Custom (freeform) Codex tools are bridged through Chat Completions as
JSON `{"input": "..."}` functions, but the Chat->Responses stream still
re-emitted them via `response.function_call_arguments.*`, leaking the
JSON wrapper and using event types the Codex client does not route for
freeform tools.
Emit `response.custom_tool_call_input.delta`/`.done` (with the unwrapped
input text) for custom tool calls instead, suppressing the intermediate
function-argument deltas since a partial `{"input":` fragment cannot be
safely unwrapped mid-stream. Custom tool call items now use a `ctc_`
item-id prefix (matching the input events' item_id) consistently across
the streaming and non-streaming paths via a shared
response_tool_call_item_id_from_chat_name helper.
Covered by new streaming and non-streaming custom-tool tests.
When proxying Codex Responses requests through the Chat Completions
format (api_format=openai_chat), the request transform only forwarded
plain `function` tools and silently dropped `tool_search`, `namespace`
(MCP), and `custom` tools, so third-party APIs never saw the official
Codex plugins and could not call them.
Introduce CodexToolContext, built from the original Responses request,
which flattens all four tool kinds into Chat functions and keeps a
chat_name -> CodexToolSpec map. The response path (streaming and
non-streaming) looks names up in this map to restore the original
tool_search_call / custom_tool_call / namespaced function_call items
instead of reparsing the flattened name. Long namespaced names are
truncated with a sha256 suffix to fit the 64-char Chat tool-name limit.
Covered by new round-trip tests for all four tool kinds across both
streaming and non-streaming paths.
No-argument tool calls produced empty argument strings that passed
through both the streaming response path and the request transform
verbatim. Strict OpenAI-compatible upstreams (e.g. Minimax) reject
`arguments: ""` with a 400 "invalid function arguments json string",
while lenient ones (OpenAI, Kimi) silently treat it as an empty object.
Add canonicalize_tool_arguments / canonicalize_tool_arguments_str helpers
that coerce empty/whitespace arguments to "{}" and apply them at the four
tool-call argument sites (streaming finalize + three request/response
transform paths). Leave function_call_output content untouched, where an
empty string is valid.
Auto-detect each Chat-routed Codex provider's reasoning interface from
its name, base URL, and model, then inject the matching thinking
parameter without manual configuration:
- Platform-first inference (OpenRouter, SiliconFlow) overrides model
rules, since the same model exposes different reasoning controls
depending on the hosting platform.
- Effort tiers are forwarded only to providers that support them
(DeepSeek, OpenRouter, and StepFun's step-3.5-flash-2603); on/off-only
providers (Kimi, GLM, Qwen, MiniMax, MiMo, SiliconFlow) drop the level
instead of sending a field the upstream rejects.
- OpenRouter uses the native reasoning:{effort} object, clamps max to
xhigh (its enum has no max), and forwards an explicit effort:"none" so
reasoning can be turned off.
- StepFun falls back to inference so per-model effort support is honored
(the static preset would have forced effort on step-3.5-flash too).
Includes the Codex provider-form reasoning controls, i18n strings
(zh/en/ja), and response-side reasoning extraction.
Responses-to-Chat conversion did not set stream_options.include_usage,
so OpenAI-compatible upstreams (kimi/MiniMax, etc.) omitted the trailing
usage chunk in streaming mode. This caused token/cost/cache stats to be
recorded as zero for third-party streaming requests routed through the
Codex Chat path.
Inject include_usage when the request is streaming, merging into any
client-provided stream_options instead of overwriting it. Add tests for
the streaming, non-streaming, and merge cases.
Thinking models like kimi/Moonshot and DeepSeek reject any assistant
message carrying tool_calls without a non-empty reasoning_content. When
cross-turn history recovery misses (proxy restart drops the in-memory
cache, ambiguous call_id, or a turn produced no reasoning upstream), the
converted assistant message has none and the whole request fails with
'reasoning_content is missing in assistant tool call message'.
Add a final-stage backfill that runs after all input items are
processed, so genuine trailing reasoning items still attach first and a
placeholder is only injected when none remains. Mirrors the placeholder
behavior in transform::anthropic_to_openai_with_reasoning_content.
The Codex Chat-to-Responses bridge already rewrites successful upstream
responses to the Responses shape, but the error branch in
handle_codex_chat_to_responses_transform passed Chat-shaped error bodies
through untouched (MiniMax base_resp, raw OpenAI Chat error, text/plain
"Unauthorized" pages, etc.), leaving Codex clients unable to recognize the
error.
Add chat_error_to_response_error in transform_codex_chat to regularize all
upstream error shapes into the standard {error: {message, type, code, param}}
envelope, then wire it through a new handle_codex_chat_error_response that
preserves the original HTTP status code. Non-JSON error bodies (HTML, plain
text) are wrapped as Value::String and truncated to 1KB at a UTF-8 char
boundary to keep diagnostic context without flooding the response.
Also fix a pre-existing append-vs-insert pitfall in three rebuilt-body
branches (Claude transform, Codex Chat normal, Codex Chat error): http
Builder::header is append, so leaking the upstream Content-Type produced
two Content-Type headers when the rewritten body was JSON. Remove the
upstream value before writing application/json.
MiniMax's OpenAI-compatible chat endpoint strict-rejects any non-leading
role=system message with "invalid params, chat content has invalid message
role: system (2013)". The Codex client uses role=developer (and occasionally
role=system) to inject collaboration_mode / permissions / skills blocks
mid-conversation, and responses_role_to_chat_role maps both to chat's
system role. The converted messages array therefore frequently contained
system entries past index 0, which DeepSeek and OpenAI tolerate but MiniMax
flags as 2013.
Collapse all system messages into a single leading system message before
returning the chat request. The rewrite preserves every system fragment
(joined by "\n\n" in original order) and leaves non-system messages
untouched, so it is lossless for permissive backends as well.
- Add collapse_system_messages_to_head in transform_codex_chat.rs.
- Run it on the messages vector at the end of responses_to_chat_completions
before serializing.
- Cover the new path with two unit tests: one repros the MiniMax-shaped
input (developer items between users) and asserts no system role past
index 0; the other verifies non-system order is preserved and content
is joined with "\n\n".
- Add Codex provider API format selection and model mapping for Chat-only upstreams.
- Convert Codex Responses requests to Chat Completions and rebuild Chat responses as Responses output.
- Preserve reasoning_content across non-streaming, streaming, tool calls, and previous_response_id follow-ups.
- Add a bounded Codex Chat history cache for restoring tool calls before tool outputs.
- Cover Chat bridge, compact routing, streaming, and history recovery with focused tests.
- Split leading <think> blocks from Chat content into Responses reasoning output.
- Keep assistant text output free of inline thinking tags.
- Support streamed inline thinking blocks before normal text deltas.
- Add regression coverage for MiniMax-style inline thinking responses.
- Add a Codex API format selector and routing badge for Chat Completions providers.
- Convert Codex Responses requests to upstream Chat Completions when routing is required.
- Convert Chat Completions JSON and SSE responses back to Responses format.
- Keep generated Codex wire_api values on Responses for Codex compatibility.
- Add i18n labels, provider metadata handling, and focused conversion tests.
Yeeyzy