notifications so a missed launch cannot strand Codex without its endpoint. Always keeps it continuously on. See the macOS tray guide for behavior and rebuild notes.

The app can also place a Dynamic-Island-style overlay at the top center of the active display. It follows the provider handling the latest request, reveals usage on hover, and expands on click. It is off on a new install; enable it under Dynamic Island in the tray Settings. The menu-bar panel is the primary surface for the all-provider overview and configuration, and stays available whether or not the overlay is on.

Windows and Linux tray control panel

Windows and Linux use the shared Tauri tray companion in apps/desktop. It provides the same connected-provider filtering, normalized quota cards, daily token graph, secure provider setup, and animated activity status as the macOS surface.

# Linux
./bin/model-router-tray

# Windows PowerShell -- build, launch, and start at logon
.\install.ps1 -CheckoutInstall -WithTray

# or build and launch it by hand
.\scripts\build-desktop-tray.ps1 -BinaryOnly
Start-Process .\apps\desktop\src-tauri\target\release\codex-router-desktop.exe

Or skip the build entirely: every release attaches codex-router-tray-<version>-windows-x64.exe, and every CI run publishes the same binary as an artifact. Windows already ships the WebView2 runtime it needs.

Windows 11 hides new tray icons in the ^ overflow next to the clock; drag the icon onto the taskbar to pin it.

Windows and Linux on X11 receive the floating top-center activity pill. Linux on Wayland uses the tray panel without the pill because the compositor owns absolute window placement. See the Windows and Linux tray guide for prerequisites, packaging, and the platform behavior matrix.

Skills for custom models

Custom models (anything routed through codex-router instead of the built-in OpenAI backend) get the Codex app's full native toolset — threads, automations, the in-app browser, computer use — in the flattened form the provider accepts. Weaker models sometimes need guidance to call those tools correctly, so the installer adds a small skill pack to ~/.codex/skills/:

• codex-router — orientation: how flattened codex_app__ / mcp__ tools work and when to read the companion skills.
• codex-app-threads — exact argument shapes for thread operations (create, list, read, message, wait, fork, archive, pin) and automations.
• codex-in-app-browser — driving the in-app browser through mcp__node_repl__js.
• codex-computer-use — driving local apps through the @oai/sky runtime.

The skills live in skills/ in this repository. bin/install copies them to ~/.codex/skills/ (each directory is marked .codex-router-managed); bin/uninstall removes exactly those, never a skill you wrote yourself. A name collision with an existing skill of your own is skipped, not overwritten. To install or remove them by hand:

node src/skills-install.mjs install
node src/skills-install.mjs uninstall

./bin/model-router codex doctor checks the pack: installed, current against the checkout, free of name collisions, and matching the app toolset snapshot the router relays.

To inspect rollout evidence for the pack, run the read-only check after using a custom model in the app:

node scripts/verify-skill-injection.mjs ~/.codex/sessions/2026/08/09/rollout-*.jsonl
node scripts/verify-skill-injection.mjs --latest --expect routed

It accepts only a standalone app-injected developer block with a turn ID, then correlates a same-turn tool call referencing the skill path with its output and checks same-turn create_thread arguments. With --expect native, that completed pack-path call is an error. Because arbitrary exec code is opaque, the rollout proves a completed path-referencing call, not that the command read specific bytes. Browser and computer-use execution remains live-only.

Common commands

./bin/model-router codex setup --guided
./bin/model-router codex doctor
./bin/model-router codex status
./bin/model-router codex start
./bin/model-router codex stop
./bin/model-router codex disable
./bin/model-router codex enable
./bin/model-router codex uninstall
./bin/control vision-bridge status
./bin/control failover status

Every command takes dsh in place of codex to act on the DeepSeek Harness integration instead:

./bin/model-router dsh enable            # publish the routed models
./bin/model-router dsh doctor
./bin/model-router dsh status
./bin/model-router dsh subagent-preset   # block to paste for a routed child model
./bin/model-router dsh disable           # remove the route, keep everything else

The optional live check makes one small request per selected provider and may consume paid quota:

./bin/model-router codex smoke-test --yes

disable removes only the Codex integration and its current service. uninstall intentionally retains the checkout, logs, backups, internal keys, and provider credentials so routine removal cannot destroy authentication or recovery data.

Updates and rollback

For a managed Git checkout:

./bin/model-router codex update
./bin/model-router codex rollback

Updates require a main checkout with no edits to tracked files, plus a recognized repository origin. Untracked files never block an update, and --force discards tracked edits without deleting untracked ones. The previous revision is retained as a local rollback ref, and a failed install restores the previous source revision. If you already ran git pull manually, run the update command anyway; it applies the pulled revision when the install manifest is older. Run doctor --fix after an update or rollback so the generated config and service match the source revision.

Tagged releases contain .tar.gz and .zip source archives, SHA-256 checksums, and GitHub build-provenance attestations.

How routing works

flowchart LR
  C["Codex Responses :4202"] --> L1["LiteLLM :4200"]
  L1 --> K1["Kimi OAuth :4201"]
  L1 --> A1["API keys :4203"]
  K1 --> P["External providers"]
  A1 --> P

Codex sends the Responses API. LiteLLM translates that contract to each provider's native protocol, including OpenAI-compatible Chat Completions and Anthropic Messages, with streaming and tool-call shapes preserved. Every listener binds to 127.0.0.1.

The router authenticates the caller before reading model traffic and passes only a random internal key to LiteLLM. The final forwarder discards that key and injects only the selected provider credential. Browser-originated requests are rejected, secrets are never exposed by public health routes, and network-facing errors are sanitized.

Codex still owns the agent loop, tools, permissions, files, plugins, skills, MCP servers, and conversation state. The router handles model inference and protocol translation; it cannot add a capability the selected model or provider does not implement.

Add future providers and models

The config/ registry tree is the validated registry for provider metadata, picker entries, upstream IDs, API protocols, context limits, request profiles, modalities, and credential sources. Tested OpenAI-compatible and Anthropic API providers share one credential-isolating forwarder and appear in the Codex picker after compatibility tests pass.

Discovery does not publish every upstream model blindly:

./bin/discover-models deepseek
./bin/test-model 'deepseek/deepseek-v4-pro' --live --yes

New models should remain unlisted until official capabilities and live text, streaming, image-input, tool-call, and context behavior are verified. See Development for the registry contract.

Documentation

• Installation, migration, and upgrades
• Compatible apps
• Troubleshooting
• Architecture and request flow
• Security and credential handling
• Provider development and tests
• Changelog

References: Kimi Code CLI OAuth, Kimi K3 API, DeepSeek model API, Anthropic models, Anthropic Messages API, Codex advanced configuration, and opencodex.

MIT licensed. See LICENSE and NOTICE.md.