codex-relay 0.2.2

Responses API ↔ Chat Completions translation bridge for Codex CLI

codex-relay

A lightweight Rust proxy that translates the OpenAI Responses API (used by Codex CLI) into the Chat Completions API, letting Codex work with any OpenAI-compatible provider — DeepSeek, Kimi, Qwen, Mistral, Groq, xAI, OpenRouter, and more.

Why

Codex CLI speaks the OpenAI Responses API, which is an OpenAI-proprietary stateful protocol. Every other provider exposes the standard Chat Completions API. codex-relay sits between Codex and your chosen provider, translating on the fly — no code changes to Codex required.

Install

# From PyPI — prebuilt binary for your platform
pip install codex-relay

# From crates.io
cargo install codex-relay

Quick start

1. Start the relay

CODEX_RELAY_UPSTREAM=https://api.deepseek.com/v1 \
CODEX_RELAY_API_KEY=$DEEPSEEK_API_KEY \
CODEX_RELAY_PORT=4446 \
codex-relay

On startup, the relay logs the available upstream models and prints a hint:

ℹ upstream models: deepseek-chat, deepseek-reasoner
⚠  To configure Codex with model metadata, run:  codex-relay --print-config --upstream ...

2. Generate your Codex config

codex-relay --print-config \
  --upstream https://api.deepseek.com/v1 \
  --api-key $DEEPSEEK_API_KEY

This prints a ready-to-use ~/.codex/config.toml snippet that includes model_properties for every upstream model, so Codex knows model capabilities and you won't see the "Model metadata … not found" warning.

If you prefer to write the config by hand, here is the minimal form:

model = "deepseek-chat"
model_provider = "deepseek-relay"

[model_providers.deepseek-relay]
name = "DeepSeek"
base_url = "http://127.0.0.1:4446/v1"
wire_api = "responses"
env_key = "DEEPSEEK_API_KEY"

[model_properties."deepseek-chat"]
context_window = 262144
max_context_window = 1048576
supports_parallel_tool_calls = true
supports_reasoning_summaries = false
input_modalities = ["text"]

⚠️ Without model_properties, Codex CLI defaults to fallback metadata for any model it doesn't recognize natively. This can degrade performance, tool-call reliability, and context-window management. The relay logs a reminder at startup and offers --print-config to eliminate this class of problem entirely.

3. Use Codex normally — it routes through the relay transparently.

CLI reference

Flag	Env var	Default	Description
--port	CODEX_RELAY_PORT	4444	Listen port
--upstream	CODEX_RELAY_UPSTREAM	https://openrouter.ai/api/v1	Upstream Chat Completions base URL
--api-key	CODEX_RELAY_API_KEY	(empty)	API key forwarded to upstream
--model-map	CODEX_RELAY_MODEL_MAP	(empty)	Comma-separated source:target model name translations
--print-config	(none)	—	Print a Codex config snippet with model_properties and exit
--session-ttl-hours	CODEX_RELAY_SESSION_TTL_HOURS	168	Retain idle previous_response_id history and reasoning state for this many hours
--max-sessions	CODEX_RELAY_MAX_SESSIONS	256	Maximum completed response histories retained for continuation
--max-session-memory-mb	CODEX_RELAY_MAX_SESSION_MEMORY_MB	512	Approximate memory budget for retained session/reasoning state

Supported providers

Provider	Base URL	Suggested port
DeepSeek	https://api.deepseek.com/v1	4446
Kimi (Moonshot)	https://api.moonshot.cn/v1	4447
Qwen	https://dashscope.aliyuncs.com/compatible-mode/v1	4448
Mistral	https://api.mistral.ai/v1	4449
Groq	https://api.groq.com/openai/v1	4450
xAI	https://api.x.ai/v1	4451
OpenRouter	https://openrouter.ai/api/v1	4452

Any OpenAI-compatible endpoint works.

Features

• Streaming — full SSE streaming with correct event sequencing
• Tool calls — accumulates streaming deltas and emits structured function_call items
• Parallel tool calls — consecutive function_call input items merged into one assistant message
• Reasoning models — preserves reasoning_content across turns (Kimi k2.6, DeepSeek-R1)
• Model catalog — proxies /v1/models from the upstream provider
• Auto-config — --print-config generates a complete Codex config with model metadata

Configuration

Variable	Default	Description
CODEX_RELAY_PORT	4444	Port to listen on
CODEX_RELAY_UPSTREAM	https://openrouter.ai/api/v1	Upstream Chat Completions base URL
CODEX_RELAY_API_KEY	(empty)	API key forwarded to upstream
CODEX_RELAY_MODEL_MAP	(empty)	Comma-separated source:target model name translations (e.g., gpt-5.4:deepseek-v4-pro)
CODEX_RELAY_SESSION_TTL_HOURS	168	Retain idle session/reasoning state for this many hours
CODEX_RELAY_MAX_SESSIONS	256	Maximum completed response histories retained for previous_response_id
CODEX_RELAY_MAX_SESSION_MEMORY_MB	512	Approximate memory budget for retained session/reasoning state
RUST_LOG	codex_relay=info	Log verbosity

Python API

from codex_relay import start

proc = start(port=4446, upstream="https://api.deepseek.com/v1", api_key="sk-...")
# ... use Codex ...
proc.terminate()

Testing

Two layers — offline tests pin behavior against captured Codex wire-shape; live tests pin behavior against real provider APIs.

Debugging tool round-trips

For tool-routing issues, enable debug logs:

RUST_LOG=codex_relay=debug codex-relay

The relay logs tool names only, never tool arguments or message content:

• response tools=... — tools received from Codex's Responses API request
• upstream tools=... — tools forwarded to the Chat Completions upstream
• upstream function_calls=... — function calls returned by a blocking upstream response
• upstream stream function_calls=... — function calls returned by a streaming upstream response

These lines are useful for checking whether a tool such as spawn_agent was preserved by the relay, and whether the failure happened before or after the model selected that tool.

Offline (always green, default cargo test)

Replays Codex CLI fixtures through the translation layer and asserts role/tool/reasoning behavior. Each fixture pins a Codex CLI version under tests/fixtures/codex_<major>_<minor>_<patch>/.

cargo test

Live (gated on provider API key, #[ignore] by default)

Spawns the relay binary on a random port, points it at the real provider, and exercises /v1/models, blocking + streaming, tool calls, and (for thinking models) the reasoning_content round-trip via an in-process recording proxy.

DEEPSEEK_API_KEY=sk-... cargo test --test compat_deepseek_live -- --ignored --test-threads=1

Regenerating fixtures after a Codex upgrade

1. Add a debug dump to the relay (write body bytes from handle_responses to a file before parsing).
2. Run a real codex exec against it; copy inbound_*.json to a new tests/fixtures/codex_<major>_<minor>_<patch>/ folder.
3. Trim each payload down to the smallest one that exercises the feature you want to lock in.
4. Add a row to tests/fixtures/VERSIONS.md and a test pointing at the new directory.

The old fixture directory stays as a regression net so the relay keeps working with the previous Codex CLI release.

Disclaimer

This project is not affiliated with, endorsed by, or sponsored by OpenAI. "Codex" refers to OpenAI Codex CLI, an open-source project licensed under Apache-2.0. codex-relay is an independent, community-built translation proxy.

Contributors

• myk5010 — system/developer message ordering fix and model name mapping (#4)
• qcnhy — streaming usage and MCP namespace bug reports plus independent verification (#5, #6)

License

MIT