Anthropic requires tool_use.id / tool_result.tool_use_id to match
^[a-zA-Z0-9_-]{1,64}$ and rejects the WHOLE request otherwise with
REQUEST_BODY_INVALID / "Invalid tool use format". OpenAI has no such rule, so
an OpenAI-compatible model can mint an id like "bash:0"
(xinjianya/moonshotai/kimi-k3 does exactly that).
In a fan-out router that id does not stay local: the client stores it in its
history and replays it to every other source. One such id therefore kills
every Claude slot at once — justwoker, tabitoken and 扇贝 are all
Claude-behind-{OpenAI,Anthropic} — and an AUTO request falls through all four
tiers to whatever tolerant model is left. Observed live: 4 consecutive 503
"all N auto providers failed" with tier 1/2/3 each reporting the same 400.
Both directions are sanitized, in both adapters:
- request: tool_calls[].id and tool_call_id, so poisoned history recovers
- response: non-streaming tool_calls[].id and the first streamed fragment,
so a bad id never enters a client session in the first place
safe_tool_id is pure and deterministic, so a call and its result are rewritten
identically within one request. A rewritten id keeps an 8-hex digest of the
original, without which distinct ids could collapse ("a:b" and "a_b") into a
duplicate/unpaired tool_use. Already-legal ids pass through byte-identical, so
well-behaved traffic is unaffected. openai.lua carries its own copy because
Lua adapters have no shared prelude.
Streamed argument fragments carry no id and must stay id-less, otherwise
index-based accumulation on the client breaks; a test pins that.
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
