The chat-completions bridge emitted Responses output items whose item ids
were raw Anthropic tool ids (toolu_/srvtoolu_), which OpenAI rejects on
replay with "Expected an ID that begins with 'fc'", breaking router
fallback conversations from gpt-5 to claude models.
Four fixes, composable and independently useful:
- emission: bridge output items get fc_/ctc_-prefixed item ids while
call_id stays raw so tool_result pairing keeps working (streaming and
non-streaming share the same helpers)
- openai replay: request transformation drops tool call item ids that do
not match OpenAI's own shapes instead of forwarding them, gated to
OpenAI and Azure, since the API accepts the items with no id at all
- anthropic replay: a replayed srvtoolu_ call whose paired server tool
result is unavailable degrades to a plain client tool_use instead of a
dangling server_tool_use that 400s the client's tool_result
- tool-only turns no longer emit a message output item with output_text
text null, matching native OpenAI output
At end of drain the pump enqueued the sentinel first and picked the
billing mode from client_detached afterward, so a client that consumed
the sentinel and tore the relay down before the pump resumed (possible
whenever the sentinel enqueue hit a full queue) had its fully delivered
response billed through the teardown path, skipping the proxy's
post-response hook. Bill or park before the sentinel goes out, and let
an unconsumed sentinel fall back to dispatching the parked billing.
A guardrail modify_response verdict on a streaming request only produced a
proper replacement on /v1/messages: the chat completions and Responses API
translations had no build_block_sse_chunks, so the ModifyResponseException
re-raised and surfaced as an in-stream 500 error frame (or a whole-request
500 in buffered mode) instead of the documented 200 replacement.
Implement build_block_sse_chunks for both OpenAI translations: chat emits a
content delta plus a finish_reason content_filter chunk with real usage;
Responses emits the typed event sequence (standalone via
build_synthetic_response_events pre-stream, or an output-item continuation
under the in-progress response id mid-stream) ending in response.completed.
GigaChat reports prompt_tokens and total_tokens after subtracting cached
tokens (the docs example is prompt_tokens=1, precached_prompt_tokens=37,
total_tokens=5, so the fields are disjoint, not a subset). Map to the
OpenAI convention by adding precached_prompt_tokens back onto prompt and
total while still surfacing it as prompt_tokens_details.cached_tokens.
xAI states the amount it charged in usage.cost_in_usd_ticks, at 10^10 ticks to
the dollar, and that figure covers tokens and every server-side tool invocation
together. The xAI chat and responses transformations restate it in USD on
usage.cost, the field litellm already carries a provider-stated cost in, and the
xAI cost calculator bills from it the way the perplexity calculator does
Routing it through usage.cost rather than a private field means the streaming
chunk assembler carries it too, and no provider-neutral file has to learn about
an xAI wire field
Only a finite, non-negative amount is trusted, so an endpoint a caller can
point litellm at cannot report a negative amount to subtract from its own
recorded spend, and cannot report a NaN, which Usage stores unvalidated and
which compares false against every budget threshold, disabling enforcement for
the key rather than mispricing one request. Absent a usable figure nothing
changes: the existing token math and the
$5 per 1,000 web search calls fallback both run as before
The web search surcharge is suppressed once the reported total applies, since
that total already covers the search calls
precached_prompt_tokens is a subset of prompt_tokens (OpenAI cached_tokens
semantics), so map it to prompt_tokens_details.cached_tokens instead of
adding it on top of prompt/total. Emit stream usage from any final chunk
carrying it rather than only finish_reason stop, which dropped tokens for
function_call and length streams. Merge auth metadata into a new dict in
the gigachat router handler instead of mutating the shared parsed-body
cache in place.
The Responses-to-chat transform dropped the filename OpenAI requires next to
file_data, so a request carrying an inline PDF counted 13 tokens instead of 36
and a real completion through the chat bridge got a 400.
Assistant list content was forwarded to /v1/responses/input_tokens as chat
`text` blocks, which the Responses API rejects (it accepts only output_text
and refusal inside an assistant turn). The 400 sent the whole request to the
local tokenizer, so any conversation with an assistant turn silently lost
provider-exact counting, including the image counting added in 73ab647b1c.
Assistant content now collapses to the plain string the Responses API counts
identically, and image parts are kept to user turns where they are legal.
* fix(vertex_ai): graft default vertex path when api_base has a version-only path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vertex_ai): keep query and fragment placement when grafting vertex path
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(vertex_ai): merge alt=sse into existing query when streaming
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
When the upstream errors while the client is still connected, the pump
forwards the exception through the relay queue so the proxy's failure
handling re-raises it. If the client disconnects before consuming that
queued exception, neither the failure hook nor billing ran and the spend
row was lost. The pump now waits for client detach and, if the exception
was never consumed, salvages partial spend like the post-disconnect
error path.
Also rewrites the bedrock disconnect logging test to the detached-pump
contract: billing fires after the upstream drain completes, not
synchronously at aclose().
The chat-to-Responses reverse transform kept only text blocks, so an image
input was dropped before the count went to OpenAI. A 256x256 image request
counted 13 tokens instead of 268.
