Three related forwarding defects found by auditing every adapter with a
tool-calling replay (assistant turn with content:[] + tool_calls).
1) content:[] -> content:{} (all 12 openai-adapter sources, plus
deepseek/trae/sensenova/agentrouter/github/groq/kimicode/mistral)
Lua adapters json.decode the request and re-encode it, and an empty Lua
table is indistinguishable from an empty JSON array — the encoder emits
{} for both. Agent clients serialise a tool-calling assistant turn with
no text as content:[], so every pass-through adapter rewrote it to
content:{} — not valid OpenAI (content is string|array|null). Verified
against a live upstream: content:[] produced "400 invalid arguments"
while content:"" was accepted.
Fixed once at the decode boundary (types.ChatMessage.UnmarshalJSON):
empty-array content normalises to "" and an empty tool_calls array is
dropped, so every adapter — including future ones — sees a valid shape.
2) gemini dropped tool_calls and never emitted functionCall /
functionResponse; the tool role also stayed as an invalid role inside
contents and system was not moved to systemInstruction.
3) ollama copied only role/content, dropping tool_calls and the call
attribution entirely (it needs tool_name, not tool_call_id).
Test: TestAdaptersPreserveToolCalls asserts, for every adapter, that the
call id (or function name where the wire format has no id), the function
name, the tool result and the trailing user turn all survive, plus a
negative control for plain text.
Four adapters handled tool_calls in transform_stream_chunk but lost them in
transform_response, so any NON-streaming tool-using conversation broke on its
second request: the client received finish_reason:"tool_calls" with no
tool_calls payload, replayed an assistant message whose function
name/arguments were empty, and the upstream rejected the next turn with
400 invalid tool_call function, function/name/arguments cannot be empty
The production audit trail shows 46 such failures on sensenova alone.
- sensenova.lua: forward message.tool_calls, decoding the arguments JSON string
into an object as the unified shape expects.
- gemini.lua: collect functionCall parts from candidates[].content.parts. Also
correct finish_reason, since Gemini reports "STOP" even when it emitted a
function call and clients keyed on it treat that as a finished answer.
- ollama.lua: the field was initialized to an empty table and never filled;
fill it and likewise correct done_reason "stop" -> "tool_calls".
trae is a different failure with the same symptom: trae-local-api's OpenAI
endpoint (/v1/chat/completions, src/server.js:353) never reads the request's
`tools` array — only its Anthropic endpoint does — so the relayed model is never
told the tool schema and instead PRINTS a <tool_call>{...}</tool_call> block into
content, leaving message.tool_calls null and finish_reason "stop". An OpenAI
client sees an ordinary completion and its agent loop ends mid-conversation.
trae.lua now recovers the structured call from that text, strips the block from
user-visible content, and corrects finish_reason. Both tag spellings
(<tool_call>/<toolcall>, the latter is what the same codebase's Anthropic prompt
asks for) and all three argument key names (arguments/params/input) are accepted.
This is a defensive fallback: fixing the upstream shim to honour `tools` remains
the real fix, since the model still guesses parameter names.
Tests: TestNonStreamToolCallsPreserved covers all ten OpenAI-shaped adapters,
TestGeminiNonStreamToolCalls and TestOllamaNonStreamToolCalls cover their native
shapes, TestTraeTextToolCallRecovery covers both tag spellings, prose around the
block, and asserts a plain text answer never gains tool_calls.
Verified end-to-end against mock upstreams reproducing each shape: a full
two-round agent loop (tool call -> tool result -> final answer) now completes for
both the structured and the text-emitted variants.
dsh displays cache-hit %, but llmsproxy dropped every upstream's cache
fields — deepseek prompt_cache_hit_tokens, OpenAI prompt_tokens_details.
cached_tokens, anthropic cache_read_input_tokens, gemini cachedContentTokenCount.
Changes:
- TokenUsage: add PromptTokensDetails (with CachedTokens) + PromptCacheHit/Miss
- MarshalJSON: emit prompt_tokens_details.cached_tokens (OpenAI v2 standard)
and prompt_cache_hit/miss_tokens (DeepSeek legacy) — dsh reads the former
first, falls back to the latter
- mergeUsage: preserve cache fields across stream chunks
- standardSSEChunk: parse the upstream raw prompt_tokens_details too
- deepseek.lua: forward prompt_cache_hit/miss_tokens + create
prompt_tokens_details from them
- openai.lua: forward prompt_tokens_details.cached_tokens and legacy
prompt_cache_hit/miss_tokens; normalize legacy hits into the standard
object so dsh sees them regardless of upstream format
- anthropic.lua: map cache_read_input_tokens → prompt_tokens_details
- gemini.lua: map cachedContentTokenCount → prompt_tokens_details
Every upstream formats errors differently, which is adapter territory:
the protocol gains an optional transform_error(status, body) hook and all
built-in adapters implement their own envelope parsing (zen free-pool
labels, anthropic/gemini/ollama/mistral shapes, sensenova quota notes,
agentrouter WAF pages). The core keeps a single uniform fallback: when no
hook yields a reason clients get "api error <status>: unknown error" and
the raw body goes to server logs only.
The gateway hardcoded "stop" on every terminating stream chunk, so
tool-call rounds reported finish_reason=stop and length caps were
invisible to clients. UnifiedChunk now carries finish_reason; adapters
emit it (with empty-string finish reasons like sensenova treated as
non-terminal), standardSSEChunk passes it through for un-adapted
upstreams, [DONE] no longer emits a duplicate reason-less done chunk,
and both streaming paths emit the real reason with "stop" as fallback.
Also vendor sensenova/agentrouter adapters into the repo: they were
WebUI-only uploads and a deploy sync silently removed them while live
AUTO-chain slots still referenced them.
The prior usage-passthrough fix only covered openai/opencode; the same
empty-choices+usage drop bug remained in the 5 sibling OpenAI-compatible
adapters, and non-OpenAI providers (anthropic/gemini/ollama) never surfaced
streaming usage at all.
- deepseek/github/groq/kimicode/mistral: preserve usage on empty-choices
chunks and attach it to normal chunks (same pattern as openai.lua)
- anthropic: emit usage from message_start (prompt) and message_delta
(completion); gateway merges split usage additively
- gemini: read usageMetadata in the stream path
- ollama: fix non-streaming key (usage -> token_usage, matches
UnifiedResponse json tag) and read prompt_eval_count/eval_count;
surface counts from the done stream chunk
- gateway: mergeUsage combines usage across chunks (non-zero fields win,
total recomputed from prompt+completion) so split usage doesn't lose
the prompt half; single-chunk case (OpenAI) preserved exactly
- usage-only chunks: done=false (no redundant terminal stop), matching
the Go fallback standardSSEChunk
