name: add-codex description: Use Codex (CLI + AppServer) as the full agent provider — planning, tool orchestration, native compaction, MCP tools, session resume — in place of the Claude Agent SDK. ChatGPT subscription or OPENAI_API_KEY. Per-group via agent_provider. Distinct from using OpenAI as an MCP tool (where Claude remains the planner).

Codex agent provider

NanoClaw runs agents in a long-lived poll loop inside the container. The backend is selected with AGENT_PROVIDER (claude | opencode | codex | mock).

Trunk ships with only the claude provider baked in. This skill copies the Codex provider files in from the providers branch, wires them into the host and container barrels, updates the Dockerfile to install the Codex CLI, and rebuilds the image.

The Codex provider runs codex app-server as a child process and speaks JSON-RPC over stdio. That gives it native session resume, streaming events, MCP tool access, and thread/compact/start compaction — same feature bar as the Claude Agent SDK, without the Anthropic-only lock-in.

Install

Pre-flight

If all of the following are already present, skip to Configuration:

• src/providers/codex.ts
• container/agent-runner/src/providers/codex.ts
• container/agent-runner/src/providers/codex-app-server.ts
• container/agent-runner/src/providers/codex.factory.test.ts
• import './codex.js'; line in src/providers/index.ts
• import './codex.js'; line in container/agent-runner/src/providers/index.ts
• ARG CODEX_VERSION and "@openai/codex@${CODEX_VERSION}" in the pnpm global-install block in container/Dockerfile

Missing pieces — continue below. All steps are idempotent; re-running is safe.

1. Fetch the providers branch

git fetch origin providers

2. Copy the Codex source files

Wholesale copies (owned entirely by this skill — user edits to these files won't survive a re-run, as designed):

git show origin/providers:src/providers/codex.ts                                      > src/providers/codex.ts
git show origin/providers:container/agent-runner/src/providers/codex.ts               > container/agent-runner/src/providers/codex.ts
git show origin/providers:container/agent-runner/src/providers/codex-app-server.ts    > container/agent-runner/src/providers/codex-app-server.ts
git show origin/providers:container/agent-runner/src/providers/codex.factory.test.ts  > container/agent-runner/src/providers/codex.factory.test.ts

3. Append the self-registration imports

Each barrel gets one line — alphabetical placement keeps diffs small.

src/providers/index.ts:

import './codex.js';

container/agent-runner/src/providers/index.ts:

import './codex.js';

4. Add the Codex CLI to the container Dockerfile

Two edits to container/Dockerfile, both idempotent (skip if already present):

(a) In the "Pin CLI versions" ARG block (around line 18), add after ARG CLAUDE_CODE_VERSION=...:

ARG CODEX_VERSION=0.124.0

(b) Add a new standalone RUN block for the Codex CLI, after the existing per-CLI install blocks (around line 106, right after the @anthropic-ai/claude-code block). The Dockerfile splits each global CLI into its own layer for cache granularity — keep that pattern; do not collapse them into a single combined pnpm install -g call:

RUN --mount=type=cache,target=/root/.cache/pnpm \
    pnpm install -g "@openai/codex@${CODEX_VERSION}"

Note: no agent-runner package dependency — Codex is a CLI binary, not a library. Unlike OpenCode, there's nothing to add to container/agent-runner/package.json.

5. Build

pnpm run build                                         # host
pnpm exec tsc -p container/agent-runner/tsconfig.json --noEmit   # container typecheck
./container/build.sh                                   # agent image

Configuration

Codex supports two primary auth paths and one experimental BYO-endpoint path. Pick the one that matches your setup.

Option A — ChatGPT subscription (recommended for individuals)

On the host (not inside the container), run Codex's OAuth login:

codex login

This writes ~/.codex/auth.json with a subscription token. The host-side Codex provider (src/providers/codex.ts) copies auth.json into a per-session ~/.codex directory mounted into the container — your host's own Codex CLI is never touched.

No .env variables required for this mode.

Option B — API key (recommended for CI or API billing)

OPENAI_API_KEY=sk-...
CODEX_MODEL=gpt-5.4-mini

The host forwards both variables into the container. If both subscription (auth.json) and OPENAI_API_KEY are present, Codex prefers the subscription.

Option C — BYO OpenAI-compatible endpoint (experimental)

Codex's built-in openai provider honors the OPENAI_BASE_URL env var directly. Point it at any OpenAI-compatible endpoint — Groq, Together, self-hosted vLLM, an OpenAI proxy, etc.

OPENAI_API_KEY=...
OPENAI_BASE_URL=https://api.groq.com/openai/v1
CODEX_MODEL=llama-3.3-70b-versatile

Codex also ships first-class local-runner flags — codex --oss --local-provider ollama or --local-provider lmstudio — that auto-detect a local server. To use those inside NanoClaw, set CODEX_MODEL to a model your local runner serves and add the corresponding base URL; see the Codex CLI docs for the full model_provider = oss configuration.

Experimental caveat: tool-calling quality depends on the model and endpoint. Not every OpenAI-compat provider implements the full function-calling spec, and smaller models (< 30B) often struggle with multi-step tool orchestration. Test before committing.

Per group / per session

Set "provider": "codex" in the group's container.json (groups/<folder>/container.json) — the in-container runner reads provider from there, not from the DB. The DB columns agent_groups.agent_provider and sessions.agent_provider (session overrides group) only drive host-side provider contribution — per-session ~/.codex mount, OPENAI_* / CODEX_MODEL env passthrough — and do not propagate into container.json at spawn time. Set both, or just edit container.json; if they disagree, the runner uses container.json and the host-side resolver falls back through session → group → container.json → 'claude'.

CODEX_MODEL applies process-wide via .env; if you need different models for different groups, set them via container_config.env on the group.

Extra MCP servers still come from NANOCLAW_MCP_SERVERS / container_config.mcpServers on the host. The runner merges them into the same mcpServers object passed to all providers.

Operational notes

• Spawn-per-query: Codex's app-server is spawned fresh per query invocation, matching the OpenCode pattern. No long-lived daemon to keep healthy across sessions.
• Per-session ~/.codex isolation: each group gets its own copy of the host's auth.json. The container can rewrite config.toml freely on every wake without touching the host's Codex config.
• Native compaction: kicks in automatically at 40K cumulative input tokens between turns, via thread/compact/start. If compaction fails, the provider logs and continues uncompacted — no fatal error.
• Approvals: auto-accepted inside the container (the container is the sandbox; same posture as Claude/OpenCode).
• Mid-turn input: Codex turns don't accept mid-turn messages. Follow-up push() calls queue and drain between turns, matching the OpenCode pattern. The poll-loop only pushes between turns anyway, so no messages are dropped.
• Stale thread recovery: isSessionInvalid matches on stale-thread-ID errors (thread not found, unknown thread, etc.) so a cold-started app-server can recover cleanly when it sees a stored continuation it no longer has.

Verify

grep -q "./codex.js" container/agent-runner/src/providers/index.ts && echo "container barrel: OK"
grep -q "./codex.js" src/providers/index.ts && echo "host barrel: OK"
grep -q "@openai/codex@" container/Dockerfile && echo "Dockerfile install: OK"
cd container/agent-runner && bun test src/providers/codex.factory.test.ts && cd -

After the image rebuild, set agent_provider = 'codex' on a test group and send a message. Successful round-trip looks like:

• init event with a stable thread ID as continuation
• One or more activity / progress events during the turn
• result event with the model's reply

If the agent hangs or errors, check ~/.codex/auth.json exists on the host (Option A) or that OPENAI_API_KEY is forwarding correctly (Option B) — docker exec into a running container and env | grep -i openai to confirm.