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.
Root cause: trae-local-api is deployed to users without the "Fold past
assistant tool_calls into [Called tool: name({...})]" text format that
their own histories already contained. Trae-Local-API-LLM then mimics this
format in subsequent responses. The trae.lua parser only recognized
<tool_call>...</tool_call> or <toolcall>...</toolcall> tags, so
[Called tool: ...] responses were left unparsed and the client received
plain text where a structured tool_calls array should be.
Fix: Add a legacy pattern match at the END of parse_text_tool_calls to
catch the [Called tool: name({args})] shape and emit proper tool_calls.
This is a fallback; models should emit <tool_call> tags per system prompt,
but we tolerate the mimicked form for robustness.
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.
sensenova: the deployed /etc/llmsproxy/adapters/sensenova.lua carried a fix that
never made it back into the repo — sensenova-6.8-flash-lite reports its chain of
thought in `reasoning` rather than `reasoning_content`, in both the single-shot
response and the stream deltas. Without this the model's output looked empty.
Repo and deployment now match byte for byte.
trae: the adapter was in use on this deployment but untracked, so a fresh
install had no way to serve the trae source.
Live testing across the zen pool showed models report
prompt_tokens_details.cached_tokens even when the hit count is 0 (e.g.
nemotron-3-ultra-free returns cached_tokens:0, audio_tokens:0,
cache_write_tokens:0). The previous >0 guard dropped those objects, so a
cache-enabled upstream looked identical to one without cache support.
- types: PromptTokensDetails.CachedTokens always emitted (drop inner
omitempty) so clients see cached_tokens:0 explicitly; dsh reads it as
a 0% hit instead of 'no data'
- adapters (9): forward prompt_tokens_details whenever the upstream
provides it (presence check instead of >0)
- Req: add cache_reported flag set when usage carried cache accounting;
WebUI shows an amber 0% tag for reported-but-missed rows and keeps
the em-dash only for sources that never report cache data
Live testing proved both sensenova and zen DO return cache fields:
- zen laguna-s-2.1-free: usage.prompt_tokens_details.cached_tokens = 32
(real hit), plus cache_write_tokens/audio_tokens
- sensenova glm-5.2: prompt_tokens_details.cached_tokens present (0 on
short prompts)
The previous round only patched deepseek/openai/anthropic/gemini.lua;
sensenova/opencode (localzen!) and the other adapters still dropped them.
- sensenova/opencode/groq/mistral/github/kimicode: stream + response
cache passthrough (same pattern as openai.lua)
- agentrouter: response passthrough + NEW stream usage forwarding (it
previously dropped the terminal usage-only chunk entirely)
- ollama skipped intentionally: its native API has no cache fields
Verified end-to-end through the gateway: localzen/laguna-s-2.1-free now
returns prompt_tokens_details.cached_tokens=32 to clients, and the request
record carries cache_hit_tokens (both chat and stream paths).
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.
zen fingerprints clients via UA + x-opencode-client/session/request/project
headers; requests without them are routed as anonymous and fail with a
single-chunk network_error stream (tools+stream) or 503 Endpoint is
unavailable (non-stream), which the adapter laundered into empty-but-valid
replies. Derive per-request session/request ids from build_headers meta
(sandbox has no os/math), bump UA to the real client format, map zen
reasoning field to reasoning_content, and set stream_options.include_usage.
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
Streaming responses now carry the upstream's real token usage instead of
gateway estimates:
- UnifiedChunk gains an optional Usage field; adapters (opencode, openai)
extract usage from upstream stream chunks (including the final chunk with
empty choices) and pass it through.
- standardSSEChunk preserves usage for passthrough adapters.
- Gateway emits the exact usage in the final stream chunk when available,
falling back to estimates only when the upstream provided none.
Non-streaming usage was already fixed to emit OpenAI-standard keys.
- adapters/opencode.lua: opencode.ai zen free pool adapter — sends the
opencode client User-Agent (zen fingerprints clients by UA; non-official
clients hit FreeUsageLimitError); pairs with api_key: public
- config: no config file ships in the repo; first run generates a default
config at the -config path with a random admin key, loopback listen and a
keyless zen source (config.EnsureDefault); remove config.example.yaml
- lua: seed bundled adapters from the embedded FS instead of a hardcoded
name list
- ui: widen model kind select (chat was clipped to 'cha')
- phase 5 bugfixes: stats ms/s bucket mixing, cleanScopes nil, ctx.Err
guards, direct-path ModelAvailable, empty stream body failure,
bestImageModel rewrite, transform failure recording, Core.mu, timer,
effective model for tool-calls
