When the headroom_retrieve tool is exposed to a client that runs its own
tool-execution loop (the LiteLLM MCP gateway path), the client executes the
retrieve call and sends the recovered original content back as a tool result
on the next turn. The guardrail then compressed that row again, and because
CCR is content-addressed it collapsed back to the exact same hash it was just
retrieved from. The model never saw the expansion and the agent looped.
Hold tool-result rows that carry headroom_retrieve output back from the
compression service, the same way the live turn and trailing tool exchange are
already protected, so the expansion survives. Retrieve calls are matched by the
direct headroom_retrieve name and the mcp__<server>__headroom_retrieve gateway
name. Because a long gateway name is truncated past 64 chars in the
OpenAI-translated view the guardrail scans, the pairing also falls back to the
tool-call id read from the request's own untranslated messages, which is never
truncated.
Fixes#38558
* fix(guardrails): run apply_guardrail-only providers in logging_only mode
A CustomGuardrail that implements only apply_guardrail inherited the CustomLogger
no-op async_logging_hook, so mode: logging_only never scanned anything and never
recorded guardrail_information. CustomGuardrail.async_logging_hook now routes the
logged request and response through the call type's guardrail translation on
copies and appends the verdict to standard_logging_object.guardrail_information.
Resolves LIT-4876
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(guardrails): keep logging_only scan copies inside the error boundary and return a fresh logging payload
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(guardrails): cover embedding scan, native-hook bypass, and unmapped call type in logging_only
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>
Gemini 3.8 Flash launches today with the same promotional pricing, limits,
and thinking settings as Gemini 3.7 Flash, so the gemini/, vertex_ai/, and
bare cost map entries mirror the 3.7 Flash ones. Regression tests lock the
launch prices, the 4096-token cache minimum, and the gemini-3 thought
signature gate in for the new model.
* fix(search): forward search-tool params through the router, complete Parallel AI v1 param mapping
SearchAPIRouter dropped every parameter configured on a search tool, forwarding
only per-request kwargs. Any tool-level setting (mode, max_results, ...) was
silently lost on the way to the adapter, for every search provider.
Also completes the Parallel AI v1 search surface: after_date, fetch_policy,
location and include_domains now nest under advanced_settings instead of being
sent as unknown top-level fields, responses preserve search_id / session_id /
warnings / raw excerpts, and search cost is derived from the request mode and
the provider's reported usage rather than a single flat rate.
* fix(parallel_ai): stop a caller from pricing its own search request
`_parallel_ai_usage` carries the provider's reported usage into cost
calculation. It was only written when the response contained a usage block, so
a caller could pass `_parallel_ai_usage=[{"name": "sku_search", "count": 0}]`
and, whenever the provider omitted usage, bill $0.00 instead of $0.005 — the
value also reached the upstream request body as an unknown field.
The key is now stripped from inbound params and written unconditionally from
the parsed response, so only the provider can populate it.
* fix(parallel_ai): price fast search mode correctly
* test(parallel_ai): fake search at HTTP boundary
* fix(parallel_ai): tolerate null search result fields
---------
Co-authored-by: khushishelat <shelatkhushi@gmail.com>
tests/e2e/test_junit_properties.py fed a hand-rolled FakeItem to
result_properties and attach_result_properties, both typed pytest.Item,
so uv run basedpyright tests/e2e reported 3 reportArgumentType errors on
litellm_internal_staging and every make check that scopes a litellm/ or
tests/e2e/ Python file failed.
Each test now looks up its own collected Item in request.session.items
and applies the covers marker at run time through request.applymarker,
so the coverage registry's collect-only pass never sees the test ids and
the production functions keep their pytest.Item signatures. No casts, no
ignores.
Resolves LIT-6669
* fix(proxy): keep passthrough logging metadata and model_info dicts when team callbacks are wired
Passing team callback vars into Logging(kwargs=...) makes get_litellm_params materialize a full litellm_params, where metadata and model_info default to None instead of being absent. Readers that resolve them as .get(key, {}).get(...) then raise, so any passthrough request from a team with logging callbacks 500s once a pre-call guardrail is on, and the router strategy loggers log a traceback per request.
* test(proxy): annotate the closure dicts the passthrough logging tests record into
The LLM Obs callback copied litellm's OpenAI-shaped objects into the span
verbatim, so every field Datadog names differently landed somewhere it does
not read: tool calls kept their nested `function` wrapper instead of DD's
name/arguments/tool_id, tool messages carried no result linking them to their
call, the request's tools were never sent, and prompt-cache counts sat inside
meta.metadata rather than the span metrics its cache dashboards chart.
One rule governs the message mapper: add the fields Datadog declares, and never
destroy content it did not understand. Content collapses to its text only when
it has text, so a content list carrying tool or image blocks rides along
unchanged, and absent messages map to an empty input rather than a fabricated
turn. Tool calls and results are read from both dialects, the OpenAI
`tool_calls` / `role: tool` shape and the Anthropic `tool_use` / `tool_result`
content blocks, so /v1/messages sessions gain tool linking they never had.
Cache counts come from the same owners the savings dashboard uses, so every
provider spelling resolves through one place rather than a second local guess.
The three cache metrics partition the input count: litellm's normalized prompt
total includes both cache categories, as the cost calculator's pricing helper
documents, so the non-cached residual subtracts reads AND writes. Counting a
primed prefix as ordinary input had inflated non-cached usage by exactly the
cache-write count on every priming request.
Correlating a result to its call reads ids and names structurally and parses no
arguments, so a tool call's arguments are decoded once per span rather than
once per pass, and arguments past a size bound ship as the raw string instead
of paying a decode that multiplies memory on hostile compact JSON.
The flat `output_tool_calls.*` metadata copies go away with this: they were a
second representation of a fact that now has its own field on the same span.
