* fix(proxy): requeue spend logs when the DB write fails with a transport error
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): hardcode the spend log queue cap and drop the stale re-export
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(proxy): keep the spend log requeue within the type discipline budget
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): apply the spend log queue cap to producer appends too
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): lower the spend log queue cap to 1k and make it env configurable
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): bound the spend log queue by bytes instead of row count
A row cap cannot bound memory: a row carries the whole prompt under store_prompts_in_spend_logs, so a cap that rides out an outage of counter-only rows is an OOM once prompts are stored. Every enqueue and dequeue now goes through one pair that tracks what the queue costs and drops the oldest rows past a 64 MB budget.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): make the spend log queue byte budget env configurable
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): use a string default for the spend log queue byte budget env read
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(proxy): make the spend log queue byte total a public attribute
The queue it accounts for is already public, and a private name only bought reportPrivateUsage errors at every call site.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: shivam <shivam@berri.ai>
Dropping it falls back to the public cost map's tier table, whose rates outrank the
zeros written beside them, so a PTU deployment on a tiered model keeps billing its
traffic per token. Stored empty, the tiers no longer apply and the zeros win
tiered_pricing is a list, so the 0.0 the flat-rate zeroing stores does not
even validate. Supplying tiers alongside PTU config gets the same 400 as a
flat rate; tiers already stored are dropped from both blobs
Images nested inside an Anthropic `tool_result` block were dropped when the
request was adapted for an OpenAI-compatible provider, because the OpenAI tool
message shape only carried text. Hoist those images out of the tool result and
into a following user message so the model can still see them, and widen the
tool message content type to accept image parts.
* fix(mcp): drop caller host and configured upstream headers from logged metadata
The synthetic request that carries MCP client headers into
add_litellm_data_to_request forwarded the caller's Host header, and
Request.url is built from it, so a caller chose the proxy_server_request
url and the metadata endpoint that every logging callback records.
_upstream_credential_headers also only knew the configured client side
auth header and the x-mcp- prefix family, so a header name declared in
mcp_servers.<name>.extra_headers reached logging metadata in cleartext.
Those names are admin chosen, so no prefix rule can recognize them; read
them off the server registry instead. The header is still forwarded
upstream, which is what extra_headers is for. authorization is left out
because clean_headers already strips it and claiming it here would move
authenticated_with_header on the oauth passthrough config.
The Responses bridge tests stub the server manager, so their fakes gain
the registry accessor the sanitizer now reads.
* fix(mcp): drop caller host from the sanitized header mapping too
The synthetic request stopped forwarding host, but the parallel sanitizer
did not, so a forged hostname still reached the guardrail payload and the
list_tools spend row. Drop it there as well.
Exempt the configured identity headers from the upstream credential set.
get_user_from_headers resolves end user attribution off the same request
this module reconstructs, and it only fills end_user_id when auth left it
unset, so claiming user_header_name or a user_header_mappings name would
lose attribution on the MCP paths that authenticate upstream.
Drop the isinstance guard on extra_headers entries: the field is typed
list[str], so the check is dead and basedpyright scores it.
* fix(mcp): accept a bare user_header_mappings entry when exempting identity headers
get_internal_user_header_from_mapping and get_customer_user_header_from_mapping
both normalize a single mapping to a one element list, and config_settings.md
documents the key as a dict. Iterating the bare form yields its keys instead,
so the exemption silently matched nothing and an identity header also named in
an MCP server's extra_headers was dropped after all.
* feat(search): add Nimble as a search provider
Adds `NimbleSearchConfig` so `search_provider: nimble` works across the SDK,
the proxy /v1/search endpoint, the Search Tools dashboard, and spend tracking.
Nimble's /v2/search already uses the Perplexity unified spec's parameter names,
so the request transform is close to a pass-through. `search_domain_filter`
splits into include_domains/exclude_domains on the spec's `-` prefix, `country`
is upper-cased to the ISO form Nimble documents, and everything else is
forwarded so focus, search_depth, time_range and the rest stay reachable. On the
response side, snippet prefers `content` and falls back to `description`, and a
malformed body raises an attributed error rather than reporting an empty search.
Also tightens `BaseSearchConfig.get_supported_perplexity_optional_params` to
return `frozenset[str]` instead of a bare mutable `set`, which every caller
already treats as read-only.
* fix(search): surface Nimble error bodies instead of empty results
Greptile flagged that a null or absent `results` degraded to a successful empty
search. A search with no hits comes back as `"results": []`, verified against the
live API, so the field is now required and anything else raises the attributed
schema error the other malformed bodies already take.
Also unwraps Nimble's second error envelope. Collection failures return
`{"success", "task_id", "message"}` rather than the `{"detail"}` shape validation
errors use, and only the latter was being read.
Drops comments that restated the adjacent code.
* docs(search): drop the Nimble param list from the transform docstring
It restated the vendor's API reference, which the module docstring already links,
and would go stale the moment Nimble adds a focus mode.
Shadow eval only answered "should this key adopt this auto-router". Once a key
is on the router it is invisible to the feature, because the sampling gate skips
any request the shadowed router already served, so post-adoption quality
regressions go unmeasured.
Reverse mode inverts the arms: sample the traffic the router did serve and
duplicate it against a fixed baseline_model, judged by the same blind pairwise
judge. Same job table, same attempt rows, same aggregates.
real_* stays the arm the caller was served and shadow_* the duplicated one, so
in reverse real_model is the router's pick and shadow_model is the baseline. The
active-job slot becomes one per (key, direction) so both directions can run at
once, and tier attribution in reverse reads the control request's routing
decision rather than the shadow call's write-back.
The MCP sub-app is attached with app.mount("/mcp", ...) and a Starlette
mount never matches its bare prefix, so POST /mcp fell through to the
router's redirect_slashes 307. Behind a TLS-terminating ingress whose
peer address is not in uvicorn's forwarded-allow-ips (default: loopback
only) the redirect Location is built from the socket scheme as http://,
and MCP clients strip the Authorization header on the cross-origin
follow, so reconnects fail with ECONNRESET right after a successful
OAuth flow. The redirect also fires before auth, so the bare spelling
never returns the RFC 9728 WWW-Authenticate challenge that OAuth
clients need to start the flow.
Add an explicit /mcp route beside the existing /toolset/{name}/mcp and
/{name}/mcp spellings, forwarding to handle_streamable_http_mcp with
the same scope rewrite those routes already use (path=/mcp,
_original_path preserved for OAuth challenge URL selection). When the
mcp package is unavailable the route 404s, matching what the bare
sub-app serves on /mcp/ in that state. /mcp/, /mcp/{server},
/{server}/mcp and /toolset/{name}/mcp spellings are unchanged; the
exact-match route and the mount have disjoint match sets so
registration order cannot matter.
Move the blocked-usage mapping for /v1/responses next to
blocked_response_usage in guardrail_translation utils, map bridged chat
prompt/completion tokens to Responses API input/output tokens, and let
raise_passthrough_exception attach the blocked response so post-call
guardrail blocks report real usage
Anthropic's Models API declares max_input_tokens and max_tokens as nullable, not
optional, and the live vendor endpoint returns both keys on every entry. The
merged Anthropic-native listing dropped either key whenever LiteLLM could not
resolve a limit, so a client validating against a nullable-but-required schema
saw a malformed entry for any model the cost map does not know.
OpenAI and Azure GPT-5.x answer a chat request whose output budget cannot fit a
single visible token with a 400, while the same models return a length-truncated
200 one or two tokens higher. Agents that probe a model with a hardcoded
max_tokens of 1 read that 400 as "model unavailable".
The four chat request helpers now recognise the provider's own sentence and hand
back the length-truncated response the provider gives at a slightly larger
budget: finish_reason "length", empty content, zero completion tokens. Any other
400 still raises. Streaming is covered by the same seam, and the caller's budget
is never raised on their behalf.
The provider bills the prompt it processed but sends no usage object with the
400, so the prompt tokens are estimated with the same token_counter every other
usage-less path uses. Reporting zero would let a caller send an arbitrarily
large prompt with max_tokens 1 and be charged nothing.
