Merge branch 'main' into litellm_lite_cli_reasoning_efforts

# Conflicts:
#	litellm/router.py

.github/scripts/smoke_test_native_wheel.py

@@ -7,6 +7,8 @@ import zipfile
 from pathlib import Path
 from typing import Final
 
+COST_MAP: Final = Path(__file__).resolve().parents[2] / "model_prices_and_context_window.json"
+
 CHILD_SCRIPT: Final = """
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
@@ -34,6 +36,9 @@ else:
 after = module.gil_stats()
 if not isinstance(after.get("releases"), int):
     raise AssertionError(f"native module unusable after panic: {after!r}")
+
+if module.bundled_model_cost_map() != Path(sys.argv[2]).read_bytes():
+    raise AssertionError("the wheel embeds a different model_prices_and_context_window.json than this checkout")
 """
 
 
@@ -55,7 +60,9 @@ def main() -> int:
 
         native_path: Final = Path(temporary_directory) / Path(native_members[0].filename).name
         native_path.write_bytes(archive.read(native_members[0]))
-        result: Final = subprocess.run((sys.executable, "-c", CHILD_SCRIPT, str(native_path)), check=False)
+        result: Final = subprocess.run(
+            (sys.executable, "-c", CHILD_SCRIPT, str(native_path), str(COST_MAP)), check=False
+        )
 
     if result.returncode != 0:
         sys.stderr.write(f"native wheel smoke test exited with status {result.returncode}\n")

.github/workflows/cost-map-guard.yml

@@ -46,3 +46,24 @@ jobs:
           HEAD_REF: ${{ github.event.pull_request.head.ref }}
         run: |
           uv run --no-project python ci_cd/cost_map_guard.py --base "$MERGE_BASE" --head "$HEAD_SHA" --head-ref "$HEAD_REF"
+      - name: Check whether the pull request changes the cost map
+        id: changed
+        env:
+          MERGE_BASE: ${{ steps.revisions.outputs.merge_base }}
+          HEAD_SHA: ${{ github.event.pull_request.head.sha }}
+        run: |
+          if git diff --quiet "$MERGE_BASE" "$HEAD_SHA" -- model_prices_and_context_window.json; then
+            echo "cost_map=false" >> "$GITHUB_OUTPUT"
+          else
+            echo "cost_map=true" >> "$GITHUB_OUTPUT"
+          fi
+      - if: steps.changed.outputs.cost_map == 'true'
+        run: rustup toolchain install --no-self-update
+      - name: Load the cost map through model-catalog
+        if: steps.changed.outputs.cost_map == 'true'
+        env:
+          HEAD_SHA: ${{ github.event.pull_request.head.sha }}
+        run: |
+          git show "${HEAD_SHA}:model_prices_and_context_window.json" > "$RUNNER_TEMP/model_prices_and_context_window.json"
+          cargo run --locked --quiet --manifest-path litellm-rust/Cargo.toml -p litellm-model-catalog --bin check-registry \
+            -- "$RUNNER_TEMP/model_prices_and_context_window.json"

.github/workflows/test-linting.yml

@@ -376,10 +376,10 @@ jobs:
             exit 0
           fi
           files="$(git diff --name-only "$base" HEAD)"
-          if grep -Eq '^(litellm/(proxy|types)/|ui/litellm-dashboard/(src/lib/http/schema\.d\.ts|scripts/gen-api-types\.mjs|package(-lock)?\.json)$|\.github/workflows/test-linting\.yml$)' <<< "$files"; then
+          if grep -Eq '^(litellm/|enterprise/|(pyproject\.toml|uv\.lock)$|ui/litellm-dashboard/(src/lib/http/schema\.d\.ts|scripts/gen-api-types\.mjs|package(-lock)?\.json)$|\.github/workflows/test-linting\.yml$)' <<< "$files"; then
             echo "relevant=true" >> "$GITHUB_OUTPUT"
           else
-            echo "No proxy, types or generator changes in this pull request, nothing to verify."
+            echo "No litellm, enterprise, dependency or generator changes in this pull request, nothing to verify."
             echo "relevant=false" >> "$GITHUB_OUTPUT"
           fi
 

.github/workflows/test-terraform-provider.yml

@@ -12,6 +12,7 @@ on:
     paths:
       - "terraform/provider/**"
       - "litellm/proxy/**"
+      - "enterprise/**"
       - ".github/workflows/test-terraform-provider.yml"
 
 permissions:

.github/workflows/test-unit.yml

@@ -322,6 +322,7 @@ jobs:
               tests/unit/llms/openai_like
               tests/unit/llms/opencode
               tests/unit/llms/opensandbox
+              tests/unit/llms/opensearch
               tests/unit/llms/openrouter
               tests/unit/llms/ovhcloud
               tests/unit/llms/parallel_ai

backend/Dockerfile

@@ -24,6 +24,7 @@ ENV UV_PYTHON_DOWNLOADS=0
 COPY pyproject.toml README.md rust-toolchain.toml ./
 COPY .cargo ./.cargo
 COPY litellm-rust ./litellm-rust
+COPY model_prices_and_context_window.json ./
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     mkdir -p litellm/rust_bridge /wheels && \

codecov.yaml

@@ -15,9 +15,10 @@ ignore:
 flag_management:
   default_rules:
     carryforward: true
-  # Dead flags no CI job uploads anymore: their carried-forward sessions were
-  # measured against old revisions, and the stale line maps mark comment lines
-  # of since-edited files as missed, sinking patch coverage on unrelated PRs.
+  # Dead flags no CI job uploads anymore, and flags whose job is path-gated on
+  # PRs: their carried-forward sessions were measured against old revisions, and
+  # the stale line maps mark comment lines of since-edited files as missed,
+  # sinking patch coverage on unrelated PRs.
   individual_flags:
     - name: proxy-mgmt-behavior
       carryforward: false
@@ -99,6 +100,10 @@ flag_management:
       carryforward: false
     - name: proxy-db-endpoints-and-responses
       carryforward: false
+    - name: roi-postgres
+      carryforward: false
+    - name: redis-compat
+      carryforward: false
 
 component_management:
   individual_components:

gateway/Dockerfile

@@ -40,6 +40,7 @@ ENV UV_PYTHON_DOWNLOADS=0
 COPY pyproject.toml README.md rust-toolchain.toml ./
 COPY .cargo ./.cargo
 COPY litellm-rust ./litellm-rust
+COPY model_prices_and_context_window.json ./
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     mkdir -p litellm/rust_bridge /wheels && \

helm/litellm-helm/README.md

@@ -259,3 +259,9 @@ itself.
 `lensWorker.mode` selects `disabled`, `bundled` or `external`. Bundled mode generates separate service and identity-signing credentials. External mode requires `lensWorker.gateway.secretName` and `lensWorker.serviceTokenSecret.name`; their keys default to `gateway-secret` and `service-token`. Provision distinct values matching the external Lens deployment
 
 Follow the [Lens Helm connection guide](https://github.com/BerriAI/lens/blob/main/helm/lens/README.md#connect-a-gateway) for complete values, verification, GitOps credential requirements and existing-data migration. Source-chart installation requires a built Lens image until the first signed Lens release is published
+
+## Shutdown accounting
+
+`GRACEFUL_SHUTDOWN_TIMEOUT` defaults to 30 seconds. Pending success callbacks share the time remaining since SIGTERM or the first `/health/drain` call. Callback flushing reserves the existing 10-second teardown headroom for cancellation, tracked accounting tasks and persistence. Exhausting this budget can discard callbacks, which is logged
+
+Set `terminationGracePeriodSeconds` to at least `GRACEFUL_SHUTDOWN_TIMEOUT + 10`, allowing additional time for preStop hooks and database writes. This is not a hard shutdown bound or a guarantee of persistence. Database retries retain their own limits, and cancellation-resistant callbacks can remain alive. The existing preStop hook can also be followed by Uvicorn's separate connection drain, as described in `values.yaml`

litellm-proxy-extras/litellm_proxy_extras/migration_data_checks.py

@@ -14,11 +14,9 @@ either, so the migration keeps today's behavior for it.
 """
 
 import os
-import re
-from collections.abc import Callable, Iterator
+from collections.abc import Callable
 from dataclasses import dataclass
 from functools import reduce
-from itertools import accumulate
 from pathlib import Path
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Final, Literal
@@ -43,7 +41,7 @@ MIGRATION_DATA_CHECK_TIMEOUT_ENV_VAR: Final = "LITELLM_MIGRATION_DATA_CHECK_TIME
 DEFAULT_MIGRATION_DATA_CHECK_TIMEOUT_SECONDS: Final = 600.0
 REFUSAL: Final = "Existing rows would make a pending schema change fail:"
 
-Kind = Literal["too_long", "duplicates", "nulls", "any_row", "orphans", "check_violations"]
+Kind = Literal["too_long"]
 
 
 @dataclass(frozen=True, slots=True)
@@ -54,9 +52,6 @@ class Check:
     statement: str
     source: str
     index: str | None = None
-    referenced_table: str | None = None
-    referenced_columns: tuple[str, ...] = ()
-    expression: str | None = None
 
 
 @dataclass(frozen=True, slots=True)
@@ -100,33 +95,6 @@ class ColumnType:
 
 ColumnTypeLookup = Callable[[str, str], ColumnType | None]
 
-_IDENT: Final = r'"?(\w+)"?'
-_QUALIFIED: Final = r'(?:"?\w+"?\s*\.\s*)?"?(\w+)"?'
-_CREATE_INDEX: Final = re.compile(
-    rf"^\s*CREATE\s+(UNIQUE\s+)?INDEX\s+(?:CONCURRENTLY\s+)?(?:IF\s+NOT\s+EXISTS\s+)?{_IDENT}\s+ON\s+(?:ONLY\s+)?"
-    rf"{_QUALIFIED}\s*(?:USING\s+(\w+)\s*)?\((.*?)\)",
-    re.IGNORECASE | re.DOTALL,
-)
-_ALTER_TABLE: Final = re.compile(
-    rf"^\s*ALTER\s+TABLE\s+(?:IF\s+EXISTS\s+)?(?:ONLY\s+)?{_QUALIFIED}\s+(.*)$", re.IGNORECASE | re.DOTALL
-)
-_SET_NOT_NULL: Final = re.compile(rf"^ALTER\s+(?:COLUMN\s+)?{_IDENT}\s+SET\s+NOT\s+NULL$", re.IGNORECASE)
-_ADD_COLUMN: Final = re.compile(
-    rf"^ADD\s+(?:COLUMN\s+)?(?:IF\s+NOT\s+EXISTS\s+)?(?!(?:CONSTRAINT|FOREIGN|UNIQUE|CHECK|PRIMARY|EXCLUDE)\b){_IDENT}\s+(.*)$",
-    re.IGNORECASE | re.DOTALL,
-)
-_CONSTRAINT: Final = rf"^ADD\s+(?:CONSTRAINT\s+{_IDENT}\s+)?"
-_ADD_FOREIGN_KEY: Final = re.compile(
-    rf"{_CONSTRAINT}FOREIGN\s+KEY\s*\(([^)]*)\)\s*REFERENCES\s+{_QUALIFIED}\s*\(([^)]*)\)(.*)$",
-    re.IGNORECASE | re.DOTALL,
-)
-_ADD_UNIQUE: Final = re.compile(rf"{_CONSTRAINT}UNIQUE\s*\(([^)]*)\)", re.IGNORECASE | re.DOTALL)
-_ADD_CHECK: Final = re.compile(rf"{_CONSTRAINT}CHECK\s*\((.*)\)\s*(NOT\s+VALID)?\s*$", re.IGNORECASE | re.DOTALL)
-_PLAIN_COLUMN: Final = re.compile(rf"^{_IDENT}(?:\s+(?:ASC|DESC|NULLS\s+(?:FIRST|LAST)))*$", re.IGNORECASE)
-_COMMENT: Final = re.compile(r"--[^\n]*")
-_NOT_NULL: Final = re.compile(r"\bNOT\s+NULL\b", re.IGNORECASE)
-_HAS_DEFAULT: Final = re.compile(r"\bDEFAULT\b|\bGENERATED\b", re.IGNORECASE)
-
 
 def migration_data_check_timeout_seconds() -> float:
     """Seconds one count query may run before the preflight gives up on verifying it."""
@@ -145,117 +113,6 @@ def migration_data_check_timeout_seconds() -> float:
         return DEFAULT_MIGRATION_DATA_CHECK_TIMEOUT_SECONDS
 
 
-def statements(migration_sql: str) -> tuple[str, ...]:
-    """The statements of one migration file, comments stripped, empty ones dropped."""
-    parts: Final = _COMMENT.sub("", migration_sql).split(";")
-    return tuple(part.strip() for part in parts if part.strip())
-
-
-def _split_top_level(text: str) -> tuple[str, ...]:
-    """Split on the commas outside parentheses."""
-    depth: Final = accumulate((1 if char == "(" else -1 if char == ")" else 0) for char in text)
-    cuts: Final = tuple(
-        position for position, (char, level) in enumerate(zip(text, depth, strict=True)) if char == "," and level == 0
-    )
-    starts: Final = (0, *(cut + 1 for cut in cuts))
-    stops: Final = (*cuts, len(text))
-    pieces: Final = tuple(text[start:stop].strip() for start, stop in zip(starts, stops, strict=True))
-    return tuple(piece for piece in pieces if piece)
-
-
-def _plain_columns(column_list: str) -> tuple[str, ...] | None:
-    """The column names of an index or constraint column list, None when one entry is an
-    expression the derivation cannot turn into a count."""
-    matches: Final = tuple(_PLAIN_COLUMN.match(entry) for entry in _split_top_level(column_list))
-    if any(match is None for match in matches):
-        return None
-    return tuple(match.group(1) for match in matches if match is not None)
-
-
-def _index_checks(match: "re.Match[str]", statement_text: str, source: str) -> Iterator[Check]:
-    unique, name, table, method, column_list = match.groups()
-    columns: Final = _plain_columns(column_list)
-    if columns is None:
-        return
-    label: Final = f'CREATE {"UNIQUE " if unique else ""}INDEX "{name}"'
-    if method is None or method.lower() == "btree":
-        yield Check("too_long", table, columns, label, source, index=name)
-    if unique:
-        yield Check("duplicates", table, columns, label, source, index=name)
-
-
-def _alter_table_checks(table: str, action: str, source: str) -> Iterator[Check]:
-    if (set_not_null := _SET_NOT_NULL.match(action)) is not None:
-        yield Check("nulls", table, (set_not_null.group(1),), f'ALTER TABLE "{table}" {action}', source)
-        return
-    if (add_column := _ADD_COLUMN.match(action)) is not None:
-        column, body = add_column.groups()
-        if _NOT_NULL.search(body) is not None and _HAS_DEFAULT.search(body) is None:
-            yield Check(
-                "any_row", table, (column,), f'ALTER TABLE "{table}" ADD COLUMN "{column}" ... NOT NULL', source
-            )
-        return
-    if (foreign_key := _ADD_FOREIGN_KEY.match(action)) is not None:
-        constraint, columns, referenced_table, referenced_columns, tail = foreign_key.groups()
-        own: Final = _plain_columns(columns)
-        theirs: Final = _plain_columns(referenced_columns)
-        if own is None or theirs is None or len(own) != len(theirs) or "NOT VALID" in tail.upper():
-            return
-        yield Check(
-            "orphans",
-            table,
-            own,
-            _constraint_label(table, constraint, "FOREIGN KEY"),
-            source,
-            referenced_table=referenced_table,
-            referenced_columns=theirs,
-        )
-        return
-    if (unique := _ADD_UNIQUE.match(action)) is not None:
-        constraint, columns = unique.groups()
-        unique_columns: Final = _plain_columns(columns)
-        if unique_columns is not None:
-            yield Check("duplicates", table, unique_columns, _constraint_label(table, constraint, "UNIQUE"), source)
-        return
-    if (check := _ADD_CHECK.match(action)) is not None:
-        constraint, expression, not_valid = check.groups()
-        if not_valid is None:
-            yield Check(
-                "check_violations",
-                table,
-                (),
-                _constraint_label(table, constraint, "CHECK"),
-                source,
-                expression=expression,
-            )
-
-
-def _constraint_label(table: str, constraint: str | None, kind: str) -> str:
-    named: Final = f'CONSTRAINT "{constraint}" ' if constraint else ""
-    return f'ALTER TABLE "{table}" ADD {named}{kind}'
-
-
-def checks_for_statement(statement_text: str, source: str) -> tuple[Check, ...]:
-    """The count queries one SQL statement needs before it can be applied, empty for a
-    statement that cannot fail on existing rows or that the derivation cannot read."""
-    if (index := _CREATE_INDEX.match(statement_text)) is not None:
-        return tuple(_index_checks(index, statement_text, source))
-    if (alter := _ALTER_TABLE.match(statement_text)) is not None:
-        table, actions = alter.groups()
-        return tuple(
-            check for action in _split_top_level(actions) for check in _alter_table_checks(table, action, source)
-        )  # comprehension-ok: flattening the per-action checks of one statement
-    return ()
-
-
-def checks_for_migration(name: str, migration_sql: str) -> tuple[Check, ...]:
-    return tuple(
-        check
-        for statement_text in statements(migration_sql)
-        for check in checks_for_statement(statement_text, f"migration {name}")
-    )  # comprehension-ok: flattening the per-statement checks of one migration
-
-
 def checks_for_index(table: str, name: str, columns: tuple[str, ...]) -> tuple[Check, ...]:
     """The entry-size check a request-log index needs before `CREATE INDEX CONCURRENTLY`."""
     return (Check("too_long", table, columns, f'CREATE INDEX "{name}"', "request-log index", index=name),)
@@ -424,42 +281,7 @@ def _entry_size_query(check: Check, schema: str, types: tuple[ColumnType, ...],
 
 
 def _count_query(check: Check, schema: str, types: tuple[ColumnType, ...], limit_bytes: int) -> "sql.Composed":
-    from psycopg import sql
-
-    table: Final = sql.Identifier(schema, check.table)
-    columns: Final = sql.SQL(", ").join(sql.Identifier(column) for column in check.columns)
-    match check.kind:
-        case "too_long":
-            return _entry_size_query(check, schema, types, limit_bytes)
-        case "duplicates":
-            not_null: Final = sql.SQL(" AND ").join(
-                sql.SQL("{} IS NOT NULL").format(sql.Identifier(column)) for column in check.columns
-            )
-            return sql.SQL(
-                "SELECT count(*) FROM (SELECT 1 FROM {t} WHERE {nn} GROUP BY {cols} HAVING count(*) > 1) AS d"
-            ).format(t=table, nn=not_null, cols=columns)
-        case "nulls":
-            return sql.SQL("SELECT count(*) FROM {t} WHERE {col} IS NULL").format(
-                t=table, col=sql.Identifier(check.columns[0])
-            )
-        case "any_row":
-            return sql.SQL("SELECT count(*) FROM {t}").format(t=table)
-        case "orphans":
-            pairs: Final = sql.SQL(" AND ").join(
-                sql.SQL("parent.{} = child.{}").format(sql.Identifier(theirs), sql.Identifier(own))
-                for own, theirs in zip(check.columns, check.referenced_columns, strict=True)
-            )
-            present: Final = sql.SQL(" AND ").join(
-                sql.SQL("child.{} IS NOT NULL").format(sql.Identifier(column)) for column in check.columns
-            )
-            return sql.SQL(
-                "SELECT count(*) FROM {t} AS child WHERE {present} AND NOT EXISTS (SELECT 1 FROM {ref} AS parent WHERE {pairs})"
-            ).format(t=table, present=present, ref=sql.Identifier(schema, check.referenced_table or ""), pairs=pairs)
-        case "check_violations":
-            return sql.SQL("SELECT count(*) FROM {t} WHERE NOT ({expr})").format(
-                t=table,
-                expr=sql.SQL(check.expression or "true"),  # pyright: ignore[reportArgumentType]  # copied from our own migration file
-            )
+    return _entry_size_query(check, schema, types, limit_bytes)
 
 
 def _applies(
@@ -474,15 +296,7 @@ def _applies(
         return False
     if check.index is not None and _index_is_valid(connection, schema, check.index):
         return False
-    if check.kind == "any_row":
-        return types is None
-    if types is None:
-        return False
-    if check.kind == "too_long":
-        return any(column.can_overflow_btree for column in types)
-    if check.kind == "orphans":
-        return check.referenced_table is not None and _table_exists(connection, schema, check.referenced_table)
-    return True
+    return types is not None and any(column.can_overflow_btree for column in types)
 
 
 def _too_long_detail(check: Check, types: tuple[ColumnType, ...], values: tuple[object, ...], limit_bytes: int) -> str:
@@ -515,21 +329,7 @@ def _run_check(
     rows: Final = _int(values[0], 0) if values else 0
     if rows == 0:
         return None
-    match check.kind:
-        case "too_long":
-            return BlockedChange(check, rows, _too_long_detail(check, types, values, limit_bytes))
-        case "duplicates":
-            return BlockedChange(check, rows, "duplicated value groups")
-        case "nulls":
-            return BlockedChange(check, rows, "NULL")
-        case "any_row":
-            return BlockedChange(
-                check, rows, "rows, and a NOT NULL column with no DEFAULT cannot be added to a table with rows"
-            )
-        case "orphans":
-            return BlockedChange(check, rows, f'with no matching row in "{check.referenced_table}"')
-        case "check_violations":
-            return BlockedChange(check, rows, f"failing CHECK ({check.expression})")
+    return BlockedChange(check, rows, _too_long_detail(check, types, values, limit_bytes))
 
 
 def find_blockers(
@@ -571,19 +371,7 @@ def find_blockers(
 
 def _remedy(check: Check) -> str:
     joined: Final = ", ".join(check.columns)
-    match check.kind:
-        case "too_long":
-            return f'Shorten or hash the long "{check.table}" ({joined}) values so every row\'s index entry fits.'
-        case "duplicates":
-            return f'Remove or merge the duplicate "{check.table}" ({joined}) values.'
-        case "nulls":
-            return f'Fill the NULL "{check.table}"."{joined}" values, or give the column a default first.'
-        case "any_row":
-            return "Give the new column a DEFAULT in the migration, or add it nullable and fill it afterwards."
-        case "orphans":
-            return f'Delete or repoint the "{check.table}" ({joined}) values with no "{check.referenced_table}" row.'
-        case "check_violations":
-            return f'Fix the "{check.table}" rows that fail the expression before adding the constraint.'
+    return f'Shorten or hash the long "{check.table}" ({joined}) values so every row\'s index entry fits.'
 
 
 def _describe(finding: Finding) -> str:
@@ -607,30 +395,4 @@ def _describe(finding: Finding) -> str:
 def report(findings: tuple[Finding, ...]) -> str:
     """The operator-facing refusal: one block per finding, then what to do next."""
     lines: Final = "\n".join(_describe(finding) for finding in findings)
-    return (
-        f"{REFUSAL}\n{lines}\n"
-        "No schema change was made. Fix the rows and start again, or run `litellm --check_migrations_against_existing_data` with the new image to check again."
-    )
-
-
-def pending_change_checks(
-    connection: "psycopg.Connection[tuple[object, ...]]",
-    schema: str,
-    migrations_dir: Path | None,
-    indexes: tuple[tuple[str, str, tuple[str, ...]], ...],
-) -> tuple[Check, ...]:
-    """Every check the pending migrations and the request-log indexes need. `migrations_dir`
-    is None when the schema is pushed rather than migrated, so only the indexes are checked."""
-    migration_checks: Final = (
-        tuple(
-            check
-            for name, migration_sql in pending_migrations(connection, schema, migrations_dir)
-            for check in checks_for_migration(name, migration_sql)
-        )  # comprehension-ok: flattening the per-migration checks
-        if migrations_dir is not None
-        else ()
-    )
-    index_checks: Final = tuple(
-        check for table, name, columns in indexes for check in checks_for_index(table, name, columns)
-    )  # comprehension-ok: flattening the per-index checks
-    return migration_checks + index_checks
+    return f"{REFUSAL}\n{lines}\nNo index was built. Fix the rows and the next index build picks it up."

litellm-proxy-extras/litellm_proxy_extras/migration_dry_run.py

@@ -0,0 +1,184 @@
+"""`litellm --check_migrations_against_existing_data`: run the pending migrations for real, each
+inside its own transaction that is always rolled back, so Postgres itself says whether this
+database's rows and schema take them. Each migration holds the locks the real upgrade will,
+and releases them before the next one runs."""
+
+from __future__ import annotations
+
+import re
+import time
+from collections.abc import Callable
+from dataclasses import dataclass
+from functools import reduce
+from pathlib import Path
+from typing import TYPE_CHECKING, Final
+
+from litellm_proxy_extras.migration_data_checks import pending_migrations
+
+if TYPE_CHECKING:
+    import psycopg
+
+_CONCURRENTLY: Final = re.compile(
+    r"\b((?:CREATE\s+(?:UNIQUE\s+)?INDEX|DROP\s+INDEX|REINDEX\s+\w+)\s+)CONCURRENTLY\s+", re.IGNORECASE
+)
+UNDEFINED_SQLSTATES: Final = frozenset({"42703", "42P01", "42704", "42883"})
+
+Migration = tuple[str, str]
+
+
+@dataclass(frozen=True, slots=True)
+class Applied:
+    name: str
+    seconds: float
+    replayed: tuple[str, ...] = ()
+
+
+@dataclass(frozen=True, slots=True)
+class Rejected:
+    name: str
+    error: str
+    seconds: float
+
+
+@dataclass(frozen=True, slots=True)
+class Baseline:
+    """A database with tables but no `_prisma_migrations`: the boot baselines it instead of replaying
+    history, so the dry run asks the same question the boot does. `refusal` is None when the boot
+    will baseline, otherwise its reason (a custom migration folder, or the schema diff)."""
+
+    refusal: str | None
+
+    @property
+    def matches(self) -> bool:
+        return self.refusal is None
+
+
+@dataclass(frozen=True, slots=True)
+class DryRun:
+    applied: tuple[Applied, ...]
+    rejected: Rejected | None
+    baseline: Baseline | None = None
+
+    @property
+    def ok(self) -> bool:
+        return self.rejected is None and (self.baseline is None or self.baseline.matches)
+
+
+def transactional(migration_sql: str) -> str:
+    """The migration as it can run inside a transaction: `CONCURRENTLY` index builds become plain ones."""
+    return _CONCURRENTLY.sub(r"\1", migration_sql)
+
+
+def _attempt(
+    connection: psycopg.Connection[tuple[object, ...]],
+    schema: str,
+    chain: tuple[Migration, ...],
+    clock: Callable[[], float],
+) -> tuple[float, psycopg.Error | None]:
+    import psycopg
+    from psycopg import sql
+
+    started: Final = clock()
+    try:
+        with connection.transaction(force_rollback=True):
+            connection.execute(sql.SQL("SET LOCAL search_path TO {}").format(sql.Identifier(schema)))
+            for _, migration_sql in chain:
+                connection.execute(transactional(migration_sql))
+    except psycopg.Error as exc:
+        return clock() - started, exc
+    return clock() - started, None
+
+
+def _run(
+    connection: psycopg.Connection[tuple[object, ...]],
+    schema: str,
+    pending: tuple[Migration, ...],
+    position: int,
+    done: DryRun,
+    clock: Callable[[], float],
+) -> DryRun:
+    """One migration in its own rolled-back transaction. A migration that fails because something
+    an earlier pending migration creates does not exist yet is rerun with those replayed in front."""
+    if done.rejected is not None:
+        return done
+    name: Final = pending[position][0]
+    seconds, error = _attempt(connection, schema, (pending[position],), clock)
+    if error is None:
+        return DryRun(done.applied + (Applied(name, seconds),), None, done.baseline)
+    if error.sqlstate not in UNDEFINED_SQLSTATES or position == 0:
+        return DryRun(done.applied, Rejected(name, str(error).strip(), seconds), done.baseline)
+    earlier: Final = pending[:position]
+    chained_seconds, chained_error = _attempt(connection, schema, earlier + (pending[position],), clock)
+    if chained_error is None:
+        replayed: Final = tuple(earlier_name for earlier_name, _ in earlier)
+        return DryRun(done.applied + (Applied(name, chained_seconds, replayed),), None, done.baseline)
+    return DryRun(done.applied, Rejected(name, str(chained_error).strip(), chained_seconds), done.baseline)
+
+
+def _has_ledger(connection: psycopg.Connection[tuple[object, ...]], schema: str) -> bool:
+    from psycopg import sql
+
+    row: Final = connection.execute(
+        "SELECT to_regclass(%s)", (sql.Identifier(schema, "_prisma_migrations").as_string(connection),)
+    ).fetchone()
+    return row is not None and row[0] is not None
+
+
+def _has_litellm_tables(connection: psycopg.Connection[tuple[object, ...]], schema: str) -> bool:
+    row: Final = connection.execute(
+        "SELECT 1 FROM information_schema.tables WHERE table_schema = %s AND table_name LIKE 'LiteLLM\\_%%' LIMIT 1",
+        (schema,),
+    ).fetchone()
+    return row is not None
+
+
+def dry_run(
+    connection: psycopg.Connection[tuple[object, ...]],
+    schema: str,
+    migrations_dir: Path,
+    baseline_check: Callable[[], str | None] | None = None,
+    clock: Callable[[], float] = time.monotonic,
+) -> DryRun:
+    """Apply every pending migration in order, each in its own transaction that is rolled back
+    before the next one starts. The first migration Postgres rejects ends the run with its error.
+    `baseline_check` (None when the boot would baseline, otherwise why it refuses) stands in for
+    the replay on a database that has tables but no migration history, which the boot baselines."""
+    if baseline_check is not None and not _has_ledger(connection, schema) and _has_litellm_tables(connection, schema):
+        return DryRun((), None, Baseline(baseline_check()))
+    pending: Final = pending_migrations(connection, schema, migrations_dir)
+    return reduce(
+        lambda done, position: _run(connection, schema, pending, position, done, clock),
+        range(len(pending)),
+        DryRun((), None),
+    )
+
+
+def _timing(applied: Applied) -> str:
+    replay: Final = f" (depends on {', '.join(applied.replayed)}, replayed in front of it)" if applied.replayed else ""
+    return f"\n  {applied.name}: {applied.seconds:.1f} s{replay}"
+
+
+def report(result: DryRun) -> str:
+    """What the operator reads: the per-migration time the real upgrade will hold its locks,
+    or the migration Postgres rejected with Postgres's own words. Nothing was applied either way."""
+    if result.baseline is not None:
+        if result.baseline.matches:
+            return (
+                "No migration history and the schema matches this build, "
+                "the proxy baselines it at boot, nothing to dry-run."
+            )
+        return f"No migration history and the proxy cannot baseline it at boot: {result.baseline.refusal}"
+    timings: Final = "".join(_timing(applied) for applied in result.applied)
+    if result.rejected is None:
+        count: Final = len(result.applied)
+        if count == 0:
+            return "No pending migrations, this database is on this version's schema."
+        return (
+            f"{count} pending migration(s) applied one at a time in a dry run, each rolled back before the next "
+            f"so its locks are released, nothing changed:{timings}"
+        )
+    return (
+        f"Migration {result.rejected.name} fails on this database after {result.rejected.seconds:.1f} s, "
+        f"nothing was applied.\n{result.rejected.error}"
+        + (f"\nApplied before it in the dry run:{timings}" if timings else "")
+    )

litellm-proxy-extras/litellm_proxy_extras/migration_recovery.py

@@ -86,6 +86,18 @@ def migration_files(directory: Path) -> tuple[tuple[str, str], ...]:
     )
 
 
+def has_packaged_history(migrations_dir: Path) -> bool:
+    """Whether `migrations_dir` carries exactly the migration files and schema.prisma this package ships,
+    the only history the boot baselines an existing database onto."""
+    packaged_dir: Final = Path(__file__).parent
+    migrations: Final = migration_files(migrations_dir)
+    return (
+        bool(migrations)
+        and migrations == migration_files(packaged_dir)
+        and (migrations_dir / "schema.prisma").read_bytes() == (packaged_dir / "schema.prisma").read_bytes()
+    )
+
+
 def baseline_current_schema(
     coordinator: MigrationCoordinator,
     schema: str,
@@ -95,14 +107,9 @@ def baseline_current_schema(
 ) -> None:
     from psycopg import sql
 
-    packaged_dir: Final = Path(__file__).parent
-    migrations: Final = migration_files(migrations_dir)
-    if (
-        not migrations
-        or migrations != migration_files(packaged_dir)
-        or (migrations_dir / "schema.prisma").read_bytes() != (packaged_dir / "schema.prisma").read_bytes()
-    ):
+    if not has_packaged_history(migrations_dir):
         raise RuntimeError("Cannot automatically baseline an existing database with custom migration history.")
+    migrations: Final = migration_files(migrations_dir)
 
     coordinator.acquire_prisma_lock()
     existing: Final = coordinator.connection.execute(

litellm-proxy-extras/litellm_proxy_extras/utils.py

@@ -11,18 +11,13 @@ from collections.abc import Callable
 from contextlib import AbstractContextManager, nullcontext
 from dataclasses import dataclass, replace
 from pathlib import Path
-from typing import TYPE_CHECKING, Final, Optional, Protocol, Union
+from typing import TYPE_CHECKING, Final, Protocol
 
 from litellm_proxy_extras import prisma_toolchain
 from litellm_proxy_extras._logging import logger
 from litellm_proxy_extras._redaction import _redact_credentials
-from litellm_proxy_extras.migration_data_checks import (
-    Finding,
-    find_blockers,
-    migration_data_check_timeout_seconds,
-    pending_change_checks,
-    report,
-)
+from litellm_proxy_extras.migration_dry_run import dry_run
+from litellm_proxy_extras.migration_dry_run import report as dry_run_report
 from litellm_proxy_extras.migration_lock import held_migration_lock
 from litellm_proxy_extras.prisma_toolchain import (
     PRISMA_COMMAND_TIMEOUT_ENV_VAR,
@@ -36,7 +31,6 @@ from litellm_proxy_extras.replica_identity import (
     apply_replica_identity_full,
 )
 from litellm_proxy_extras.request_log_indexes import (
-    REQUEST_LOG_INDEXES,
     ensure_request_log_indexes,
     filter_request_log_index_diff,
 )
@@ -52,7 +46,7 @@ class LensCheckConnect(Protocol):
     ) -> "psycopg.Connection[tuple[object, ...]]": ...
 
 
-def str_to_bool(value: Optional[str]) -> bool:
+def str_to_bool(value: str | None) -> bool:
     if value is None:
         return False
     return value.lower() in ("true", "1", "t", "y", "yes")
@@ -169,7 +163,7 @@ def _without_sql_comments(statement: str) -> str:
     ).strip()
 
 
-def _without_spend_logs_pk_clauses(statement: str) -> Optional[str]:
+def _without_spend_logs_pk_clauses(statement: str) -> str | None:
     prefix_match = _SPEND_LOGS_ALTER_RE.match(statement)
     if not prefix_match:
         return statement
@@ -217,7 +211,7 @@ def _max_migration_timestamp(names) -> int:
     return max(_migration_timestamp(n) for n in names)
 
 
-def _redacted_command(command: object) -> Union[str, tuple[str, ...], list[str]]:
+def _redacted_command(command: object) -> str | tuple[str, ...] | list[str]:
     if isinstance(command, tuple):
         return tuple(_redact_credentials(str(argument)) for argument in command)
     if isinstance(command, list):
@@ -404,7 +398,7 @@ class ProxyExtrasDBManager:
         return tuple(row) if row is not None else ()
 
     @staticmethod
-    def _failed_migration_logs(migration_name: str, started_at: str) -> Optional[str]:
+    def _failed_migration_logs(migration_name: str, started_at: str) -> str | None:
         row: Final = ProxyExtrasDBManager._read_migration_ledger(
             "SELECT logs FROM {} WHERE migration_name = %s AND started_at = %s::timestamptz "
             "AND finished_at IS NULL AND rolled_back_at IS NULL",
@@ -721,10 +715,10 @@ class ProxyExtrasDBManager:
         return row is not None
 
     @staticmethod
-    def _prisma_schema_param(url: str) -> Optional[str]:
+    def _prisma_schema_param(url: str) -> str | None:
         """The `schema` query param Prisma uses to pick its target schema,
         or None when the URL does not set one."""
-        from urllib.parse import urlparse, parse_qsl
+        from urllib.parse import parse_qsl, urlparse
 
         return next(
             (v for k, v in parse_qsl(urlparse(url).query) if k == "schema"),
@@ -1424,7 +1418,6 @@ class ProxyExtrasDBManager:
             bool: True if setup was successful, False otherwise
         """
         ensure_prisma_toolchain(prisma_command=_get_prisma_command(), prisma_env=_get_prisma_env())
-        ProxyExtrasDBManager.raise_if_rows_block_pending_changes(use_migrate=use_migrate)
         migrated = ProxyExtrasDBManager._run_migrations(use_migrate=use_migrate, use_v2_resolver=use_v2_resolver)
         if not migrated:
             return False
@@ -1433,46 +1426,69 @@ class ProxyExtrasDBManager:
         return True
 
     @staticmethod
-    def migration_data_check_findings(use_migrate: bool = True) -> tuple[Finding, ...]:
-        """What in the existing rows would make a pending migration or request-log index
-        fail, read with count queries only: no write, no table lock. Empty when nothing
-        blocks, when no database is configured, or when it cannot be reached, so the
-        migration step that follows reports the connection problem the way it does today."""
+    def _baseline_refusal(prisma_dir: Path) -> str | None:
+        """Why `baseline_current_schema` would refuse this database at boot, None when it would baseline:
+        the migrations folder must be the packaged one, then `prisma migrate diff` must find the live
+        database and this build's schema.prisma identical."""
+        from litellm_proxy_extras.migration_lock import migration_environment
+        from litellm_proxy_extras.migration_recovery import has_packaged_history
+
+        if not has_packaged_history(prisma_dir):
+            return (
+                f"{prisma_dir} is a custom migration history (LITELLM_MIGRATION_DIR), it differs from the "
+                "migrations this build ships, and the proxy only baselines onto the packaged history."
+            )
+        try:
+            prisma_toolchain.run_prisma(
+                (
+                    _get_prisma_command(),
+                    "migrate",
+                    "diff",
+                    "--from-schema-datasource",
+                    str(prisma_dir / "schema.prisma"),
+                    "--to-schema-datamodel",
+                    str(prisma_dir / "schema.prisma"),
+                    "--exit-code",
+                ),
+                timeout=prisma_toolchain.prisma_command_timeout(),
+                env=migration_environment(_get_prisma_env()),
+            )
+        except subprocess.CalledProcessError as exc:
+            diff: Final = str(exc.stdout or exc.stderr or exc).strip()
+            return f"the schema differs from this build. prisma migrate diff:\n{diff}"
+        except subprocess.TimeoutExpired as exc:
+            return f"prisma migrate diff did not finish within {exc.timeout} s"
+        return None
+
+    @staticmethod
+    def migration_dry_run_summary(use_migrate: bool = True) -> tuple[bool, str]:
+        """`--check_migrations_against_existing_data`: the pending migrations applied for real on
+        DIRECT_URL or DATABASE_URL inside one transaction that is rolled back, so Postgres reports
+        what the upgrade would hit. (True, the per-migration timings) when they all apply,
+        (False, Postgres's own error) when one does not."""
         import psycopg
 
+        if not use_migrate:
+            return (True, "The schema is pushed rather than migrated, there is nothing to dry-run.")
         database_url: Final = os.environ.get("DATABASE_URL")
         if not database_url:
-            return ()
+            return (False, "DATABASE_URL is not set.")
         direct_url: Final = ProxyExtrasDBManager._strip_prisma_query_params(
             os.environ.get("DIRECT_URL") or database_url
         )
         schema: Final = ProxyExtrasDBManager._prisma_schema_param(database_url) or "public"
-        migrations_dir: Final = Path(ProxyExtrasDBManager._get_prisma_dir()) / "migrations" if use_migrate else None
-        indexes: Final = tuple((index.table, index.name, index.columns) for index in REQUEST_LOG_INDEXES)
+        prisma_dir: Final = Path(ProxyExtrasDBManager._get_prisma_dir())
         try:
             with psycopg.connect(direct_url, connect_timeout=10, autocommit=True) as connection:
-                checks: Final = pending_change_checks(connection, schema, migrations_dir, indexes)
-                return find_blockers(connection, schema, checks, migration_data_check_timeout_seconds())
+                result: Final = dry_run(
+                    connection,
+                    schema,
+                    prisma_dir / "migrations",
+                    baseline_check=lambda: ProxyExtrasDBManager._baseline_refusal(prisma_dir),
+                )
         except psycopg.OperationalError as exc:
-            logger.warning(
-                "Could not reach the database for the pre-migration row check: %s", _redact_credentials(str(exc))
-            )
-            return ()
-
-    @staticmethod
-    def migration_data_check_report(use_migrate: bool = True) -> Optional[str]:
-        """The operator-facing refusal for `migration_data_check_findings`, None when nothing blocks."""
-        findings: Final = ProxyExtrasDBManager.migration_data_check_findings(use_migrate=use_migrate)
-        return report(findings) if findings else None
-
-    @staticmethod
-    def raise_if_rows_block_pending_changes(use_migrate: bool) -> None:
-        """Refuse to run migrations that the existing rows would make fail, before any is
-        applied. The RuntimeError carries the report and reaches the operator through the
-        proxy's "Database migration cannot proceed" exit."""
-        refusal: Final = ProxyExtrasDBManager.migration_data_check_report(use_migrate=use_migrate)
-        if refusal is not None:
-            raise RuntimeError(refusal)
+            return (False, f"Could not reach the database: {_redact_credentials(str(exc))}")
+        return (result.ok, dry_run_report(result))
 
     @staticmethod
     def build_request_log_indexes(build: Callable[[str, str], bool] = ensure_request_log_indexes) -> bool:
@@ -1724,5 +1740,4 @@ class ProxyExtrasDBManager:
                 time.sleep(random.randrange(5, 15))
             finally:
                 os.chdir(original_dir)
-                pass
         return False

litellm-rust/Cargo.lock

@@ -4345,6 +4345,7 @@ dependencies = [
  "litellm-inference-transcription",
  "litellm-llms",
  "litellm-llms-types",
+ "litellm-model-catalog",
  "litellm-router-types",
  "litellm-secrets",
  "litellm-secrets-aws",

litellm-rust/crates/core-utils/src/exception_mapping_utils/openai.rs

@@ -53,6 +53,7 @@ pub(super) const RULES: &[Rule] = &[
         |mapping| {
             mapping.error_str.contains("invalid_request_error")
                 && !mapping.error_str.contains("Incorrect API key provided")
+                && !matches!(mapping.status, Some(401 | 404))
         },
         PublicError::BadRequest,
     ),
@@ -132,6 +133,23 @@ mod tests {
         assert_eq!(classified(Some(400), text), Some(expected));
     }
 
+    #[rstest::rstest]
+    #[case::unauthorized(Some(401), None)]
+    #[case::not_found(Some(404), None)]
+    #[case::forbidden(Some(403), Some(PublicError::BadRequest))]
+    #[case::rate_limited(Some(429), Some(PublicError::BadRequest))]
+    #[case::server_error(Some(500), Some(PublicError::BadRequest))]
+    #[case::no_status(None, Some(PublicError::BadRequest))]
+    fn invalid_request_errors_leave_401_and_404_to_the_status_mapping(
+        #[case] status: Option<u16>,
+        #[case] expected: Option<PublicError>,
+    ) {
+        assert_eq!(
+            classified(status, "invalid_request_error bad field"),
+            expected
+        );
+    }
+
     #[rstest::rstest]
     #[case::rate_limit_before_context_window(
         "rate limit and This model's maximum context length is 10",

litellm-rust/crates/model-catalog/AGENTS.md

@@ -25,3 +25,5 @@ A new `N`, image size or resolution in an existing key shape parses with no Rust
 ## Tests
 
 `BUNDLED_REGISTRY` embeds the root `model_prices_and_context_window.json`, so the tests over it fail a JSON change with a bad value or a key no type reads
+
+`Catalog::problems` is the one definition of a mergeable cost map. The bundled test asserts it is empty, and the cost map guard workflow runs it on a pull request's JSON through `cargo run --bin check-registry <path>`

litellm-rust/crates/model-catalog/Cargo.toml

@@ -10,6 +10,7 @@ schema = ["dep:schemars", "litellm-cost/schema"]
 
 [dependencies]
 litellm-cost = { workspace = true, features = ["serde"] }
+litellm-error.workspace = true
 
 indexmap = { version = "2.14.0", features = ["serde"] }
 jiff = { version = "0.2.37", default-features = false, features = ["std", "tzdb-bundle-always"] }
@@ -24,5 +25,4 @@ time.workspace = true
 
 [dev-dependencies]
 jsonschema = { version = "0.55.1", default-features = false }
-litellm-error.workspace = true
 rstest.workspace = true

litellm-rust/crates/model-catalog/src/bin/check-registry.rs

@@ -0,0 +1,26 @@
+use std::{env, fs, path::PathBuf, process::ExitCode};
+
+use litellm_model_catalog::Catalog;
+
+fn main() -> ExitCode {
+    let Some(path) = env::args_os().nth(1).map(PathBuf::from) else {
+        eprintln!("usage: check-registry <model_prices_and_context_window.json>");
+        return ExitCode::from(2);
+    };
+    let body = match fs::read(&path) {
+        Ok(body) => body,
+        Err(error) => {
+            eprintln!("{}: {error}", path.display());
+            return ExitCode::FAILURE;
+        }
+    };
+    let problems = Catalog::problems(&body);
+    for problem in &problems {
+        eprintln!("{}", litellm_error::Report::new(problem));
+    }
+    if problems.is_empty() {
+        ExitCode::SUCCESS
+    } else {
+        ExitCode::FAILURE
+    }
+}

litellm-rust/crates/model-catalog/src/catalog.rs

@@ -5,7 +5,7 @@ use indexmap::IndexMap;
 use serde::Deserialize;
 use serde_json::Value;
 
-use crate::error::{Error, Loaded, Rejects, Repr};
+use crate::error::{Error, Loaded, Rejects, Repr, pointer_child, pointer_for_key};
 use crate::fallback::{FallbackGeneralizations, FallbackRule};
 use crate::model::Model;
 use crate::store::{Rows, Store};
@@ -211,6 +211,36 @@ impl Catalog {
         })
     }
 
+    pub fn problems(body: &[u8]) -> Vec<Error> {
+        let Loaded {
+            value: catalog,
+            errors,
+        } = match Self::parse(body) {
+            Ok(loaded) => loaded,
+            Err(fatal) => return vec![fatal],
+        };
+        let unread = catalog.rows().entries.iter().flat_map(|(model, entry)| {
+            EntryWire::read(entry.fields())
+                .unknown
+                .into_iter()
+                .map(move |(key, _)| {
+                    Error::new(
+                        pointer_child(&pointer_for_key(model), &key),
+                        Repr::UnreadField,
+                    )
+                })
+        });
+        let no_fallback_rules = catalog
+            .fallback_rules()
+            .is_none_or(<[_]>::is_empty)
+            .then(|| Error::from_key(FALLBACK_GENERALIZATIONS, Repr::NoFallbackRules));
+        errors
+            .into_iter()
+            .chain(unread)
+            .chain(no_fallback_rules)
+            .collect()
+    }
+
     pub fn row_errors(model: &str, fields: &Fields) -> Vec<Error> {
         let (_, _, errors) = read_row(fields);
         errors
@@ -404,3 +434,85 @@ fn unknown_timezone(wire: &EntryWire) -> Option<Error> {
         )
     })
 }
+
+#[cfg(test)]
+mod tests {
+    use rstest::rstest;
+
+    use super::*;
+
+    const RULES: &str = r#""fallback_generalizations":{"rules":[{"name":"family","pattern":"^new-","model_info":{"mode":"chat"}}]}"#;
+
+    #[rstest]
+    #[case::clean(format!(r#"{{{RULES},"m":{{"litellm_provider":"test"}}}}"#), vec![])]
+    #[case::not_json("{".to_owned(), vec![("", "json")])]
+    #[case::row_error(format!(r#"{{{RULES},"m":{{}}}}"#), vec![("/m/litellm_provider", "missing_provider")])]
+    #[case::unread_key(
+        format!(r#"{{{RULES},"m":{{"litellm_provider":"test","typo_cost":1}}}}"#),
+        vec![("/m/typo_cost", "unread_field")]
+    )]
+    #[case::empty_model_key(
+        format!(r#"{{{RULES},"":{{"litellm_provider":"test","future_field":true}}}}"#),
+        vec![("//future_field", "unread_field")]
+    )]
+    #[case::unread_key_on_invalid_row(
+        format!(r#"{{{RULES},"m":{{"typo_cost":1}}}}"#),
+        vec![("/m/litellm_provider", "missing_provider"), ("/m/typo_cost", "unread_field")]
+    )]
+    #[case::escaped_unread_key_on_invalid_row(
+        format!(r#"{{{RULES},"open/router~v1":{{"typo/cost~":1}}}}"#),
+        vec![("/open~1router~0v1/litellm_provider", "missing_provider"), ("/open~1router~0v1/typo~1cost~0", "unread_field")]
+    )]
+    #[case::no_rules(
+        r#"{"m":{"litellm_provider":"test"}}"#.to_owned(),
+        vec![("/fallback_generalizations", "no_fallback_rules")]
+    )]
+    #[case::empty_rules(
+        r#"{"fallback_generalizations":{"rules":[]},"m":{"litellm_provider":"test"}}"#.to_owned(),
+        vec![("/fallback_generalizations", "no_fallback_rules")]
+    )]
+    fn problems_list_what_a_cost_map_change_must_fix(
+        #[case] body: String,
+        #[case] expected: Vec<(&str, &str)>,
+    ) {
+        let problems: Vec<_> = Catalog::problems(body.as_bytes())
+            .iter()
+            .map(|problem| (problem.path().to_owned(), <&str>::from(problem.kind())))
+            .collect();
+        let expected: Vec<_> = expected
+            .into_iter()
+            .map(|(path, kind)| (path.to_owned(), kind))
+            .collect();
+        assert_eq!(problems, expected);
+    }
+
+    #[rstest]
+    fn runtime_parsing_preserves_custom_fields_and_recovers_valid_rows() {
+        let body = br#"{
+            "good":{"litellm_provider":"test","custom_metadata":true},
+            "bad":{"litellm_provider":"test","input_cost_per_token":"wrong"}
+        }"#;
+        let loaded = Catalog::parse(body).unwrap();
+        assert_eq!(loaded.errors.len(), 1);
+        assert_eq!(loaded.errors[0].path(), "/bad/input_cost_per_token");
+        let (catalog, rejected) = loaded.value.without_rejected(&loaded.errors);
+        assert_eq!(rejected.len(), 1);
+        assert!(catalog.lookup("bad").is_none());
+        let good = catalog.lookup("good").unwrap();
+        assert!(good.entry.model().is_some());
+        assert_eq!(
+            good.entry.field("custom_metadata"),
+            Some(&Value::Bool(true))
+        );
+        assert!(
+            Catalog::problems(body)
+                .iter()
+                .any(|problem| problem.kind() == crate::ErrorKind::UnreadField)
+        );
+        assert!(
+            Catalog::problems(body)
+                .iter()
+                .any(|problem| problem.kind() == crate::ErrorKind::NoFallbackRules)
+        );
+    }
+}

litellm-rust/crates/model-catalog/src/error.rs

@@ -109,6 +109,12 @@ pub(crate) enum Repr {
         #[source]
         cause: Option<DateCause>,
     },
+    #[error("not a field litellm reads")]
+    #[strum_discriminants(strum(serialize = "unread_field"))]
+    UnreadField,
+    #[error("has no rules")]
+    #[strum_discriminants(strum(serialize = "no_fallback_rules"))]
+    NoFallbackRules,
 }
 
 /// What a problem takes out of the served map
@@ -148,7 +154,7 @@ impl Error {
             | Repr::NoOffPeakHours
             | Repr::EmptyList
             | Repr::InvalidDate { .. } => Rejects::Row,
-            Repr::MissingProvider => Rejects::Nothing,
+            Repr::MissingProvider | Repr::UnreadField | Repr::NoFallbackRules => Rejects::Nothing,
         }
     }
 }
@@ -334,6 +340,8 @@ mod tests {
     #[case::no_off_peak_hours(Repr::NoOffPeakHours, Rejects::Row)]
     #[case::empty_list(Repr::EmptyList, Rejects::Row)]
     #[case::invalid_date(Repr::InvalidDate { cause: None }, Rejects::Row)]
+    #[case::unread_field(Repr::UnreadField, Rejects::Nothing)]
+    #[case::no_fallback_rules(Repr::NoFallbackRules, Rejects::Nothing)]
     fn policy_classifies_each_error(#[case] error: Repr, #[case] expected: Rejects) {
         assert_eq!(Error::from(error).rejects(), expected);
     }

litellm-rust/crates/model-catalog/tests/catalog.rs

@@ -366,18 +366,14 @@ fn written_is_the_document_in_order_including_sections_and_bad_entries() {
 }
 
 #[rstest]
-fn the_bundled_registry_parses_with_no_problems_and_every_key_understood() {
-    let loaded = Catalog::parse(BUNDLED_REGISTRY).unwrap();
-    let catalog = loaded.value;
-    assert!(loaded.errors.is_empty(), "{:#?}", loaded.errors);
-    let unrecognized: Vec<_> = catalog.unrecognized().collect();
-    assert!(unrecognized.is_empty(), "{unrecognized:#?}");
-    assert!(
-        catalog
-            .fallback_rules()
-            .is_some_and(|rules| !rules.is_empty())
-    );
+fn the_bundled_registry_has_no_problems() {
+    let problems: Vec<_> = Catalog::problems(BUNDLED_REGISTRY)
+        .iter()
+        .map(report)
+        .collect();
+    assert!(problems.is_empty(), "{problems:#?}");
     const REFERENCE_MODEL_COUNT: usize = 4303;
+    let catalog = Catalog::parse(BUNDLED_REGISTRY).unwrap().value;
     assert!(catalog.model_count() as f64 >= REFERENCE_MODEL_COUNT as f64 * 0.9);
 }
 

litellm-rust/crates/python-bridge/AGENTS.md

@@ -106,6 +106,18 @@ GIL handling to `litellm-host-python`.
 
 ## Data Handling
 
+- Reuse large immutable bundled data and parsed catalogs across calls, including in
+  replacement APIs. Share Rust storage and cache repeated Python projections instead
+  of reparsing, serializing or copying the full payload on every access
+- Cache owned Python objects with `PyOnceLock<Py<T>>`, then bind and clone references
+  while attached. Do not use blocking `OnceLock::get_or_init` while attached
+  ([PyO3 initialization](https://pyo3.rs/v0.29.2/faq.html),
+  [reference ownership](https://pyo3.rs/v0.29.2/types.html))
+- Never reuse Python objects across interpreters or interpreter reinitialization.
+  Once-only caches are for immutable data. Reloadable/custom data needs versioned
+  entries or invalidation, and caller-mutable Python containers must not be shared
+- Test unchanged contents and allocation reuse, not just equal values. Keep this
+  coverage when replacing a bundled-data accessor with a catalog API
 - OCR payloads can contain personal data and large base64 images. Do not log
   payloads or provider responses.
 - Avoid copying large payloads more than needed. The current JSON round-trip is

litellm-rust/crates/python-bridge/Cargo.toml

@@ -49,6 +49,7 @@ litellm-secrets-types.workspace = true
 litellm-llms-types.workspace = true
 litellm-router-types.workspace = true
 litellm-host-python.workspace = true
+litellm-model-catalog.workspace = true
 litellm-token-counter = { path = "../token-counter", default-features = false }
 pyo3.workspace = true
 prost.workspace = true

litellm-rust/crates/python-bridge/src/lib.rs

@@ -8,6 +8,7 @@ mod execution;
 mod http;
 mod logger;
 mod marshal;
+mod model_registry;
 mod preflight;
 mod python_settings;
 mod routes;
@@ -30,6 +31,8 @@ mod _native {
     #[pymodule_export]
     use crate::logger::NativeDiagnosticProcessor;
     #[pymodule_export]
+    use crate::model_registry::bundled_model_cost_map;
+    #[pymodule_export]
     use crate::routes::audio_transcription::{atranscription, transcription};
     #[pymodule_export]
     use crate::routes::chat_completions::{acompletion, completion};
@@ -110,6 +113,7 @@ mod tests {
                 "NativeClickHouseSpendConfig",
                 "NativeClickHouseSpendStorage",
                 "trace_encode_error",
+                "bundled_model_cost_map",
                 "TokenCounter",
                 "Tokenizer",
                 "gil_stats",

litellm-rust/crates/python-bridge/src/model_registry.rs

@@ -0,0 +1,30 @@
+use pyo3::prelude::*;
+use pyo3::sync::PyOnceLock;
+use pyo3::types::PyBytes;
+
+#[pyfunction]
+pub fn bundled_model_cost_map(py: Python<'_>) -> Bound<'_, PyBytes> {
+    static BYTES: PyOnceLock<Py<PyBytes>> = PyOnceLock::new();
+    BYTES
+        .get_or_init(py, || {
+            PyBytes::new(py, litellm_model_catalog::BUNDLED_REGISTRY).unbind()
+        })
+        .bind(py)
+        .clone()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[rstest::rstest]
+    fn repeated_calls_reuse_the_bundled_registry_bytes() {
+        Python::initialize();
+        let first = Python::attach(|py| bundled_model_cost_map(py).unbind());
+        Python::attach(|py| {
+            let second = bundled_model_cost_map(py);
+            assert!(first.bind(py).is(&second));
+            assert_eq!(second.as_bytes(), litellm_model_catalog::BUNDLED_REGISTRY);
+        });
+    }
+}

litellm/__init__.py

@@ -685,6 +685,7 @@ moonshot_models: Set = set()
 publicai_models: Set = set()
 darkbloom_models: Set = set()
 openinfer_models: Final[Set[str]] = set()
+kyma_models: Final[set[str]] = set()  # mutable-ok: populated when the shared model catalog loads
 streamlake_models: Final[Set[str]] = set()
 zerogpu_models: Final[Set[str]] = set()
 v0_models: Set = set()
@@ -981,6 +982,8 @@ def _populate_provider_model_sets(model_cost_map: Mapping[str, Mapping[str, obje
             darkbloom_models.add(key)
         elif value.get("litellm_provider") == "openinfer":
             openinfer_models.add(key)
+        elif value.get("litellm_provider") == "kyma":
+            kyma_models.add(key)
         elif value.get("litellm_provider") == "streamlake":
             streamlake_models.add(key)
         elif value.get("litellm_provider") == "zerogpu":
@@ -1159,6 +1162,7 @@ model_list = list(
     | publicai_models
     | darkbloom_models
     | openinfer_models
+    | kyma_models
     | streamlake_models
     | zerogpu_models
     | v0_models
@@ -1290,6 +1294,7 @@ def _build_models_by_provider() -> dict:
         "publicai": publicai_models,
         "darkbloom": darkbloom_models,
         "openinfer": openinfer_models,
+        "kyma": kyma_models,
         "streamlake": streamlake_models,
         "zerogpu": zerogpu_models,
         "v0": v0_models,

litellm/_internal_context.py

@@ -36,6 +36,8 @@ REDIS_FAMILIES_METADATA_KEY: Final = "families"
 
 _service_caller: Final[ContextVar[str | None]] = ContextVar("service_caller", default=None)
 
+_mirrored_call: Final[ContextVar[bool]] = ContextVar("mirrored_call", default=False)
+
 
 @contextmanager
 def internal_sub_call() -> Generator[None]:
@@ -123,6 +125,30 @@ def current_service_caller() -> str | None:
     return _service_caller.get()
 
 
+@contextmanager
+def mirrored_call() -> Generator[None]:
+    """A silent_model mirror of a request: every litellm call inside, its retries and fallbacks included."""
+    token: Final = _mirrored_call.set(True)
+    try:
+        yield
+    finally:
+        _restore(_mirrored_call, token)
+
+
+def in_mirrored_call() -> bool:
+    return _mirrored_call.get()
+
+
+@contextmanager
+def outside_mirrored_call() -> Generator[None]:
+    """Logging a mirrored call: the calls its callbacks start are not mirrors themselves."""
+    token: Final = _mirrored_call.set(False)
+    try:
+        yield
+    finally:
+        _restore(_mirrored_call, token)
+
+
 @contextmanager
 def pinned_billing_time(moment: datetime) -> Generator[None]:
     """Price every rate lookup inside this block at ``moment`` rather than at each one's own clock read."""

litellm/anthropic_beta_gated_fields.py

@@ -0,0 +1,325 @@
+"""Body fields that an ``anthropic-beta`` value gates.
+
+``anthropic_beta_headers_config.json`` says which beta each provider leg
+carries. When a leg drops a beta, the request fields that beta unlocks
+would make the provider 400. Under ``drop_params`` the same request degrades on
+that leg by dropping those fields too; without it the request goes out as sent
+and the provider answers its own 400, like any unsupported param. Every helper
+here is copy-on-write: the caller's containers are returned untouched when
+nothing is stripped, and a shallow copy is returned otherwise, so a fallback
+leg still sends the full request.
+"""
+
+from collections.abc import Mapping, Sequence, Set
+from dataclasses import dataclass
+from types import MappingProxyType
+from typing import Final
+
+from pydantic import TypeAdapter, ValidationError
+
+from litellm._logging import verbose_logger
+from litellm.litellm_core_utils.core_helpers import drops_unsupported_params
+from litellm.types.llms.anthropic import (
+    ANTHROPIC_BETA_HEADER_VALUES,
+    ANTHROPIC_MID_CONVERSATION_OUTPUT_CONFIG_BETA_HEADER,
+    ANTHROPIC_MID_CONVERSATION_TOOL_CHANGES_BETA_HEADER,
+    ANTHROPIC_THINKING_BINDING_CONTROLS_BETA_HEADER,
+    ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER,
+)
+
+ANTHROPIC_MID_CONVERSATION_SYSTEM_CLEAR_AT_BETA_HEADER: Final = "mid-conversation-system-clear-at-2026-08-21"
+ANTHROPIC_INLINE_TOOLS_BETA_HEADER: Final = "inline-tools-2026-09-15"
+
+THINKING_BLOCK_BINDING: Final = "thinking.block_binding"
+THINKING_DISPLAY_UPDATES: Final = "thinking.display=updates"
+SYSTEM_CLEAR_AT: Final = "messages[].system.clear_at"
+SYSTEM_OUTPUT_CONFIG: Final = "messages[].system.output_config"
+SYSTEM_TOOL_CHANGES: Final = "messages[].system.content[].tool_addition|tool_removal"
+SYSTEM_MESSAGE_EMPTIED: Final = "messages[].system (emptied by the strip)"
+
+_TOOL_CHANGE_BLOCK_TYPES: Final = frozenset({"tool_addition", "tool_removal"})
+
+BETA_GATED_FIELDS: Final[Mapping[str, tuple[str, ...]]] = MappingProxyType(
+    {
+        ANTHROPIC_THINKING_BINDING_CONTROLS_BETA_HEADER: (THINKING_BLOCK_BINDING,),
+        ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER: (THINKING_DISPLAY_UPDATES,),
+        ANTHROPIC_MID_CONVERSATION_SYSTEM_CLEAR_AT_BETA_HEADER: (SYSTEM_CLEAR_AT,),
+        ANTHROPIC_MID_CONVERSATION_OUTPUT_CONFIG_BETA_HEADER: (SYSTEM_OUTPUT_CONFIG,),
+        ANTHROPIC_BETA_HEADER_VALUES.PER_TURN_CONTROL_2026_07_01.value: (SYSTEM_OUTPUT_CONFIG,),
+        ANTHROPIC_MID_CONVERSATION_TOOL_CHANGES_BETA_HEADER: (SYSTEM_TOOL_CHANGES,),
+        ANTHROPIC_INLINE_TOOLS_BETA_HEADER: (SYSTEM_TOOL_CHANGES,),
+    }
+)
+
+
+def _betas_unlocking(field: str) -> frozenset[str]:
+    return frozenset(beta for beta, fields in BETA_GATED_FIELDS.items() if field in fields)
+
+
+_FIELD_BETAS: Final[Mapping[str, frozenset[str]]] = MappingProxyType(
+    {
+        field: _betas_unlocking(field) for fields in BETA_GATED_FIELDS.values() for field in fields
+    }  # comprehension-ok: flattening the beta-to-fields map into a field-to-betas index
+)
+
+
+def field_is_gated(field: str, active_betas: Set[str]) -> bool:
+    """True when no active beta unlocks ``field``, so the field must be stripped."""
+    return _FIELD_BETAS[field].isdisjoint(active_betas)
+
+
+_MAPPING_ADAPTER: Final = TypeAdapter(Mapping[str, object])
+_SEQUENCE_ADAPTER: Final = TypeAdapter(tuple[object, ...])
+_HEADERS_ADAPTER: Final = TypeAdapter(Mapping[str, str])
+
+
+def _as_mapping(value: object) -> Mapping[str, object] | None:
+    try:
+        return _MAPPING_ADAPTER.validate_python(value, strict=True)
+    except ValidationError:
+        return None
+
+
+def _as_sequence(value: object) -> tuple[object, ...] | None:
+    try:
+        return _SEQUENCE_ADAPTER.validate_python(value)
+    except ValidationError:
+        return None
+
+
+def _is_tool_change_block(block: object) -> bool:
+    mapping: Final = _as_mapping(block)
+    block_type: Final = mapping.get("type") if mapping is not None else None
+    return isinstance(block_type, str) and block_type in _TOOL_CHANGE_BLOCK_TYPES
+
+
+def _header_beta_values(headers: Mapping[str, str]) -> frozenset[str]:
+    joined: Final = ",".join(value for key, value in headers.items() if key.lower() == "anthropic-beta")
+    return frozenset(piece.strip() for piece in joined.split(",") if piece.strip())
+
+
+def _validated_headers(headers: object) -> Mapping[str, str]:
+    try:
+        return _HEADERS_ADAPTER.validate_python(headers)
+    except ValidationError:
+        return MappingProxyType({})
+
+
+def active_beta_values(headers: object, body_betas: object = ()) -> frozenset[str]:
+    """The ``anthropic-beta`` values a request will send: the comma-joined header (key matched
+    case-insensitively) plus the body's ``anthropic_beta`` list on legs that carry betas in the body."""
+    body_values: Final = _as_sequence(body_betas) or ()
+    body_betas_as_strings: Final = frozenset(beta for beta in body_values if isinstance(beta, str))
+    return _header_beta_values(_validated_headers(headers)) | body_betas_as_strings
+
+
+@dataclass(frozen=True, slots=True)
+class StrippedThinking:
+    thinking: object
+    stripped: tuple[str, ...]
+
+
+@dataclass(frozen=True, slots=True)
+class StrippedMessages:
+    messages: Sequence[object]
+    stripped: tuple[str, ...]
+
+
+@dataclass(frozen=True, slots=True)
+class StrippedRequest:
+    request: Mapping[str, object]
+    stripped: tuple[str, ...]
+
+
+def strip_beta_gated_thinking(thinking: object, active_betas: Set[str], *, drop_params: object) -> StrippedThinking:
+    """Drop ``thinking`` sub-fields whose beta is absent. Returns the same object when nothing is gated."""
+    mapping: Final = _as_mapping(thinking)
+    if mapping is None or not drops_unsupported_params(drop_params):
+        return StrippedThinking(thinking=thinking, stripped=())
+    binding_gated: Final = "block_binding" in mapping and field_is_gated(THINKING_BLOCK_BINDING, active_betas)
+    display_gated: Final = mapping.get("display") == "updates" and field_is_gated(
+        THINKING_DISPLAY_UPDATES, active_betas
+    )
+    if not binding_gated and not display_gated:
+        return StrippedThinking(thinking=thinking, stripped=())
+    gated_keys: Final = frozenset(
+        {*(("block_binding",) if binding_gated else ()), *(("display",) if display_gated else ())}
+    )
+    stripped: Final = (
+        *((THINKING_BLOCK_BINDING,) if binding_gated else ()),
+        *((THINKING_DISPLAY_UPDATES,) if display_gated else ()),
+    )
+    return StrippedThinking(
+        thinking={key: value for key, value in mapping.items() if key not in gated_keys},
+        stripped=stripped,
+    )
+
+
+def _strip_beta_gated_system_message(message: Mapping[str, object], active_betas: Set[str]) -> StrippedMessages:
+    """One mid-conversation ``role: system`` message. ``messages`` holds the rewritten message, or nothing
+    when the strip left it with neither content nor ``output_config``, since such a system turn is itself a 400."""
+    clear_at_gated: Final = "clear_at" in message and field_is_gated(SYSTEM_CLEAR_AT, active_betas)
+    output_config_gated: Final = "output_config" in message and field_is_gated(SYSTEM_OUTPUT_CONFIG, active_betas)
+    content: Final = message.get("content")
+    blocks: Final = _as_sequence(content) if not isinstance(content, str) else None
+    tool_changes_gated: Final = (
+        blocks is not None
+        and any(_is_tool_change_block(block) for block in blocks)
+        and field_is_gated(SYSTEM_TOOL_CHANGES, active_betas)
+    )
+    if not clear_at_gated and not output_config_gated and not tool_changes_gated:
+        return StrippedMessages(messages=(message,), stripped=())
+    kept_content: Final[object] = (
+        [block for block in blocks if not _is_tool_change_block(block)]
+        if tool_changes_gated and blocks is not None
+        else content
+    )
+    gated_keys: Final = frozenset(
+        {*(("clear_at",) if clear_at_gated else ()), *(("output_config",) if output_config_gated else ())}
+    )
+    rewritten: Final = {
+        key: (kept_content if key == "content" else value) for key, value in message.items() if key not in gated_keys
+    }
+    stripped: Final = (
+        *((SYSTEM_CLEAR_AT,) if clear_at_gated else ()),
+        *((SYSTEM_OUTPUT_CONFIG,) if output_config_gated else ()),
+        *((SYSTEM_TOOL_CHANGES,) if tool_changes_gated else ()),
+    )
+    if kept_content in (None, "", []) and "output_config" not in rewritten:
+        return StrippedMessages(messages=(), stripped=(*stripped, SYSTEM_MESSAGE_EMPTIED))
+    return StrippedMessages(messages=(rewritten,), stripped=stripped)
+
+
+def _strip_beta_gated_message(message: object, active_betas: Set[str]) -> StrippedMessages:
+    mapping: Final = _as_mapping(message)
+    if mapping is None or mapping.get("role") != "system":
+        return StrippedMessages(messages=(message,), stripped=())
+    return _strip_beta_gated_system_message(mapping, active_betas)
+
+
+def _ends_on_assistant(messages: Sequence[object]) -> bool:
+    last: Final = _as_mapping(messages[-1]) if messages else None
+    return last is not None and last.get("role") == "assistant"
+
+
+def strip_beta_gated_system_messages(
+    messages: Sequence[object], active_betas: Set[str], *, drop_params: object
+) -> StrippedMessages:
+    """Drop the gated fields of every mid-conversation ``role: system`` message. Returns the same
+    sequence when nothing is gated, or when dropping an emptied system turn would leave the conversation
+    ending on an assistant turn, which the model would read as a prefill to continue."""
+    if not drops_unsupported_params(drop_params):
+        return StrippedMessages(messages=messages, stripped=())
+    per_message: Final = tuple(_strip_beta_gated_message(message, active_betas) for message in messages)
+    stripped: Final = tuple(
+        name for result in per_message for name in result.stripped
+    )  # comprehension-ok: flattening per-message strip reports
+    if not stripped:
+        return StrippedMessages(messages=messages, stripped=())
+    kept: Final = [
+        message for result in per_message for message in result.messages
+    ]  # comprehension-ok: flattening per-message results
+    if _ends_on_assistant(kept) and not _ends_on_assistant(messages):
+        return StrippedMessages(messages=messages, stripped=())
+    return StrippedMessages(messages=kept, stripped=stripped)
+
+
+def strip_beta_gated_request_fields(
+    request: Mapping[str, object], active_betas: Set[str], *, drop_params: object
+) -> StrippedRequest:
+    """Apply both strips to an Anthropic ``/v1/messages`` shaped body. Returns the same mapping when
+    nothing is gated, otherwise a shallow copy with ``thinking`` and ``messages`` replaced."""
+    thinking: Final = strip_beta_gated_thinking(request.get("thinking"), active_betas, drop_params=drop_params)
+    messages: Final = request.get("messages")
+    message_sequence: Final = _as_sequence(messages) if not isinstance(messages, str) else None
+    system_messages: Final = (
+        strip_beta_gated_system_messages(message_sequence, active_betas, drop_params=drop_params)
+        if message_sequence is not None
+        else StrippedMessages(messages=(), stripped=())
+    )
+    stripped: Final = (*thinking.stripped, *system_messages.stripped)
+    if not stripped:
+        return StrippedRequest(request=request, stripped=())
+    rewritten: Final = {
+        **request,
+        **({"thinking": thinking.thinking} if thinking.stripped else {}),
+        **({"messages": system_messages.messages} if system_messages.stripped else {}),
+    }
+    return StrippedRequest(request=rewritten, stripped=stripped)
+
+
+@dataclass(frozen=True, slots=True)
+class StrippedNativeRequest:
+    thinking: object
+    thinking_stripped: bool
+    messages: Sequence[object]
+    messages_stripped: bool
+    stripped: tuple[str, ...]
+
+
+def unstripped_native_request(optional_params: object, messages: object) -> StrippedNativeRequest:
+    """The identity result for legs that strip later, once their final ``anthropic_beta`` list exists."""
+    params: Final = _as_mapping(optional_params) or MappingProxyType({})
+    message_sequence: Final = _as_sequence(messages) if not isinstance(messages, str) else None
+    return StrippedNativeRequest(
+        thinking=params.get("thinking"),
+        thinking_stripped=False,
+        messages=message_sequence if message_sequence is not None else (),
+        messages_stripped=False,
+        stripped=(),
+    )
+
+
+def strip_beta_gated_native_request(
+    headers: object, optional_params: object, messages: object, *, body_betas: object = (), drop_params: object
+) -> StrippedNativeRequest:
+    """The ``/v1/messages`` seam, fed the final (already filtered) headers, the final body ``anthropic_beta``
+    list on legs that carry betas in the body, plus the body parts it may rewrite."""
+    if not drops_unsupported_params(drop_params):
+        return unstripped_native_request(optional_params, messages)
+    active_betas: Final = active_beta_values(headers, body_betas)
+    params: Final = _as_mapping(optional_params) or MappingProxyType({})
+    thinking: Final = strip_beta_gated_thinking(params.get("thinking"), active_betas, drop_params=drop_params)
+    message_sequence: Final = _as_sequence(messages) if not isinstance(messages, str) else None
+    system_messages: Final = (
+        strip_beta_gated_system_messages(message_sequence, active_betas, drop_params=drop_params)
+        if message_sequence is not None
+        else StrippedMessages(messages=(), stripped=())
+    )
+    return StrippedNativeRequest(
+        thinking=thinking.thinking,
+        thinking_stripped=bool(thinking.stripped),
+        messages=system_messages.messages,
+        messages_stripped=bool(system_messages.stripped),
+        stripped=(*thinking.stripped, *system_messages.stripped),
+    )
+
+
+def log_beta_gated_strips(stripped: Sequence[str], model: str | None, provider: str) -> None:
+    if not stripped:
+        return
+    described: Final = ", ".join(
+        f"{field} (unlocked by anthropic-beta {' or '.join(sorted(_FIELD_BETAS[field]))})"
+        if field in _FIELD_BETAS
+        else field
+        for field in sorted(set(stripped))
+    )
+    verbose_logger.warning(
+        "Dropped %s for model=%s provider=%s because drop_params is on and the provider would reject them on "
+        "this request, either since this provider leg does not send that anthropic-beta (see "
+        "anthropic_beta_headers_config.json) or since the model turns mid-conversation system messages into user "
+        "messages.",
+        described,
+        model,
+        provider,
+    )
+
+
+def strip_beta_gated_request_body(
+    request: Mapping[str, object], headers: object, *, drop_params: object, model: str | None, provider: str
+) -> Mapping[str, object]:
+    gated: Final = strip_beta_gated_request_fields(
+        request, active_beta_values(headers, request.get("anthropic_beta") or ()), drop_params=drop_params
+    )
+    log_beta_gated_strips(gated.stripped, model=model, provider=provider)
+    return gated.request

litellm/anthropic_beta_headers_config.json

@@ -248,6 +248,7 @@
     "text_editor_20241022": null,
     "text_editor_20250124": null,
     "thinking-binding-controls-2026-08-01": "thinking-binding-controls-2026-08-01",
+    "thinking-display-updates-2026-08-18": "thinking-display-updates-2026-08-18",
     "token-efficient-tools-2025-02-19": "token-efficient-tools-2025-02-19",
     "web-fetch-2025-09-10": null,
     "web-search-2025-03-05": "web-search-2025-03-05"

litellm/anthropic_beta_headers_manager.py

@@ -241,6 +241,13 @@ def get_provider_name(provider: str) -> str:
     return aliases.get(provider, provider)
 
 
+def has_beta_headers_config(provider: str) -> bool:
+    config: Final = _bundled_beta_headers_config()
+    aliases: Final = config.get("provider_aliases", {})
+    canonical_provider: Final = (aliases.get(provider) if isinstance(aliases, Mapping) else None) or provider
+    return canonical_provider in config
+
+
 def filter_and_transform_beta_headers(
     beta_headers: list[str],
     provider: str,
@@ -397,7 +404,7 @@ def update_request_with_filtered_beta(
     headers: dict,
     request_data: dict,
     provider: str,
-) -> tuple[dict, dict]:
+) -> tuple[dict[str, str], dict[str, object]]:
     """
     Update both headers and request body beta fields based on provider support.
     Modifies both dicts in place and returns them.

litellm/caching/caching.py

@@ -455,17 +455,33 @@ class Cache:
         3. Else use the `model` passed in kwargs, plus `model_id` when set (e.g. a SageMaker inference component)
         """
         litellm_params: Final[dict] = kwargs.get("litellm_params", {}) or {}
-        metadata_sources: Final[tuple[dict, ...]] = (
-            kwargs.get("metadata") or {},
-            kwargs.get("litellm_metadata") or {},
-            litellm_params.get("metadata") or {},
-            litellm_params.get("litellm_metadata") or {},
+        metadata_sources: Final = cast(  # cast-ok: each source is the untyped request's metadata dict or empty
+            "tuple[dict[str, object], dict[str, object], dict[str, object], dict[str, object]]",
+            (
+                kwargs.get("metadata") or {},
+                kwargs.get("litellm_metadata") or {},
+                litellm_params.get("metadata") or {},
+                litellm_params.get("litellm_metadata") or {},
+            ),
         )
-        model_group: Final[str | None] = next(
-            (source["model_group"] for source in metadata_sources if source.get("model_group")), None
+        metadata, litellm_metadata, params_metadata, params_litellm_metadata = metadata_sources
+        call_model_info: Final[object] = kwargs.get("model_info")
+        routed_source: Final = next(
+            (
+                source
+                for source in (litellm_metadata, metadata, params_litellm_metadata, params_metadata)
+                if call_model_info and source.get("model_group") and source.get("model_info") == call_model_info
+            ),
+            None,
         )
+        model_group: Final = (
+            str(routed_source["model_group"])
+            if routed_source is not None
+            else next((str(source["model_group"]) for source in metadata_sources if source.get("model_group")), None)
+        )
+        caching_group_sources: Final = (routed_source,) if routed_source is not None else metadata_sources
         caching_group: Final = next(
-            (group for source in metadata_sources if (group := self._get_caching_group(source, model_group))), None
+            (group for source in caching_group_sources if (group := self._get_caching_group(source, model_group))), None
         )
         model_id_suffix: Final = (
             f" model_id: {kwargs['model_id']}" if "model_id" in kwargs and kwargs["model_id"] is not None else ""

litellm/caching/qdrant_semantic_cache.py

@@ -13,7 +13,7 @@ import asyncio
 import json
 import os
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Final, Protocol, cast
+from typing import TYPE_CHECKING, Any, Final, Protocol
 
 from pydantic import TypeAdapter
 
@@ -292,10 +292,7 @@ class QdrantSemanticCache(BaseCache):
         prompt: Final = get_semantic_cache_prompt_from_messages(messages)
 
         # create an embedding for prompt
-        embedding_response: Final = cast(
-            EmbeddingResponse,
-            self._get_embedding(prompt, metadata=kwargs.get("metadata")),
-        )
+        embedding_response: Final = self._get_embedding(prompt, metadata=kwargs.get("metadata"))
 
         # get the embedding
         embedding: Final = _EMBEDDING_VECTOR.validate_python(embedding_response["data"][0]["embedding"])
@@ -331,10 +328,7 @@ class QdrantSemanticCache(BaseCache):
         prompt: Final = get_semantic_cache_prompt_from_messages(messages)
 
         # convert to embedding
-        embedding_response: Final = cast(
-            EmbeddingResponse,
-            self._get_embedding(prompt, metadata=kwargs.get("metadata")),
-        )
+        embedding_response: Final = self._get_embedding(prompt, metadata=kwargs.get("metadata"))
 
         # get the embedding
         embedding: Final = _EMBEDDING_VECTOR.validate_python(embedding_response["data"][0]["embedding"])

litellm/caching/redis_semantic_cache.py

@@ -14,7 +14,9 @@ import asyncio
 import json
 import os
 from collections.abc import Callable, Mapping
-from typing import TYPE_CHECKING, Any, Final, cast
+from typing import TYPE_CHECKING, Final, cast
+
+from pydantic import ConfigDict, TypeAdapter
 
 import litellm
 from litellm._logging import print_verbose, verbose_logger
@@ -38,6 +40,11 @@ if TYPE_CHECKING:
     from litellm.router import Router
 
 
+_CACHE_RESPONSE_ADAPTER: Final[TypeAdapter[str | bytes | None]] = TypeAdapter(
+    str | bytes | None, config=ConfigDict(strict=True, hide_input_in_errors=True)
+)
+
+
 class RedisSemanticCache(BaseCache):
     """
     Redis-backed semantic cache for LLM responses.
@@ -380,7 +387,7 @@ class RedisSemanticCache(BaseCache):
             )
         return embedding_response["data"][0]["embedding"]
 
-    def _get_cache_logic(self, cached_response: Any) -> object:
+    def _get_cache_logic(self, cached_response: str | bytes | None) -> object:
         """
         Process the cached response to prepare it for use.
 
@@ -391,24 +398,23 @@ class RedisSemanticCache(BaseCache):
             The processed cache response, or None if input was None
         """
         if cached_response is None:
-            return cached_response
+            return None
 
         # Convert bytes to string if needed
-        if isinstance(cached_response, bytes):
-            cached_response = cached_response.decode("utf-8")
+        response_text: Final = (
+            cached_response.decode("utf-8") if isinstance(cached_response, bytes) else cached_response
+        )
 
         # Convert string representation to Python object
         try:
-            cached_response = json.loads(cached_response)
+            return json.loads(response_text)
         except json.JSONDecodeError:
             try:
-                cached_response = ast.literal_eval(cached_response)
+                return ast.literal_eval(response_text)
             except (ValueError, SyntaxError) as e:
                 print_verbose(f"Error parsing cached response: {e}")
                 return None
 
-        return cached_response
-
     def set_cache(self, key: str, value: object, **kwargs) -> None:
         """
         Store a value in the semantic cache.
@@ -494,7 +500,7 @@ class RedisSemanticCache(BaseCache):
             similarity: Final = 1 - vector_distance
 
             cached_prompt: Final = cache_hit["prompt"]
-            cached_response: Final = cache_hit["response"]
+            cached_response: Final = _CACHE_RESPONSE_ADAPTER.validate_python(cache_hit["response"])
 
             # update kwargs["metadata"] with similarity, don't rewrite the original metadata
             kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity
@@ -645,7 +651,7 @@ class RedisSemanticCache(BaseCache):
             similarity: Final = 1 - vector_distance
 
             cached_prompt: Final = cache_hit["prompt"]
-            cached_response: Final = cache_hit["response"]
+            cached_response: Final = _CACHE_RESPONSE_ADAPTER.validate_python(cache_hit["response"])
 
             # update kwargs["metadata"] with similarity, don't rewrite the original metadata
             kwargs.setdefault("metadata", {})["semantic-similarity"] = similarity

litellm/cost_calculator.py

@@ -22,7 +22,7 @@ from litellm.constants import (
     OPENAI_LIVE_TERMINAL_RESPONSE_EVENT_TYPES,
     REALTIME_METERED_USAGE_KEY,
 )
-from litellm.litellm_core_utils.core_helpers import as_str_mapping
+from litellm.litellm_core_utils.core_helpers import as_str_mapping, billed_usage_cost
 from litellm.litellm_core_utils.cost_discount import resolve_cost_discount
 from litellm.litellm_core_utils.decisions_pricing import decisions_pricing_fields, is_decisions_call_type
 from litellm.litellm_core_utils.hidden_params import HIDDEN_PARAMS_ATTR
@@ -50,6 +50,7 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import (
     parse_prompt_tokens_details,
     select_cost_metric_for_model,
 )
+from litellm.litellm_core_utils.llm_response_utils.response_metadata import response_ms_since_logging_start
 from litellm.llms.anthropic.cost_calculation import (
     cost_per_token as anthropic_cost_per_token,
 )
@@ -1396,9 +1397,9 @@ def _apply_cost_margin(
 
 def _store_cost_breakdown_in_logging_obj(
     litellm_logging_obj: LitellmLoggingObject | None,
-    prompt_tokens_cost_usd_dollar: float,
-    completion_tokens_cost_usd_dollar: float,
-    cost_for_built_in_tools_cost_usd_dollar: float,
+    prompt_tokens_cost_usd_dollar: float | None,
+    completion_tokens_cost_usd_dollar: float | None,
+    cost_for_built_in_tools_cost_usd_dollar: float | None,
     total_cost_usd_dollar: float,
     additional_costs: dict | None = None,
     original_cost: float | None = None,
@@ -1500,9 +1501,45 @@ def _response_time_ms_for_cost(
         return float(stamped)
     if total_time:
         return total_time
-    if litellm_logging_obj is not None:
-        return litellm_logging_obj.get_response_ms()
-    return 0.0
+    if litellm_logging_obj is None:
+        return 0.0
+    reported_ms: Final = response_ms_since_logging_start(litellm_logging_obj)
+    if reported_ms is not None and litellm_logging_obj.stream is not True:
+        return reported_ms
+    return litellm_logging_obj.get_response_ms()
+
+
+def _adjusted_provider_billed_cost(
+    reported_cost: float,
+    custom_llm_provider: str | None,
+    model: str | None,
+    region_name: str | None,
+    litellm_logging_obj: LitellmLoggingObject | None,
+    service_tier: str | None,
+    data_residency: str | None,
+) -> float:
+    discounted_cost, discount_percent, discount_amount = _apply_cost_discount(
+        base_cost=reported_cost, custom_llm_provider=custom_llm_provider, model=model, region_name=region_name
+    )
+    adjusted_cost, margin_percent, margin_fixed_amount, margin_total_amount = _apply_cost_margin(
+        base_cost=discounted_cost, custom_llm_provider=custom_llm_provider
+    )
+    _store_cost_breakdown_in_logging_obj(
+        litellm_logging_obj=litellm_logging_obj,
+        prompt_tokens_cost_usd_dollar=None,
+        completion_tokens_cost_usd_dollar=None,
+        cost_for_built_in_tools_cost_usd_dollar=None,
+        total_cost_usd_dollar=adjusted_cost,
+        original_cost=reported_cost,
+        discount_percent=discount_percent,
+        discount_amount=discount_amount,
+        margin_percent=margin_percent,
+        margin_fixed_amount=margin_fixed_amount,
+        margin_total_amount=margin_total_amount,
+        service_tier=service_tier,
+        data_residency=data_residency,
+    )
+    return adjusted_cost
 
 
 def completion_cost(
@@ -1616,6 +1653,9 @@ def completion_cost(
             )
 
         explicit_pricing: Final = custom_pricing is True or base_model is not None
+        caller_set_prices: Final = (
+            explicit_pricing or custom_cost_per_token is not None or custom_cost_per_second is not None
+        )
         custom_priced_request_model: Final = model if custom_pricing is True else None
         configured_cost_per_query: Final = (
             _configured_search_cost_per_query(litellm_logging_obj) if custom_pricing else None
@@ -1666,6 +1706,7 @@ def completion_cost(
             raw_candidates if custom_pricing is True else _priced_candidates_before_unpriced_rules(raw_candidates)
         )
 
+        priced_response: Final[object] = completion_response
         for idx, (candidate_model, candidate_pricing_provider) in enumerate(potential_model_names):
             model = candidate_model
             try:
@@ -1984,6 +2025,19 @@ def completion_cost(
                     total_time=total_time,
                 )
                 model_for_discount = custom_priced_request_model or model
+                if (
+                    not caller_set_prices
+                    and (reported_cost := billed_usage_cost(priced_response, custom_llm_provider)) is not None
+                ):
+                    return _adjusted_provider_billed_cost(
+                        reported_cost=reported_cost,
+                        custom_llm_provider=custom_llm_provider,
+                        model=model_for_discount,
+                        region_name=region_name if isinstance(region_name, str) else None,
+                        litellm_logging_obj=litellm_logging_obj,
+                        service_tier=service_tier,
+                        data_residency=data_residency,
+                    )
                 # Calculate cost based on prompt_tokens, completion_tokens
                 if (
                     "togethercomputer" in model or "together_ai" in model or custom_llm_provider == "together_ai"

litellm/exceptions.py

@@ -139,6 +139,7 @@ class AuthenticationError(openai.AuthenticationError):
         litellm_debug_info: str | None = None,
         max_retries: int | None = None,
         num_retries: int | None = None,
+        body: Mapping[str, object] | None = None,
     ):
         self.status_code = 401
         self.message = f"litellm.AuthenticationError: {message}"
@@ -152,7 +153,7 @@ class AuthenticationError(openai.AuthenticationError):
             request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
         )
         super().__init__(
-            self.message, response=self.response, body=None
+            self.message, response=self.response, body=body
         )  # Call the base class constructor with the parameters it needs
 
     def __str__(self):
@@ -189,6 +190,7 @@ class NotFoundError(openai.NotFoundError):
         litellm_debug_info: str | None = None,
         max_retries: int | None = None,
         num_retries: int | None = None,
+        body: Mapping[str, object] | None = None,
     ):
         self.status_code = 404
         self.message = f"litellm.NotFoundError: {message}"
@@ -202,7 +204,7 @@ class NotFoundError(openai.NotFoundError):
             request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
         )
         super().__init__(
-            self.message, response=self.response, body=None
+            self.message, response=self.response, body=body
         )  # Call the base class constructor with the parameters it needs
 
     def __str__(self):
@@ -321,6 +323,17 @@ class UnsupportedEndpointError(BadRequestError):
     pass
 
 
+class EndpointNotSupportedByModelError(BadRequestError):
+    def __init__(self, message: str, model: str, llm_provider: str = "") -> None:
+        super().__init__(  # pyright: ignore[reportUnknownMemberType]  # inherited public exception constructor is untyped
+            message=message, model=model, llm_provider=llm_provider
+        )
+        self.message = message
+        self.type = "invalid_request_error"
+        self.param = "model"
+        self.code = "400"
+
+
 class UnprocessableEntityError(openai.UnprocessableEntityError):
     def __init__(
         self,

litellm/images/main.py

@@ -68,6 +68,11 @@ from litellm.utils import (
 )
 
 _PROVIDER_ROUTING_HINTS: Final = TypeAdapter(ProviderRoutingHints)
+_PROVIDERS_RESOLVING_THEIR_OWN_IMAGE_BASE: Final = (
+    LlmProviders.DASHSCOPE,
+    LlmProviders.QWENCLOUD,
+    LlmProviders.QWEN_AI_PLATFORM,
+)
 
 # Cache for ImageEditRequestUtils to avoid repeated __getattr__ calls
 _ImageEditRequestUtils_cache: type["ImageEditRequestUtils"] | None = None
@@ -271,6 +276,7 @@ def image_generation(
     """
     try:
         args: Final = locals()
+        requested_api_base: Final = api_base
         aimg_generation: Final = kwargs.get("aimg_generation", False)
         litellm_call_id: Final = kwargs.get("litellm_call_id", None)
         logger_fn: Final = kwargs.get("logger_fn", None)
@@ -468,7 +474,11 @@ def image_generation(
                 raise ValueError(f"image generation config is not supported for {custom_llm_provider}")
 
             # Resolve api_base from litellm.api_base if not explicitly provided
-            _api_base: Final = api_base or litellm.api_base
+            _api_base: Final = (
+                requested_api_base
+                if custom_llm_provider in _PROVIDERS_RESOLVING_THEIR_OWN_IMAGE_BASE
+                else api_base or litellm.api_base
+            )
             litellm_params_dict["api_base"] = _api_base
 
             return llm_http_handler.image_generation_handler(
@@ -641,6 +651,8 @@ def image_generation(
                     timeout=timeout,
                     client=custom_client,
                 )
+        elif custom_llm_provider == LlmProviders.KYMA.value:
+            raise LiteLLMUnknownProvider(model=model, custom_llm_provider=custom_llm_provider)
 
         return model_response
     except Exception as e:

litellm/integrations/custom_logger.py

@@ -513,7 +513,7 @@ class CustomLogger:  # https://docs.litellm.ai/docs/observability/custom_callbac
         data: dict,
         user_api_key_dict: UserAPIKeyAuth,
         call_type: CallTypesLiteral,
-    ) -> Any:
+    ) -> object:
         pass
 
     async def async_post_call_streaming_hook(

litellm/integrations/datadog/datadog_cost_management.py

@@ -205,11 +205,12 @@ class DatadogCostManagementLogger(CustomBatchLogger):
         # see _RESERVED_TAG_KEYS for the rationale.
         if self.cost_tag_keys:
             allow: Final = set(self.cost_tag_keys)
+            client_allow: Final = allow - {"is_silent_experiment"}
             for rt in log.get("request_tags") or []:
                 if not isinstance(rt, str) or ":" not in rt:
                     continue
                 k, _, v = rt.partition(":")
-                if k in allow and v:
+                if k in client_allow and v:
                     self._set_custom_tag(tags, k, v)
             for k, v in metadata.items():
                 if k in allow and v is not None and not isinstance(v, (dict, list)):
@@ -218,7 +219,7 @@ class DatadogCostManagementLogger(CustomBatchLogger):
                 nested = metadata.get(nested_key)
                 if isinstance(nested, dict):
                     for k, v in nested.items():
-                        if k in allow and v is not None and not isinstance(v, (dict, list)):
+                        if k in client_allow and v is not None and not isinstance(v, (dict, list)):
                             self._set_custom_tag(tags, k, str(v))
 
         return tags

litellm/integrations/langfuse/langfuse.py

@@ -833,6 +833,12 @@ class LangFuseLogger:
                 ("vertex_location", vertex_location, bool(vertex_location)),
                 ("aws_region_name", aws_region_name, bool(aws_region_name)),
                 ("cache_hit", kwargs.get("cache_hit") or False, "cache_hit" in kwargs),
+                (
+                    "is_silent_experiment",
+                    "is_silent_experiment" in allowlisted_metadata
+                    and allowlisted_metadata["is_silent_experiment"] is True,
+                    "is_silent_experiment" in allowlisted_metadata,
+                ),
             )
             enrichments: Final[Mapping[str, object]] = {
                 key: value for key, value, include in candidate_enrichments if include

litellm/integrations/opentelemetry_utils/base_otel_llm_obs_attributes.py

@@ -15,7 +15,7 @@ class BaseLLMObsOTELAttributes(ABC):
         pass
 
 
-def cast_as_primitive_value_type(value) -> str | bool | int | float:
+def cast_as_primitive_value_type(value: object) -> str | bool | int | float:
     """
     Converts a value to an OTEL-supported primitive for Arize/Phoenix observability.
     """

litellm/integrations/prometheus.py

@@ -5148,6 +5148,12 @@ def _get_combined_custom_metadata_from_standard_logging_payload(
         **(requester_metadata if isinstance(requester_metadata, dict) else {}),
         **(user_api_key_auth_metadata if isinstance(user_api_key_auth_metadata, dict) else {}),
         **(spend_logs_metadata if isinstance(spend_logs_metadata, dict) else {}),
+        "is_silent_experiment": (
+            "true"
+            if "is_silent_experiment" in standard_logging_metadata
+            and standard_logging_metadata["is_silent_experiment"] is True
+            else None
+        ),
     }
 
 

litellm/integrations/websearch_interception/handler.py

@@ -37,6 +37,7 @@ from litellm.integrations.websearch_interception.transformation import (
 from litellm.litellm_core_utils.agentic_loop_settings import (
     validated_max_agentic_loops,
 )
+from litellm.litellm_core_utils.core_helpers import qualify_provider_stripped_model
 from litellm.llms.base_llm.search.transformation import SearchResponse
 from litellm.types.integrations.custom_logger import (
     CHAT_COMPLETION_AGENTIC_SURFACE,
@@ -114,6 +115,18 @@ class _WebSearchSettingsView(TypedDict):
     websearch_interception_params: WebSearchInterceptionConfig
 
 
+def _caller_model(model: str, logging_obj: "LiteLLMLoggingObj | None", kwargs: Mapping[str, object]) -> str:
+    agentic_view: Final[_AgenticLoopParamsView] = {
+        "agentic_loop_params": logging_obj.model_call_details.get("agentic_loop_params", {}) if logging_obj else {}
+    }
+    recorded_model: Final = agentic_view["agentic_loop_params"].get("model")
+    custom_llm_provider: Final = kwargs.get("custom_llm_provider")
+    return qualify_provider_stripped_model(
+        recorded_model if isinstance(recorded_model, str) and recorded_model else model,
+        custom_llm_provider if isinstance(custom_llm_provider, str) else "",
+    )
+
+
 class _SearchToolLitellmParams(TypedDict, total=False):
     search_provider: ReadOnly[str | None]
 
@@ -1155,6 +1168,7 @@ class WebSearchInterceptionLogger(CustomLogger):
             tool_calls=tool_calls,
             optional_params=optional_params,
             kwargs=kwargs,
+            logging_obj=logging_obj,
             response_format=response_format,
         )
         return AgenticLoopPlan(
@@ -1181,6 +1195,7 @@ class WebSearchInterceptionLogger(CustomLogger):
             tool_calls=tool_calls,
             optional_params=optional_params,
             kwargs=kwargs,
+            logging_obj=logging_obj,
         )
         return AgenticLoopPlan(
             run_agentic_loop=True,
@@ -1195,6 +1210,7 @@ class WebSearchInterceptionLogger(CustomLogger):
         tool_calls: list[dict],
         optional_params: dict,
         kwargs: dict,
+        logging_obj: "LiteLLMLoggingObj | None",
     ) -> AgenticLoopRequestPatch:
         """Execute litellm.asearch() and build a Responses API rerun patch."""
         search_tasks: Final = [
@@ -1254,9 +1270,7 @@ class WebSearchInterceptionLogger(CustomLogger):
             }
         }
 
-        full_model_name = model
-        if "/" not in model and isinstance(kwargs.get("custom_llm_provider"), str):
-            full_model_name = f"{kwargs['custom_llm_provider']}/{model}"
+        full_model_name: Final = _caller_model(model, logging_obj, kwargs)
 
         verbose_logger.debug(
             "WebSearchInterception: Built responses request patch model=%s input_items=%d searches=%d",
@@ -1814,6 +1828,7 @@ class WebSearchInterceptionLogger(CustomLogger):
             tool_calls=tool_calls,
             optional_params=dict[str, object](optional_params),
             kwargs=dict[str, object](kwargs),
+            logging_obj=logging_obj,
             response_format=response_format,
         )
         if request_patch.messages is None:
@@ -1836,7 +1851,8 @@ class WebSearchInterceptionLogger(CustomLogger):
         messages: list[dict],
         tool_calls: list[dict],
         optional_params: dict,
-        kwargs: dict,
+        kwargs: dict[str, object],
+        logging_obj: "LiteLLMLoggingObj | None",
         response_format: str = "openai",
     ) -> AgenticLoopRequestPatch:
         """Execute litellm.search() and build chat-completion rerun patch."""
@@ -1922,11 +1938,7 @@ class WebSearchInterceptionLogger(CustomLogger):
             k: v for k, v in kwargs.items() if not k.startswith("_websearch_interception") and k not in internal_params
         }
 
-        full_model_name = model
-        if "custom_llm_provider" in kwargs:
-            custom_llm_provider: Final = kwargs["custom_llm_provider"]
-            if not model.startswith(custom_llm_provider) and "/" not in model:
-                full_model_name = f"{custom_llm_provider}/{model}"
+        full_model_name: Final = _caller_model(model, logging_obj, kwargs)
 
         verbose_logger.debug(
             "WebSearchInterception: Built chat completion request patch model=%s messages=%d",

litellm/interactions/litellm_responses_transformation/handler.py

@@ -103,6 +103,7 @@ class LiteLLMResponsesInteractionsHandler:
         return LiteLLMResponsesInteractionsConfig.transform_responses_response_to_interactions_response(
             responses_response=responses_api_response,
             model=model,
+            custom_llm_provider=custom_llm_provider,
         )
 
     async def async_interactions_api_handler(
@@ -138,4 +139,5 @@ class LiteLLMResponsesInteractionsHandler:
         return LiteLLMResponsesInteractionsConfig.transform_responses_response_to_interactions_response(
             responses_response=responses_api_response,
             model=model,
+            custom_llm_provider=responses_request.get("custom_llm_provider"),
         )

litellm/interactions/litellm_responses_transformation/transformation.py

@@ -12,6 +12,7 @@ from typing import Final, cast
 
 from pydantic import BaseModel
 
+from litellm.litellm_core_utils.core_helpers import billed_usage_cost
 from litellm.types.interactions import (
     InteractionInput,
     InteractionsAPIOptionalRequestParams,
@@ -190,6 +191,7 @@ class LiteLLMResponsesInteractionsConfig:
     def transform_responses_response_to_interactions_response(
         responses_response: ResponsesAPIResponse,
         model: str | None = None,
+        custom_llm_provider: str | None = None,
     ) -> InteractionsAPIResponse:
         """
         Transform a Responses API response to an Interactions API response.
@@ -266,9 +268,11 @@ class LiteLLMResponsesInteractionsConfig:
         # (total_input_tokens, total_output_tokens)
         usage: Final[object] = getattr(responses_response, "usage", None)
         if usage:
+            reported_cost: Final = billed_usage_cost(responses_response, custom_llm_provider)
             interactions_response_dict["usage"] = {
                 "total_input_tokens": getattr(usage, "input_tokens", 0),
                 "total_output_tokens": getattr(usage, "output_tokens", 0),
+                **({} if reported_cost is None else {"cost": reported_cost}),
             }
 
         # Add updated (same as created for now)

litellm/litellm_core_utils/chat_completion_agentic_loop.py

@@ -15,6 +15,7 @@ from litellm.litellm_core_utils.agentic_loop_settings import (
     DEFAULT_MAX_AGENTIC_LOOPS,
     validated_max_agentic_loops,
 )
+from litellm.litellm_core_utils.core_helpers import qualify_agentic_followup_model
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObject
 from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
 from litellm.types.integrations.custom_logger import (
@@ -182,9 +183,7 @@ async def _execute_chat_completion_agentic_plan(
     if patch.messages is None:
         raise ValueError("Agentic loop plan missing patched messages")
 
-    full_model_name = patch.model or model
-    if "/" not in full_model_name:
-        full_model_name = f"{custom_llm_provider}/{full_model_name}"
+    full_model_name: Final = qualify_agentic_followup_model(patch.model, model, custom_llm_provider)
 
     optional_params_for_followup: Final = {**optional_params, **patch.optional_params}
     if patch.tools is not None:

litellm/litellm_core_utils/core_helpers.py

@@ -2,6 +2,7 @@
 ## Helper utilities
 import copy
 import logging
+import math
 import re
 from collections.abc import Collection, Iterable, Iterator, Mapping
 from types import MappingProxyType
@@ -34,6 +35,49 @@ def is_codex_user_agent(user_agent: str) -> bool:
     return bool(_CODEX_CLIENT_PREFIX_RE.match(user_agent))
 
 
+def qualify_provider_stripped_model(model: str, custom_llm_provider: str) -> str:
+    """Put the provider prefix back on a provider-stripped model."""
+    if not custom_llm_provider or model.startswith(f"{custom_llm_provider}/"):
+        return model
+    if _already_routes_to(model, custom_llm_provider):
+        return model
+    return f"{custom_llm_provider}/{model}"
+
+
+def _already_routes_to(model: str, custom_llm_provider: str) -> bool:
+    from litellm.litellm_core_utils.get_llm_provider_logic import inferred_provider
+
+    return _leads_with_provider(model) and inferred_provider(model) == custom_llm_provider
+
+
+def _leads_with_provider(model: str) -> bool:
+    leading_segment, separator, _ = model.partition("/")
+    return bool(separator) and names_a_provider(leading_segment)
+
+
+def names_a_provider(candidate: str) -> bool:
+    """Whether a model name's leading segment is a provider rather than part of the model."""
+    import litellm
+    from litellm.litellm_core_utils.get_llm_provider_logic import is_registered_custom_provider
+    from litellm.llms.openai_like.json_loader import JSONProviderRegistry
+
+    provider_names: Final = {getattr(provider, "value", provider) for provider in litellm.provider_list}
+    return (
+        candidate in provider_names
+        or JSONProviderRegistry.exists(candidate)
+        or is_registered_custom_provider(candidate)
+    )
+
+
+def qualify_agentic_followup_model(patch_model: str | None, model: str, custom_llm_provider: str) -> str:
+    """Resolve the model an agentic follow-up re-dispatches, keeping a hook's own provider."""
+    if not patch_model or patch_model == model:
+        return qualify_provider_stripped_model(model, custom_llm_provider)
+    if _leads_with_provider(patch_model):
+        return patch_model
+    return qualify_provider_stripped_model(patch_model, custom_llm_provider)
+
+
 def safe_divide_seconds(seconds: float, denominator: float, default: float | None = None) -> float | None:
     """
     Safely divide seconds by denominator, handling zero division.
@@ -973,6 +1017,16 @@ def chunk_usage(chunk: object) -> object:
     return getattr(chunk, "usage", None)
 
 
+USAGE_COST_BILLED_PROVIDERS: Final = frozenset({"kyma"})
+
+
+def billed_usage_cost(response: object, custom_llm_provider: str | None) -> float | None:
+    if custom_llm_provider not in USAGE_COST_BILLED_PROVIDERS:
+        return None
+    cost: Final = provider_reported_usage_cost(_usage_field(response, "usage"), custom_llm_provider)
+    return cost if cost is not None and math.isfinite(cost) and cost >= 0 else None
+
+
 _MESSAGES_REPORTED_COST_PROVIDERS: Final = frozenset({"opencode_go"})
 
 

litellm/litellm_core_utils/exception_mapping_utils.py

@@ -413,6 +413,25 @@ def _map_openai_exception(
             body=getattr(original_exception, "body", None),
         )
     elif "invalid_request_error" in error_str and "Incorrect API key provided" not in error_str:
+        invalid_request_status: Final = getattr(original_exception, "status_code", None)
+        if invalid_request_status == 401:
+            raise AuthenticationError(
+                message=f"{exception_provider} - {message}",
+                llm_provider=custom_llm_provider,
+                model=model,
+                response=response,
+                litellm_debug_info=extra_information,
+                body=getattr(original_exception, "body", None),
+            )
+        if invalid_request_status == 404:
+            raise NotFoundError(
+                message=f"{exception_provider} - {message}",
+                model=model,
+                llm_provider=custom_llm_provider,
+                response=response,
+                litellm_debug_info=extra_information,
+                body=getattr(original_exception, "body", None),
+            )
         raise BadRequestError(
             message=f"{exception_provider} - {message}",
             llm_provider=custom_llm_provider,

litellm/litellm_core_utils/health_check_helpers.py

@@ -31,7 +31,13 @@ EXTRACTION_HEALTH_CHECK_FIELDS: Final[Mapping[str, Mapping[str, str]]] = Mapping
 )
 
 
-def native_health_check_mode(model: str, custom_llm_provider: str | None) -> Literal["anthropic_messages"] | None:
+def native_health_check_mode(
+    model: str, custom_llm_provider: str | None
+) -> Literal["anthropic_messages", "realtime"] | None:
+    if custom_llm_provider == "soniox":
+        from litellm.llms.soniox.common_utils import soniox_health_check_mode
+
+        return soniox_health_check_mode(model)
     if custom_llm_provider != "bedrock_mantle":
         return None
     from litellm.llms.bedrock_mantle.common_utils import mantle_health_check_mode

litellm/litellm_core_utils/litellm_logging.py

@@ -23,7 +23,7 @@ from httpx import Response, Timeout
 from pydantic import BaseModel, JsonValue, TypeAdapter
 
 import litellm
-from litellm._internal_context import post_response_phase
+from litellm._internal_context import in_mirrored_call, outside_mirrored_call, post_response_phase
 from litellm._logging import (
     is_debugging_on,
     redact_string,
@@ -162,9 +162,10 @@ from litellm.types.llms.openai import (
 from litellm.types.mcp import MCPPostCallResponseObject
 from litellm.types.prompts.init_prompts import PromptSpec
 from litellm.types.rerank import RerankResponse
-from litellm.types.router import RouterRateLimitError
+from litellm.types.router import RouterRateLimitError, SilentExperimentFlag
 from litellm.types.utils import (
     DEPLOYMENT_SCOPED_PRICING_FIELDS,
+    AgenticLoopParams,
     CachingDetails,
     CallTypes,
     CostBreakdown,
@@ -314,7 +315,7 @@ else:
 _in_memory_loggers: Final[list[CustomLogger]] = []
 
 _STANDARD_LOGGING_METADATA_RESOLVED_KEYS: Final[frozenset[str]] = frozenset(
-    ("used_client_oauth_token", "usage_object", KUBERNETES_POD_ROUTING_KEY)
+    ("used_client_oauth_token", "usage_object", KUBERNETES_POD_ROUTING_KEY, "is_silent_experiment")
 )
 _STANDARD_LOGGING_METADATA_KEYS: Final[frozenset[str]] = (
     frozenset(StandardLoggingMetadata.__annotations__.keys()) - _STANDARD_LOGGING_METADATA_RESOLVED_KEYS
@@ -808,6 +809,7 @@ class Logging(LiteLLMLoggingBaseClass):
             litellm_params = scrub_sensitive_keys_in_metadata(litellm_params)
 
         self.litellm_params = litellm_params
+        self.is_silent_experiment: Final = in_mirrored_call()
 
         # Initialize cost breakdown field
         self.cost_breakdown: CostBreakdown | None = None
@@ -1158,6 +1160,9 @@ class Logging(LiteLLMLoggingBaseClass):
         if "custom_llm_provider" in self.model_call_details:
             self.custom_llm_provider = self.model_call_details["custom_llm_provider"]
 
+    def record_agentic_loop_params(self, params: AgenticLoopParams) -> None:
+        self.model_call_details["agentic_loop_params"] = params
+
     def record_applied_timeout(self, timeout: float | Timeout) -> None:
         self.applied_timeout = timeout
         self.litellm_params["timeout"] = timeout
@@ -1937,10 +1942,10 @@ class Logging(LiteLLMLoggingBaseClass):
 
     def set_cost_breakdown(
         self,
-        input_cost: float,
-        output_cost: float,
+        input_cost: float | None,
+        output_cost: float | None,
         total_cost: float,
-        cost_for_built_in_tools_cost_usd_dollar: float,
+        cost_for_built_in_tools_cost_usd_dollar: float | None,
         additional_costs: dict | None = None,
         original_cost: float | None = None,
         discount_percent: float | None = None,
@@ -1979,14 +1984,17 @@ class Logging(LiteLLMLoggingBaseClass):
 
         self.billed_token_rates = billed_token_rates
         self.cost_breakdown = CostBreakdown(
-            input_cost=input_cost,
-            output_cost=output_cost,
             total_cost=total_cost,
-            tool_usage_cost=cost_for_built_in_tools_cost_usd_dollar,
             service_tier=service_tier,
             data_residency=data_residency,
             vertex_location=vertex_location,
         )
+        if input_cost is not None:
+            self.cost_breakdown["input_cost"] = input_cost
+        if output_cost is not None:
+            self.cost_breakdown["output_cost"] = output_cost
+        if cost_for_built_in_tools_cost_usd_dollar is not None:
+            self.cost_breakdown["tool_usage_cost"] = cost_for_built_in_tools_cost_usd_dollar
         if cache_read_cost is not None and cache_read_cost > 0:
             self.cost_breakdown["cache_read_cost"] = cache_read_cost
         if cache_creation_cost is not None and cache_creation_cost > 0:
@@ -3249,7 +3257,7 @@ class Logging(LiteLLMLoggingBaseClass):
         """Restores trace_id/session_id contextvars once this attempt's own success
         logging (including any nested calls its callbacks trigger) is fully done."""
         try:
-            with post_response_phase():
+            with post_response_phase(), outside_mirrored_call():
                 return self._success_handler_body(
                     result=result,
                     start_time=start_time,
@@ -3749,7 +3757,7 @@ class Logging(LiteLLMLoggingBaseClass):
         """Restores trace_id/session_id contextvars once this attempt's own success
         logging (including any nested calls its callbacks trigger) is fully done."""
         try:
-            with post_response_phase():
+            with post_response_phase(), outside_mirrored_call():
                 return await self._async_success_handler_body(
                     result=result, start_time=start_time, end_time=end_time, cache_hit=cache_hit, **kwargs
                 )
@@ -4266,13 +4274,14 @@ class Logging(LiteLLMLoggingBaseClass):
         ``for_async_request`` marks the sync run of a failure whose async run also logs it, so a
         CustomLogger that gets ``async_log_failure_event`` from that async run skips its sync hook."""
         try:
-            return self._failure_handler_body(
-                exception=exception,
-                traceback_exception=traceback_exception,
-                start_time=start_time,
-                end_time=end_time,
-                for_async_request=for_async_request,
-            )
+            with outside_mirrored_call():
+                return self._failure_handler_body(
+                    exception=exception,
+                    traceback_exception=traceback_exception,
+                    start_time=start_time,
+                    end_time=end_time,
+                    for_async_request=for_async_request,
+                )
         finally:
             self._restore_correlation_context()
 
@@ -4465,12 +4474,13 @@ class Logging(LiteLLMLoggingBaseClass):
         """Restores trace_id/session_id contextvars once this attempt's own failure
         logging (including any nested calls its callbacks trigger) is fully done."""
         try:
-            return await self._async_failure_handler_body(
-                exception=exception,
-                traceback_exception=traceback_exception,
-                start_time=start_time,
-                end_time=end_time,
-            )
+            with outside_mirrored_call():
+                return await self._async_failure_handler_body(
+                    exception=exception,
+                    traceback_exception=traceback_exception,
+                    start_time=start_time,
+                    end_time=end_time,
+                )
         finally:
             self._restore_correlation_context()
 
@@ -6559,6 +6569,7 @@ class StandardLoggingPayloadSetup:
         start_time: dt_object | None = None,
         response_id: str | None = None,
         custom_llm_provider: str | None = None,
+        is_silent_experiment: bool = False,
     ) -> StandardLoggingMetadata:
         """
         Clean and filter the metadata dictionary to include only the specified keys in StandardLoggingMetadata.
@@ -6632,6 +6643,7 @@ class StandardLoggingPayloadSetup:
                 proxy_stamped_used_client_oauth_token(metadata, litellm_params),
                 custom_llm_provider,
             ),
+            **(SilentExperimentFlag(is_silent_experiment=True) if is_silent_experiment else SilentExperimentFlag()),
         )
         if isinstance(metadata, dict):
             for key in metadata.keys() & _STANDARD_LOGGING_METADATA_KEYS:
@@ -7432,6 +7444,7 @@ def get_standard_logging_object_payload(
             start_time=start_time,
             response_id=id,
             custom_llm_provider=selected_provider if isinstance(selected_provider, str) else None,
+            is_silent_experiment=logging_obj.is_silent_experiment is True,
         )
         _request_body: Final = proxy_server_request.get("body", {})
         end_user_id: Final = clean_metadata["user_api_key_end_user_id"] or _request_body.get(
@@ -7716,9 +7729,8 @@ def get_standard_logging_metadata(
     )
     if isinstance(metadata, dict):
         # Update the clean_metadata with values from input metadata that match StandardLoggingMetadata fields
-        for key in StandardLoggingMetadata.__annotations__:
-            if key in metadata:
-                clean_metadata[key] = metadata[key]
+        for key in (metadata.keys() & StandardLoggingMetadata.__annotations__.keys()) - {"is_silent_experiment"}:
+            clean_metadata[key] = metadata[key]
 
         if metadata.get("user_api_key") is not None:
             if is_valid_sha256_hash(str(metadata.get("user_api_key"))):

litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py

@@ -3,7 +3,7 @@ import json
 import re
 import time
 import traceback
-from collections.abc import Mapping, Sequence
+from collections.abc import Generator, Iterator, Mapping, Sequence
 from typing import Final, Literal, cast
 
 from pydantic import ConfigDict, TypeAdapter, ValidationError, with_config
@@ -19,7 +19,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
 from litellm.llms.ollama.common_utils import OllamaResponseIdNormalizer
 from litellm.types.llms.databricks import DatabricksTool
 from litellm.types.llms.openai import (
-    ChatCompletionReasoningItem,
+    ChatCompletionAnnotation,
     ChatCompletionThinkingBlock,
     ImageURLListItem,
     OpenAIModerationResponse,
@@ -59,6 +59,31 @@ _CHOICES_FIELDS: Final[frozenset] = frozenset(Choices.model_fields.keys())
 _MODEL_RESPONSE_FIELDS: Final[frozenset] = frozenset(ModelResponse.model_fields.keys()) | {"usage"}
 
 
+_ANNOTATION_ENTRIES: Final = TypeAdapter(tuple[object, ...])
+
+
+def _is_provider_specific_annotation(annotation: object) -> bool:
+    match annotation:
+        case {"type": "url_citation"}:
+            return False
+        case {"type": _}:
+            return True
+        case _:
+            return False
+
+
+def _annotation_entries(annotations: object) -> tuple[object, ...]:
+    return _ANNOTATION_ENTRIES.validate_python(annotations) if isinstance(annotations, list) else ()
+
+
+def partition_annotations(annotations: object) -> tuple[object, tuple[object, ...]]:
+    entries: Final = _annotation_entries(annotations)
+    others: Final = tuple(annotation for annotation in entries if _is_provider_specific_annotation(annotation))
+    if not others:
+        return annotations, ()
+    return [annotation for annotation in entries if not _is_provider_specific_annotation(annotation)] or None, others
+
+
 def _normalize_images_for_message(
     images: list[dict] | None,
 ) -> list[ImageURLListItem] | None:
@@ -145,31 +170,89 @@ def _split_assembled_content_for_replay(content: str | None) -> list[str]:
     return _REPLAY_CONTENT_SLICE_RE.findall(content)
 
 
-def _clear_later_replay_slice_metadata(choice: StreamingChoices) -> None:
-    # Rebuild the delta as content-only so every accumulate-able field (role,
-    # tool_calls, reasoning_content, thinking_blocks, audio, images,
-    # annotations, ...) is dropped on later slices instead of an enumerated
-    # subset; repeating any of them makes downstream handlers that accumulate
-    # streamed deltas collect it once per slice, and a field added to Delta
-    # later can't silently re-introduce the duplication.
-    choice.delta = Delta(content=choice.delta.content)
-    choice.logprobs = None
-    if hasattr(choice, "enhancements"):
-        del choice.enhancements
+_REPLAY_REASONING_FIELDS: Final = frozenset({"reasoning_content", "thinking_blocks", "reasoning_items"})
+_REPLAY_LEAD_FIELDS: Final = _REPLAY_REASONING_FIELDS | {"role", "provider_specific_fields"}
+_REPLAY_TOOL_FIELDS: Final = frozenset({"tool_calls", "function_call"})
+_REPLAY_TEXT_FIELDS: Final = frozenset({"content", "annotations"})
 
 
-def _first_replay_delta(
-    message: Mapping[str, object], reasoning_items: Sequence[ChatCompletionReasoningItem] | None
-) -> Delta:
-    return Delta(
-        content=message.get("content"),
-        role=message["role"],
-        function_call=message.get("function_call"),
-        tool_calls=message.get("tool_calls"),
-        reasoning_items=None if reasoning_items is None else list(reasoning_items),
-        provider_specific_fields=_cached_provider_specific_fields(
-            _CACHED_MESSAGE_PROVIDER_FIELDS.validate_python(message)
-        ),
+def _fields_in(fields: Mapping[str, object], keys: frozenset[str]) -> dict[str, object]:
+    return {key: value for key, value in fields.items() if key in keys}
+
+
+def _replay_deltas(delta: Delta) -> tuple[Delta, ...]:
+    fields: Final[dict[str, object]] = delta.model_dump(exclude_none=True)
+    lead: Final = _fields_in(fields, _REPLAY_LEAD_FIELDS) if _REPLAY_REASONING_FIELDS.intersection(fields) else {}
+    pieces: Final = _split_assembled_content_for_replay(delta.content) or ([delta.content] if delta.content else [])
+    if not pieces and not lead:
+        return (delta,)
+    if not pieces:
+        held_back: Final = lead.keys() | {"content"}
+        rest: Final = {key: value for key, value in fields.items() if key not in held_back}
+        return (Delta.model_validate(lead),) + ((Delta.model_validate(rest),) if rest else ())
+    tools: Final = _fields_in(fields, _REPLAY_TOOL_FIELDS)
+    annotations: Final = _fields_in(fields, frozenset({"annotations"}))
+    moved: Final = lead.keys() | _REPLAY_TOOL_FIELDS | _REPLAY_TEXT_FIELDS
+    first_slice: Final = {key: value for key, value in fields.items() if key not in moved}
+    last_index: Final = len(pieces) - 1
+    return (
+        ((Delta.model_validate(lead),) if lead else ())
+        + tuple(
+            Delta.model_validate(
+                {
+                    **(first_slice if index == 0 else {}),
+                    "content": piece,
+                    **(annotations if index == last_index else {}),
+                }
+            )
+            for index, piece in enumerate(pieces)
+        )
+        + ((Delta.model_validate(tools),) if tools else ())
+    )
+
+
+def _replay_frame(
+    response: ModelResponseStream, delta: Delta, finish_reason: str | None, keeps_choice_metadata: bool
+) -> ModelResponseStream:
+    frame: Final = response.model_copy(deep=True)
+    choice: Final = frame.choices[0]
+    choice.delta = delta
+    choice.finish_reason = finish_reason
+    if not keeps_choice_metadata:
+        choice.logprobs = None
+        if hasattr(choice, "enhancements"):
+            del choice.enhancements
+    return frame
+
+
+def _replay_frames(response: ModelResponseStream) -> Iterator[ModelResponseStream]:
+    if len(response.choices) != 1:
+        yield response
+        return
+    usage: Final = getattr(response, "usage", None)
+    if usage is not None:
+        delattr(response, "usage")
+    choice: Final = response.choices[0]
+    deltas: Final = _replay_deltas(choice.delta)
+    first_text: Final = next((index for index, delta in enumerate(deltas) if delta.content), 0)
+    for index, delta in enumerate(deltas):
+        yield _replay_frame(response, delta, finish_reason=None, keeps_choice_metadata=index == first_text)
+    finish_frame: Final = _replay_frame(
+        response, Delta(), finish_reason=choice.finish_reason, keeps_choice_metadata=False
+    )
+    if usage is not None:
+        setattr(finish_frame, "usage", usage)
+    yield finish_frame
+
+
+def _first_replay_delta(message: Mapping[str, object]) -> Delta:
+    return Delta.model_validate(
+        {
+            **message,
+            "provider_specific_fields": _cached_provider_specific_fields(
+                _CACHED_MESSAGE_PROVIDER_FIELDS.validate_python(message)
+            ),
+        }
     )
 
 
@@ -234,7 +317,7 @@ async def convert_to_streaming_response_async(
                     t["index"] = index
                 pydantic_tool_calls.append(ChatCompletionDeltaToolCall(**t))
             choice["message"]["tool_calls"] = pydantic_tool_calls
-        delta = _first_replay_delta(choice["message"], choice["message"].get("reasoning_items"))
+        delta = _first_replay_delta(choice["message"])
         finish_reason = choice.get("finish_reason", None)
 
         if finish_reason is None:
@@ -266,41 +349,14 @@ async def convert_to_streaming_response_async(
     if "model" in response_object:
         model_response_object.model = response_object["model"]
 
-    # Replay cached content with per-word cadence so stream=true cache hits
-    # don't arrive as a single SSE frame. Multi-choice (n>1) responses and
-    # unsplittable content (None/empty/whitespace-free) keep the original
-    # single-yield behavior.
-    slices: list[str] = []
-    if len(model_response_object.choices) == 1:
-        slices = _split_assembled_content_for_replay(model_response_object.choices[0].delta.content)
-    if len(slices) <= 1:
-        yield model_response_object
-        await asyncio.sleep(0)
-        return
-
-    # Detach usage from the base object so we can re-attach it only to the
-    # final slice chunk. A non-None usage always lives in __pydantic_extra__
-    # here (set via setattr above), so delattr cannot fail.
-    original_usage: Final = getattr(model_response_object, "usage", None)
-    if original_usage is not None:
-        delattr(model_response_object, "usage")
-    original_finish_reason: Final = model_response_object.choices[0].finish_reason
-    last_idx: Final = len(slices) - 1
-    for i, piece in enumerate(slices):
-        slice_chunk = model_response_object.model_copy(deep=True)
-        slice_chunk.choices[0].delta.content = piece
-        if i > 0:
-            _clear_later_replay_slice_metadata(slice_chunk.choices[0])
-        slice_chunk.choices[0].finish_reason = original_finish_reason if i == last_idx else None
-        if i == last_idx and original_usage is not None:
-            setattr(slice_chunk, "usage", original_usage)
-        yield slice_chunk
+    for frame in _replay_frames(model_response_object):
+        yield frame
         await asyncio.sleep(0)
 
 
 def convert_to_streaming_response(
     response_object: dict | None = None,
-):
+) -> Generator[ModelResponseStream, None, None]:
     # used for yielding Cache hits when stream == True
     if response_object is None:
         raise Exception("Error in response object format")
@@ -356,30 +412,7 @@ def convert_to_streaming_response(
     if "model" in response_object:
         model_response_object.model = response_object["model"]
 
-    # Replay cached content with per-word cadence on sync cache-hit paths
-    # (S3Cache, sync completion()). See convert_to_streaming_response_async
-    # for the full rationale — this mirrors its tail.
-    slices: list[str] = []
-    if len(model_response_object.choices) == 1:
-        slices = _split_assembled_content_for_replay(model_response_object.choices[0].delta.content)
-    if len(slices) <= 1:
-        yield model_response_object
-        return
-
-    original_usage: Final = getattr(model_response_object, "usage", None)
-    if original_usage is not None:
-        delattr(model_response_object, "usage")
-    original_finish_reason: Final = model_response_object.choices[0].finish_reason
-    last_idx: Final = len(slices) - 1
-    for i, piece in enumerate(slices):
-        slice_chunk = model_response_object.model_copy(deep=True)
-        slice_chunk.choices[0].delta.content = piece
-        if i > 0:
-            _clear_later_replay_slice_metadata(slice_chunk.choices[0])
-        slice_chunk.choices[0].finish_reason = original_finish_reason if i == last_idx else None
-        if i == last_idx and original_usage is not None:
-            setattr(slice_chunk, "usage", original_usage)
-        yield slice_chunk
+    yield from _replay_frames(model_response_object)
 
 
 from collections import defaultdict
@@ -787,6 +820,9 @@ def convert_to_model_response_object(
                     provider_specific_fields = dict(choice["message"].get("provider_specific_fields", None) or {})
                     for f in choice["message"].keys() - _MESSAGE_FIELDS:
                         provider_specific_fields[f] = choice["message"][f]
+                    annotations, other_annotations = partition_annotations(choice["message"].get("annotations", None))
+                    if other_annotations:
+                        provider_specific_fields["annotations"] = list(other_annotations)
 
                     # Handle reasoning models that display `reasoning_content` within `content`
                     reasoning_content, content = extract_reasoning_content(choice["message"])
@@ -809,7 +845,9 @@ def convert_to_model_response_object(
                         reasoning_content=reasoning_content,
                         thinking_blocks=thinking_blocks,
                         reasoning_items=choice["message"].get("reasoning_items", None),
-                        annotations=choice["message"].get("annotations", None),
+                        annotations=cast(  # cast-ok: provider annotation dicts pass through whole, keeping keys such as url_citation.content
+                            list[ChatCompletionAnnotation] | None, annotations
+                        ),
                         images=_normalize_images_for_message(choice["message"].get("images", None)),
                     )
                     finish_reason = choice.get("finish_reason", None)

litellm/litellm_core_utils/llm_response_utils/response_metadata.py

@@ -27,6 +27,15 @@ def _timing_window_start(
     return start_time, False
 
 
+def response_ms_since_logging_start(logging_obj: LiteLLMLoggingObject) -> float | None:
+    reported_ms: Final = logging_obj.response_timing_metrics.get("_response_ms")
+    if reported_ms is None:
+        return None
+    logging_start: Final = logging_obj.start_time
+    window_start: Final = _timing_window_start(logging_start, logging_obj)[0]
+    return reported_ms - max(0.0, logging_start.timestamp() - window_start.timestamp()) * 1000
+
+
 def _union_duration_ms(windows: object, lower: float, upper: float) -> float | None:
     if not isinstance(windows, (list, tuple)):
         return None

litellm/litellm_core_utils/logging_worker.py

@@ -6,7 +6,7 @@ import atexit
 import contextvars
 import inspect
 import logging
-from collections.abc import Coroutine, Iterator
+from collections.abc import Awaitable, Callable, Coroutine, Iterator
 from typing import Final
 
 from typing_extensions import TypedDict
@@ -66,14 +66,19 @@ class LoggingWorker:
         max_queue_size: int = LOGGING_WORKER_MAX_QUEUE_SIZE,
         concurrency: int = LOGGING_WORKER_CONCURRENCY,
         timeout_summary_window: float = LOGGING_WORKER_TIMEOUT_SUMMARY_WINDOW_SECONDS,
+        *,
+        retry_sleep: Callable[[float], Awaitable[None]] = asyncio.sleep,
     ):
         self.timeout = timeout
         self.max_queue_size = max_queue_size
         self.concurrency = concurrency
         self.timeout_summary_window = timeout_summary_window
         self._queue: asyncio.Queue[LoggingTask] | None = None
-        self._worker_task: asyncio.Task | None = None
-        self._running_tasks: set[asyncio.Task] = set()
+        self._worker_task: asyncio.Task[None] | None = None
+        self._running_tasks: set[asyncio.Task[None]] = set()
+        self._overflow_tasks: set[asyncio.Task[None]] = set()  # mutable-ok: done callbacks discard refs
+        self._pending_enqueues: dict[int, LoggingTask] = {}  # mutable-ok: retries and shutdown share callback ownership
+        self._retry_sleep = retry_sleep
         self._dequeued_tasks: dict[int, LoggingTask] = {}  # mutable-ok: refs so flush can rescue never-started tasks
         self._sem: asyncio.Semaphore | None = None
         self._bound_loop: asyncio.AbstractEventLoop | None = None
@@ -168,6 +173,7 @@ class LoggingWorker:
             self._sem = None
             self._worker_task = None
             self._running_tasks.clear()
+            self._overflow_tasks.clear()
             self._timeout_summary_task = None
             self._timeout_burst_count = 0
             self._queue = new_queue
@@ -185,6 +191,10 @@ class LoggingWorker:
             self._sem = asyncio.Semaphore(self.concurrency)
         if self._worker_task is None or self._worker_task.done():
             self._worker_task = asyncio.create_task(self._worker_loop())
+        if not self._overflow_tasks:
+            self._aggressive_clear_in_progress = False
+            for pending in tuple(self._pending_enqueues.values()):
+                self._schedule_delayed_enqueue_retry(pending)
 
     async def _process_log_task(
         self, task: LoggingTask, sem: asyncio.Semaphore, queue: "asyncio.Queue[LoggingTask]"
@@ -278,12 +288,24 @@ class LoggingWorker:
         # Capture the current context when enqueueing
         task: Final = LoggingTask(coroutine=coroutine, context=contextvars.copy_context())
 
+        self._enqueue_task(task)
+
+    def _enqueue_task(self, task: LoggingTask) -> None:
+        if self._queue is None:
+            return
         try:
             self._queue.put_nowait(task)
         except asyncio.QueueFull:
-            # Queue is full - handle it appropriately
             verbose_logger.exception("LoggingWorker queue is full")
+            self._pending_enqueues[id(task)] = task
             self._handle_queue_full(task)
+        else:
+            self._pending_enqueues.pop(id(task), None)
+
+    def _start_overflow_task(self, coroutine: Coroutine[object, object, None]) -> None:
+        task: Final = asyncio.create_task(coroutine)
+        self._overflow_tasks.add(task)
+        task.add_done_callback(self._overflow_tasks.discard)
 
     def _should_start_aggressive_clear(self) -> bool:
         """
@@ -326,7 +348,7 @@ class LoggingWorker:
         if self._should_start_aggressive_clear():
             self._mark_aggressive_clear_started()
             # Schedule clearing as async task so enqueue returns immediately (non-blocking)
-            asyncio.create_task(self._aggressively_clear_queue_async(task))
+            self._start_overflow_task(self._aggressively_clear_queue_async(task))
         else:
             # Cooldown active or clear in progress, schedule a delayed retry
             self._schedule_delayed_enqueue_retry(task)
@@ -363,7 +385,7 @@ class LoggingWorker:
             delay: Final = self._calculate_retry_delay()
 
             # Schedule the retry as a background task
-            asyncio.create_task(self._retry_enqueue_task(task, delay))
+            self._start_overflow_task(self._retry_enqueue_task(task, delay))
         except RuntimeError:
             # No event loop, drop the task as we can't schedule a retry
             pass
@@ -373,17 +395,8 @@ class LoggingWorker:
         Retry enqueueing the task after delay, preserving original context.
         This is called as a background task from _schedule_delayed_enqueue_retry.
         """
-        await asyncio.sleep(delay)
-
-        # Try to enqueue the task directly, preserving its original context
-        if self._queue is None:
-            return
-
-        try:
-            self._queue.put_nowait(task)
-        except asyncio.QueueFull:
-            # Still full - handle it appropriately (clear or retry again)
-            self._handle_queue_full(task)
+        await self._retry_sleep(delay)
+        self._enqueue_task(task)
 
     def _extract_tasks_from_queue(self) -> list[LoggingTask]:
         """
@@ -423,15 +436,10 @@ class LoggingWorker:
                 return
 
             extracted_tasks: Final = self._extract_tasks_from_queue()
-
-            # Add new task to extracted tasks to process directly
             if new_task is not None:
-                self._track_dequeued(new_task)
-                extracted_tasks.append(new_task)
-
-            # Process extracted tasks directly
+                self._enqueue_task(new_task)
             if extracted_tasks:
-                await self._process_extracted_tasks(extracted_tasks)
+                await asyncio.gather(*(self._process_single_task(task) for task in extracted_tasks))
         except Exception as e:
             verbose_logger.exception("LoggingWorker error during aggressive clear: %s", e)
         finally:
@@ -439,7 +447,7 @@ class LoggingWorker:
             self._aggressive_clear_in_progress = False
 
     async def _process_single_task(self, task: LoggingTask) -> None:
-        """Process a single task and mark it done."""
+        """Process a task extracted from the queue and mark it done."""
         if self._queue is None:
             return
 
@@ -455,17 +463,6 @@ class LoggingWorker:
             self._untrack_dequeued(task)
             self._queue.task_done()
 
-    async def _process_extracted_tasks(self, tasks: list[LoggingTask]) -> None:
-        """
-        Process tasks that were extracted from the queue to make room.
-        Processes them concurrently without semaphore limits for maximum speed.
-        """
-        if not tasks or self._queue is None:
-            return
-
-        # Process all tasks concurrently for maximum speed
-        await asyncio.gather(*[self._process_single_task(task) for task in tasks])
-
     def ensure_initialized_and_enqueue(self, async_coroutine: Coroutine[object, None, None]) -> None:
         """
         Ensure the logging worker is initialized and enqueue the coroutine.
@@ -473,32 +470,63 @@ class LoggingWorker:
         self.start()
         self.enqueue(async_coroutine)
 
-    async def stop(self) -> None:
+    @property
+    def pending_count(self) -> int:
+        return (
+            (self._queue.qsize() if self._queue is not None else 0)
+            + len(self._dequeued_tasks)
+            + len(self._pending_enqueues)
+        )
+
+    async def stop(self, *, discard_pending: bool = False, timeout: float | None = None) -> None:
         """Stop the logging worker and clean up resources."""
+        if discard_pending and self._queue is not None:
+            discarded: Final = self._drain_pending(self._queue)
+            for task in discarded:
+                task["coroutine"].close()
+                self._queue.task_done()
+            if discarded:
+                verbose_logger.warning("LoggingWorker shutdown: %d queued callbacks discarded", len(discarded))
         if self._timeout_summary_task is not None:
             self._timeout_summary_task.cancel()
             self._timeout_summary_task = None
         self._emit_timeout_summary()
 
-        if self._worker_task is None and not self._running_tasks:
-            # No worker launched and no in-flight tasks to drain.
-            return
+        if discard_pending:
+            rejected: Final = tuple(self._pending_enqueues.values())
+            self._pending_enqueues.clear()
+            for task in rejected:
+                task["coroutine"].close()
+            if rejected:
+                verbose_logger.warning("LoggingWorker shutdown: %d overflow callbacks discarded", len(rejected))
 
-        tasks_to_cancel: Final[list[asyncio.Task]] = list(self._running_tasks)
-        if self._worker_task:
-            # Include the main worker loop so it stops fetching work.
-            tasks_to_cancel.append(self._worker_task)
+        tasks_to_cancel: Final = (
+            *self._running_tasks,
+            *self._overflow_tasks,
+            *((self._worker_task,) if self._worker_task is not None else ()),
+        )
+        if not tasks_to_cancel:
+            return
 
         for task in tasks_to_cancel:
             # Propagate cancellation to every pending task.
             task.cancel()
 
         # Wait for cancellation to settle; ignore errors raised during shutdown.
-        await asyncio.gather(*tasks_to_cancel, return_exceptions=True)
+        done, pending = await asyncio.wait(tasks_to_cancel, timeout=timeout)
+        await asyncio.gather(*done, return_exceptions=True)
+        if pending:
+            verbose_logger.warning("LoggingWorker shutdown: %d tasks still alive after cancellation", len(pending))
 
-        self._worker_task = None
+        if discard_pending and self._queue is not None:
+            for task in self._unstarted_dequeued_tasks():
+                task["coroutine"].close()
+                self._untrack_dequeued(task)
+                self._queue.task_done()
+        if self._worker_task is not None and self._worker_task.done():
+            self._worker_task = None
         # Drop references to completed tasks so we can restart cleanly.
-        self._running_tasks.clear()
+        self._running_tasks.difference_update(done)
 
     async def flush(self) -> None:
         """Flush the logging queue.
@@ -516,6 +544,9 @@ class LoggingWorker:
             return
         self.start()
         await self._queue.join()
+        while self._overflow_tasks:
+            await asyncio.wait(tuple(self._overflow_tasks))
+            await self._queue.join()
 
     async def clear_queue(self):
         """

litellm/litellm_core_utils/prompt_templates/common_utils.py

@@ -424,6 +424,109 @@ def anthropic_image_source_to_openai_url(image_source: Mapping[str, object]) ->
     return None
 
 
+@dataclass(frozen=True, slots=True)
+class InlineDocument:
+    filename: str
+    file_data: str
+
+
+def _document_media_type(source: object) -> str:
+    match source:
+        case {"media_type": str() as media_type} if media_type:
+            return media_type
+        case _:
+            return "application/pdf"
+
+
+def anthropic_inline_document(document: Mapping[str, object]) -> InlineDocument | None:
+    source: Final = document.get("source")
+    match source:
+        case {"type": "base64", "data": str() as data} if data:
+            title: Final = document.get("title")
+            return InlineDocument(
+                filename=title if isinstance(title, str) and title else "document.pdf",
+                file_data=f"data:{_document_media_type(source)};base64,{data}",
+            )
+        case _:
+            return None
+
+
+def is_pdf_file_part(part: object) -> bool:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data.startswith("data:"):
+            return file_data.startswith("data:application/pdf")
+        case {"type": "file", "file": {"format": str() as file_format}}:
+            return file_format == "application/pdf"
+        case {"type": "file", "file": {"filename": str() as filename}}:
+            return filename.lower().endswith(".pdf")
+        case _:
+            return False
+
+
+_CONTENT_PARTS: Final = TypeAdapter(tuple[object, ...])
+
+
+def content_parts(message: Mapping[str, object]) -> Sequence[object]:
+    content: Final = message.get("content")
+    return _CONTENT_PARTS.validate_python(content) if isinstance(content, list) else ()
+
+
+def _has_pdf_file_part(message: Mapping[str, object]) -> bool:
+    return any(is_pdf_file_part(part) for part in content_parts(message))
+
+
+def messages_have_pdf_file_part(messages: Sequence[Mapping[str, object]]) -> bool:
+    return any(_has_pdf_file_part(message) for message in messages)
+
+
+def _message_without_pdf_file_parts(message: AllMessageValues) -> AllMessageValues:
+    if not _has_pdf_file_part(message):
+        return message
+    kept: Final = [part for part in content_parts(message) if not is_pdf_file_part(part)]
+    return cast(AllMessageValues, {**message, "content": kept})  # cast-ok: same message with fewer content parts
+
+
+def _is_pdf_only_user_turn(message: AllMessageValues) -> bool:
+    parts: Final = content_parts(message)
+    return message["role"] == "user" and bool(parts) and all(is_pdf_file_part(part) for part in parts)
+
+
+def messages_without_pdf_file_parts(messages: Sequence[AllMessageValues]) -> tuple[AllMessageValues, ...]:
+    return tuple(
+        _message_without_pdf_file_parts(message) for message in messages if not _is_pdf_only_user_turn(message)
+    )
+
+
+_CONTENT_PART: Final = TypeAdapter(Mapping[str, object])
+
+
+def _file_part_as_image_url(part: object) -> object:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data:
+            fields: Final = _CONTENT_PART.validate_python(part)
+            return {key: value for key, value in fields.items() if key != "file"} | {
+                "type": "image_url",
+                "image_url": {"url": file_data},
+            }
+        case _:
+            return part
+
+
+def message_with_file_parts_as_image_urls(message: Mapping[str, object]) -> Mapping[str, object]:
+    parts: Final = content_parts(message)
+    if not parts:
+        return message
+    return {**message, "content": [_file_part_as_image_url(part) for part in parts]}
+
+
+def _typed_message_with_file_parts_as_image_urls(message: AllMessageValues) -> AllMessageValues:
+    return cast(AllMessageValues, message_with_file_parts_as_image_urls(message))  # cast-ok: same message, image parts
+
+
+def messages_with_file_parts_as_image_urls(messages: Sequence[AllMessageValues]) -> tuple[AllMessageValues, ...]:
+    return tuple(_typed_message_with_file_parts_as_image_urls(message) for message in messages)
+
+
 def _image_part_url(part: Mapping[str, object]) -> str | None:
     """The image URL carried by one content part, whichever of the three dialects wrote it."""
     part_type: Final = part.get("type")

litellm/litellm_core_utils/realtime_streaming.py

@@ -20,6 +20,7 @@ from litellm.constants import (
     REALTIME_SESSION_SUCCESS_LOGGED_KEY,
 )
 from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
+from litellm.llms.base_llm.realtime.transcription_protocol import RealtimeTranscriptionProtocolError
 from litellm.llms.base_llm.realtime.transformation import BaseRealtimeConfig, RealtimeBackend
 from litellm.types.llms.openai import (
     OpenAIRealtimeEvents,
@@ -1281,24 +1282,29 @@ class RealTimeStreaming:
             verbose_logger.exception("Connection closed in backend to client send messages - %s", e)
             close: Final = backend_close_from(e)
             self._flush_unbilled_transcription_usage()
-            if self._backend_refused_session(close):
-                await self.log_backend_refusal(e)
-            else:
-                await self.log_messages()
+            await self._log_session_end(e if self._backend_refused_session(close) else None)
             return close
         except asyncio.CancelledError:
             self._flush_unbilled_transcription_usage()
-            await self.log_messages()
+            await self._log_session_end(None)
             raise
         except Exception as e:
             verbose_logger.exception("Error in backend to client send messages: %s", e)
             self._flush_unbilled_transcription_usage()
-            await self.log_messages()
+            await self._log_session_end(None)
             return BackendClose(code=1011, reason="proxy failed while relaying the upstream websocket")
 
     def _backend_refused_session(self, close: BackendClose) -> bool:
         return close.code != 1000 and not self._backend_sent_frames
 
+    async def _log_session_end(self, transport_refusal: Exception | None) -> None:
+        provider_refusal: Final = None if self.provider_config is None else self.provider_config.session_refusal()
+        refusal: Final = provider_refusal or transport_refusal
+        if refusal is None:
+            await self.log_messages()
+            return
+        await self.log_backend_refusal(refusal)
+
     async def log_backend_refusal(self, error: Exception) -> None:
         if not self.logging_obj:
             return
@@ -1691,7 +1697,13 @@ class RealTimeStreaming:
                 # ``_maybe_send_guardrail_turn_detection_update`` would retry.
                 if msg_type == "session.update" and self.provider_config is None:
                     await self._send_session_gate_once()
-                sent = await self._send_to_backend(message)
+                try:
+                    sent = await self._send_to_backend(message)
+                except RealtimeTranscriptionProtocolError as e:
+                    await self.websocket.send_text(realtime_error_event(str(e), error_type="invalid_request_error"))
+                    if self._uses_deferred_backend_setup():
+                        raise
+                    continue
                 if guardrail_turn_detection_injected and sent:
                     self._guardrail_turn_detection_update_sent = True
 

litellm/litellm_core_utils/served_output_texts.py

@@ -4,12 +4,13 @@ post-call guardrail rewrote rather than the provider response the proxy assemble
 from __future__ import annotations
 
 from collections.abc import Sequence
+from itertools import chain
 from typing import Final, Literal
 
-from pydantic import BaseModel, Field, TypeAdapter, ValidationError
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, ValidationError
 
 from litellm.types.llms.base import LiteLLMBaseModel
-from litellm.types.utils import ModelResponse, ModelResponseStream
+from litellm.types.utils import ModelResponse, ModelResponseStream, TextCompletionResponse
 
 SERVED_OUTPUT_TEXTS_KEY: Final = "served_output_texts"
 
@@ -44,6 +45,19 @@ class _ChatChoices(LiteLLMBaseModel):
     choices: list[object]
 
 
+class _TextCompletionChoice(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    index: int | None = None
+    text: str | None = None
+
+
+class _TextCompletionChunk(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    choices: tuple[_TextCompletionChoice, ...]
+
+
 def _as_json_object(response: object) -> dict[str, object] | None:
     candidate: Final = response.model_dump() if isinstance(response, BaseModel) else response
     try:
@@ -108,8 +122,13 @@ def served_output_texts(response: object) -> ServedTexts | None:
 
 
 def served_stream_output_texts(chunks: Sequence[object]) -> ServedTexts | None:
-    if chunks and all(isinstance(chunk, ModelResponseStream) for chunk in chunks):
-        return _chat_stream_texts(tuple(chunk for chunk in chunks if isinstance(chunk, ModelResponseStream)))
+    chat_chunks: Final = tuple(chunk for chunk in chunks if isinstance(chunk, ModelResponseStream))
+    if chunks and len(chat_chunks) == len(chunks):
+        return _indexed_texts_by_choice(tuple(chain.from_iterable(_chat_chunk_texts(chunk) for chunk in chat_chunks)))
+    if chunks and all(isinstance(chunk, TextCompletionResponse) for chunk in chunks):
+        return _indexed_texts_by_choice(
+            tuple(chain.from_iterable(_text_completion_chunk_texts(chunk) for chunk in chunks))
+        )
     from litellm.llms.anthropic.messages.stream_assembly import (
         assemble_anthropic_sse_stream,
         is_anthropic_sse_stream,
@@ -121,19 +140,26 @@ def served_stream_output_texts(chunks: Sequence[object]) -> ServedTexts | None:
     return _chat_texts(assembled) if assembled is not None else None
 
 
-def _chat_stream_choice_text(chunks: Sequence[ModelResponseStream], index: int) -> str | None:
-    contents: Final = tuple(
-        content
-        for chunk in chunks
+def _chat_chunk_texts(chunk: ModelResponseStream) -> tuple[tuple[int, str | None], ...]:
+    return tuple(
+        (choice.index or 0, content if isinstance(content := choice.delta.content, str) else None)
         for choice in chunk.choices
-        if (choice.index or 0) == index and isinstance(content := choice.delta.content, str)
     )
-    return "".join(contents) if contents else None
 
 
-def _chat_stream_texts(chunks: Sequence[ModelResponseStream]) -> ServedTexts | None:
-    choice_count: Final = max(((choice.index or 0) + 1 for chunk in chunks for choice in chunk.choices), default=0)
-    texts: Final = tuple(_chat_stream_choice_text(chunks, index) for index in range(choice_count))
+def _text_completion_chunk_texts(chunk: object) -> tuple[tuple[int, str | None], ...]:
+    parsed: Final = _TextCompletionChunk.model_validate(_as_json_object(chunk))
+    return tuple((choice.index or 0, choice.text) for choice in parsed.choices)
+
+
+def _joined_choice_text(indexed_texts: Sequence[tuple[int, str | None]], index: int) -> str | None:
+    texts: Final = tuple(text for text_index, text in indexed_texts if text_index == index and text is not None)
+    return "".join(texts) if texts else None
+
+
+def _indexed_texts_by_choice(indexed_texts: Sequence[tuple[int, str | None]]) -> ServedTexts | None:
+    choice_count: Final = max((index + 1 for index, _ in indexed_texts), default=0)
+    texts: Final = tuple(_joined_choice_text(indexed_texts, index) for index in range(choice_count))
     return texts if any(text is not None for text in texts) else None
 
 
@@ -170,6 +196,8 @@ def _choice_with_text(choice: object, text: str | None) -> object:
     if choice_obj is None or text is None:
         return choice
     message: Final = _as_json_object(choice_obj.get("message"))
-    if message is None or message.get("content") == text:
-        return choice
-    return {**choice_obj, "message": {**message, "content": text}}
+    if message is not None:
+        return choice if message.get("content") == text else {**choice_obj, "message": {**message, "content": text}}
+    if isinstance(choice_obj.get("text"), str):
+        return {**choice_obj, "text": text}
+    return choice

litellm/litellm_core_utils/streaming_chunk_builder_utils.py

@@ -12,9 +12,14 @@ from typing_extensions import ReadOnly, Required
 
 import litellm
 from litellm._logging import verbose_logger
-from litellm.litellm_core_utils.core_helpers import chunk_usage, provider_reported_usage_cost
+from litellm.litellm_core_utils.core_helpers import (
+    USAGE_COST_BILLED_PROVIDERS,
+    chunk_usage,
+    provider_reported_usage_cost,
+)
 from litellm.litellm_core_utils.hidden_params import HIDDEN_PARAMS_ATTR, set_hidden_params
 from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import BEDROCK_TRACE_FIELDS, object_items, object_mapping
+from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import partition_annotations
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     get_content_from_model_response,  # pyright: ignore[reportUnknownVariableType]  # response param accepts an untyped dict
 )
@@ -1352,7 +1357,7 @@ def stream_chunk_builder_text_completion(
     return TextCompletionResponse(**response)
 
 
-_CALCULATOR_PRICED_REPORTED_COST_PROVIDERS: Final = frozenset({LlmProviders.XAI.value})
+_CALCULATOR_PRICED_REPORTED_COST_PROVIDERS: Final = frozenset({LlmProviders.XAI.value}) | USAGE_COST_BILLED_PROVIDERS
 
 
 def _logged_provider(logging_obj: Optional["Logging"]) -> object:
@@ -1696,10 +1701,17 @@ def stream_chunk_builder(
             # Merge annotations from ALL chunks — providers may spread
             # them across multiple streaming chunks or send them only in
             # the final chunk.
-            all_annotations: Final[list] = []
+            all_annotations: Final[list[object]] = []
             for ac in annotation_chunks:
                 all_annotations.extend(ac["choices"][0]["delta"]["annotations"])
-            response["choices"][0]["message"]["annotations"] = all_annotations
+            message_annotations, provider_annotations = partition_annotations(all_annotations)
+            if message_annotations is not None:
+                response["choices"][0]["message"]["annotations"] = message_annotations
+            if provider_annotations:
+                response.choices[0].message.provider_specific_fields = {
+                    **(response.choices[0].message.provider_specific_fields or {}),
+                    "annotations": list(provider_annotations),
+                }
 
         audio_chunks: Final = [
             chunk
@@ -1762,7 +1774,10 @@ def stream_chunk_builder(
 
             if combined_provider_fields:
                 _choice = response.choices[0]
-                _choice.message.provider_specific_fields = combined_provider_fields
+                _choice.message.provider_specific_fields = {
+                    **(_choice.message.provider_specific_fields or {}),
+                    **combined_provider_fields,
+                }
 
         completion_output = get_content_from_model_response(response)
 

litellm/llms/anthropic/chat/guardrail_translation/handler.py

@@ -432,27 +432,30 @@ class AnthropicMessagesHandler(BaseTranslation):
         message, _ = serialize_http_exception_detail(exc.detail)
         return tuple(anthropic_sse_error_frames(message))
 
-    def _standalone_block_chunks(self, exc: "ModifyResponseException") -> list[bytes]:
+    def build_block_response(self, exc: "ModifyResponseException") -> "AnthropicMessagesResponse":
         import uuid
 
-        from litellm.llms.anthropic.messages.native.fake_stream_iterator import (
-            FakeAnthropicMessagesStreamIterator,
-        )
         from litellm.llms.base_llm.guardrail_translation.utils import (
             blocked_response_usage,
         )
         from litellm.types.utils import AnthropicMessagesResponse
 
-        block_response: Final = AnthropicMessagesResponse(
+        return AnthropicMessagesResponse(
             id=f"msg_{uuid.uuid4()}",
             type="message",
             role="assistant",
             content=[{"type": "text", "text": exc.message}],
             model=exc.model,
             stop_reason="end_turn",
-            usage=blocked_response_usage(getattr(exc, "original_response", None)),
+            usage=blocked_response_usage(exc.original_response),
         )
-        return list(FakeAnthropicMessagesStreamIterator(response=block_response))
+
+    def _standalone_block_chunks(self, exc: "ModifyResponseException") -> list[bytes]:
+        from litellm.llms.anthropic.messages.native.fake_stream_iterator import (
+            FakeAnthropicMessagesStreamIterator,
+        )
+
+        return list(FakeAnthropicMessagesStreamIterator(response=self.build_block_response(exc)))
 
     def _block_continuation_chunks(
         self, exc: "ModifyResponseException", responses_so_far: Sequence[object]

litellm/llms/anthropic/chat/handler.py

@@ -17,6 +17,7 @@ import litellm
 import litellm.litellm_core_utils
 import litellm.types
 import litellm.types.utils
+from litellm.anthropic_beta_gated_fields import strip_beta_gated_request_body
 from litellm.anthropic_beta_headers_manager import (
     update_request_with_filtered_beta,
 )
@@ -506,11 +507,20 @@ class AnthropicChatCompletion(BaseLLM):
             The pair stays mutable because the streaming path rewrites it in
             place (`data["stream"] = True`) before sending.
             """
-            request_headers, data = update_request_with_filtered_beta(
+            request_headers, filtered_data = update_request_with_filtered_beta(
                 headers=headers,
                 request_data=request_data,
                 provider=custom_llm_provider,
             )
+            data: Final = dict(
+                strip_beta_gated_request_body(
+                    filtered_data,
+                    request_headers,
+                    drop_params=litellm_params.get("drop_params"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # litellm_params is the handler's bare dict
+                    model=model,
+                    provider=custom_llm_provider,
+                )
+            )
             logging_obj.pre_call(
                 input=messages,
                 api_key=api_key,

litellm/llms/anthropic/common_utils.py

@@ -51,6 +51,7 @@ from litellm.types.llms.anthropic import (
     ANTHROPIC_OAUTH_BETA_HEADER,
     ANTHROPIC_OAUTH_TOKEN_PREFIX,
     ANTHROPIC_SERVER_TOOL_USE_ID_PREFIX,
+    ANTHROPIC_THINKING_BINDING_CONTROLS_BETA_HEADER,
     ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER,
     ANTHROPIC_TOOL_SEARCH_TOOL_TYPES,
     AllAnthropicToolsValues,
@@ -600,6 +601,20 @@ class AnthropicModelInfo(BaseLLMModelInfo):
             return False
         return thinking.get("type") in ("adaptive", "enabled") and thinking.get("display") == "updates"
 
+    def thinking_betas(self, thinking: AnthropicThinkingParam | None) -> tuple[str, ...]:
+        return (
+            *(
+                (ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER,)
+                if self.is_thinking_display_updates_used(thinking)
+                else ()
+            ),
+            *(
+                (ANTHROPIC_THINKING_BINDING_CONTROLS_BETA_HEADER,)
+                if isinstance(thinking, dict) and "block_binding" in thinking
+                else ()
+            ),
+        )
+
     def is_mid_conversation_tool_change_used(self, messages: Sequence[object]) -> bool:
         for message in messages:
             if message_field(message, "role") != "system":
@@ -1180,7 +1195,7 @@ class AnthropicModelInfo(BaseLLMModelInfo):
         *,
         custom_llm_provider: str,
         is_mid_conversation_output_config_used: bool = False,
-        is_thinking_display_updates_used: bool = False,
+        thinking: AnthropicThinkingParam | None = None,
         is_mid_conversation_tool_change_used: bool = False,
     ) -> list[str]:
         """
@@ -1217,13 +1232,10 @@ class AnthropicModelInfo(BaseLLMModelInfo):
         if is_mid_conversation_output_config_used:
             betas.append(ANTHROPIC_MID_CONVERSATION_OUTPUT_CONFIG_BETA_HEADER)
 
-        thinking_display_betas: Final = (
-            (ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER,) if is_thinking_display_updates_used else ()
-        )
         tool_change_betas: Final = (
             (ANTHROPIC_MID_CONVERSATION_TOOL_CHANGES_BETA_HEADER,) if is_mid_conversation_tool_change_used else ()
         )
-        return list(set(betas).union(thinking_display_betas, tool_change_betas))
+        return list(set(betas).union(self.thinking_betas(thinking), tool_change_betas))
 
     @staticmethod
     def _make_api_key_auth_header(
@@ -1285,7 +1297,7 @@ class AnthropicModelInfo(BaseLLMModelInfo):
         api_base: str | None = None,
         use_bearer_for_custom_base: bool = False,
         is_mid_conversation_output_config_used: bool = False,
-        is_thinking_display_updates_used: bool = False,
+        thinking: AnthropicThinkingParam | None = None,
         is_mid_conversation_tool_change_used: bool = False,
         wif_minted: bool = False,
     ) -> dict:
@@ -1346,7 +1358,7 @@ class AnthropicModelInfo(BaseLLMModelInfo):
             betas.update(user_anthropic_beta_headers)
 
         all_betas: Final = betas.union(
-            (ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER,) if is_thinking_display_updates_used else (),
+            self.thinking_betas(thinking),
             (ANTHROPIC_MID_CONVERSATION_TOOL_CHANGES_BETA_HEADER,) if is_mid_conversation_tool_change_used else (),
         )
 
@@ -1433,7 +1445,7 @@ class AnthropicModelInfo(BaseLLMModelInfo):
             auth_token=auth_token,
             file_id_used=file_id_used,
             is_mid_conversation_output_config_used=is_mid_conversation_output_config_used,
-            is_thinking_display_updates_used=self.is_thinking_display_updates_used(optional_params.get("thinking")),
+            thinking=optional_params.get("thinking"),
             is_mid_conversation_tool_change_used=self.is_mid_conversation_tool_change_used(messages),
             web_search_tool_used=web_search_tool_used,
             is_vertex_request=optional_params.get("is_vertex_request", False),

litellm/llms/anthropic/messages/adapters/transformation.py

@@ -123,6 +123,7 @@ from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingCho
 
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     anthropic_image_source_to_openai_url,
+    anthropic_inline_document,
     parse_tool_call_arguments,
     reasoning_content_from_thinking_blocks,
     with_prompt_cache_breakpoint,
@@ -185,6 +186,8 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionAssistantMessage,
     ChatCompletionAssistantToolCall,
+    ChatCompletionFileObject,
+    ChatCompletionFileObjectFile,
     ChatCompletionImageObject,
     ChatCompletionImageUrlObject,
     ChatCompletionRedactedThinkingBlock,
@@ -203,7 +206,7 @@ from litellm.types.llms.openai import (
     ChatCompletionUserMessage,
     ToolMessageContentPart,
 )
-from litellm.types.utils import Choices, ModelResponse, StreamingChoices, Usage
+from litellm.types.utils import Choices, LlmProviders, ModelResponse, StreamingChoices, Usage
 from litellm.utils import supports_mid_conversation_system
 
 from .streaming_iterator import AnthropicSSEStream, AnthropicStreamWrapper
@@ -362,6 +365,9 @@ class AnthropicAdapter:
 _BlockT: Final = TypeVar("_BlockT", bound=Mapping[str, object])
 
 
+_CLAUDE_OVER_REASONING_EFFORT_PROVIDERS: Final = frozenset({LlmProviders.KYMA.value})
+
+
 class LiteLLMAnthropicMessagesAdapter:
     def __init__(self):
         pass
@@ -495,7 +501,9 @@ class LiteLLMAnthropicMessagesAdapter:
         for m in ordered_messages:
             user_message: ChatCompletionUserMessage | None = None
             tool_message_list: list[ChatCompletionToolMessage] = []
-            new_user_content_list: list[ChatCompletionTextObject | ChatCompletionImageObject] = []
+            new_user_content_list: list[
+                ChatCompletionTextObject | ChatCompletionImageObject | ChatCompletionFileObject
+            ] = []
             if m["role"] == "system":
                 system_message = self._translate_midturn_system_message_to_openai(m, model)
                 if system_message is not None:
@@ -528,13 +536,8 @@ class LiteLLMAnthropicMessagesAdapter:
                                     self._add_prompt_cache_breakpoint_if_present(content, image_obj)
                                 )
                         elif content.get("type") == "document":
-                            # Convert Anthropic document format (PDF, etc.) to OpenAI format
-                            source = content.get("source", {})
-                            openai_image_url = self._translate_anthropic_image_to_openai(cast(dict, source))
-
-                            if openai_image_url:
-                                image_url_obj = ChatCompletionImageUrlObject(url=openai_image_url)
-                                doc_obj = ChatCompletionImageObject(type="image_url", image_url=image_url_obj)
+                            doc_obj = self._translate_anthropic_document_to_openai(content)
+                            if doc_obj is not None:
                                 self._add_cache_control_if_applicable(content, doc_obj, model)
                                 new_user_content_list.append(doc_obj)
                         elif content.get("type") == "tool_result":
@@ -1128,7 +1131,8 @@ class LiteLLMAnthropicMessagesAdapter:
         """Translate Anthropic thinking to either thinking or reasoning_effort.
 
         A Claude-family target keeps ``thinking`` verbatim, since every bridged provider serving one
-        speaks that param. Carrying its adaptive effort tier alongside takes two different params,
+        speaks that param, except Kyma, which serves Claude over the OpenAI dialect and takes only
+        ``reasoning_effort``, so its Claude models get the non-Claude mapping. Carrying its adaptive effort tier alongside takes two different params,
         because the two are not interchangeable at the provider mapping below.
 
         Bedrock takes ``output_config`` directly, which attaches the tier and leaves ``thinking``
@@ -1159,7 +1163,9 @@ class LiteLLMAnthropicMessagesAdapter:
         is_bedrock_target: Final = model.startswith(("bedrock/", "converse/", "invoke/")) or self.is_bedrock_arn_model(
             model
         )
-        is_claude_target: Final = self.is_anthropic_claude_model(model) or self.is_bedrock_arn_model(model)
+        is_claude_target: Final = (
+            self.is_anthropic_claude_model(model) or self.is_bedrock_arn_model(model)
+        ) and custom_llm_provider not in _CLAUDE_OVER_REASONING_EFFORT_PROVIDERS
         output_config: Final = anthropic_message_request.get("output_config")
 
         if is_claude_target:
@@ -1351,7 +1357,7 @@ class LiteLLMAnthropicMessagesAdapter:
                 text: Final = tool_result_text_part(block)
                 return None if text is None else ChatCompletionTextObject(type="text", text=text)
             case "image" | "document":
-                return self._tool_result_image_part(block.get("source"))
+                return self._source_image_url_part(block.get("source"))
             case "tool_reference":
                 return ChatCompletionToolReferenceObject(
                     type="tool_reference", tool_name=str(block.get("tool_name") or "")
@@ -1359,7 +1365,18 @@ class LiteLLMAnthropicMessagesAdapter:
             case _:
                 return None
 
-    def _tool_result_image_part(self, image_source: object) -> ChatCompletionImageObject | None:
+    def _translate_anthropic_document_to_openai(
+        self, document: Mapping[str, object]
+    ) -> ChatCompletionFileObject | ChatCompletionImageObject | None:
+        inline_document: Final = anthropic_inline_document(document)
+        if inline_document is None:
+            return self._source_image_url_part(document.get("source"))
+        return ChatCompletionFileObject(
+            type="file",
+            file=ChatCompletionFileObjectFile(filename=inline_document.filename, file_data=inline_document.file_data),
+        )
+
+    def _source_image_url_part(self, image_source: object) -> ChatCompletionImageObject | None:
         if not isinstance(image_source, dict):
             return None
         openai_image_url = self._translate_anthropic_image_to_openai(image_source)

litellm/llms/anthropic/messages/native/transformation.py

@@ -2,10 +2,21 @@ from collections.abc import AsyncIterator, Mapping, Sequence
 from typing import ClassVar, Final
 
 import httpx
+from pydantic import TypeAdapter
 
 import litellm
 from litellm._logging import verbose_logger
-from litellm.anthropic_beta_headers_manager import get_unsupported_headers, is_beta_header_supported_by_bundled_config
+from litellm.anthropic_beta_gated_fields import (
+    log_beta_gated_strips,
+    strip_beta_gated_native_request,
+    strip_beta_gated_system_messages,
+    unstripped_native_request,
+)
+from litellm.anthropic_beta_headers_manager import (
+    get_unsupported_headers,
+    has_beta_headers_config,
+    is_beta_header_supported_by_bundled_config,
+)
 from litellm.constants import ANTHROPIC_BILLING_METADATA_PREFIX
 from litellm.exceptions import AuthenticationError
 from litellm.litellm_core_utils.core_helpers import drops_unsupported_params
@@ -20,7 +31,6 @@ from litellm.types.llms.anthropic import (
     ANTHROPIC_ADVISOR_TOOL_TYPE,
     ANTHROPIC_BETA_HEADER_VALUES,
     ANTHROPIC_MID_CONVERSATION_TOOL_CHANGES_BETA_HEADER,
-    ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER,
     AnthropicMessagesRequest,
 )
 from litellm.types.llms.anthropic_messages.anthropic_response import (
@@ -51,6 +61,8 @@ DEFAULT_ANTHROPIC_API_VERSION: Final = "2023-06-01"
 
 _CALLER_CREDENTIAL_HEADERS: Final = frozenset({"x-api-key", "authorization"})
 
+_MESSAGE_MAPPINGS: Final = TypeAdapter(tuple[Mapping[str, object], ...])
+
 
 def _carries_caller_credential(headers: Mapping[str, str]) -> bool:
     """Whether the caller sent their own Anthropic credential, in which case this passthrough
@@ -85,6 +97,9 @@ def _ensure_stop_sequence(response: AnthropicMessagesResponse) -> AnthropicMessa
     return response
 
 
+_MESSAGES_ADAPTER: Final = TypeAdapter(list[dict[str, object]])
+
+
 class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
     _workload_identity_eligible: ClassVar[bool] = True
 
@@ -229,7 +244,18 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
         else:
             return system_param
 
-    def _normalize_system_role_messages(self, anthropic_messages_request: dict, model: str) -> None:
+    def _strip_beta_gated_system_turns(
+        self, messages: Sequence[Mapping[str, object]], model: str, drop_params: object
+    ) -> Sequence[Mapping[str, object]]:
+        if not has_beta_headers_config(self._resolved_provider):
+            return messages
+        gated: Final = strip_beta_gated_system_messages(messages, frozenset(), drop_params=drop_params)
+        log_beta_gated_strips(gated.stripped, model=model, provider=self._resolved_provider)
+        return _MESSAGE_MAPPINGS.validate_python(gated.messages) if gated.stripped else messages
+
+    def _normalize_system_role_messages(
+        self, anthropic_messages_request: dict, model: str, drop_params: object
+    ) -> None:
         """Normalize ``role: "system"`` entries in ``messages`` per the Anthropic
         ``/v1/messages`` contract, which the first-party API, Bedrock Invoke,
         Vertex, and Azure Foundry all enforce identically.
@@ -277,7 +303,15 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
                 custom_llm_provider=self.custom_llm_provider,
                 key="supports_mid_conversation_system",
             )
-            else list(convert_mid_conversation_system_turns(messages[leading_count:]))
+            else list(
+                convert_mid_conversation_system_turns(
+                    self._strip_beta_gated_system_turns(
+                        messages[leading_count:],  # pyright: ignore[reportUnknownArgumentType]  # messages comes from the bare request dict
+                        model=model,
+                        drop_params=drop_params,
+                    )
+                )
+            )
         )
         if hoisted or remaining != messages:
             anthropic_messages_request["messages"] = remaining
@@ -336,7 +370,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
                     model=model,
                 ),
             )
-        return self._finalize_messages_headers(headers, optional_params, messages), api_base
+        return self.finalize_messages_headers(headers, optional_params, messages), api_base
 
     async def avalidate_anthropic_messages_environment(
         self,
@@ -376,7 +410,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
                     model=model,
                 ),
             )
-        return self._finalize_messages_headers(oauth_headers, optional_params, messages), api_base
+        return self.finalize_messages_headers(oauth_headers, optional_params, messages), api_base
 
     def _require_auth_header(self, auth_header: Mapping[str, str] | None, model: str) -> Mapping[str, str]:
         if auth_header is None:
@@ -410,7 +444,7 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
 
         return config_allows_workload_identity(self)
 
-    def _finalize_messages_headers(
+    def finalize_messages_headers(
         self,
         headers: dict,  # mutable-ok: out-param
         optional_params: dict,  # mutable-ok: out-param
@@ -619,6 +653,12 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
         if thinking_enabled or effort_enabled:
             optional_params.pop("temperature", None)
 
+    def strips_beta_gated_fields_from_final_headers(self) -> bool:
+        """False on legs whose final ``anthropic_beta`` list is only known after this transform (Bedrock Invoke),
+        which strip beta-gated fields themselves once that list exists, and on providers with no row in
+        ``anthropic_beta_headers_config.json``, whose filter drops every beta and so cannot say which fields fail."""
+        return has_beta_headers_config(self._resolved_provider)
+
     def transform_anthropic_messages_request(
         self,
         model: str,
@@ -720,18 +760,37 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
         if not _has_advisor:
             messages = strip_advisor_blocks_from_messages(messages)
 
+        gated: Final = (
+            strip_beta_gated_native_request(
+                headers,  # pyright: ignore[reportUnknownArgumentType]  # the base signature types this as a bare dict
+                anthropic_messages_optional_request_params,  # pyright: ignore[reportUnknownArgumentType]  # the base signature types this as a bare dict
+                messages,  # pyright: ignore[reportUnknownArgumentType]  # the base signature types this as a bare list
+                drop_params=getattr(litellm_params, "drop_params", None),
+            )
+            if self.strips_beta_gated_fields_from_final_headers()
+            else unstripped_native_request(anthropic_messages_optional_request_params, messages)  # pyright: ignore[reportUnknownArgumentType]  # the base signature types these as bare dicts
+        )
+        log_beta_gated_strips(gated.stripped, model=model, provider=self._resolved_provider)
         anthropic_messages_request: Final[AnthropicMessagesRequest] = AnthropicMessagesRequest(
             messages=strip_encrypted_reasoning_blocks_from_anthropic_messages(
-                drop_non_string_tool_result_texts(messages, getattr(litellm_params, "drop_params", None))
+                drop_non_string_tool_result_texts(
+                    _MESSAGES_ADAPTER.validate_python(gated.messages) if gated.messages_stripped else messages,
+                    getattr(litellm_params, "drop_params", None),
+                )
             ),
             max_tokens=max_tokens,
             model=model,
             **anthropic_messages_optional_request_params,
         )
+        request: Final = (
+            {**dict(anthropic_messages_request), "thinking": gated.thinking}
+            if gated.thinking_stripped
+            else dict(anthropic_messages_request)  # pyright: ignore[reportUnknownVariableType]  # built from the base signature's bare dict
+        )
         if not isinstance(tools, list):
-            return dict(anthropic_messages_request)
+            return request
         return {
-            **anthropic_messages_request,
+            **request,
             "tools": self.tools_for_upstream(
                 OBJECT_LIST_ADAPTER.validate_python(tools), model, litellm_params.drop_params
             ),
@@ -866,17 +925,13 @@ class AnthropicMessagesConfig(BaseAnthropicMessagesConfig):
         if AnthropicModelInfo().is_tool_search_used(tools):
             beta_values.add(get_tool_search_beta_header(custom_llm_provider))
 
-        thinking_display_betas: Final = (
-            (ANTHROPIC_THINKING_DISPLAY_UPDATES_BETA_HEADER,)
-            if AnthropicModelInfo().is_thinking_display_updates_used(optional_params.get("thinking"))
-            else ()
-        )
+        thinking_betas: Final = AnthropicModelInfo().thinking_betas(optional_params.get("thinking"))
         tool_change_betas: Final = (
             (ANTHROPIC_MID_CONVERSATION_TOOL_CHANGES_BETA_HEADER,)
             if AnthropicModelInfo().is_mid_conversation_tool_change_used(messages)
             else ()
         )
-        all_beta_values: Final = beta_values.union(thinking_display_betas, tool_change_betas)
+        all_beta_values: Final = beta_values.union(thinking_betas, tool_change_betas)
         dangerous_tool_use: Final = ANTHROPIC_BETA_HEADER_VALUES.DANGEROUS_TOOL_USE_2026_09_03.value
         forwarded_beta_values: Final = (  # pyright: ignore[reportUnknownVariableType]  # derived from the bare beta set
             all_beta_values - {dangerous_tool_use}

litellm/llms/anthropic/messages/responses_adapters/transformation.py

@@ -13,6 +13,7 @@ from typing import Any, Final, cast
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     TOOL_RESULT_IMAGE_BOUNDARY,
     TOOL_RESULT_IMAGE_PLACEHOLDER,
+    anthropic_inline_document,
     encrypted_reasoning_signature,
     responses_reasoning_items_from_thinking_blocks,
     with_prompt_cache_breakpoint,
@@ -106,27 +107,18 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
         block: Mapping[str, object],
     ) -> dict[str, str] | None:  # mutable-ok: API message payload
         """Convert an Anthropic document block to a Responses input_file part."""
+        inline_document: Final = anthropic_inline_document(block)
+        if inline_document is not None:
+            return {
+                "type": "input_file",
+                "filename": inline_document.filename,
+                "file_data": inline_document.file_data,
+            }
         raw_source: Final = block.get("source")
         if not isinstance(raw_source, Mapping):
             return None
         source: Final = cast(Mapping[str, object], raw_source)  # cast-ok: untrusted client payload
-        source_type: Final = source.get("type")
-        if source_type == "base64":
-            data: Final = source.get("data")
-            if not isinstance(data, str) or not data:
-                return None
-            raw_media_type: Final = source.get("media_type")
-            media_type: Final = (
-                raw_media_type if isinstance(raw_media_type, str) and raw_media_type else "application/pdf"
-            )
-            raw_title: Final = block.get("title")
-            filename: Final = raw_title if isinstance(raw_title, str) and raw_title else "document.pdf"
-            return {
-                "type": "input_file",
-                "filename": filename,
-                "file_data": f"data:{media_type};base64,{data}",
-            }
-        if source_type == "url":
+        if source.get("type") == "url":
             url: Final = source.get("url")
             if not isinstance(url, str) or not url:
                 return None

litellm/llms/anthropic/prompt_cache_prediction.py

@@ -661,12 +661,13 @@ def prepare_native_baseline_body(request: Mapping[str, object], model: str) -> M
             TypeAdapter(tuple[str, ...]).validate_python(owned.get("additional_drop_params") or ()),
             dict(options),
         )
-        body: Final = AnthropicMessagesConfig().transform_anthropic_messages_request(
+        config: Final = AnthropicMessagesConfig()
+        body: Final = config.transform_anthropic_messages_request(
             model=resolved_model,
             messages=strip_provider_specific_fields_from_anthropic_messages(messages),
             anthropic_messages_optional_request_params=filtered,
             litellm_params=GenericLiteLLMParams(),
-            headers={},
+            headers=config.finalize_messages_headers({}, dict(filtered), list(messages)),  # pyright: ignore[reportUnknownMemberType]  # the shared helper takes and returns bare dicts
         )
         return MappingProxyType(_JSON_OBJECT.validate_python(body))
     except Exception:  # noqa: BLE001  # an unsupported hypothetical request is unavailable, never an inference failure

litellm/llms/azure/chat/gpt_reasoning_transformation.py

@@ -24,6 +24,7 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config):
 
     GPT5_SERIES_ROUTE = "gpt5_series/"
     OPT_IN_REASONING_EFFORTS: ClassVar[frozenset[str]] = frozenset(("xhigh", "none"))
+    LOOKS_UP_BASE_MODEL: ClassVar[bool] = True
 
     @classmethod
     def _model_map_lookup_name(cls, model: str) -> str:
@@ -88,7 +89,7 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config):
         drop_params: bool,
         api_version: str = "",
         *,
-        declared_reasoning_efforts: Collection[str] = (),
+        declared_reasoning_efforts: Collection[str] | None = None,
     ) -> dict:
         return OpenAIGPT5Config.map_openai_params(
             self,

litellm/llms/azure_ai/anthropic/handler.py

@@ -4,11 +4,12 @@ Azure Anthropic handler - reuses AnthropicChatCompletion logic with Azure authen
 
 import copy
 import json
-from collections.abc import Callable
+from collections.abc import Callable, Mapping
 from typing import Final
 
 import httpx
 
+from litellm.anthropic_beta_gated_fields import strip_beta_gated_request_body
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion
 from litellm.llms.anthropic.chat.transformation import JsonModeRequest
@@ -75,13 +76,22 @@ class AzureAnthropicChatCompletion(AnthropicChatCompletion):
             litellm_params=litellm_params,
         )
 
-        data: Final = config.transform_request(
+        request_body: Final[Mapping[str, object]] = config.transform_request(
             model=model,
             messages=messages,
             optional_params=optional_params,
             litellm_params=litellm_params,
             headers=headers,
         )
+        data: Final = dict(
+            strip_beta_gated_request_body(
+                request_body,
+                headers,  # pyright: ignore[reportUnknownArgumentType]  # validate_environment returns a bare dict
+                drop_params=litellm_params.get("drop_params"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # litellm_params is the handler's bare dict
+                model=model,
+                provider=custom_llm_provider,
+            )
+        )
 
         ## LOGGING
         logging_obj.pre_call(

litellm/llms/azure_ai/anthropic/messages_transformation.py

@@ -145,6 +145,8 @@ class AzureAnthropicMessagesConfig(AnthropicMessagesConfig):
             litellm_params=litellm_params,
             headers=headers,
         )
-        self._normalize_system_role_messages(anthropic_messages_request, model=model)
+        self._normalize_system_role_messages(
+            anthropic_messages_request, model=model, drop_params=getattr(litellm_params, "drop_params", None)
+        )
         self._remove_scope_from_cache_control(anthropic_messages_request)
         return anthropic_messages_request

litellm/llms/azure_ai/anthropic/transformation.py

@@ -107,9 +107,11 @@ class AzureAnthropicConfig(AnthropicConfig):
             is_vertex_request=optional_params.get("is_vertex_request", False),
             user_anthropic_beta_headers=user_anthropic_beta_headers,
             mcp_server_used=mcp_server_used,
+            thinking=optional_params.get("thinking"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # optional_params is the base config's bare dict
         )
         # Merge headers - Azure auth (api-key or Authorization) takes precedence
-        headers = {**anthropic_headers, **headers}
+        merged_beta_header: Final = {k: v for k, v in anthropic_headers.items() if k == "anthropic-beta"}
+        headers = {**anthropic_headers, **headers, **merged_beta_header}
 
         # Ensure anthropic-version header is set
         if "anthropic-version" not in headers:

litellm/llms/azure_ai/chat/transformation.py

@@ -1,7 +1,7 @@
 import copy
 import enum
 import re
-from collections.abc import AsyncIterator, Collection, Iterator
+from collections.abc import AsyncIterator, Collection, Iterator, Mapping
 from typing import TYPE_CHECKING, Final, cast
 
 import httpx
@@ -10,10 +10,11 @@ from httpx import Response
 import litellm
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
-    audio_or_image_in_message_content,
+    content_parts,
     convert_content_list_to_str,
     drop_tool_reference_parts_from_tool_messages,
     filter_value_from_dict,
+    is_pdf_file_part,
 )
 from litellm.llms.azure.common_utils import BaseAzureLLM
 from litellm.llms.azure_ai.common_utils import (
@@ -46,6 +47,20 @@ NON_OPENAI_SPEC_MESSAGE_FIELDS: Final = (
     "cache_control",
 )
 
+LIST_ONLY_CONTENT_PART_TYPES: Final = frozenset({"image_url", "input_audio"})
+
+
+def _is_list_only_content_part(part: object) -> bool:
+    match part:
+        case {"type": str() as part_type} if part_type in LIST_ONLY_CONTENT_PART_TYPES:
+            return True
+        case _:
+            return is_pdf_file_part(part)
+
+
+def _has_list_only_content_part(message: Mapping[str, object]) -> bool:
+    return any(_is_list_only_content_part(part) for part in content_parts(message))
+
 
 class AzureAIChatCompletionResponseIterator(OpenAIChatCompletionResponseIterator):
     def chunk_parser(self, chunk: dict[str, object]) -> ModelResponseStream:  # mutable-ok: mirrors override contract
@@ -106,7 +121,7 @@ class AzureAIStudioConfig(OpenAIConfig):
         model: str,
         drop_params: bool,
         *,
-        declared_reasoning_efforts: Collection[str] = (),
+        declared_reasoning_efforts: Collection[str] | None = None,
     ) -> dict[str, object]:  # mutable-ok: OpenAIConfig.map_openai_params signature
         if not azureAIGPT5Config.is_model_gpt_5_model(model):
             return super().map_openai_params(
@@ -246,7 +261,7 @@ class AzureAIStudioConfig(OpenAIConfig):
                these with "Extra inputs are not permitted", which breaks multi-turn
                Anthropic-format clients that echo thinking blocks back as history.
             2. Transforms list content to a string.
-            3. If message contains an image or audio, send as is (user-intended)
+            3. If message contains an image, audio, or PDF file part, send as is (user-intended)
 
         Operates on a deep copy so the caller's messages keep their thinking blocks
         and provider metadata, which a fallback to another provider still needs.
@@ -257,8 +272,7 @@ class AzureAIStudioConfig(OpenAIConfig):
             for field in NON_OPENAI_SPEC_MESSAGE_FIELDS:
                 filter_value_from_dict(message_dict, field)
 
-            # Do nothing if the message contains an image or audio
-            if audio_or_image_in_message_content(message):
+            if _has_list_only_content_part(message):
                 continue
 
             texts = convert_content_list_to_str(message=message)

litellm/llms/azure_ai/passthrough/transformation.py

@@ -5,8 +5,9 @@ from types import MappingProxyType
 from typing import TYPE_CHECKING, Final
 
 import httpx
-from pydantic import TypeAdapter, ValidationError
+from pydantic import ConfigDict, TypeAdapter, ValidationError
 
+from litellm._lazy_imports import get_default_encoding
 from litellm._logging import verbose_logger
 from litellm.llms.azure_ai.common_utils import (
     AzureFoundryModelInfo,
@@ -22,10 +23,15 @@ from litellm.llms.base_llm.passthrough.transformation import (
     relayed_body,
     strip_leading_model_segment,
 )
+from litellm.llms.cohere.embed.transformation import (
+    CohereEmbeddingRowShape,
+    CohereImageTokenBilling,
+    cohere_embedding_response,
+)
 from litellm.rust_bridge.ocr.entrypoints import NATIVE_OCR_PASSTHROUGH_RESPONSE, NativeOcrPassthroughResponse
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.rerank import RerankResponse
-from litellm.types.utils import CallTypes, ImageResponse, StandardPassThroughResponseObject
+from litellm.types.utils import CallTypes, EmbeddingResponse, ImageResponse, StandardPassThroughResponseObject
 
 if TYPE_CHECKING:
     from httpx import URL, Response
@@ -35,6 +41,7 @@ if TYPE_CHECKING:
 
 
 EMPTY_QUERY: Final[Mapping[str, object]] = MappingProxyType({})
+ECHOED_TEXTS: Final = TypeAdapter(tuple[str, ...], config=ConfigDict(hide_input_in_errors=True))
 
 
 def api_version_from(litellm_params: Mapping[str, object]) -> str | None:
@@ -85,9 +92,29 @@ def relay_query_params(
     return MappingProxyType({**(request_query_params or EMPTY_QUERY), "api-version": api_version})
 
 
+def echoed_texts(body: Mapping[str, object]) -> tuple[str, ...]:
+    try:
+        return ECHOED_TEXTS.validate_python(body.get("texts"))
+    except ValidationError:
+        return ()
+
+
+def cohere_embed_relay_response(body: Mapping[str, object]) -> EmbeddingResponse:
+    return cohere_embedding_response(
+        body,
+        None,
+        get_default_encoding(),
+        echoed_texts(body),
+        CohereImageTokenBilling.FOLDED_INTO_INPUT_TOKENS,
+        CohereEmbeddingRowShape.TYPED,
+    )
+
+
 FOUNDRY_RELAY_SHAPES: Final = (
     RelayShape("/rerank", CallTypes.arerank, RerankResponse.model_validate),
     RelayShape("/providers/blackforestlabs/v1/flux-2-pro", CallTypes.aimage_generation, ImageResponse.model_validate),
+    RelayShape("/providers/cohere/v1/embed", CallTypes.aembedding, cohere_embed_relay_response),
+    RelayShape("/providers/cohere/v2/embed", CallTypes.aembedding, cohere_embed_relay_response),
 )
 
 

litellm/llms/azure_ai/responses/transformation.py

@@ -232,7 +232,7 @@ class AzureAIAgentResponsesAPIConfig(AzureAIResponsesAPIConfig):
         model: str,
         drop_params: bool,
         *,
-        declared_reasoning_efforts: Collection[str],
+        declared_reasoning_efforts: Collection[str] | None,
     ) -> Mapping[str, object]:
         mapped: Final = super().map_openai_params_for_deployment(
             response_api_optional_params, model, drop_params, declared_reasoning_efforts=declared_reasoning_efforts

litellm/llms/base_llm/realtime/transformation.py

@@ -4,7 +4,6 @@ from types import TracebackType
 from typing import TYPE_CHECKING, Any, Protocol
 
 import httpx
-from typing_extensions import Self
 
 from litellm.types.llms.openai import OpenAIRealtimeStreamSessionEvents
 from litellm.types.realtime import (
@@ -24,7 +23,7 @@ else:
 
 
 class RealtimeBackend(Protocol):
-    async def __aenter__(self) -> Self: ...
+    async def __aenter__(self) -> object: ...
 
     async def __aexit__(
         self,
@@ -97,9 +96,15 @@ class BaseRealtimeConfig(ABC):
     def unbilled_usage_on_session_close(self, model: str) -> RealtimeInputAudioTranscriptionUsage | None:
         return None
 
+    def session_refusal(self) -> Exception | None:
+        return None
+
     async def open_backend(self, url: str, headers: Mapping[str, str]) -> RealtimeBackend | None:
         return None
 
+    def wrap_backend(self, backend: RealtimeBackend) -> RealtimeBackend:
+        return backend
+
     def transform_session_created_event(
         self,
         model: str,

litellm/llms/base_llm/vector_store/transformation.py

@@ -226,6 +226,9 @@ class BaseVectorStoreConfig:
         ``get_complete_url`` adds a query string, like Azure, override this."""
         return f"{api_base}/{encoded_vector_store_id}"
 
+    def get_vector_store_list_url(self, api_base: str) -> str:
+        return api_base
+
     def get_error_class(self, error_message: str, status_code: int, headers: dict | httpx.Headers) -> BaseLLMException:
         from ..chat.transformation import BaseLLMException
 

litellm/llms/bedrock/chat/converse_transformation.py

@@ -17,6 +17,8 @@ from typing_extensions import ReadOnly, TypedDict
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm.anthropic_beta_gated_fields import active_beta_values, log_beta_gated_strips, strip_beta_gated_thinking
+from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers
 from litellm.constants import (
     BEDROCK_MIN_THINKING_BUDGET_TOKENS,
     RESPONSE_FORMAT_TOOL_NAME,
@@ -117,6 +119,7 @@ from litellm.utils import (
 )
 
 from ..common_utils import (
+    DROP_DISABLED_REASONING_EFFORT_WARNING,
     BedrockError,
     BedrockModelInfo,
     bedrock_cache_point_ttl_supported,
@@ -1192,11 +1195,7 @@ class AmazonConverseConfig(BaseConfig):
                         ),
                         status_code=400,
                     )
-                verbose_logger.debug(
-                    "Dropping unsupported `reasoning_effort=%s` for Bedrock model=%s.",
-                    value,
-                    model,
-                )
+                verbose_logger.warning(DROP_DISABLED_REASONING_EFFORT_WARNING, "reasoning_effort", value, model)
             elif param == "reasoning_effort" and isinstance(value, str):
                 self._handle_reasoning_effort_parameter(
                     model=model, reasoning_effort=value, optional_params=optional_params
@@ -1828,7 +1827,16 @@ class AmazonConverseConfig(BaseConfig):
                 filtered_tools.append(tool)
 
         base_model: Final = BedrockModelInfo.get_base_model(model)
-        client_beta_list: Final = get_anthropic_beta_from_headers(headers or {})
+        header_beta_list: Final = get_anthropic_beta_from_headers(headers or {})
+        client_beta_list: Final = header_beta_list or filter_and_transform_beta_headers(
+            sorted(
+                active_beta_values(
+                    {},
+                    additional_request_params.get("anthropic_beta"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # additional_request_params is a bare dict
+                )
+            ),
+            provider="bedrock_converse",
+        )
         eager_beta: Final = (
             (ANTHROPIC_FINE_GRAINED_TOOL_STREAMING_BETA_HEADER,)
             if base_model.startswith("anthropic")
@@ -1836,7 +1844,15 @@ class AmazonConverseConfig(BaseConfig):
             and ANTHROPIC_FINE_GRAINED_TOOL_STREAMING_BETA_HEADER not in client_beta_list
             else ()
         )
-        anthropic_beta_list: Final = [*client_beta_list, *eager_beta]
+        thinking_betas: Final = AnthropicModelInfo().thinking_betas(
+            additional_request_params.get("thinking"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # additional_request_params is a bare dict
+        )
+        missing_thinking_betas: Final = (
+            tuple(beta for beta in thinking_betas if beta not in client_beta_list)
+            if base_model.startswith("anthropic")
+            else ()
+        )
+        anthropic_beta_list: Final = [*client_beta_list, *eager_beta, *missing_thinking_betas]
 
         # Only separate tools if computer use tools are actually present
         if filtered_tools and self.is_computer_use_tool_used(filtered_tools, model):
@@ -1906,7 +1922,8 @@ class AmazonConverseConfig(BaseConfig):
                 else:
                     computer_use_header = "computer-use-2024-10-22"
 
-                anthropic_beta_list.append(computer_use_header)
+                if computer_use_header not in anthropic_beta_list:
+                    anthropic_beta_list.append(computer_use_header)
                 # Transform computer use tools to proper Bedrock format
                 transformed_computer_tools: Final = self._transform_computer_use_tools(computer_use_tools)
                 additional_request_params["tools"] = transformed_computer_tools
@@ -2031,6 +2048,16 @@ class AmazonConverseConfig(BaseConfig):
         bedrock_tools, anthropic_beta_list = self._process_tools_and_beta(
             original_tools, model, headers, additional_request_params, litellm_params, deployment_model
         )
+        requested_thinking: Final = additional_request_params.get("thinking")  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType]  # _prepare_request_params hands back a bare dict
+        body_betas: Final = additional_request_params.get("anthropic_beta")  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType]  # same bare dict
+        gated_thinking: Final = strip_beta_gated_thinking(
+            requested_thinking,  # pyright: ignore[reportUnknownArgumentType]  # same bare dict
+            active_beta_values({}, body_betas) | frozenset(anthropic_beta_list),  # pyright: ignore[reportUnknownArgumentType]  # _process_tools_and_beta hands back a bare list
+            drop_params=drop_params,
+        )
+        if gated_thinking.stripped:
+            log_beta_gated_strips(gated_thinking.stripped, model=model, provider="bedrock_converse")
+            additional_request_params["thinking"] = gated_thinking.thinking
 
         # Append cachePoint to tools if cache_control_injection_points has tool_config
         cache_injection_points: Final = additional_request_params.pop("cache_control_injection_points", None)

litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude_transformation.py

@@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Any, Final
 import httpx
 from pydantic import TypeAdapter
 
+from litellm.anthropic_beta_gated_fields import strip_beta_gated_request_body
 from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers
 from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import object_mapping
 from litellm.litellm_core_utils.prompt_templates.factory import (
@@ -210,7 +211,15 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
         if beta_list:
             _anthropic_request["anthropic_beta"] = beta_list
 
-        return _anthropic_request
+        return dict(
+            strip_beta_gated_request_body(
+                _anthropic_request,  # pyright: ignore[reportUnknownArgumentType]  # built by the base signature's bare dict transform
+                {},
+                drop_params=litellm_params.get("drop_params"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # litellm_params is the base signature's bare dict
+                model=model,
+                provider="bedrock_invoke",
+            )
+        )
 
     @property
     def uses_async_transform_request(self) -> bool:
@@ -294,7 +303,6 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
         programmatic_tool_calling_used: Final = self.is_programmatic_tool_calling_used(tools)
         input_examples_used: Final = self.is_input_examples_used(tools)
         is_mid_conversation_output_config_used: Final = self.is_mid_conversation_output_config_used(messages)
-        is_thinking_display_updates_used: Final = self.is_thinking_display_updates_used(thinking)
 
         client_beta_set: Final = set(get_anthropic_beta_from_headers(headers))
         user_beta_set: Final = (
@@ -312,7 +320,7 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig):
             mcp_server_used=self.is_mcp_server_used(optional_params.get("mcp_servers")),
             custom_llm_provider="bedrock",
             is_mid_conversation_output_config_used=is_mid_conversation_output_config_used,
-            is_thinking_display_updates_used=is_thinking_display_updates_used,
+            thinking=thinking,
         )
         beta_set.update(auto_betas)
 

litellm/llms/bedrock/claude_platform/transformation.py

@@ -72,6 +72,7 @@ class BedrockClaudePlatformConfig(BedrockClaudePlatformMixin, AnthropicConfig):
             ),
             code_execution_tool_used=self.is_code_execution_tool_used(tools=optional_params.get("tools")),
             container_with_skills_used=self.is_container_with_skills_used(optional_params=optional_params),
+            thinking=optional_params.get("thinking"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # optional_params is the base config's bare dict
         )
         anthropic_headers["anthropic-workspace-id"] = workspace_id
         return {**headers, **anthropic_headers}

litellm/llms/bedrock/common_utils.py

@@ -1331,6 +1331,12 @@ def _mantle_api_base_from_env() -> str | None:
     return next((base[: -len(suffix)] for suffix in _MANTLE_OPENAI_BASE_SUFFIXES if base.endswith(suffix)), base)
 
 
+DROP_DISABLED_REASONING_EFFORT_WARNING: Final = (
+    "Dropping %s=%r for Bedrock model=%r (drop_params=True) because the model map marks that level unsupported. "
+    "Send a level the model supports."
+)
+
+
 def bedrock_reasoning_effort_disabled(model: str, effort: str) -> bool:
     from litellm.utils import is_explicitly_disabled_factory
 

litellm/llms/bedrock/embed/embedding.py

@@ -9,9 +9,11 @@ from collections.abc import Callable, Mapping
 from typing import TYPE_CHECKING, Final, get_args, overload
 
 import httpx
+from pydantic import TypeAdapter
 
 import litellm
 from litellm.constants import BEDROCK_EMBEDDING_PROVIDERS_LITERAL
+from litellm.litellm_core_utils.hidden_params import set_hidden_param
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.llms.cohere.embed.handler import embedding as cohere_embedding
 from litellm.llms.custom_httpx.http_handler import (
@@ -36,7 +38,7 @@ from ..base_aws_llm import (
     pop_aws_auth_params,
     run_aws_signing,
 )
-from ..common_utils import BedrockError
+from ..common_utils import BedrockError, split_bedrock_region_path
 from .amazon_nova_transformation import AmazonNovaEmbeddingConfig
 from .amazon_titan_g1_transformation import AmazonTitanG1Config
 from .amazon_titan_multimodal_transformation import (
@@ -64,12 +66,21 @@ def _sign_get_request(
     return request.prepare()
 
 
+_OPTIONAL_STR: Final[TypeAdapter[str | None]] = TypeAdapter(str | None)
+
+
+def _without_region_path(model: str) -> str:
+    region, model_id = split_bedrock_region_path(model)
+    return model if region is None else model_id
+
+
 class BedrockEmbedding(BaseAWSLLM):
     @overload
     def _load_credentials(
         self,
         optional_params: dict,  # mutable-ok: the implementation pops the aws_* keys out of the caller's dict in place
         bearer_token: None = None,
+        region_from_model: str | None = None,
     ) -> tuple[Credentials, str]: ...
 
     @overload
@@ -77,15 +88,18 @@ class BedrockEmbedding(BaseAWSLLM):
         self,
         optional_params: dict,  # mutable-ok: the implementation pops the aws_* keys out of the caller's dict in place
         bearer_token: str,
+        region_from_model: str | None = None,
     ) -> tuple[None, str]: ...
 
     def _load_credentials(
         self,
         optional_params: dict,
         bearer_token: str | None = None,
+        region_from_model: str | None = None,
     ) -> tuple[Credentials | None, str]:
         auth_params: Final = pop_aws_auth_params(optional_params)
-        aws_region_name = optional_params.pop("aws_region_name", None)
+        explicit_region_name: Final = optional_params.pop("aws_region_name", None)
+        aws_region_name = region_from_model if explicit_region_name is None else explicit_region_name
 
         ### SET REGION NAME ###
         if aws_region_name is None:
@@ -184,14 +198,17 @@ class BedrockEmbedding(BaseAWSLLM):
         provider: BEDROCK_EMBEDDING_PROVIDERS_LITERAL,
         is_async_invoke: bool | None = False,
         batch_data: list[dict] | None = None,
+        aws_region_name: str | None = None,
     ) -> EmbeddingResponse | None:
         """
         Transforms the response from the Bedrock embedding provider to the OpenAI format.
         """
         returned_response: EmbeddingResponse | None = None
+        bedrock_model: Final = _without_region_path(model)
+        is_async_job: Final = bool(is_async_invoke) and len(response_list) == 1 and "invocationArn" in response_list[0]
 
         # Handle async invoke responses (single response with invocationArn)
-        if is_async_invoke and len(response_list) == 1 and "invocationArn" in response_list[0]:
+        if is_async_job:
             if provider == "twelvelabs":
                 returned_response = TwelveLabsMarengoEmbeddingConfig().transform_async_invoke_response(
                     response=response_list[0], model=model
@@ -227,15 +244,15 @@ class BedrockEmbedding(BaseAWSLLM):
                 )
         else:
             # Handle regular invoke responses
-            if model == "amazon.titan-embed-image-v1":
+            if bedrock_model == "amazon.titan-embed-image-v1":
                 returned_response = AmazonTitanMultimodalEmbeddingG1Config().transform_response(
                     response_list=response_list, model=model, batch_data=batch_data
                 )
-            elif model == "amazon.titan-embed-text-v1":
+            elif bedrock_model == "amazon.titan-embed-text-v1":
                 returned_response = AmazonTitanG1Config().transform_response(response_list=response_list, model=model)
-            elif model == "amazon.titan-embed-text-v2:0":
+            elif bedrock_model == "amazon.titan-embed-text-v2:0":
                 returned_response = AmazonTitanV2Config().transform_response(response_list=response_list, model=model)
-            elif model == "amazon.titan-embed-g1-text-02":
+            elif bedrock_model == "amazon.titan-embed-g1-text-02":
                 returned_response = AmazonTitanG1Config().transform_response(response_list=response_list, model=model)
             elif provider == "twelvelabs":
                 returned_response = TwelveLabsMarengoEmbeddingConfig().transform_response(
@@ -251,6 +268,8 @@ class BedrockEmbedding(BaseAWSLLM):
         ##########################################################
         if returned_response is None:
             raise Exception(f"Unable to map model response to known provider format. model={model}")
+        if aws_region_name is not None and not is_async_job:
+            set_hidden_param(returned_response, "region_name", aws_region_name)
         return returned_response
 
     def _single_func_embeddings(
@@ -319,6 +338,7 @@ class BedrockEmbedding(BaseAWSLLM):
             provider=provider,
             is_async_invoke=is_async_invoke,
             batch_data=batch_data,
+            aws_region_name=aws_region_name,
         )
 
     async def _async_single_func_embeddings(
@@ -390,6 +410,7 @@ class BedrockEmbedding(BaseAWSLLM):
             provider=provider,
             is_async_invoke=is_async_invoke,
             batch_data=batch_data,
+            aws_region_name=aws_region_name,
         )
 
     def embeddings(
@@ -409,26 +430,29 @@ class BedrockEmbedding(BaseAWSLLM):
         litellm_params: dict,
         api_key: str | None = None,
     ) -> EmbeddingResponse:
+        model_id_param: Final = _OPTIONAL_STR.validate_python(optional_params.pop("model_id", None)) or None
+        has_async_invoke: Final = "async_invoke/" in model
+        model_without_async_route: Final = model.replace("async_invoke/", "", 1)
+        path_region: Final = split_bedrock_region_path(model_without_async_route)[0] if model_id_param is None else None
+        bedrock_model: Final = _without_region_path(model_without_async_route)
         credentials, aws_region_name = self._load_credentials(
-            optional_params, bearer_token=bedrock_bearer_token(api_key)
+            optional_params,
+            bearer_token=bedrock_bearer_token(api_key),
+            region_from_model=self._get_aws_region_from_model_arn(model_id_param) or path_region,
         )
 
         ### TRANSFORMATION ###
-        unencoded_model_id: Final = optional_params.pop("model_id", None) or model  # default to model if not passed
+        unencoded_model_id: Final = model_id_param or bedrock_model
         modelId: Final = urllib.parse.quote(unencoded_model_id, safe="")
         aws_region_name = self._get_aws_region_name(
             optional_params={"aws_region_name": aws_region_name},
-            model=model,
+            model=bedrock_model,
             model_id=unencoded_model_id,
         )
-        # Check async invoke needs to be used
-        has_async_invoke: Final = "async_invoke/" in model
-        if has_async_invoke:
-            model = model.replace("async_invoke/", "", 1)
-        provider: Final = self.get_bedrock_embedding_provider(model)
+        provider: Final = self.get_bedrock_embedding_provider(bedrock_model)
         if provider is None:
             raise Exception(
-                f"Unable to determine bedrock embedding provider for model: {model}. "
+                f"Unable to determine bedrock embedding provider for model: {bedrock_model}. "
                 f"Supported providers: {list(get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL))}"
             )
         inference_params = copy.deepcopy(optional_params)
@@ -441,9 +465,9 @@ class BedrockEmbedding(BaseAWSLLM):
         batch_data: list | None = None
         if provider == "cohere":
             data = BedrockCohereEmbeddingConfig().transform_request(
-                model=model, input=input, inference_params=inference_params
+                model=bedrock_model, input=input, inference_params=inference_params
             )
-        elif provider == "amazon" and model in [
+        elif provider == "amazon" and bedrock_model in [
             "amazon.titan-embed-image-v1",
             "amazon.titan-embed-text-v1",
             "amazon.titan-embed-text-v2:0",
@@ -451,28 +475,28 @@ class BedrockEmbedding(BaseAWSLLM):
         ]:
             batch_data = []
             for i in input:
-                if model == "amazon.titan-embed-image-v1":
+                if bedrock_model == "amazon.titan-embed-image-v1":
                     transformed_request: AmazonEmbeddingRequest = (
                         AmazonTitanMultimodalEmbeddingG1Config().transform_request(
                             input=i, inference_params=inference_params
                         )
                     )
-                elif model == "amazon.titan-embed-text-v1":
+                elif bedrock_model == "amazon.titan-embed-text-v1":
                     transformed_request = AmazonTitanG1Config().transform_request(
                         input=i, inference_params=inference_params
                     )
-                elif model == "amazon.titan-embed-text-v2:0":
+                elif bedrock_model == "amazon.titan-embed-text-v2:0":
                     transformed_request = AmazonTitanV2Config().transform_request(
                         input=i, inference_params=inference_params
                     )
-                elif model == "amazon.titan-embed-g1-text-02":
+                elif bedrock_model == "amazon.titan-embed-g1-text-02":
                     transformed_request = AmazonTitanG1Config().transform_request(
                         input=i, inference_params=inference_params
                     )
                 else:
                     raise Exception(
                         "Unmapped model. Received={}. Expected={}".format(
-                            model,
+                            bedrock_model,
                             [
                                 "amazon.titan-embed-image-v1",
                                 "amazon.titan-embed-text-v1",
@@ -485,7 +509,7 @@ class BedrockEmbedding(BaseAWSLLM):
         elif provider == "twelvelabs":
             batch_data = []
             for i in input:
-                twelvelabs_request = TwelveLabsMarengoEmbeddingConfig(model=model).transform_request(
+                twelvelabs_request = TwelveLabsMarengoEmbeddingConfig(model=bedrock_model).transform_request(
                     input=i,
                     inference_params=inference_params,
                     async_invoke_route=has_async_invoke,
@@ -527,7 +551,7 @@ class BedrockEmbedding(BaseAWSLLM):
                     extra_headers=extra_headers,
                     endpoint_url=endpoint_url,
                     aws_region_name=aws_region_name,
-                    model=model,
+                    model=model_without_async_route,
                     logging_obj=logging_obj,
                     api_key=api_key,
                     provider=provider,
@@ -541,7 +565,7 @@ class BedrockEmbedding(BaseAWSLLM):
                 extra_headers=extra_headers,
                 endpoint_url=endpoint_url,
                 aws_region_name=aws_region_name,
-                model=model,
+                model=model_without_async_route,
                 logging_obj=logging_obj,
                 api_key=api_key,
                 provider=provider,
@@ -570,8 +594,9 @@ class BedrockEmbedding(BaseAWSLLM):
         ## ROUTING ##
         # Convert CaseInsensitiveDict to regular dict for httpx compatibility
         headers_for_request: Final = dict(prepped.headers) if hasattr(prepped, "headers") else {}
+        set_hidden_param(model_response, "region_name", aws_region_name)
         return cohere_embedding(
-            model=model,
+            model=model_without_async_route,
             input=input,
             model_response=model_response,
             logging_obj=logging_obj,

litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude_transformation.py

@@ -7,6 +7,7 @@ from pydantic import TypeAdapter
 
 import litellm
 from litellm._logging import verbose_logger
+from litellm.anthropic_beta_gated_fields import log_beta_gated_strips, strip_beta_gated_native_request
 from litellm.anthropic_beta_headers_manager import filter_and_transform_beta_headers
 from litellm.constants import (
     BEDROCK_MIN_THINKING_BUDGET_TOKENS,
@@ -549,6 +550,9 @@ class AmazonAnthropicClaudeMessagesConfig(
             "edits": retained_edits,
         }
 
+    def strips_beta_gated_fields_from_final_headers(self) -> bool:
+        return False
+
     def _get_bedrock_invoke_anthropic_beta_headers(
         self,
         model: str,
@@ -584,9 +588,7 @@ class AmazonAnthropicClaudeMessagesConfig(
             ),
             custom_llm_provider="bedrock",
             is_mid_conversation_output_config_used=is_mid_conversation_output_config_used,
-            is_thinking_display_updates_used=anthropic_model_info.is_thinking_display_updates_used(
-                anthropic_messages_request.get("thinking")
-            ),
+            thinking=anthropic_messages_request.get("thinking"),
             is_mid_conversation_tool_change_used=anthropic_model_info.is_mid_conversation_tool_change_used(
                 outgoing_messages_typed
             ),
@@ -757,7 +759,9 @@ class AmazonAnthropicClaudeMessagesConfig(
                 ),
                 "display": "updates",
             }
-        self._normalize_system_role_messages(anthropic_messages_request, model=model)
+        self._normalize_system_role_messages(
+            anthropic_messages_request, model=model, drop_params=getattr(litellm_params, "drop_params", None)
+        )
         #########################################################
         ############## BEDROCK Invoke SPECIFIC TRANSFORMATION ###
         #########################################################
@@ -825,6 +829,20 @@ class AmazonAnthropicClaudeMessagesConfig(
         if filtered_betas:
             anthropic_messages_request["anthropic_beta"] = filtered_betas
 
+        final_request: Final = OBJECT_MAPPING_ADAPTER.validate_python(anthropic_messages_request)  # pyright: ignore[reportUnknownArgumentType]  # built from the base signature's bare dict
+        gated: Final = strip_beta_gated_native_request(
+            {},
+            final_request,
+            final_request.get("messages"),
+            body_betas=filtered_betas,
+            drop_params=getattr(litellm_params, "drop_params", None),
+        )
+        log_beta_gated_strips(gated.stripped, model=model, provider="bedrock_invoke")
+        if gated.thinking_stripped:
+            anthropic_messages_request["thinking"] = gated.thinking
+        if gated.messages_stripped:
+            anthropic_messages_request["messages"] = list(gated.messages)
+
         outbound_tools: Final = tools_without_eager_input_streaming(anthropic_messages_request)
         if outbound_tools is not None:
             anthropic_messages_request["tools"] = outbound_tools

litellm/llms/bedrock/responses/transformation.py

@@ -52,6 +52,7 @@ from litellm.llms.base_llm.responses.codex_compat import drop_unsupported_tools,
 from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
 from litellm.llms.bedrock.common_utils import (
     BEDROCK_CHAT_COMPLETIONS_ROUTE_PREFIX,
+    DROP_DISABLED_REASONING_EFFORT_WARNING,
     BedrockError,
     bedrock_reasoning_effort_disabled,
     bedrock_supports_openai_responses,
@@ -173,7 +174,7 @@ def _without_disabled_reasoning_effort(
             ),
             status_code=400,
         )
-    verbose_logger.debug("Dropping unsupported `reasoning.effort=%s` for Bedrock model=%s.", effort, model)
+    verbose_logger.warning(DROP_DISABLED_REASONING_EFFORT_WARNING, "reasoning.effort", effort, model)
     rest: Final = {key: value for key, value in reasoning.items() if key != "effort"}
     without_reasoning: Final = {key: value for key, value in params.items() if key != "reasoning"}
     return {**without_reasoning, "reasoning": rest} if rest else without_reasoning

litellm/llms/cohere/embed/transformation.py

@@ -10,11 +10,13 @@ Convers
 Docs - https://docs.cohere.com/v2/reference/embed
 """
 
-from collections.abc import Iterable, Mapping, Sized
-from typing import Final, Protocol, cast
+from collections.abc import Iterable, Iterator, Mapping, Sequence, Sized
+from enum import Enum
+from itertools import chain, starmap
+from typing import Final, Protocol, TypeAlias, cast
 
 import httpx
-from pydantic import ConfigDict, TypeAdapter
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
 
 import litellm
 from litellm import COHERE_DEFAULT_EMBEDDING_INPUT_TYPE
@@ -32,14 +34,117 @@ from litellm.utils import is_base64_encoded
 from ..common_utils import CohereError
 
 _JSON_OBJECT: Final = TypeAdapter(Mapping[str, object], config=ConfigDict(hide_input_in_errors=True))
-_EMBEDDINGS_BY_TYPE: Final = TypeAdapter(Mapping[str, Iterable[object]], config=ConfigDict(hide_input_in_errors=True))
 _DECODED_JSON: Final = TypeAdapter(object)
 
 
 class _SupportsEncode(Protocol):
     """Tokenizer handle: the embedding usage path only encodes text to measure its token length."""
 
-    def encode(self, text: str, /) -> Sized: ...
+    def encode_ordinary(self, text: str, /) -> Sized: ...
+
+
+class CohereImageTokenBilling(Enum):
+    SEPARATE_FROM_INPUT_TOKENS = "separate_from_input_tokens"
+    FOLDED_INTO_INPUT_TOKENS = "folded_into_input_tokens"
+
+
+class CohereEmbeddingRowShape(Enum):
+    OPENAI = "openai"
+    TYPED = "typed"
+
+
+class _CohereBilledUnits(BaseModel):
+    model_config = ConfigDict(frozen=True, hide_input_in_errors=True)
+
+    input_tokens: int | None = None
+    image_tokens: int | None = None
+    images: int | None = None
+
+
+class _CohereEmbedMeta(BaseModel):
+    model_config = ConfigDict(frozen=True, hide_input_in_errors=True)
+
+    billed_units: _CohereBilledUnits = Field(default_factory=_CohereBilledUnits)
+
+
+_CohereEmbeddings: TypeAlias = Mapping[str, Sequence[object]] | Sequence[object]
+
+
+class _CohereEmbedBody(BaseModel):
+    model_config = ConfigDict(frozen=True, hide_input_in_errors=True)
+
+    embeddings: _CohereEmbeddings
+    meta: _CohereEmbedMeta = Field(default_factory=_CohereEmbedMeta)
+
+
+def _embedding_rows(vectors: Sequence[object]) -> Iterator[Mapping[str, object]]:
+    return ({"object": "embedding", "index": index, "embedding": vector} for index, vector in enumerate(vectors))
+
+
+def _typed_embedding_rows(embedding_type: str, vectors: Sequence[object]) -> Iterator[Mapping[str, object]]:
+    return ({**row, "type": embedding_type} for row in _embedding_rows(vectors))
+
+
+def _response_rows(embeddings: _CohereEmbeddings, row_shape: CohereEmbeddingRowShape) -> Iterable[Mapping[str, object]]:
+    if not isinstance(embeddings, Mapping):
+        return _embedding_rows(embeddings)
+    match row_shape:
+        case CohereEmbeddingRowShape.OPENAI:
+            return chain.from_iterable(map(_embedding_rows, embeddings.values()))
+        case CohereEmbeddingRowShape.TYPED:
+            return chain.from_iterable(starmap(_typed_embedding_rows, embeddings.items()))
+
+
+def _prompt_usage(prompt_tokens: int, prompt_tokens_details: PromptTokensDetailsWrapper | None) -> Usage:
+    return Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=0,
+        total_tokens=prompt_tokens,
+        prompt_tokens_details=prompt_tokens_details,
+    )
+
+
+def _billed_usage(billed_units: _CohereBilledUnits, image_billing: CohereImageTokenBilling) -> Usage:
+    match image_billing:
+        case CohereImageTokenBilling.SEPARATE_FROM_INPUT_TOKENS:
+            image_units: Final = (billed_units.image_tokens or 0) + (billed_units.images or 0)
+            return _prompt_usage(
+                (billed_units.input_tokens or 0) + image_units,
+                PromptTokensDetailsWrapper(
+                    text_tokens=billed_units.input_tokens,
+                    image_tokens=image_units or None,
+                    image_count=billed_units.images,
+                ),
+            )
+        case CohereImageTokenBilling.FOLDED_INTO_INPUT_TOKENS:
+            return _prompt_usage(billed_units.input_tokens or 0, None)
+
+
+def _cohere_embed_usage(
+    billed_units: _CohereBilledUnits,
+    input_texts: Iterable[str],
+    encoding: _SupportsEncode,
+    image_billing: CohereImageTokenBilling,
+) -> Usage:
+    if billed_units.input_tokens is None and billed_units.image_tokens is None and billed_units.images is None:
+        return _prompt_usage(sum(len(encoding.encode_ordinary(text)) for text in input_texts), None)
+    return _billed_usage(billed_units, image_billing)
+
+
+def cohere_embedding_response(
+    body: Mapping[str, object],
+    model: str | None,
+    encoding: _SupportsEncode,
+    input_texts: Iterable[str],
+    image_billing: CohereImageTokenBilling,
+    row_shape: CohereEmbeddingRowShape,
+) -> EmbeddingResponse:
+    cohere_body: Final = _CohereEmbedBody.model_validate(body)
+    return EmbeddingResponse(
+        model=model,
+        data=list(_response_rows(cohere_body.embeddings, row_shape)),
+        usage=_cohere_embed_usage(cohere_body.meta.billed_units, input_texts, encoding, image_billing),
+    )
 
 
 class CohereEmbeddingConfig(BaseEmbeddingConfig):
@@ -145,44 +250,16 @@ class CohereEmbeddingConfig(BaseEmbeddingConfig):
             ),
         )
 
-    def _calculate_usage(self, input: list[str], encoding: _SupportsEncode, meta: dict) -> Usage:
-        input_tokens = 0
-
-        text_tokens: Final[int | None] = meta.get("billed_units", {}).get("input_tokens")
-
-        image_tokens: Final[int | None] = meta.get("billed_units", {}).get("images")
-
-        prompt_tokens_details: PromptTokensDetailsWrapper | None = None
-        if image_tokens is None and text_tokens is None:
-            for text in input:
-                input_tokens += len(encoding.encode(text))
-        else:
-            prompt_tokens_details = PromptTokensDetailsWrapper(
-                image_tokens=image_tokens,
-                text_tokens=text_tokens,
-            )
-            if image_tokens:
-                input_tokens += image_tokens
-            if text_tokens:
-                input_tokens += text_tokens
-
-        return Usage(
-            prompt_tokens=input_tokens,
-            completion_tokens=0,
-            total_tokens=input_tokens,
-            prompt_tokens_details=prompt_tokens_details,
-        )
-
-    def _transform_response(
+    def transform_native_embed_response(
         self,
         response: httpx.Response,
         api_key: str | None,
         logging_obj: LiteLLMLoggingObj,
-        data: dict | CohereEmbeddingRequest,
-        model_response: EmbeddingResponse,
+        data: Mapping[str, object] | CohereEmbeddingRequest,
         model: str,
         encoding: _SupportsEncode,
-        input: list,
+        input: Sequence[str],
+        row_shape: CohereEmbeddingRowShape,
     ) -> EmbeddingResponse:
         response_json: Final = _DECODED_JSON.validate_python(response.json())
         ## LOGGING
@@ -192,38 +269,15 @@ class CohereEmbeddingConfig(BaseEmbeddingConfig):
             additional_args={"complete_input_dict": data},
             original_response=response_json,
         )
-        """
-            response 
-            {
-                'object': "list",
-                'data': [
-                
-                ]
-                'model', 
-                'usage'
-            }
-        """
-        payload: Final = _JSON_OBJECT.validate_python(response_json)
-        embeddings: Final = _EMBEDDINGS_BY_TYPE.validate_python(payload["embeddings"])
-        output_data: Final = []
-        for k, embedding_list in embeddings.items():
-            for idx, embedding in enumerate(embedding_list):
-                output_data.append({"object": "embedding", "index": idx, "embedding": embedding})
-        model_response.object = "list"
-        model_response.data = output_data
-        model_response.model = model
-        input_tokens = 0
-        for text in input:
-            input_tokens += len(encoding.encode(text))
-
-        setattr(
-            model_response,
-            "usage",
-            self._calculate_usage(input, encoding, dict(_JSON_OBJECT.validate_python(payload.get("meta", {})))),
+        return cohere_embedding_response(
+            _JSON_OBJECT.validate_python(response_json),
+            model,
+            encoding,
+            input,
+            CohereImageTokenBilling.SEPARATE_FROM_INPUT_TOKENS,
+            row_shape,
         )
 
-        return model_response
-
     def transform_embedding_response(
         self,
         model: str,
@@ -235,15 +289,15 @@ class CohereEmbeddingConfig(BaseEmbeddingConfig):
         optional_params: dict,
         litellm_params: dict,
     ) -> EmbeddingResponse:
-        return self._transform_response(
+        return self.transform_native_embed_response(
             response=raw_response,
             api_key=api_key,
             logging_obj=logging_obj,
             data=request_data,
-            model_response=model_response,
             model=model,
             encoding=litellm.encoding,
             input=logging_obj.model_call_details["input"],
+            row_shape=CohereEmbeddingRowShape.OPENAI,
         )
 
     def get_error_class(self, error_message: str, status_code: int, headers: dict | httpx.Headers) -> BaseLLMException:

litellm/llms/custom_httpx/llm_http_handler.py

@@ -50,7 +50,11 @@ from litellm.litellm_core_utils.audio_utils.subtitle_utils import (
     SUBTITLE_RESPONSE_FORMATS,
     synthesize_subtitle_document,
 )
-from litellm.litellm_core_utils.core_helpers import as_str_mapping, set_provider_response_headers_in_hidden_params
+from litellm.litellm_core_utils.core_helpers import (
+    as_str_mapping,
+    qualify_agentic_followup_model,
+    set_provider_response_headers_in_hidden_params,
+)
 from litellm.litellm_core_utils.dot_notation_indexing import delete_nested_value
 from litellm.litellm_core_utils.get_litellm_params import AWS_CREDENTIAL_KWARGS_KEYS
 from litellm.litellm_core_utils.get_llm_provider_logic import get_provider_affinity_header
@@ -2577,6 +2581,7 @@ class BaseLLMHTTPHandler:
                 "preset_cache_key": None,
                 "stream_response": {},
                 "model_info": kwargs.get("model_info"),
+                **litellm_params.per_second_pricing(),
                 **vertex_location_params,
                 **anthropic_messages_optional_request_params,
             },
@@ -6275,9 +6280,7 @@ class BaseLLMHTTPHandler:
         if patch.messages is None:
             raise ValueError("Agentic loop plan missing patched messages")
 
-        full_model_name = patch.model or model
-        if "/" not in full_model_name:
-            full_model_name = f"{custom_llm_provider}/{full_model_name}"
+        full_model_name: Final = qualify_agentic_followup_model(patch.model, model, custom_llm_provider)
 
         optional_params_for_followup: Final = dict(optional_params)
         optional_params_for_followup.update(patch.optional_params)
@@ -6920,7 +6923,7 @@ class BaseLLMHTTPHandler:
         try:
             ssl_context: Final = realtime_ssl_for_url(url)
             provider_backend: Final = await provider_config.open_backend(url, headers)
-            backend_ws: Final = (
+            backend_ws: Final = provider_config.wrap_backend(
                 provider_backend
                 if provider_backend is not None
                 else await self._open_realtime_backend_ws(websockets, url, headers, ssl_context)
@@ -6958,9 +6961,7 @@ class BaseLLMHTTPHandler:
                         await backend_ws.send(_session_config)
                         realtime_streaming.session_configuration_request = _session_config
 
-                # For providers that defer setup until client session.update, optionally
-                # send synthetic session.created to unblock clients waiting on connect.
-                if not provider_config.requires_session_configuration():
+                if not _session_config:
                     synthetic_session: Final = provider_config.transform_session_created_event(
                         model=model,
                         logging_session_id=logging_obj.litellm_trace_id,
@@ -11149,7 +11150,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url: Final = api_base
+        url: Final = vector_store_provider_config.get_vector_store_list_url(api_base)
 
         params: Final[dict[str, object]] = {}
         if after is not None:
@@ -11228,7 +11229,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url: Final = api_base
+        url: Final = vector_store_provider_config.get_vector_store_list_url(api_base)
 
         params: Final[dict[str, object]] = {}
         if after is not None:

litellm/llms/dashscope/chat/transformation.py

@@ -10,6 +10,7 @@ from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllMessageValues, ChatCompletionToolParam
 
 from ...openai.chat.gpt_transformation import OpenAIGPTConfig
+from ..common_utils import resolve_dashscope_openai_api_base
 
 
 class DashScopeChatConfig(OpenAIGPTConfig):
@@ -55,11 +56,9 @@ class DashScopeChatConfig(OpenAIGPTConfig):
     def _get_openai_compatible_provider_info(
         self, api_base: str | None, api_key: str | None
     ) -> tuple[str | None, str | None]:
-        api_base = (
-            api_base or get_secret_str("DASHSCOPE_API_BASE") or "https://dashscope.aliyuncs.com/compatible-mode/v1"
-        )
+        resolved_api_base: Final = resolve_dashscope_openai_api_base(api_base)
         dynamic_api_key: Final = api_key or get_secret_str("DASHSCOPE_API_KEY")
-        return api_base, dynamic_api_key
+        return resolved_api_base, dynamic_api_key
 
     def get_openai_compatible_provider_info(
         self,
@@ -69,7 +68,7 @@ class DashScopeChatConfig(OpenAIGPTConfig):
         return self._get_openai_compatible_provider_info(api_base, api_key)
 
     def _resolve_chat_api_base(self, api_base: str | None) -> str:
-        return api_base or "https://dashscope.aliyuncs.com/compatible-mode/v1"
+        return resolve_dashscope_openai_api_base(api_base)
 
     def get_complete_url(
         self,

litellm/llms/dashscope/common_utils.py

@@ -2,6 +2,7 @@
 Common utilities for the DashScope LLM provider.
 """
 
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Final
 from urllib.parse import urlparse
 
@@ -19,10 +20,123 @@ if TYPE_CHECKING:
     from litellm.llms.base_llm.videos.transformation import BaseVideoConfig
 
 DASHSCOPE_CHAT_COMPATIBLE_PATH: Final = "/compatible-mode/v1"
-DASHSCOPE_RERANK_PATH: Final = "/compatible-api/v1/reranks"
+DASHSCOPE_ANTHROPIC_PATH: Final = "/apps/anthropic"
+DASHSCOPE_NATIVE_V1_PATH: Final = "/api/v1"
+DASHSCOPE_COMPATIBLE_API_PATH: Final = "/compatible-api/v1"
+DASHSCOPE_RERANK_PATH: Final = f"{DASHSCOPE_COMPATIBLE_API_PATH}/reranks"
+DASHSCOPE_IMAGE_PATH: Final = "/api/v1/services/aigc/multimodal-generation/generation"
+DASHSCOPE_DEFAULT_ORIGIN: Final = "https://dashscope.aliyuncs.com"
+
+_STANDARD_DASHSCOPE_HOSTS: Final = frozenset(
+    {
+        "dashscope.aliyuncs.com",
+        "dashscope-intl.aliyuncs.com",
+        "dashscope-us.aliyuncs.com",
+    }
+)
+_WORKSPACE_HOST_SUFFIX: Final = ".maas.aliyuncs.com"
+_EXCLUDED_WORKSPACE_PREFIXES: Final = ("trial.", "token-plan.")
+_PROTOCOL_PATH_PREFIXES: Final = (
+    DASHSCOPE_CHAT_COMPATIBLE_PATH,
+    DASHSCOPE_ANTHROPIC_PATH,
+    DASHSCOPE_NATIVE_V1_PATH,
+    DASHSCOPE_COMPATIBLE_API_PATH,
+)
 
 
-def _rerank_base_for_chat_shaped_base(api_base: str | None) -> str | None:
+@dataclass(frozen=True, slots=True)
+class DashScopeEndpoints:
+    origin: str
+    openai: str
+    anthropic: str
+    native: str
+
+
+def _is_dashscope_host(host: str) -> bool:
+    if host in _STANDARD_DASHSCOPE_HOSTS:
+        return True
+    return host.endswith(_WORKSPACE_HOST_SUFFIX) and not host.startswith(_EXCLUDED_WORKSPACE_PREFIXES)
+
+
+def _dashscope_origin(api_base: str | None) -> str | None:
+    if not api_base:
+        return None
+    parsed: Final = urlparse(api_base)
+    if parsed.scheme not in ("http", "https") or not parsed.netloc:
+        return None
+    if parsed.query or parsed.fragment:
+        return None
+    host: Final = (parsed.hostname or "").lower()
+    if not _is_dashscope_host(host):
+        return None
+    path: Final = parsed.path.rstrip("/")
+    if path and not any(path == prefix or path.startswith(f"{prefix}/") for prefix in _PROTOCOL_PATH_PREFIXES):
+        return None
+    return f"{parsed.scheme}://{parsed.netloc}"
+
+
+def resolve_dashscope_endpoints(api_base: str | None) -> DashScopeEndpoints | None:
+    """
+    Derive DashScope's protocol-specific bases from any standard DashScope base.
+
+    Accepts the domain root, an OpenAI-compatible base, an Anthropic Messages
+    base, or a native `/api/v1` base for `dashscope.aliyuncs.com`,
+    `dashscope-intl.aliyuncs.com`, `dashscope-us.aliyuncs.com`, and
+    `{workspace}.{region}.maas.aliyuncs.com`. Returns None for any other host
+    or path so callers keep the caller-supplied base untouched.
+    """
+    origin: Final = _dashscope_origin(api_base)
+    if origin is None:
+        return None
+    return DashScopeEndpoints(
+        origin=origin,
+        openai=f"{origin}{DASHSCOPE_CHAT_COMPATIBLE_PATH}",
+        anthropic=f"{origin}{DASHSCOPE_ANTHROPIC_PATH}",
+        native=f"{origin}{DASHSCOPE_NATIVE_V1_PATH}",
+    )
+
+
+def is_dashscope_native_endpoint_url(api_base: str) -> bool:
+    return urlparse(api_base).path.rstrip("/").startswith(f"{DASHSCOPE_NATIVE_V1_PATH}/")
+
+
+def resolve_dashscope_openai_api_base(
+    api_base: str | None,
+    env_var: str = "DASHSCOPE_API_BASE",
+    default: str = f"{DASHSCOPE_DEFAULT_ORIGIN}{DASHSCOPE_CHAT_COMPATIBLE_PATH}",
+) -> str:
+    candidate: Final = api_base or get_secret_str(env_var)
+    if candidate and is_dashscope_native_endpoint_url(candidate):
+        return candidate
+    endpoints: Final = resolve_dashscope_endpoints(candidate)
+    if endpoints is not None:
+        return endpoints.openai
+    return candidate or default
+
+
+def resolve_dashscope_anthropic_api_base(
+    api_base: str | None,
+    env_var: str = "DASHSCOPE_API_BASE",
+    default: str = f"{DASHSCOPE_DEFAULT_ORIGIN}{DASHSCOPE_ANTHROPIC_PATH}",
+) -> str:
+    candidate: Final = api_base or get_secret_str(env_var)
+    endpoints: Final = resolve_dashscope_endpoints(candidate)
+    if endpoints is not None:
+        return endpoints.anthropic
+    return candidate or default
+
+
+def dashscope_base_selects_anthropic_messages(api_base: str | None) -> bool:
+    candidate: Final = api_base or get_secret_str("DASHSCOPE_API_BASE")
+    if not candidate:
+        return False
+    path: Final = urlparse(candidate).path.rstrip("/")
+    if path.endswith(DASHSCOPE_ANTHROPIC_PATH) or f"{DASHSCOPE_ANTHROPIC_PATH}/" in path:
+        return True
+    return not path and resolve_dashscope_endpoints(candidate) is not None
+
+
+def _chat_shaped_aliyun_origin(api_base: str | None) -> str | None:
     if api_base is None:
         return None
     parsed: Final = urlparse(api_base)
@@ -30,14 +144,24 @@ def _rerank_base_for_chat_shaped_base(api_base: str | None) -> str | None:
     on_aliyun_host: Final = host == "aliyuncs.com" or host.endswith(".aliyuncs.com")
     if not on_aliyun_host or parsed.path.rstrip("/") != DASHSCOPE_CHAT_COMPATIBLE_PATH:
         return None
-    return f"{parsed.scheme}://{parsed.netloc}{DASHSCOPE_RERANK_PATH}"
+    return f"{parsed.scheme}://{parsed.netloc}"
+
+
+def _is_compatible_api_base(api_base: str) -> bool:
+    path: Final = urlparse(api_base).path.rstrip("/")
+    return path == DASHSCOPE_COMPATIBLE_API_PATH or path.startswith(f"{DASHSCOPE_COMPATIBLE_API_PATH}/")
 
 
 def resolve_dashscope_family_rerank_api_base(api_base: str | None, env_var: str, default_rerank_base: str) -> str:
-    remapped: Final = _rerank_base_for_chat_shaped_base(api_base)
-    if api_base is not None and remapped is None:
+    if api_base is None:
+        return get_secret_str(env_var) or default_rerank_base
+    if _is_compatible_api_base(api_base):
         return api_base
-    return get_secret_str(env_var) or remapped or default_rerank_base
+    chat_origin: Final = _chat_shaped_aliyun_origin(api_base)
+    if chat_origin is not None:
+        return get_secret_str(env_var) or f"{chat_origin}{DASHSCOPE_RERANK_PATH}"
+    endpoints: Final = resolve_dashscope_endpoints(api_base)
+    return f"{endpoints.origin}{DASHSCOPE_RERANK_PATH}" if endpoints is not None else api_base
 
 
 def get_dashscope_family_embedding_config(custom_llm_provider: str) -> "BaseEmbeddingConfig":

litellm/llms/dashscope/embed/transformation.py

@@ -25,7 +25,7 @@ from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import AllEmbeddingInputValues, AllMessageValues
 from litellm.types.utils import EmbeddingResponse, Usage
 
-from ..common_utils import DashScopeError
+from ..common_utils import DashScopeError, resolve_dashscope_openai_api_base
 
 DEFAULT_API_BASE: Final = "https://dashscope.aliyuncs.com/compatible-mode/v1"
 
@@ -91,7 +91,7 @@ class DashScopeEmbeddingConfig(BaseEmbeddingConfig):
         return resolved_api_key
 
     def _resolve_embedding_api_base(self, api_base: str | None) -> str:
-        return api_base or get_secret_str("DASHSCOPE_API_BASE") or DEFAULT_API_BASE
+        return resolve_dashscope_openai_api_base(api_base, default=DEFAULT_API_BASE)
 
     def validate_environment(
         self,

litellm/llms/dashscope/image_generation/transformation.py

@@ -39,6 +39,13 @@ from litellm.types.llms.openai import (
 )
 from litellm.types.utils import ImageObject, ImageResponse
 
+from ..common_utils import (
+    DASHSCOPE_CHAT_COMPATIBLE_PATH,
+    DASHSCOPE_IMAGE_PATH,
+    is_dashscope_native_endpoint_url,
+    resolve_dashscope_endpoints,
+)
+
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
     from litellm.litellm_core_utils.tokenizer import Encoding as Tokenizer
@@ -53,8 +60,6 @@ _DECODED_JSON: Final = TypeAdapter(object)
 
 DEFAULT_API_BASE: Final = "https://dashscope-intl.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation"
 
-CHAT_COMPATIBLE_MODE_PATH: Final = "/compatible-mode/v1"
-
 # Maps OpenAI size strings (WxH) to DashScope size strings (W*H)
 OPENAI_TO_DASHSCOPE_SIZE: Final[dict] = {
     "256x256": "256*256",
@@ -102,8 +107,14 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig):
             raise ValueError("DASHSCOPE_API_KEY is not set")
         return resolved_api_key
 
-    def _resolve_image_api_base(self, image_api_base: str | None) -> str:
-        return image_api_base or get_secret_str("DASHSCOPE_API_BASE_IMAGE") or DEFAULT_API_BASE
+    def _image_api_base_env(self) -> str | None:
+        return get_secret_str("DASHSCOPE_API_BASE_IMAGE")
+
+    def _default_image_api_base(self) -> str:
+        return DEFAULT_API_BASE
+
+    def _configured_chat_api_base(self) -> str | None:
+        return get_secret_str("DASHSCOPE_API_BASE")
 
     def get_complete_url(
         self,
@@ -114,10 +125,18 @@ class DashScopeImageGenerationConfig(BaseImageGenerationConfig):
         litellm_params: dict,
         stream: bool | None = None,
     ) -> str:
-        image_api_base: Final = (
-            api_base if api_base and not api_base.rstrip("/").endswith(CHAT_COMPATIBLE_MODE_PATH) else None
-        )
-        return self._resolve_image_api_base(image_api_base)
+        image_env_api_base: Final = self._image_api_base_env()
+        configured_api_base: Final = api_base or self._configured_chat_api_base()
+        if not configured_api_base or configured_api_base.rstrip("/").endswith(DASHSCOPE_CHAT_COMPATIBLE_PATH):
+            return image_env_api_base or self._default_image_api_base()
+        if is_dashscope_native_endpoint_url(configured_api_base):
+            return configured_api_base
+        endpoints: Final = resolve_dashscope_endpoints(configured_api_base)
+        if endpoints is None:
+            return configured_api_base
+        if not api_base and image_env_api_base:
+            return image_env_api_base
+        return f"{endpoints.origin}{DASHSCOPE_IMAGE_PATH}"
 
     def validate_environment(
         self,

litellm/llms/dashscope/messages/__init__.py

@@ -0,0 +1,7 @@
+"""
+DashScope Anthropic-compatible Messages Module
+"""
+
+from .transformation import DashScopeAnthropicMessagesConfig
+
+__all__ = ["DashScopeAnthropicMessagesConfig"]

litellm/llms/dashscope/messages/transformation.py

@@ -0,0 +1,96 @@
+"""
+DashScope Anthropic-compatible Messages transformation config.
+"""
+
+from typing import ClassVar, Final
+
+import litellm
+from litellm.exceptions import AuthenticationError
+from litellm.litellm_core_utils.url_utils import ensure_api_route
+from litellm.llms.anthropic.messages.native.transformation import (
+    AnthropicMessagesConfig,
+)
+from litellm.secret_managers.main import get_secret_str, normalize_nonempty_secret_str
+
+from ..common_utils import (
+    dashscope_base_selects_anthropic_messages,
+    resolve_dashscope_anthropic_api_base,
+)
+
+
+class DashScopeAnthropicMessagesConfig(AnthropicMessagesConfig):
+    """
+    DashScope exposes an Anthropic-compatible Messages API at
+    ``{origin}/apps/anthropic`` for every DashScope domain, including
+    ``dashscope.aliyuncs.com`` and ``{workspace}.{region}.maas.aliyuncs.com``.
+    """
+
+    _workload_identity_eligible: ClassVar[bool] = False
+
+    @property
+    def custom_llm_provider(self) -> str | None:
+        return "dashscope"
+
+    def should_strip_billing_metadata(self) -> bool:
+        return True
+
+    def handles_web_search_natively(self) -> bool:
+        return False
+
+    def serves_api_base(self, model: str, api_base: str | None) -> bool:
+        return dashscope_base_selects_anthropic_messages(api_base)
+
+    @staticmethod
+    def get_api_key(api_key: str | None = None) -> str | None:
+        return (
+            normalize_nonempty_secret_str(api_key)
+            or normalize_nonempty_secret_str(get_secret_str("DASHSCOPE_API_KEY"))
+            or normalize_nonempty_secret_str(litellm.api_key)
+        )
+
+    @staticmethod
+    def get_api_base(api_base: str | None = None) -> str:
+        return resolve_dashscope_anthropic_api_base(api_base)
+
+    def validate_anthropic_messages_environment(
+        self,
+        headers: dict[str, str],  # mutable-ok: mirrors the legacy base override signature
+        model: str,
+        messages: list[object],  # mutable-ok: mirrors the legacy base override signature
+        optional_params: dict[str, object],  # mutable-ok: mirrors the legacy base override signature
+        litellm_params: dict[str, object],  # mutable-ok: mirrors the legacy base override signature
+        api_key: str | None = None,
+        api_base: str | None = None,
+    ) -> tuple[dict[str, str], str | None]:  # mutable-ok: mirrors the base signature
+        resolved_api_key: Final = self.get_api_key(api_key=api_key)
+        if resolved_api_key is None:
+            present_headers: Final = {name.lower() for name in headers}
+            if "x-api-key" not in present_headers and "authorization" not in present_headers:
+                raise AuthenticationError(
+                    message=(
+                        "Missing DashScope API key. Set DASHSCOPE_API_KEY or pass api_key explicitly. "
+                        "Anthropic credentials are never sent to a DashScope endpoint."
+                    ),
+                    llm_provider="dashscope",
+                    model=model,
+                )
+        return super().validate_anthropic_messages_environment(  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType, reportReturnType]  # base signature uses an unparameterized dict
+            headers=headers,
+            model=model,
+            messages=messages,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            api_key=resolved_api_key,
+            api_base=api_base,
+        )
+
+    def get_complete_url(
+        self,
+        api_base: str | None,
+        api_key: str | None,
+        model: str,
+        optional_params: dict[str, object],  # mutable-ok: mirrors the base get_complete_url signature
+        litellm_params: dict[str, object],  # mutable-ok: mirrors the base get_complete_url signature
+        stream: bool | None = None,
+    ) -> str:
+        return ensure_api_route(self.get_api_base(api_base=api_base), "v1/messages")

litellm/llms/dashscope/qwen_ai_platform.py

@@ -3,7 +3,7 @@ from typing import Final
 from litellm.secret_managers.main import get_secret_str
 
 from .chat.transformation import DashScopeChatConfig
-from .common_utils import resolve_dashscope_family_rerank_api_base
+from .common_utils import resolve_dashscope_family_rerank_api_base, resolve_dashscope_openai_api_base
 from .embed.transformation import DashScopeEmbeddingConfig
 from .image_generation.transformation import DashScopeImageGenerationConfig
 from .rerank.transformation import DashScopeRerankConfig
@@ -38,7 +38,7 @@ class QwenAIPlatformChatConfig(DashScopeChatConfig):
         return self._resolve_chat_api_base(api_base), _resolve_qwen_ai_platform_api_key(api_key)
 
     def _resolve_chat_api_base(self, api_base: str | None) -> str:
-        return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE
+        return resolve_dashscope_openai_api_base(api_base, "QWEN_AI_PLATFORM_API_BASE", QWEN_AI_PLATFORM_API_BASE)
 
 
 class QwenAIPlatformEmbeddingConfig(DashScopeEmbeddingConfig):
@@ -46,7 +46,7 @@ class QwenAIPlatformEmbeddingConfig(DashScopeEmbeddingConfig):
         return _require_qwen_ai_platform_api_key(api_key)
 
     def _resolve_embedding_api_base(self, api_base: str | None) -> str:
-        return api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE") or QWEN_AI_PLATFORM_API_BASE
+        return resolve_dashscope_openai_api_base(api_base, "QWEN_AI_PLATFORM_API_BASE", QWEN_AI_PLATFORM_API_BASE)
 
 
 class QwenAIPlatformRerankConfig(DashScopeRerankConfig):
@@ -63,8 +63,14 @@ class QwenAIPlatformImageGenerationConfig(DashScopeImageGenerationConfig):
     def _resolve_api_key(self, api_key: str | None) -> str:
         return _require_qwen_ai_platform_api_key(api_key)
 
-    def _resolve_image_api_base(self, image_api_base: str | None) -> str:
-        return image_api_base or get_secret_str("QWEN_AI_PLATFORM_API_BASE_IMAGE") or QWEN_AI_PLATFORM_IMAGE_API_BASE
+    def _image_api_base_env(self) -> str | None:
+        return get_secret_str("QWEN_AI_PLATFORM_API_BASE_IMAGE")
+
+    def _default_image_api_base(self) -> str:
+        return QWEN_AI_PLATFORM_IMAGE_API_BASE
+
+    def _configured_chat_api_base(self) -> str | None:
+        return get_secret_str("QWEN_AI_PLATFORM_API_BASE")
 
 
 class QwenAIPlatformVideoConfig(DashScopeVideoConfig):

litellm/llms/dashscope/qwencloud.py

@@ -3,7 +3,7 @@ from typing import Final
 from litellm.secret_managers.main import get_secret_str
 
 from .chat.transformation import DashScopeChatConfig
-from .common_utils import resolve_dashscope_family_rerank_api_base
+from .common_utils import resolve_dashscope_family_rerank_api_base, resolve_dashscope_openai_api_base
 from .embed.transformation import DashScopeEmbeddingConfig
 from .image_generation.transformation import DashScopeImageGenerationConfig
 from .rerank.transformation import DashScopeRerankConfig
@@ -38,7 +38,7 @@ class QwenCloudChatConfig(DashScopeChatConfig):
         return self._resolve_chat_api_base(api_base), _resolve_qwencloud_api_key(api_key)
 
     def _resolve_chat_api_base(self, api_base: str | None) -> str:
-        return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE
+        return resolve_dashscope_openai_api_base(api_base, "QWENCLOUD_API_BASE", QWENCLOUD_API_BASE)
 
 
 class QwenCloudEmbeddingConfig(DashScopeEmbeddingConfig):
@@ -46,7 +46,7 @@ class QwenCloudEmbeddingConfig(DashScopeEmbeddingConfig):
         return _require_qwencloud_api_key(api_key)
 
     def _resolve_embedding_api_base(self, api_base: str | None) -> str:
-        return api_base or get_secret_str("QWENCLOUD_API_BASE") or QWENCLOUD_API_BASE
+        return resolve_dashscope_openai_api_base(api_base, "QWENCLOUD_API_BASE", QWENCLOUD_API_BASE)
 
 
 class QwenCloudRerankConfig(DashScopeRerankConfig):
@@ -63,8 +63,14 @@ class QwenCloudImageGenerationConfig(DashScopeImageGenerationConfig):
     def _resolve_api_key(self, api_key: str | None) -> str:
         return _require_qwencloud_api_key(api_key)
 
-    def _resolve_image_api_base(self, image_api_base: str | None) -> str:
-        return image_api_base or get_secret_str("QWENCLOUD_API_BASE_IMAGE") or QWENCLOUD_IMAGE_API_BASE
+    def _image_api_base_env(self) -> str | None:
+        return get_secret_str("QWENCLOUD_API_BASE_IMAGE")
+
+    def _default_image_api_base(self) -> str:
+        return QWENCLOUD_IMAGE_API_BASE
+
+    def _configured_chat_api_base(self) -> str | None:
+        return get_secret_str("QWENCLOUD_API_BASE")
 
 
 class QwenCloudVideoConfig(DashScopeVideoConfig):

litellm/llms/dashscope/videos/transformation.py

@@ -38,6 +38,8 @@ from litellm.types.videos.utils import (
 )
 from litellm.utils import get_model_info
 
+from ..common_utils import resolve_dashscope_endpoints
+
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging
     from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
@@ -206,6 +208,9 @@ def _parse_task_response(raw_response: httpx.Response) -> _TaskResponse:
 
 
 def _normalized_api_base(api_base: str) -> str:
+    endpoints: Final = resolve_dashscope_endpoints(api_base)
+    if endpoints is not None:
+        return endpoints.origin
     trimmed: Final = api_base.rstrip("/")
     if trimmed.endswith(DASHSCOPE_COMPATIBLE_MODE_PATH):
         return trimmed[: -len(DASHSCOPE_COMPATIBLE_MODE_PATH)]

litellm/llms/databricks/chat/transformation.py

@@ -10,6 +10,7 @@ from typing import TYPE_CHECKING, Any, Final, Literal, cast, overload
 import httpx
 from pydantic import BaseModel, ConfigDict, TypeAdapter
 
+from litellm.anthropic_beta_gated_fields import log_beta_gated_strips, strip_beta_gated_thinking
 from litellm.constants import RESPONSE_FORMAT_TOOL_NAME
 from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
     handle_invalid_parallel_tool_calls,
@@ -514,6 +515,14 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
         self.update_optional_params_with_thinking_tokens(
             non_default_params=non_default_params, optional_params=mapped_params
         )
+        gated_thinking: Final = strip_beta_gated_thinking(
+            mapped_params.get("thinking"),  # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType]  # mapped_params is the base config's bare dict
+            frozenset(),
+            drop_params=drop_params,
+        )
+        if gated_thinking.stripped:
+            log_beta_gated_strips(gated_thinking.stripped, model=model, provider="databricks")
+            mapped_params["thinking"] = gated_thinking.thinking
 
         return mapped_params
 

litellm/llms/databricks/messages/transformation.py

@@ -144,7 +144,7 @@ class DatabricksAnthropicMessagesConfig(DatabricksBase, AnthropicMessagesConfig)
             ("content-type" if key.lower() == "content-type" else key): value
             for key, value in {**auth_headers, **client_anthropic_headers}.items()
         }
-        finalized_headers: Final[dict[str, str]] = self._finalize_messages_headers(  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType]  # the shared helper returns a bare dict  # mutable-ok: becomes the outbound headers
+        finalized_headers: Final[dict[str, str]] = self.finalize_messages_headers(  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType]  # the shared helper returns a bare dict  # mutable-ok: becomes the outbound headers
             headers=normalized_headers,
             optional_params=optional_params,
             messages=messages,
@@ -191,6 +191,8 @@ class DatabricksAnthropicMessagesConfig(DatabricksBase, AnthropicMessagesConfig)
             litellm_params=litellm_params,
             headers=headers,
         )
-        self._normalize_system_role_messages(anthropic_messages_request, model=model)  # pyright: ignore[reportUnknownMemberType]  # the shared helper takes a bare dict
+        self._normalize_system_role_messages(  # pyright: ignore[reportUnknownMemberType]  # the shared helper takes a bare dict
+            anthropic_messages_request, model=model, drop_params=getattr(litellm_params, "drop_params", None)
+        )
         self._remove_scope_from_cache_control(anthropic_messages_request)  # pyright: ignore[reportUnknownMemberType]  # the shared helper takes a bare dict
         return anthropic_messages_request

litellm/llms/gigachat/chat/transformation.py

@@ -38,6 +38,15 @@ else:
 
 _EMPTY_FUNCTION: Final[Mapping[str, object]] = MappingProxyType({})
 _JSON_VALUE: Final = TypeAdapter(object)
+_CONTENT_PART: Final = TypeAdapter(Mapping[str, object])
+
+
+def _file_part_data(part: Mapping[str, object]) -> str | None:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data:
+            return file_data
+        case _:
+            return None
 
 
 def is_valid_json(value: str) -> bool:
@@ -295,7 +304,7 @@ class GigaChatConfig(BaseConfig):
 
     def _transform_list_content(self, content: Sequence) -> tuple[str, Sequence[object]]:
         """
-        Extract text and image attachments from a multimodal message content list.
+        Extract text, image and file attachments from a multimodal message content list.
 
         Args:
             content: List of content parts (OpenAI multimodal format)
@@ -320,6 +329,10 @@ class GigaChatConfig(BaseConfig):
                         file_id = self._upload_image(upload_url)
                         if file_id:
                             attachments.append(file_id)
+                elif (file_data := _file_part_data(_CONTENT_PART.validate_python(part))) and (
+                    uploaded_file_id := self._upload_image(file_data)
+                ):
+                    attachments.append(uploaded_file_id)
         text: Final = "\n".join(texts) if texts else ""
         return text, attachments
 

litellm/llms/oci/chat/transformation.py

@@ -20,6 +20,7 @@ import litellm
 from litellm._logging import verbose_logger
 from litellm.constants import DEFAULT_OCI_CHAT_MAX_TOKENS
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
+from litellm.litellm_core_utils.prompt_templates.common_utils import messages_with_file_parts_as_image_urls
 from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
@@ -681,7 +682,9 @@ class OCIChatConfig(BaseConfig):
                 servingMode=serving_mode,
                 chatRequest=OCIChatRequestPayload(
                     apiFormat=vendor.value,
-                    messages=adapt_messages_to_generic_oci_standard(messages),
+                    messages=adapt_messages_to_generic_oci_standard(
+                        list(messages_with_file_parts_as_image_urls(messages))
+                    ),
                     **self._get_optional_params(vendor, optional_params, model),
                 ),
             )

litellm/llms/openai/chat/gpt_reasoning_transformation.py

@@ -5,8 +5,10 @@ from collections.abc import Collection
 from typing import ClassVar, Final
 
 import litellm
+from litellm._logging import verbose_logger
 from litellm.utils import (
     declared_value_factory,
+    has_catalog_entry,
     is_explicitly_disabled_factory,
     supports_factory,
 )
@@ -16,6 +18,25 @@ from .gpt_transformation import OpenAIGPTConfig
 _GPT_SERIES_VERSION: Final = re.compile(r"^gpt-(\d+)(?:\.(\d+))?(?=[.-]|$)")
 
 
+def refused_reasoning_effort_warning(*, suggest_declared_levels: bool, suggest_base_model: bool) -> str:
+    return "".join(
+        (
+            "Dropping %s=%r for model=%r (drop_params=True) because the model map does not mark that level "
+            "supported for %r.",
+            (
+                " If the deployment supports it, list every level it supports in model_info.reasoning_effort_levels."
+                if suggest_declared_levels
+                else ""
+            ),
+            (
+                " If the deployment name is not its underlying model, set model_info.base_model to that model."
+                if suggest_base_model
+                else ""
+            ),
+        )
+    )
+
+
 def _catalogue_declares_default_effort() -> bool:
     """Whether the loaded cost map carries default_reasoning_effort for ANY entry.
 
@@ -93,6 +114,7 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
 
     OPT_IN_REASONING_EFFORTS: ClassVar[frozenset[str]] = frozenset(("xhigh",))
     ROUTE_SPECIFIC_REASONING_EFFORTS: ClassVar[frozenset[str]] = frozenset(("max",))
+    LOOKS_UP_BASE_MODEL: ClassVar[bool] = False
 
     @classmethod
     def is_model_gpt_5_model(cls, model: str) -> bool:
@@ -307,7 +329,7 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
         model: str,
         drop_params: bool,
         *,
-        declared_reasoning_efforts: Collection[str] = (),
+        declared_reasoning_efforts: Collection[str] | None = None,
     ) -> dict:
         if self.is_model_gpt_5_search_model(model):
             if "max_tokens" in non_default_params:
@@ -338,10 +360,21 @@ class OpenAIGPT5Config(OpenAIGPTConfig):
 
         if (
             requested_effort is not None
-            and requested_effort not in declared_reasoning_efforts
+            and requested_effort not in (declared_reasoning_efforts or ())
             and self.refuses_reasoning_effort_level(model, requested_effort)
         ):
             if litellm.drop_params or drop_params:
+                verbose_logger.warning(
+                    refused_reasoning_effort_warning(
+                        suggest_declared_levels=declared_reasoning_efforts is not None,
+                        suggest_base_model=self.LOOKS_UP_BASE_MODEL
+                        and not has_catalog_entry(self._model_map_lookup_name(model), None),
+                    ),
+                    "reasoning_effort",
+                    requested_effort,
+                    model,
+                    self._model_map_lookup_name(model),
+                )
                 non_default_params.pop("reasoning_effort", None)
                 optional_params.pop("reasoning_effort", None)
             else:

litellm/llms/openai/common_utils.py

@@ -104,6 +104,19 @@ def is_openai_backed_api_base(api_base: str) -> bool:
     return hostname is not None and (hostname == _OPENAI_API_HOST or hostname.endswith(f".{_OPENAI_API_HOST}"))
 
 
+_OPENAI_AGENTS_API_RESOURCES: Final = frozenset({"agents", "vaults"})
+
+
+def openai_api_resource(path: str) -> str | None:
+    segments: Final = tuple(segment for segment in path.split("/") if segment)
+    resource_segments: Final = segments[1:] if segments[:1] == ("v1",) else segments
+    return next(iter(resource_segments), None)
+
+
+def is_openai_agents_api_path(path: str) -> bool:
+    return openai_api_resource(path) in _OPENAI_AGENTS_API_RESOURCES
+
+
 class OpenAIError(BaseLLMException):
     def __init__(
         self,

litellm/llms/openai/completion/guardrail_translation/handler.py

@@ -5,18 +5,30 @@ This module provides guardrail translation support for OpenAI's text completion
 The handler processes the 'prompt' parameter for guardrails.
 """
 
-from collections.abc import Mapping
+import json
+from collections.abc import Mapping, Sequence
+from itertools import chain
 from typing import TYPE_CHECKING, Final
 
 from litellm._logging import verbose_proxy_logger
-from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation
-from litellm.types.utils import GenericGuardrailAPIInputs
+from litellm.llms.base_llm.guardrail_translation.base_translation import (
+    BaseTranslation,
+    StreamingScanKey,
+    StreamTransformSink,
+)
+from litellm.llms.base_llm.guardrail_translation.utils import (
+    blocked_chat_stream_usage,
+    stream_item_field,
+    stream_item_items,
+)
+from litellm.types.utils import GenericGuardrailAPIInputs, TextChoices, TextCompletionResponse, Usage
 
 if TYPE_CHECKING:
-    from litellm.integrations.custom_guardrail import CustomGuardrail
+    from fastapi import HTTPException
+
+    from litellm.integrations.custom_guardrail import CustomGuardrail, ModifyResponseException
     from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
     from litellm.types.proxy.auth.user_api_key_auth import UserAPIKeyAuth
-    from litellm.types.utils import TextCompletionResponse
 
 
 class OpenAITextCompletionHandler(BaseTranslation):
@@ -32,6 +44,9 @@ class OpenAITextCompletionHandler(BaseTranslation):
     - A list of strings (for batch completions)
     """
 
+    delivers_ended_stream_rewrites = True
+    assembles_streamed_response = True
+
     async def process_input_messages(
         self,
         data: dict,
@@ -125,7 +140,7 @@ class OpenAITextCompletionHandler(BaseTranslation):
         guardrail_to_apply: "CustomGuardrail",
         litellm_logging_obj: "LiteLLMLoggingObj | None" = None,
         user_api_key_dict: "UserAPIKeyAuth | None" = None,
-        request_data: dict | None = None,
+        request_data: dict[str, object] | None = None,
     ) -> "TextCompletionResponse":
         """
         Process output response by applying guardrails to completion text.
@@ -162,11 +177,7 @@ class OpenAITextCompletionHandler(BaseTranslation):
                 if "response" not in request_data:
                     request_data["response"] = response
 
-            # Add user API key metadata with prefixed keys
-            if "litellm_metadata" not in request_data:
-                user_metadata: Final = self.transform_user_api_key_dict_to_metadata(user_api_key_dict)
-                if user_metadata:
-                    request_data["litellm_metadata"] = user_metadata
+            self.merge_user_api_key_metadata_into_request(request_data, user_api_key_dict)
 
             inputs: Final = GenericGuardrailAPIInputs(texts=texts_to_check)
             # Include model information from the response if available
@@ -195,3 +206,168 @@ class OpenAITextCompletionHandler(BaseTranslation):
                     )
 
         return response
+
+    async def process_output_streaming_response(
+        self,
+        responses_so_far: list[object],  # mutable-ok: rewrites the caller's buffered chunks in place
+        guardrail_to_apply: "CustomGuardrail",
+        litellm_logging_obj: "LiteLLMLoggingObj | None" = None,
+        user_api_key_dict: "UserAPIKeyAuth | None" = None,
+        request_data: dict[str, object] | None = None,  # mutable-ok: the hooks share and mutate the request payload
+        stream_transform_sink: StreamTransformSink | None = None,
+        deliver_ended_stream_rewrites: bool = False,
+    ) -> Sequence[object]:
+        chunks: Final = _text_completion_chunks(responses_so_far)
+        if not self._check_streaming_has_ended(chunks):
+            return responses_so_far
+        assembled: Final = self._assemble_ended_stream(chunks)
+        pre_guardrail_texts: Final = _choice_texts(assembled)
+        await self.process_output_response(
+            response=assembled,
+            guardrail_to_apply=guardrail_to_apply,
+            litellm_logging_obj=litellm_logging_obj,
+            user_api_key_dict=user_api_key_dict,
+            request_data=request_data,
+        )
+        if not deliver_ended_stream_rewrites:
+            return responses_so_far
+        rewrites: Final = tuple(
+            after for before, after in zip(pre_guardrail_texts, _choice_texts(assembled)) if after != before
+        )
+        for choice_index, text in rewrites:
+            self._write_choice_text(chunks, choice_index, text)
+        return responses_so_far
+
+    def get_streaming_scan_key(self, responses_so_far: Sequence[object]) -> StreamingScanKey | None:
+        chunks: Final = _text_completion_chunks(responses_so_far)
+        if not self._check_streaming_has_ended(chunks):
+            return None
+        assembled: Final = self._assemble_ended_stream(chunks)
+        return StreamingScanKey(texts=tuple(text for _, text in _choice_texts(assembled)), stream_ended=True)
+
+    def released_stream_as_ended(self, responses_so_far: Sequence[object]) -> tuple[object, ...]:
+        chunks: Final = _text_completion_chunks(responses_so_far)
+        streamed_choices: Final = tuple(chain.from_iterable(chunk.choices for chunk in chunks))
+        finished_indices: Final = frozenset(
+            _choice_index(choice) for choice in streamed_choices if choice.finish_reason
+        )
+        unfinished_indices: Final = tuple(
+            dict.fromkeys(
+                _choice_index(choice) for choice in streamed_choices if _choice_index(choice) not in finished_indices
+            )
+        )
+        if not unfinished_indices:
+            return tuple(responses_so_far)
+        terminator: Final = TextCompletionResponse(
+            id=chunks[0].id,
+            model=chunks[0].model,
+            object="text_completion",
+            choices=[TextChoices(index=index, text="", finish_reason="length") for index in unfinished_indices],
+        )
+        return (*responses_so_far, terminator)
+
+    def build_block_sse_chunks(
+        self,
+        exc: "ModifyResponseException",
+        stream_started: bool = False,
+        responses_so_far: Sequence[object] | None = None,
+    ) -> Sequence[bytes]:
+        streamed: Final = _text_completion_chunks(responses_so_far or ())
+        source: Final = streamed[0] if streamed else TextCompletionResponse(model=exc.model)
+        message_chunk: Final = TextCompletionResponse(
+            id=source.id,
+            created=source.created,
+            model=source.model or exc.model,
+            object="text_completion",
+            choices=[TextChoices(index=0, text=exc.message)],
+        ).model_copy(update={"usage": None})
+        final_chunk: Final = TextCompletionResponse(
+            id=source.id,
+            created=source.created,
+            model=source.model or exc.model,
+            object="text_completion",
+            choices=[TextChoices(index=0, text="", finish_reason="content_filter")],
+        ).model_copy(update={"usage": _blocked_stream_usage(exc.original_response)})
+        return tuple(
+            f"data: {chunk.model_dump_json(exclude_none=True)}\n\n".encode() for chunk in (message_chunk, final_chunk)
+        )
+
+    def build_stream_error_items(
+        self,
+        exc: "HTTPException",
+        responses_so_far: Sequence[object] | None = None,
+    ) -> Sequence[bytes] | None:
+        from litellm.proxy.common_request_processing import sse_error_payload
+
+        _, error_obj = sse_error_payload(exc)
+        return (f'data: {{"error": {json.dumps(error_obj)}}}\n\n'.encode(),)
+
+    def _check_streaming_has_ended(self, responses_so_far: Sequence[object]) -> bool:
+        streamed_choices: Final = chain.from_iterable(stream_item_items(item, "choices") for item in responses_so_far)
+        return any(stream_item_field(choice, "finish_reason") is not None for choice in streamed_choices)
+
+    @staticmethod
+    def _assemble_ended_stream(responses_so_far: Sequence[TextCompletionResponse]) -> TextCompletionResponse:
+        streamed_choices: Final = tuple(chain.from_iterable(item.choices for item in responses_so_far))
+        choice_indices: Final = tuple(dict.fromkeys(_choice_index(choice) for choice in streamed_choices))
+        assembled_choices: Final = [
+            TextChoices(
+                index=index,
+                text="".join(_choice_text(choice) for choice in streamed_choices if _choice_index(choice) == index),
+                finish_reason=next(
+                    (
+                        choice.finish_reason
+                        for choice in streamed_choices
+                        if _choice_index(choice) == index and choice.finish_reason
+                    ),
+                    None,
+                ),
+            )
+            for index in choice_indices
+        ]
+        first: Final = responses_so_far[0]
+        return TextCompletionResponse(
+            id=first.id,
+            created=first.created,
+            model=first.model,
+            object="text_completion",
+            choices=assembled_choices,
+            usage=next((item.usage for item in reversed(responses_so_far) if item.usage is not None), None),
+        )
+
+    @staticmethod
+    def _write_choice_text(responses_so_far: Sequence[TextCompletionResponse], choice_index: int, text: str) -> None:
+        streamed_choices: Final = chain.from_iterable(item.choices for item in responses_so_far)
+        head, *tail = (choice for choice in streamed_choices if _choice_index(choice) == choice_index)
+        head.text = text
+        for choice in tail:
+            if isinstance(stream_item_field(choice, "text"), str):
+                choice.text = ""
+
+
+def _blocked_stream_usage(original_response: object) -> Usage | None:
+    prompt_tokens, completion_tokens = blocked_chat_stream_usage(original_response)
+    if isinstance(original_response, list) and prompt_tokens == completion_tokens == 0:
+        return None
+    return Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=prompt_tokens + completion_tokens,
+    )
+
+
+def _text_completion_chunks(responses_so_far: Sequence[object]) -> tuple[TextCompletionResponse, ...]:
+    return tuple(item for item in responses_so_far if isinstance(item, TextCompletionResponse))
+
+
+def _choice_text(choice: TextChoices) -> str:
+    text: Final = stream_item_field(choice, "text")
+    return text if isinstance(text, str) else ""
+
+
+def _choice_index(choice: TextChoices) -> int:
+    return choice.index or 0
+
+
+def _choice_texts(response: TextCompletionResponse) -> tuple[tuple[int, str], ...]:
+    return tuple((_choice_index(choice), _choice_text(choice)) for choice in response.choices)

litellm/llms/openai/responses/transformation.py

@@ -32,6 +32,7 @@ from litellm.litellm_core_utils.url_utils import encode_url_path_segment, join_a
 from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
 from litellm.llms.openai.chat.gpt_reasoning_transformation import (
     is_gpt_reasoning_series_name,
+    refused_reasoning_effort_warning,
     unsupported_reasoning_effort_message,
 )
 from litellm.responses.litellm_completion_transformation.custom_tools import TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE
@@ -55,6 +56,9 @@ from ..common_utils import OpenAIError
 from ..workload_identity import get_workload_identity_bearer_token, resolve_openai_workload_identity_config
 
 OPENAI_RESPONSES_API_MIN_MAX_OUTPUT_TOKENS: Final = 16
+DROP_UNSUPPORTED_REASONING_WARNING: Final = (
+    "Dropping reasoning=%r for model=%r (drop_params=True) because its model cost map entry lacks supports_reasoning."
+)
 _RAW_RESPONSE_JSON: Final = TypeAdapter(dict[str, object], config=ConfigDict(strict=True))
 
 if TYPE_CHECKING:
@@ -417,12 +421,19 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
             return declared
         return info["key"] in _bundled_openai_reasoning_models()
 
+    @staticmethod
+    def _is_known_non_reasoning_model(model: str) -> bool:
+        from litellm.utils import is_known_non_reasoning_model
+
+        return is_known_non_reasoning_model(model.split("/")[-1], LlmProviders.OPENAI.value)
+
     @staticmethod
     def _requests_reasoning_effort(reasoning: object) -> bool:
-        effort: Final = (
-            reasoning.get("effort") if isinstance(reasoning, Mapping) else getattr(reasoning, "effort", None)
-        )
-        return effort is not None
+        return OpenAIResponsesAPIConfig._requested_effort(reasoning) is not None
+
+    @staticmethod
+    def _requested_effort(reasoning: object) -> object:
+        return reasoning.get("effort") if isinstance(reasoning, Mapping) else getattr(reasoning, "effort", None)
 
     @staticmethod
     def _enforce_min_max_output_tokens(max_output_tokens: "int | None") -> "int | None":
@@ -464,17 +475,20 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
     ) -> dict:
         return dict(
             self.map_openai_params_for_deployment(
-                response_api_optional_params, model, drop_params, declared_reasoning_efforts=()
+                response_api_optional_params, model, drop_params, declared_reasoning_efforts=None
             )
         )
 
+    def honors_declared_reasoning_efforts(self) -> bool:
+        return type(self).map_openai_params is OpenAIResponsesAPIConfig.map_openai_params
+
     def map_openai_params_for_deployment(
         self,
         response_api_optional_params: ResponsesAPIOptionalRequestParams,
         model: str,
         drop_params: bool,
         *,
-        declared_reasoning_efforts: Collection[str],
+        declared_reasoning_efforts: Collection[str] | None,
     ) -> Mapping[str, object]:
         """No mapping applied since inputs are in OpenAI spec already.
 
@@ -494,7 +508,9 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
             and not self._supports_reasoning_param(model=model)
         ):
             if drop_params or litellm.drop_params:
-                params.pop("reasoning", None)
+                dropped_reasoning: Final = params.pop("reasoning", None)
+                if self._requested_effort(dropped_reasoning) != "none" or not self._is_known_non_reasoning_model(model):
+                    verbose_logger.warning(DROP_UNSUPPORTED_REASONING_WARNING, dropped_reasoning, model)
             else:
                 raise litellm.UnsupportedParamsError(
                     message=(
@@ -512,7 +528,7 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
             if (
                 isinstance(reasoning, dict)
                 and isinstance(requested_effort, str)
-                and requested_effort not in declared_reasoning_efforts
+                and requested_effort not in (declared_reasoning_efforts or ())
                 and self._effort_level_is_disabled(lookup_name, requested_effort)
             ):
                 if not (drop_params or litellm.drop_params):
@@ -520,6 +536,15 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
                         message=unsupported_reasoning_effort_message("reasoning.effort", requested_effort, model),
                         status_code=400,
                     )
+                verbose_logger.warning(
+                    refused_reasoning_effort_warning(
+                        suggest_declared_levels=declared_reasoning_efforts is not None, suggest_base_model=False
+                    ),
+                    "reasoning.effort",
+                    requested_effort,
+                    model,
+                    lookup_name,
+                )
                 remaining: Final = reasoning.copy()
                 remaining.pop("effort", None)
                 if remaining:

litellm/llms/openai_like/providers.json

@@ -270,6 +270,13 @@
       "/v1/responses"
     ]
   },
+  "kyma": {
+    "base_url": "https://kymaapi.com/v1",
+    "api_key_env": "KYMA_API_KEY",
+    "provider_info": {
+      "supports_function_calling": true
+    }
+  },
   "streamlake": {
     "base_url": "https://vanchin.streamlake.ai/api/gateway/v1/endpoints",
     "api_key_env": "STREAMLAKE_API_KEY",