Merge origin/main into litellm_presidio_logging_only_mask_failure_logs

.circleci/scripts/run_integration.sh

@@ -1,14 +1,14 @@
 #!/usr/bin/env bash
 set -euo pipefail
 
-if [ "${GITHUB_ACTIONS:-}" = true ]; then
-  echo "Integration contracts are owned by CircleCI" >&2
-  exit 1
-fi
-
 suite="${1:?integration suite required}"
 mode="${2:-standard}"
 side="${3:-}"
+requested_workers="${INTEGRATION_WORKERS:-}"
+license_env=()
+if [ -n "${LITELLM_LICENSE:-}" ]; then
+  license_env=("LITELLM_LICENSE=$LITELLM_LICENSE")
+fi
 if [ "$mode" = replica ]; then
   results="test-results/integration-${suite}-replica"
 elif [ "$mode" = parity ]; then
@@ -84,7 +84,7 @@ if [ "$suite" = browser ]; then
   export LITELLM_UI_PATH="$PWD/ui/litellm-dashboard/out"
   test -f "$LITELLM_UI_PATH/index.html"
 fi
-export INTEGRATION_SEED="$(.venv/bin/python -c 'import hashlib,os; print(int(hashlib.sha256((os.environ.get("CIRCLE_SHA1", "local") + os.environ.get("CIRCLE_WORKFLOW_ID", "local")).encode()).hexdigest()[:8],16))')"
+export INTEGRATION_SEED="$(.venv/bin/python -c 'import hashlib,os; print(int(hashlib.sha256(((os.environ.get("CIRCLE_SHA1") or os.environ.get("GITHUB_SHA") or "local") + (os.environ.get("CIRCLE_WORKFLOW_ID") or os.environ.get("GITHUB_RUN_ID") or "local")).encode()).hexdigest()[:8],16))')"
 export INTEGRATION_ORDER_SEED="$INTEGRATION_SEED"
 
 uv run --no-sync prisma generate --schema litellm/proxy/schema.prisma > "$results/prisma-generate.log" 2>&1
@@ -107,7 +107,7 @@ sudo iptables -N integration_only
 guard_created=true
 sudo iptables -A integration_only -o lo -j ACCEPT
 sudo iptables -A integration_only -m conntrack --ctstate ESTABLISHED,RELATED -j ACCEPT
-for service in postgres-db redis-cache; do
+for service in "${INTEGRATION_POSTGRES_CONTAINER:-postgres-db}" "${INTEGRATION_REDIS_CONTAINER:-redis-cache}"; do
   address="$(docker inspect --format '{{range .NetworkSettings.Networks}}{{.IPAddress}}{{end}}' "$service")"
   sudo iptables -A integration_only -d "$address" -j ACCEPT
 done
@@ -145,6 +145,9 @@ fi
 if [ "$suite" = mcp ]; then
   export INTEGRATION_WORKERS=2 INTEGRATION_COVERAGE=1
 fi
+if [ -n "$requested_workers" ]; then
+  export INTEGRATION_WORKERS="$requested_workers"
+fi
 isolated_workers=false
 if [ "${INTEGRATION_WORKERS:-1}" -gt 1 ]; then
   isolated_workers=true
@@ -185,7 +188,7 @@ start_proxy() {
     "${database_env[@]}" REDIS_HOST="$REDIS_HOST" REDIS_PORT="$REDIS_PORT" \
     INTEGRATION_UPSTREAM_URL="$INTEGRATION_UPSTREAM_URL" \
     LITELLM_MASTER_KEY="$LITELLM_MASTER_KEY" LITELLM_SALT_KEY="$LITELLM_SALT_KEY" LITELLM_UI_PATH="$LITELLM_UI_PATH" PROXY_BASE_URL="http://127.0.0.1:$port" \
-    LITELLM_LICENSE="${LITELLM_LICENSE:-}" \
+    "${license_env[@]}" \
     LITELLM_MODE=PRODUCTION STORE_MODEL_IN_DB=True LITELLM_ENABLE_MCP_STDIO=true "${cost_map_env[@]}" \
     AWS_EC2_METADATA_DISABLED=true DO_NOT_TRACK=1 COVERAGE_FILE="$coverage_data" \
     "${proxy_command[@]}" --config tests/integration/proxy_config.yaml \
@@ -257,7 +260,7 @@ env -i PATH="$PATH" HOME="$HOME" PYTHONPATH="$PYTHONPATH" \
   "${shared_services[@]}" "${worker_coverage[@]}" \
   INTEGRATION_WORKERS="${INTEGRATION_WORKERS:-1}" \
   LITELLM_MODE=PRODUCTION \
-  LITELLM_LICENSE="${LITELLM_LICENSE:-}" \
+  "${license_env[@]}" \
   INTEGRATION_SEED="$INTEGRATION_SEED" \
   INTEGRATION_ORDER_SEED="$INTEGRATION_ORDER_SEED" \
   INTEGRATION_CASE_SHARDS="${CIRCLE_NODE_TOTAL:-1}" INTEGRATION_CASE_SHARD_INDEX="${CIRCLE_NODE_INDEX:-0}" \

.github/scripts/assert_secret_absent.py

@@ -0,0 +1,119 @@
+import base64
+import io
+import os
+import sys
+import urllib.parse
+import zipfile
+from collections.abc import Callable, Iterator, Mapping
+from dataclasses import dataclass
+from functools import reduce
+from itertools import chain
+from pathlib import Path
+from types import MappingProxyType
+from typing import Final, TextIO
+
+SECRET_VARIABLE: Final = "SCANNED_SECRET"
+MIN_ENCODED_LENGTH: Final = 12
+MAX_ARCHIVE_DEPTH: Final = 3
+MASK: Final = "***"
+
+Forms = tuple[tuple[str, str], ...]
+
+
+@dataclass(frozen=True, slots=True)
+class Hit:
+    location: str
+    form: str
+
+
+def _aligned_encodings(raw: bytes, name: str, encode: Callable[[bytes], bytes]) -> Iterator[tuple[str, str]]:
+    for offset in range(3):
+        chunk: Final = raw[offset:]
+        yield f"{name} offset {offset}", encode(chunk[: len(chunk) - len(chunk) % 3]).decode()
+
+
+def secret_forms(secret: str) -> Forms:
+    encoded: Final = (
+        ("url-encoded", urllib.parse.quote(secret, safe="")),
+        *_aligned_encodings(secret.encode(), "base64", base64.b64encode),
+        *_aligned_encodings(secret.encode(), "base64url", base64.urlsafe_b64encode),
+    )
+    by_value: Final = MappingProxyType(
+        {value: name for name, value in encoded if value != secret and len(value) >= MIN_ENCODED_LENGTH}
+    )
+    return (("raw", secret), *((name, value) for value, name in by_value.items()))
+
+
+def mask(text: str, forms: Forms) -> str:
+    return reduce(lambda masked, form: masked.replace(form[1], MASK), forms, text)
+
+
+def _name_hits(location: str, name: str, forms: Forms) -> Iterator[Hit]:
+    for form, value in forms:
+        if value in name:
+            yield Hit(mask(location, forms), f"{form} in name")
+
+
+def _content_hits(location: str, data: bytes, forms: Forms, depth: int) -> Iterator[Hit]:
+    for form, value in forms:
+        if value.encode() in data:
+            yield Hit(mask(location, forms), form)
+    if depth >= MAX_ARCHIVE_DEPTH or not zipfile.is_zipfile(io.BytesIO(data)):
+        return
+    with zipfile.ZipFile(io.BytesIO(data)) as archive:
+        for member in archive.infolist():
+            if member.is_dir():
+                continue
+            yield from _content_hits(f"{location}!{member.filename}", archive.read(member), forms, depth + 1)
+
+
+def _walk(root: Path) -> Iterator[tuple[Path, bool]]:
+    for directory, subdirectories, files in os.walk(root):
+        yield from ((Path(directory, name), True) for name in subdirectories if Path(directory, name).is_symlink())
+        yield from ((Path(directory, name), False) for name in files)
+
+
+def _path_hits(root: Path, path: Path, linked_directory: bool, forms: Forms) -> Iterator[Hit]:
+    location: Final = str(path.relative_to(root))
+    if linked_directory:
+        yield Hit(mask(location, forms), "directory symlink, not scanned")
+        return
+    yield from _name_hits(location, location, forms)
+    yield from _content_hits(location, path.read_bytes(), forms, 0)
+
+
+def scan(root: Path, secret: str) -> tuple[int, tuple[Hit, ...]]:
+    forms: Final = secret_forms(secret)
+    entries: Final = tuple(_walk(root))
+    hits: Final = tuple(chain.from_iterable(_path_hits(root, path, linked, forms) for path, linked in entries))
+    return len(entries), hits
+
+
+def main(argv: tuple[str, ...], environ: Mapping[str, str], out: TextIO) -> int:
+    if len(argv) != 1:
+        print(f"usage: assert_secret_absent.py <directory>, with the secret in ${SECRET_VARIABLE}", file=out)
+        return 2
+    secret: Final = environ.get(SECRET_VARIABLE, "")
+    root: Final = Path(argv[0])
+    if not secret:
+        print(f"{SECRET_VARIABLE} is empty, nothing to scan for", file=out)
+        return 0
+    if not root.is_dir():
+        print(f"{root} is not a directory, nothing to scan", file=out)
+        return 0
+    try:
+        scanned, hits = scan(root, secret)
+    except Exception as error:
+        print(f"scan failed with {type(error).__name__}, treating the results as unsafe", file=out)
+        return 1
+    for hit in hits:
+        print(f"secret found: {hit.location} ({hit.form})", file=out)
+    if hits:
+        print(f"{len(hits)} copies of the secret under {root}, refusing to upload", file=out)
+        return 1
+    print(f"no copy of the secret in {scanned} files under {root}", file=out)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main(tuple(sys.argv[1:]), os.environ, sys.stdout))

.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/image-scan.yml

@@ -13,8 +13,10 @@ on:
       - migrations/Dockerfile
       - migrations/run.py
       - gateway/Dockerfile
+      - gateway/launch.py
       - gateway/main.py
       - gateway/routes/allowlist.py
+      - litellm_cli/**
       - backend/Dockerfile
       - backend/main.py
       - litellm-rust/**

.github/workflows/test-integration.yml

@@ -0,0 +1,199 @@
+name: integration
+
+on:
+  pull_request:
+    types: [labeled]
+  schedule:
+    - cron: "5 8-20/3 * * 1-6"
+      timezone: America/Los_Angeles
+  workflow_dispatch:
+    inputs:
+      suite:
+        description: Integration group to run
+        type: choice
+        options:
+          - all
+          - providers
+          - extensions
+          - accounting
+          - management
+          - management-replica
+          - database
+          - database-replica
+          - mcp
+          - security
+          - cost
+          - sdk
+          - browser
+        default: all
+      workers:
+        description: Workers per group, blank keeps each group's default
+        type: string
+        default: ""
+
+permissions:
+  contents: read
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}-${{ github.event.label.name }}-${{ inputs.suite }}-${{ inputs.workers }}
+  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
+
+jobs:
+  integration:
+    name: integration ${{ matrix.suite }}
+    if: github.event_name != 'pull_request' || github.event.label.name == 'run-ci'
+    environment: integration-license
+    runs-on: ${{ fromJSON('{"providers":"ubuntu-latest-64-cores","extensions":"ubuntu-latest-64-cores","accounting":"ubuntu-latest-32-cores","management":"ubuntu-latest-32-cores","management-replica":"ubuntu-latest-32-cores"}')[matrix.suite] || 'ubuntu-latest-16-cores' }}
+    timeout-minutes: 60
+    strategy:
+      fail-fast: false
+      matrix:
+        suite: ${{ fromJSON((inputs.suite || 'all') == 'all' && '["providers","extensions","accounting","management","management-replica","database","database-replica","mcp","security","cost","sdk","browser"]' || format('["{0}"]', inputs.suite)) }}
+    env:
+      UV_PYTHON: "3.12"
+      INTEGRATION_WORKERS: ${{ inputs.workers || fromJSON('{"providers":"32","extensions":"48","accounting":"16","management":"16","management-replica":"16","cost":"16","mcp":"8","security":"8","database":"4","database-replica":"4"}')[matrix.suite] || '' }}
+    services:
+      postgres:
+        image: postgres:16@sha256:e17e86066e5ef83e0952a9347f5c792b7ece00972e2aa787a6986f471b3dd3d5
+        command: -c max_connections=1000 -c shared_preload_libraries=pg_stat_statements -c pg_stat_statements.track=all -c pg_stat_statements.max=20000
+        env:
+          POSTGRES_USER: postgres
+          POSTGRES_PASSWORD: postgres
+          POSTGRES_DB: circle_test
+        ports:
+          - 5432:5432
+        options: >-
+          --shm-size 1g
+          --health-cmd "pg_isready -U postgres"
+          --health-interval 5s
+          --health-timeout 5s
+          --health-retries 20
+      redis:
+        image: redis:7-alpine@sha256:7aec734b2bb298a1d769fd8729f13b8514a41bf90fcdd1f38ec52267fbaa8ee6
+        ports:
+          - 6379:6379
+        options: >-
+          --health-cmd "redis-cli ping"
+          --health-interval 5s
+          --health-timeout 5s
+          --health-retries 20
+    steps:
+      - uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
+        with:
+          persist-credentials: false
+
+      - name: Set up Python
+        timeout-minutes: 3
+        uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
+        with:
+          python-version: ${{ env.UV_PYTHON }}
+
+      - name: Set up uv
+        timeout-minutes: 3
+        uses: ./.github/actions/setup-uv-with-retries
+        with:
+          version: "0.10.9"
+
+      - name: Cache the Rust build
+        timeout-minutes: 5
+        uses: ./.github/actions/cache-cargo-build
+
+      - name: Cache Prisma binaries
+        timeout-minutes: 5
+        uses: ./.github/actions/cache-prisma-binaries
+
+      - name: Install dependencies
+        timeout-minutes: 20
+        run: .github/scripts/uv_sync_with_retries.sh --frozen --all-groups --all-extras
+
+      - name: Install redis-server 7.2.16
+        timeout-minutes: 3
+        run: |
+          mkdir -p "$RUNNER_TEMP/redis-bin"
+          cid="$(docker create redis:7.2.16@sha256:0637954999d01b7c9ce9167db2da50656e2590d3b884f1c600c5f63bb6e6773c)"
+          docker cp "${cid}:/usr/local/bin/redis-server" "$RUNNER_TEMP/redis-bin/redis-server"
+          docker rm "$cid"
+          "$RUNNER_TEMP/redis-bin/redis-server" --version | grep -q 'v=7.2.16'
+          echo "$RUNNER_TEMP/redis-bin" >> "$GITHUB_PATH"
+
+      - name: Install MCP SDK1 peer
+        if: matrix.suite == 'mcp'
+        timeout-minutes: 5
+        run: |
+          uv venv --python 3.12 .venv-mcp-peer
+          uv pip install --no-config --exclude-newer "3 days" --python .venv-mcp-peer 'mcp==1.28.1' 'langchain-mcp-adapters==0.2.1'
+          echo "MCP_TEST_PEER_PYTHON=$PWD/.venv-mcp-peer/bin/python" >> "$GITHUB_ENV"
+
+      - name: Set up Node.js
+        if: matrix.suite == 'browser'
+        timeout-minutes: 5
+        uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0
+        with:
+          node-version-file: ui/litellm-dashboard/.nvmrc
+          cache: "npm"
+          cache-dependency-path: |
+            ui/litellm-dashboard/package-lock.json
+            tests/e2e/ui/package-lock.json
+
+      - name: Install browser dependencies and build the dashboard
+        if: matrix.suite == 'browser'
+        timeout-minutes: 20
+        run: |
+          (cd ui/litellm-dashboard && npm ci)
+          (cd tests/e2e/ui && npm ci)
+          sudo env PATH="$PATH" DEBIAN_FRONTEND=noninteractive \
+            node tests/e2e/ui/node_modules/@playwright/test/cli.js install-deps chromium
+          node tests/e2e/ui/node_modules/@playwright/test/cli.js install chromium
+          (cd ui/litellm-dashboard && NEXT_TELEMETRY_DISABLED=1 npm run build)
+
+      - name: Run integration contracts
+        timeout-minutes: 45
+        env:
+          SUITE: ${{ matrix.suite }}
+          INTEGRATION_POSTGRES_CONTAINER: ${{ job.services.postgres.id }}
+          INTEGRATION_REDIS_CONTAINER: ${{ job.services.redis.id }}
+          LITELLM_LICENSE: ${{ secrets.LITELLM_LICENSE }}
+        run: |
+          case "$SUITE" in
+            *-replica) bash .circleci/scripts/run_integration.sh "${SUITE%-replica}" replica ;;
+            *) bash .circleci/scripts/run_integration.sh "$SUITE" ;;
+          esac
+
+      - name: Verify every case ran exactly once
+        if: ${{ !cancelled() && matrix.suite != 'browser' }}
+        timeout-minutes: 2
+        run: |
+          record="$(find test-results -mindepth 2 -maxdepth 2 -name execution.json -print -quit)"
+          test -n "$record"
+          mkdir -p "$RUNNER_TEMP/shards/0"
+          cp "$record" "$RUNNER_TEMP/shards/0/execution.json"
+          .venv/bin/python -m tests.integration._support.sharding "$RUNNER_TEMP/shards" --shards 1
+
+      - name: Report MCP coverage
+        if: ${{ !cancelled() && matrix.suite == 'mcp' }}
+        timeout-minutes: 5
+        run: |
+          coverage_dir=test-results/integration-mcp/coverage
+          test -f "$coverage_dir/coverage.txt" && exit 0
+          export COVERAGE_FILE="$PWD/$coverage_dir/data"
+          .venv/bin/python -m coverage combine --rcfile=tests/integration/mcp_coverage.toml
+          .venv/bin/python -m coverage report --rcfile=tests/integration/mcp_coverage.toml > "$coverage_dir/coverage.txt"
+          .venv/bin/python -m coverage html --rcfile=tests/integration/mcp_coverage.toml -d "$coverage_dir/html"
+          tail -n 1 "$coverage_dir/coverage.txt"
+
+      - name: Check results for the license
+        id: secret-scan
+        if: ${{ !cancelled() }}
+        timeout-minutes: 5
+        env:
+          SCANNED_SECRET: ${{ secrets.LITELLM_LICENSE }}
+        run: python .github/scripts/assert_secret_absent.py test-results
+
+      - name: Upload results
+        if: ${{ !cancelled() && steps.secret-scan.outcome == 'success' }}
+        timeout-minutes: 10
+        uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1
+        with:
+          name: integration-${{ matrix.suite }}-${{ github.run_id }}-${{ github.run_attempt }}
+          path: test-results/
+          retention-days: 14

.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

@@ -236,6 +236,7 @@ jobs:
               tests/unit/llms/azure_ai
               tests/unit/llms/base_llm
               tests/unit/llms/baseten
+              tests/unit/llms/bigmodel
               tests/unit/llms/black_forest_labs
               tests/unit/llms/brave
               tests/unit/llms/bytez
@@ -321,6 +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
@@ -1001,9 +1003,9 @@ jobs:
         run: |
           diff -u model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json
           if [ -z "$RUST_BRIDGE_ARTIFACT" ]; then
-            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra caching --extra extra_proxy --extra proxy-runtime --extra utils
+            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra extra_proxy --extra proxy-runtime --extra utils
           else
-            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra caching --extra extra_proxy --extra proxy-runtime --extra utils --no-install-project
+            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra extra_proxy --extra proxy-runtime --extra utils --no-install-project
             uv pip install --no-deps --python .venv/bin/python rust-bridge-dist/*.whl
             cp rust-bridge-dist/litellm/rust_bridge/_native.abi3.so litellm/rust_bridge/_native.abi3.so
             uv run --no-sync python -c "import importlib.metadata; import litellm.rust_bridge._native; print(importlib.metadata.version('litellm'))"
@@ -1449,9 +1451,9 @@ jobs:
           RUST_BRIDGE_ARTIFACT: ${{ needs.rust-bridge.outputs.artifact }}
         run: |
           if [ -z "$RUST_BRIDGE_ARTIFACT" ]; then
-            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra caching --extra extra_proxy --extra proxy-runtime --extra utils
+            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra extra_proxy --extra proxy-runtime --extra utils
           else
-            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra caching --extra extra_proxy --extra proxy-runtime --extra utils --no-install-project
+            .github/scripts/uv_sync_with_retries.sh --frozen --group ci --group proxy-dev --extra google --extra proxy --extra semantic-router --extra saml --extra extra_proxy --extra proxy-runtime --extra utils --no-install-project
             uv pip install --no-deps --python .venv/bin/python rust-bridge-dist/*.whl
             cp rust-bridge-dist/litellm/rust_bridge/_native.abi3.so litellm/rust_bridge/_native.abi3.so
           fi

README.md

@@ -141,12 +141,56 @@ response = client.chat.completions.create(
 )
 ```
 
-[**Docs: LLM Providers**](https://docs.litellm.ai/docs/providers)
+</details>
+
+<details>
+<summary><b>Auto Routing</b> - Choose a model for each prompt (Python SDK + AI Gateway)</summary>
+
+### Python SDK
+
+[**Docs: Autorouter SDK**](https://docs.litellm.ai/docs/autorouter_sdk)
+
+Install with `uv add litellm` and set `OPENAI_API_KEY` for the models below.
+
+```python
+import asyncio
+from litellm import Router
+
+router = Router(model_list=[
+    {"model_name": "fast", "litellm_params": {"model": "openai/gpt-6-luna"}},
+    {"model_name": "capable", "litellm_params": {"model": "openai/gpt-6.1-sol"}},
+    {"model_name": "auto", "litellm_params": {
+        "model": "auto_router/complexity_router",
+        "complexity_router_config": {
+            "classifier_type": "heuristic",
+            "tiers": {"SIMPLE": "fast", "MEDIUM": "fast",
+                      "COMPLEX": "capable", "REASONING": "capable"},
+            "return_raw_model_name": True,
+        },
+        "complexity_router_default_model": "capable",
+    }},
+])
+response = asyncio.run(router.acompletion(
+    model="auto", messages=[{"role": "user", "content": "What is 2+2?"}],
+))
+print(response.model, response.choices[0].message.content)
+```
+
+### AI Gateway (Proxy Server)
+
+[**Create an Auto Router**](https://docs.litellm.ai/docs/auto_router/setup), then call its model name. Replace `auto` and the URL below with yours, and set `LITELLM_API_KEY` to your virtual key.
+
+```shell
+curl http://localhost:4000/v1/chat/completions \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{"model":"auto","messages":[{"role":"user","content":"What is 2+2?"}]}'
+```
 
 </details>
 
 <details>
-<summary><b>Agents</b> - Invoke A2A Agents (Python SDK + AI Gateway)</summary>
+<summary><b>A2A</b> - Invoke A2A Agents (Python SDK + AI Gateway)</summary>
 
 [**Supported Providers**](https://docs.litellm.ai/docs/a2a#add-a2a-agents) - LangGraph, Vertex AI Agent Engine, Azure AI Foundry, Bedrock AgentCore, Pydantic AI
 
@@ -354,6 +398,7 @@ Set `LITELLM_PROXY_API_BASE` and `LITELLM_PROXY_API_KEY` and every model call th
 | [Eden AI (`edenai`)](https://docs.litellm.ai/docs/providers/edenai) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |  |  |  |
 | [ElevenLabs (`elevenlabs`)](https://docs.litellm.ai/docs/providers/elevenlabs) | ✅ | ✅ | ✅ |  |  | ✅ | ✅ |  |  |  |
 | [Empower (`empower`)](https://docs.litellm.ai/docs/providers/empower) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
+| [eu/jev Decisions API (`eu_jev`)](https://docs.litellm.ai/docs/pass_through/eu_jev) |  |  |  |  |  |  |  |  |  |  |
 | [Fal AI (`fal_ai`)](https://docs.litellm.ai/docs/providers/fal_ai) | ✅ | ✅ | ✅ |  | ✅ |  |  |  |  |  |
 | [Featherless AI (`featherless_ai`)](https://docs.litellm.ai/docs/providers/featherless_ai) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
 | [Fireworks AI (`fireworks_ai`)](https://docs.litellm.ai/docs/providers/fireworks_ai) | ✅ | ✅ | ✅ |  |  |  |  |  |  |  |
@@ -442,7 +487,7 @@ You can use LiteLLM through either the Proxy Server or Python SDK. Both give you
 <tr>
 <th style={{width: '14%'}}></th>
 <th style={{width: '43%'}}><strong><a href="https://docs.litellm.ai/docs/simple_proxy">LiteLLM AI Gateway</a></strong></th>
-<th style={{width: '43%'}}><strong><a href="https://docs.litellm.ai/docs/">LiteLLM Python SDK</a></strong></th>
+<th style={{width: '43%'}}><strong><a href="https://docs.litellm.ai/docs/python_sdk">LiteLLM Python SDK</a></strong></th>
 </tr>
 </thead>
 <tbody>

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:

enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py

@@ -398,7 +398,7 @@ class CheckBatchCost:
         deployment. Once that deployment leaves the router, default fallbacks can
         silently send the retrieve to a provider that never saw the batch, so its
         404 must not retire a potentially recoverable row."""
-        return self.llm_router.get_deployment(model_id=model_id) is not None
+        return self.llm_router.registry().deployment(model_id) is not None
 
     @staticmethod
     def _is_output_file_gone_at_provider(error: Exception, output_file_id: Optional[str]) -> bool:
@@ -564,7 +564,7 @@ class CheckBatchCost:
     def _get_deployment_id_for_bare_model(
         self, bare_model_name: str, llm_provider: str
     ) -> Optional[str]:
-        model_group = self.llm_router.resolve_model_name_from_model_id(bare_model_name)
+        model_group = self.llm_router.registry().resolve(bare_model_name, kind="from_id")
         deployment_id = (
             self._get_deployment_id_for_provider(model_group, llm_provider) if model_group else None
         )
@@ -580,7 +580,7 @@ class CheckBatchCost:
     ) -> Optional[str]:
         from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
 
-        for deployment in self.llm_router.get_model_list(model_name=None) or []:
+        for deployment in self.llm_router.registry().deployments() or []:
             litellm_params = deployment.get("litellm_params") or {}
             actual_model = litellm_params.get("model")
             if not isinstance(actual_model, str):
@@ -623,8 +623,9 @@ class CheckBatchCost:
         """
         from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
 
-        for deployment_id in self.llm_router.get_model_ids(model_name=model_group):
-            deployment_info = self.llm_router.get_deployment(model_id=deployment_id)
+        registry = self.llm_router.registry()
+        for deployment_id in registry.names(model_group, kind="ids"):
+            deployment_info = registry.deployment(deployment_id)
             if deployment_info is None:
                 continue
             try:
@@ -743,7 +744,7 @@ class CheckBatchCost:
             except (IndexError, AttributeError):
                 pass
 
-        credentials: Final = self.llm_router.get_deployment_credentials_with_provider(model_id) or {}
+        credentials: Final = self.llm_router.registry().credentials(model_id, form="with_provider") or {}
         _file_content = await afile_content(
             file_id=cast(str, raw_output_file_id),
             _litellm_internal_model_credentials=MappingProxyType(dict(credentials)),
@@ -766,7 +767,7 @@ class CheckBatchCost:
             except Exception:
                 pass
 
-        deployment_info = self.llm_router.get_deployment(model_id=model_id)
+        deployment_info = self.llm_router.registry().deployment(model_id)
         if deployment_info is None:
             verbose_proxy_logger.info(
                 f"Skipping job {job.unified_object_id} because it is not a valid deployment info"
@@ -996,7 +997,7 @@ class CheckBatchCost:
         )
 
         try:
-            deployment_info: Final = self.llm_router.get_deployment(model_id=model_id)
+            deployment_info: Final = self.llm_router.registry().deployment(model_id)
             if deployment_info is None:
                 verbose_proxy_logger.error(
                     f"CheckBatchCost: deployment {model_id} is no longer in the router, so the line items of batch "
@@ -1009,7 +1010,7 @@ class CheckBatchCost:
                 custom_llm_provider=deployment_info.litellm_params.custom_llm_provider,
             )
             cost_provider: Final = cast(BatchFileProvider, llm_provider)
-            credentials: Final = self.llm_router.get_deployment_credentials_with_provider(model_id) or {}
+            credentials: Final = self.llm_router.registry().credentials(model_id, form="with_provider") or {}
             file_access: Final = {
                 **credentials,
                 "_litellm_internal_model_credentials": MappingProxyType(dict(credentials)),

enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py

@@ -30,7 +30,7 @@ from collections.abc import Awaitable, Callable, Mapping
 from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Final, cast
+from typing import TYPE_CHECKING, Final
 
 from litellm_enterprise.proxy.common_utils.managed_object_attribution import (
     ManagedObjectCreatorAttribution,
@@ -273,7 +273,7 @@ class CheckResponsesCost:
 
     def _resolve_deployment(self, response_id: str) -> Deployment | None:
         model_id: str | None = ResponsesAPIRequestUtils.get_model_id_from_response_id(response_id)
-        return self.llm_router.get_deployment(model_id=model_id) if model_id is not None else None
+        return self.llm_router.registry().deployment(model_id) if model_id is not None else None
 
     async def _get_response(
         self,
@@ -290,12 +290,14 @@ class CheckResponsesCost:
         sees provider env vars, so it fails for every deployment whose credentials
         live in the config; the row then never leaves ``queued``.
         """
-        if not via_router:
-            return await litellm.aget_responses(response_id=response_id, litellm_metadata=litellm_metadata)
-        router_response = await self.llm_router.aget_responses(
-            response_id=response_id, litellm_metadata=litellm_metadata
+        retrieved: Final[object] = (
+            await self.llm_router.aget_responses(response_id=response_id, litellm_metadata=litellm_metadata)
+            if via_router
+            else await litellm.aget_responses(response_id=response_id, litellm_metadata=litellm_metadata)
         )
-        return cast(ResponsesAPIResponse, router_response)
+        if not isinstance(retrieved, ResponsesAPIResponse):
+            raise TypeError(f"Retrieving {response_id} did not return a response object")
+        return retrieved
 
     async def _fetch_unbilled(self, target: _PollTarget) -> ResponsesAPIResponse:
         return await self._get_response(
@@ -413,7 +415,7 @@ class CheckResponsesCost:
         if target.deployment is None or not isinstance(requested, str):
             return MappingProxyType({})
         routed: Final = _ROUTED_DEPLOYMENTS.validate_python(
-            self.llm_router.get_model_list(model_name=requested, team_id=team_id) or ()
+            self.llm_router.registry().deployments(requested, team_id=team_id) or ()
         )
         serving_id: Final = target.deployment.model_info.id
         return MappingProxyType(

enterprise/litellm_enterprise/proxy/hooks/managed_files.py

@@ -245,7 +245,7 @@ def _provider_file_retrieve_credentials(
     if llm_router is None or model_id is None:
         return None
     try:
-        credentials: Final = llm_router.get_deployment_credentials_with_provider(model_id)
+        credentials: Final = llm_router.registry().credentials(model_id, form="with_provider")
     except Exception as error:
         verbose_logger.warning(
             "Failed to retrieve credentials for provider file "
@@ -2650,7 +2650,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             k: v for k, v in data.items() if k not in ("model", "file_id", "_litellm_internal_model_credentials")
         }
         for model_id, model_file_id in specific_model_file_id_mapping.items():
-            credentials = llm_router.get_deployment_credentials_with_provider(model_id=model_id)
+            credentials = llm_router.registry().credentials(model_id, form="with_provider")
             delete_data = {
                 **filtered_data,
                 **(
@@ -2694,7 +2694,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                     # against the deployment's configured bucket, which they only
                     # trust from this immutable server-side snapshot, never from
                     # request params.
-                    credentials = llm_router.get_deployment_credentials_with_provider(model_id=model_id)
+                    credentials = llm_router.registry().credentials(model_id, form="with_provider")
                     if credentials is not None:
                         data["_litellm_internal_model_credentials"] = cast(Dict, MappingProxyType(dict(credentials)))
                     else:

enterprise/litellm_enterprise/proxy/management_endpoints/project_endpoints.py

@@ -290,7 +290,7 @@ def _check_team_project_limits(
 
 
 def _router_access_group_names(llm_router: "Router | None") -> frozenset[str]:
-    return frozenset(llm_router.get_model_access_groups()) if llm_router is not None else frozenset()
+    return frozenset(llm_router.registry().access_groups()) if llm_router is not None else frozenset()
 
 
 def _project_models_expanding_at_request_time(

enterprise/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "litellm-enterprise"
-version = "0.1.80"
+version = "0.1.81"
 description = "Package for LiteLLM Enterprise features"
 readme = "README.md"
 requires-python = ">=3.9"
@@ -26,7 +26,7 @@ required-version = ">=0.10.9"
 module-root = ""
 
 [tool.commitizen]
-version = "0.1.80"
+version = "0.1.81"
 version_files = [
     "pyproject.toml:^version",
     "../pyproject.toml:litellm-enterprise==",

examples/bigmodel.md

@@ -0,0 +1,63 @@
+# BigModel (Zhipu AI)
+
+Use `bigmodel/` for the domestic BigModel platform and `zai/` for international Z.AI. Each platform uses its own credentials and default URL
+
+```yaml
+model_list:
+  - model_name: glm
+    litellm_params:
+      model: bigmodel/glm-5.3
+      api_key: os.environ/BIGMODEL_API_KEY
+      api_base: https://open.bigmodel.cn
+```
+
+One model configuration supports all three native APIs. The provider selects the upstream endpoint from the requested API
+
+| Proxy endpoint | BigModel endpoint |
+| --- | --- |
+| `/v1/chat/completions` | `/api/paas/v4/chat/completions` |
+| `/v1/responses` | `/api/v1/responses` |
+| `/v1/messages` | `/api/anthropic/v1/messages` |
+
+`api_base` is optional and defaults to `https://open.bigmodel.cn`. Set `BIGMODEL_API_BASE` to override the default, or pass an explicit `api_base` to override the environment value. Existing API-specific base URLs and complete endpoint URLs are normalized, preserving a custom proxy's path prefix
+
+A GLM Coding Plan key uses `https://open.bigmodel.cn/api/coding/paas/v4` as its `api_base`, since Coding Plan and standard keys do not work on each other's endpoints. Chat Completions then calls the Coding Plan endpoint, and Responses and Messages keep the endpoints in the table above
+
+The Admin UI lists BigModel (Zhipu AI), displays its official logo, and provides a required password API Key field and an optional API Base field with the default URL. Authentication uses an explicit `api_key` or `BIGMODEL_API_KEY`
+
+```python
+import litellm
+
+chat = litellm.completion(
+    model="bigmodel/glm-5.3",
+    messages=[{"role": "user", "content": "Hello"}],
+    thinking={"type": "enabled"},
+    reasoning_effort="low",
+)
+
+response = litellm.responses(
+    model="bigmodel/glm-5.3",
+    input="Hello",
+    reasoning={"effort": "low"},
+    store=True,
+)
+
+async def messages():
+    return await litellm.anthropic.messages.acreate(
+        model="bigmodel/glm-5.3",
+        max_tokens=1024,
+        messages=[{"role": "user", "content": "Hello"}],
+        thinking={"type": "adaptive"},
+        output_config={"effort": "low"},
+    )
+```
+
+Each API retains its native response format and streaming events. The native Messages SDK entry point is asynchronous, as is the proxy's `/v1/messages` route
+
+GLM-5.3, GLM-5.3-Flash, and GLM-5.3-FlashX use always-on thinking. GLM-5.3 supports `low`, `high`, and `max` reasoning effort. Messages preserves native `thinking`, `output_config`, and `reasoning_effort` parameters
+
+Set `store=True` when creating a Responses request if you need continuation, retrieval, or deletion. Use the response ID returned by LiteLLM. BigModel documents a seven-day retention period and does not support cancel, compact, native WebSocket, or file_search
+
+The model catalog and packaged backup include GLM-5.3, GLM-5.3-Flash, and GLM-5.3-FlashX. Prices are expressed in USD/token using a static 7.0 CNY/USD conversion, with the source and verification date in each entry's metadata. Use existing `litellm_params` cost fields for contract-specific deployment prices
+
+API references: [Chat Completions](https://docs.bigmodel.cn/cn/guide/develop/openai/introduction), [Responses](https://docs.bigmodel.cn/cn/guide/develop/responses/introduction), [Messages](https://docs.bigmodel.cn/cn/guide/develop/claude/introduction), and [pricing](https://docs.bigmodel.cn/cn/guide/start/pricing), checked on 2026-10-27

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 && \
@@ -144,6 +145,7 @@ COPY --from=builder --chown=nonroot:nonroot /app/.venv /app/.venv
 COPY --from=builder --chown=nonroot:nonroot /app/litellm /app/litellm
 COPY --from=builder --chown=nonroot:nonroot /app/enterprise /app/enterprise
 COPY --from=builder --chown=nonroot:nonroot /app/gateway /app/gateway
+COPY --from=builder --chown=nonroot:nonroot /app/litellm_cli /app/litellm_cli
 COPY --from=builder --chown=nonroot:nonroot /app/docker/component_entrypoint.sh /app/docker/component_entrypoint.sh
 COPY --from=builder --chown=nonroot:nonroot /app/schema.prisma /app/schema.prisma
 COPY --from=builder /opt/prisma /opt/prisma

gateway/launch.py

@@ -8,6 +8,11 @@ handed the loopback URL it listens on. A pre-existing ``DATABASE_URL`` wins in
 wins under token auth too, so exporting it here is enough for every worker to
 pick the pooled URL up unchanged.
 
+LiteLLM imports wait for ``main``: every uvicorn worker imports this module again
+before it can answer its first 5 s health ping, so a slow host would kill each one.
+Workers then load the gateway app through ``worker_app`` without garbage collection
+pauses, which hold the GIL long enough to miss a ping.
+
 Run with:
     python -m gateway.launch --workers 4 --host 0.0.0.0 --port 4000
 """
@@ -15,19 +20,19 @@ Run with:
 import os
 import sys
 from collections.abc import Callable, Mapping, Sequence
-from typing import Final
+from typing import TYPE_CHECKING, Final
 
 from uvicorn.main import main as uvicorn_main
 
-from litellm.proxy.db.db_url_settings import DatabaseURLSettings
-from litellm.proxy.db.pgbouncer import (
-    PgBouncerError,
-    PgBouncerSettings,
-    export_pooled_database_url,
-    start_in_container_pgbouncer,
-)
+from litellm_cli.main import without_gc_pauses
 
-GATEWAY_APP: Final = "gateway.main:app"
+if TYPE_CHECKING:
+    from fastapi import FastAPI
+
+    from litellm.proxy.db.db_url_settings import DatabaseURLSettings
+    from litellm.proxy.db.pgbouncer import PgBouncerError, PgBouncerSettings
+
+GATEWAY_APP: Final = "gateway.launch:worker_app"
 KEEPALIVE_FLAG: Final = "--timeout-keep-alive"
 
 
@@ -35,21 +40,23 @@ def uvicorn_argv(argv: Sequence[str], environ: Mapping[str, str]) -> tuple[str,
     """Honor ``KEEPALIVE_TIMEOUT`` like ``proxy_cli.py`` does, unless the flag was passed explicitly."""
     keepalive: Final = environ.get("KEEPALIVE_TIMEOUT")
     if keepalive is None or any(arg == KEEPALIVE_FLAG or arg.startswith(f"{KEEPALIVE_FLAG}=") for arg in argv):
-        return (GATEWAY_APP, *argv)
-    return (GATEWAY_APP, *argv, KEEPALIVE_FLAG, keepalive)
+        return (GATEWAY_APP, "--factory", *argv)
+    return (GATEWAY_APP, "--factory", *argv, KEEPALIVE_FLAG, keepalive)
 
 
 def pool_database_url(
-    settings: DatabaseURLSettings,
-    pgbouncer: PgBouncerSettings,
+    settings: "DatabaseURLSettings",
+    pgbouncer: "PgBouncerSettings",
     environ: Mapping[str, str],
-) -> str | PgBouncerError | None:
+) -> "str | PgBouncerError | None":
     """Start the in-container PgBouncer and return its loopback URL, or None when ``pgbouncer.enabled`` is off.
 
     The upstream URL is whatever ``apply_to_env`` assembled from the discrete
     ``DATABASE_*`` vars (or an operator-pinned ``DATABASE_URL``). Under token
     auth the pooler mints and renews the upstream token itself.
     """
+    from litellm.proxy.db.pgbouncer import PgBouncerError, start_in_container_pgbouncer
+
     if not pgbouncer.enabled:
         return None
     upstream_url: Final = environ.get("DATABASE_URL")
@@ -58,11 +65,20 @@ def pool_database_url(
     return start_in_container_pgbouncer(pgbouncer, upstream_url, token_auth=settings.token_auth())
 
 
+def worker_app() -> "FastAPI":
+    with without_gc_pauses():
+        from gateway.main import app
+    return app
+
+
 def _serve(argv: Sequence[str]) -> None:
     uvicorn_main(tuple(argv), prog_name="uvicorn")
 
 
 def main(argv: Sequence[str], serve: Callable[[Sequence[str]], None] = _serve) -> None:
+    from litellm.proxy.db.db_url_settings import DatabaseURLSettings
+    from litellm.proxy.db.pgbouncer import PgBouncerError, PgBouncerSettings, export_pooled_database_url
+
     settings: Final = DatabaseURLSettings.from_env()
     settings.apply_to_env()
     pooled_url: Final = pool_database_url(settings, PgBouncerSettings(), os.environ)

gateway/routes/allowlist.py

@@ -142,6 +142,7 @@ GATEWAY_EXACT_PATHS: frozenset[str] = frozenset(
         "/test",
         "/debug/memory/summary",
         "/api/event_logging/batch",
+        "/eu_jev/v1/systemone",
     }
 )
 

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-proxy-extras/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "litellm-proxy-extras"
-version = "0.4.111"
+version = "0.4.112"
 description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
 readme = "README.md"
 requires-python = ">=3.9"
@@ -30,7 +30,7 @@ required-version = ">=0.10.9"
 module-root = ""
 
 [tool.commitizen]
-version = "0.4.111"
+version = "0.4.112"
 version_files = [
     "pyproject.toml:^version",
     "../pyproject.toml:litellm-proxy-extras==",

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

@@ -39,6 +39,7 @@ from litellm._logging import (
     turn_on_json,
     log_level,
 )
+from litellm._logging import suppress_startup_output as suppress_startup_output
 import re
 from litellm.constants import (
     DEFAULT_BATCH_SIZE,
@@ -519,7 +520,7 @@ aiohttp_trust_env: bool = False  # set to true to use HTTP_ Proxy settings
 disable_aiohttp_transport: bool = False  # Set this to true to use httpx instead
 disable_aiohttp_trust_env: bool = False  # When False, aiohttp will respect HTTP(S)_PROXY env vars
 force_ipv4: bool = False  # when True, litellm will force ipv4 for all LLM requests. Some users have seen httpx ConnectionError when using ipv6.
-http2: bool = False
+http2: bool | None = None
 network_mock: bool = False  # When True, use mock transport — no real network calls
 
 ####### STOP SEQUENCE LIMIT #######
@@ -637,6 +638,7 @@ watsonx_models: Set = set()
 gemini_models: Set = set()
 xai_models: Set = set()
 zai_models: Set = set()
+bigmodel_models: Final[set[str]] = set()  # mutable-ok: provider registry is populated and extended at runtime
 deepseek_models: Set = set()
 tencent_models: Set = set()
 runwayml_models: Set = set()
@@ -683,6 +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()
@@ -883,6 +886,8 @@ def _populate_provider_model_sets(model_cost_map: Mapping[str, Mapping[str, obje
             xai_models.add(key)
         elif value.get("litellm_provider") == "zai":
             zai_models.add(key)
+        elif value.get("litellm_provider") == "bigmodel":
+            bigmodel_models.add(key)
         elif value.get("litellm_provider") == "fal_ai":
             fal_ai_models.add(key)
         elif value.get("litellm_provider") == "deepseek":
@@ -977,6 +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":
@@ -1113,6 +1120,7 @@ model_list = list(
     | text_completion_inception_models
     | xai_models
     | zai_models
+    | bigmodel_models
     | fal_ai_models
     | deepseek_models
     | modelscope_models
@@ -1154,6 +1162,7 @@ model_list = list(
     | publicai_models
     | darkbloom_models
     | openinfer_models
+    | kyma_models
     | streamlake_models
     | zerogpu_models
     | v0_models
@@ -1236,6 +1245,7 @@ def _build_models_by_provider() -> dict:
         "text-completion-inception": text_completion_inception_models,
         "xai": xai_models,
         "zai": zai_models,
+        "bigmodel": bigmodel_models,
         "fal_ai": fal_ai_models,
         "deepseek": deepseek_models,
         "tencent": tencent_models,
@@ -1284,6 +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,
@@ -1772,6 +1783,9 @@ if TYPE_CHECKING:
     from .llms.bespoke.decisions.transformation import (
         BespokeDecisionsConfig as BespokeDecisionsConfig,
     )
+    from .llms.eu_jev.decisions.transformation import (
+        EuJevDecisionsConfig as EuJevDecisionsConfig,
+    )
     from .llms.openai.decisions.transformation import (
         OpenAIDecisionsConfig as OpenAIDecisionsConfig,
     )

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/_lazy_imports_registry.py

@@ -174,6 +174,7 @@ LLM_CONFIG_NAMES: Final = (
     "OllamaDecisionsConfig",
     "LayaDecisionsConfig",
     "BespokeDecisionsConfig",
+    "EuJevDecisionsConfig",
     "OpenAIDecisionsConfig",
     "AzureDecisionsConfig",
     "AzureAIDecisionsConfig",
@@ -753,6 +754,7 @@ _LLM_CONFIGS_IMPORT_MAP: Final = {
     "OllamaDecisionsConfig": (".llms.ollama.decisions.transformation", "OllamaDecisionsConfig"),
     "LayaDecisionsConfig": (".llms.laya.decisions.transformation", "LayaDecisionsConfig"),
     "BespokeDecisionsConfig": (".llms.bespoke.decisions.transformation", "BespokeDecisionsConfig"),
+    "EuJevDecisionsConfig": (".llms.eu_jev.decisions.transformation", "EuJevDecisionsConfig"),
     "OpenAIDecisionsConfig": (".llms.openai.decisions.transformation", "OpenAIDecisionsConfig"),
     "AzureDecisionsConfig": (".llms.azure.decisions.transformation", "AzureDecisionsConfig"),
     "AzureAIDecisionsConfig": (".llms.azure_ai.decisions.transformation", "AzureAIDecisionsConfig"),

litellm/_logging.py

@@ -10,10 +10,12 @@ import sys
 from collections.abc import Iterator, Mapping
 from datetime import datetime
 from logging import Formatter
+from types import FrameType
 from typing import Final, TextIO
 from urllib.parse import unquote
 
 from pydantic import TypeAdapter
+from typing_extensions import ReadOnly, TypedDict
 
 import litellm
 from litellm.constants import (
@@ -26,9 +28,11 @@ from litellm.litellm_core_utils.env_utils import get_env_int
 from litellm.litellm_core_utils.safe_json_dumps import UNSERIALIZABLE_OBJECT, safe_dumps, safe_json_structure
 from litellm.litellm_core_utils.safe_json_loads import safe_json_loads
 from litellm.litellm_core_utils.secret_redaction import (
+    REDACTED,
     python_redact_string,
     python_redact_structured_value,
     redact_internal_details,
+    redact_structured_value,
 )
 from litellm.litellm_core_utils.secret_redaction import (
     redact_string as redact_secret_string,
@@ -138,6 +142,32 @@ def redact_internal_details_from_client_message(value: str) -> str:
     return redact_internal_details(value)
 
 
+_CLIENT_FIELDS: Final = TypeAdapter(dict[str, object])
+_CLIENT_ITEMS: Final = TypeAdapter(list[object])
+
+
+def _redacted_client_field(key: str | None, value: object) -> object:
+    if isinstance(value, str):
+        return redact_internal_details(redact_structured_value(key, value))
+    if isinstance(value, dict):
+        return {
+            child_key: _redacted_client_field(child_key, child)
+            for child_key, child in _CLIENT_FIELDS.validate_python(value).items()
+        }
+    if isinstance(value, (list, tuple)):
+        return [_redacted_client_field(key, child) for child in _CLIENT_ITEMS.validate_python(value)]
+    if not isinstance(value, (int, float)):
+        return value
+    text: Final = str(value)
+    return value if redact_structured_value(key, text) == text else REDACTED
+
+
+def redact_internal_details_from_client_fields(fields: Mapping[str, object]) -> object:
+    if not _ENABLE_SECRET_REDACTION:
+        return fields
+    return _redacted_client_field(None, dict(fields))
+
+
 def _substituted_color_message(record: logging.LogRecord) -> str | None:
     """Render a record's ``color_message`` against its args, or None if absent.
 
@@ -598,6 +628,7 @@ _LOG_FORMAT_PREFIX: Final = "%(asctime)s - %(name)s:%(levelname)s"
 _LOG_FORMAT_SUFFIX: Final = ": %(filename)s:%(lineno)s - %(message)s"
 _PLAIN_LOG_FORMAT: Final = _LOG_FORMAT_PREFIX + _LOG_FORMAT_SUFFIX
 _COLOR_LOG_FORMAT: Final = f"\033[92m{_LOG_FORMAT_PREFIX}\033[0m{_LOG_FORMAT_SUFFIX}"
+_ANSI_ESCAPE: Final = re.compile(r"\x1b\[[0-9;]*m")
 
 
 def _stream_is_tty(stream: TextIO | None) -> bool:
@@ -612,15 +643,28 @@ def _stream_is_tty(stream: TextIO | None) -> bool:
         return False
 
 
-def _plain_log_format(stdout: TextIO | None, stderr: TextIO | None) -> str:
-    """The plain-text log format, colorized only when both streams are an interactive terminal.
+def _color_enabled(stream: TextIO | None) -> bool:
+    """True when ANSI colors written to the stream reach an interactive terminal.
 
     Honors the NO_COLOR convention from no-color.org: color is disabled when
     NO_COLOR is present with a non-empty value.
     """
-    if os.environ.get("NO_COLOR"):
-        return _PLAIN_LOG_FORMAT
-    return _COLOR_LOG_FORMAT if _stream_is_tty(stdout) and _stream_is_tty(stderr) else _PLAIN_LOG_FORMAT
+    return not os.environ.get("NO_COLOR") and _stream_is_tty(stream)
+
+
+def _terminal_colors(stdout: TextIO | None, stderr: TextIO | None) -> bool:
+    """True when both streams are an interactive color terminal, the one rule for log lines and startup prints."""
+    return _color_enabled(stdout) and _color_enabled(stderr)
+
+
+def terminal_text(message: str) -> str:
+    """The message with its ANSI colors kept only on a color terminal, by the rule the plain log format uses."""
+    return message if _terminal_colors(sys.stdout, sys.stderr) else _ANSI_ESCAPE.sub("", message)
+
+
+def _plain_log_format(stdout: TextIO | None, stderr: TextIO | None) -> str:
+    """The plain-text log format, colorized only when both streams are an interactive terminal."""
+    return _COLOR_LOG_FORMAT if _terminal_colors(stdout, stderr) else _PLAIN_LOG_FORMAT
 
 
 class LevelRoutingStreamHandler(logging.StreamHandler):
@@ -642,8 +686,8 @@ class LevelRoutingStreamHandler(logging.StreamHandler):
         super().emit(record)
 
 
-def _parse_json_logs_env(value: str | None) -> bool:
-    """Strict opt-in parse for the JSON_LOGS env var: only "true" (any case) enables JSON logs.
+def _parse_env_flag(value: str | None) -> bool:
+    """Strict opt-in parse for a boolean env var such as JSON_LOGS: only "true" (any case) enables it.
 
     Matches the reader in litellm-proxy-extras/_logging.py. The previous
     bool(os.getenv(...)) treated any non-empty value, including "false" and "0",
@@ -656,7 +700,18 @@ def resolve_log_level(log_level: str) -> int:
     return getattr(logging, log_level.upper())
 
 
-json_logs: Final = _parse_json_logs_env(os.getenv("JSON_LOGS"))
+json_logs: Final = _parse_env_flag(os.getenv("JSON_LOGS"))
+suppress_startup_output: Final = _parse_env_flag(os.getenv("LITELLM_SUPPRESS_STARTUP_OUTPUT"))
+
+
+class StartupOutputSettings(TypedDict, total=False):
+    json_logs: ReadOnly[object]
+    suppress_startup_output: ReadOnly[object]
+
+
+STARTUP_OUTPUT_SETTINGS: Final = TypeAdapter(StartupOutputSettings)
+
+
 # Create a handler for the logger (you may need to adapt this based on your needs)
 log_level: Final = os.getenv("LITELLM_LOG", "DEBUG")
 numeric_level: Final[int] = resolve_log_level(log_level)
@@ -743,7 +798,7 @@ class JsonFormatter(Formatter):
         return dt.isoformat()
 
     def format(self, record):
-        message_str: Final = record.getMessage()
+        message_str: Final = _ANSI_ESCAPE.sub("", record.getMessage())
         json_record: Final[dict[str, object]] = {
             "message": message_str,
             "level": record.levelname,
@@ -799,12 +854,14 @@ class CorrelationPlainFormatter(logging.Formatter):
     """Appends trace_id/session_id to plain-text log lines stamped by CorrelationContextFilter.
 
     Mirrors JsonFormatter's handling of these two fields so request_correlation_in_logs
-    behaves the same whether or not json_logs is enabled.
+    behaves the same whether or not json_logs is enabled. Unless it was built with the color format,
+    it also drops the ANSI color codes a message carries, which a piped log would store as text.
     """
 
     def format(self, record: logging.LogRecord) -> str:
         rendered: Final = super().format(record)
-        formatted: Final = rendered if _is_redacted(record) else redact_string(rendered)
+        uncolored: Final = rendered if self._fmt == _COLOR_LOG_FORMAT else _ANSI_ESCAPE.sub("", rendered)
+        formatted: Final = uncolored if _is_redacted(record) else redact_string(uncolored)
         trace_id: Final = getattr(record, "trace_id", None)
         session_id: Final = getattr(record, "session_id", None)
         if not trace_id and not session_id:
@@ -898,29 +955,46 @@ verbose_proxy_logger.addFilter(_diagnostic_filter)
 verbose_proxy_stdout_logger.addFilter(_diagnostic_filter)
 verbose_logger.addFilter(_diagnostic_filter)
 
-_ANSI_ESCAPE: Final = re.compile(r"\x1b\[[0-9;]*m")
-
 
 def print_startup_message(message: str) -> None:
     """Write a boot-time message meant for the operator's terminal.
 
-    Plain mode prints it unchanged, colors and spacing included. With JSON logs on, every line of
-    stdout is read by a log pipeline, so the message goes out as one INFO record of the proxy logger
-    instead, on stdout like the print whatever handlers the logging config gave that logger, with the
-    ANSI color codes and surrounding blank lines removed, located at the caller and emitted at any log
-    level, whole and unredacted as the print showed it; a blank spacer produces no record.
+    LITELLM_SUPPRESS_STARTUP_OUTPUT or litellm_settings.suppress_startup_output turns every one of them
+    off; a warning goes through print_startup_warning instead. Plain mode prints it with its spacing,
+    keeping the ANSI colors only on a color terminal by the same check as the plain log format, so a
+    container log never stores escape codes.
+    With JSON logs on, every line of stdout is read by a log pipeline, so the message goes out as one
+    INFO record of the proxy logger instead, on stdout like the print whatever handlers the logging
+    config gave that logger, with the ANSI color codes and surrounding blank lines removed, located at
+    the caller and emitted at any log level, whole and unredacted as the print showed it; a blank
+    spacer produces no record.
     """
+    if litellm.suppress_startup_output is True:
+        return
+    frame: Final = inspect.currentframe()
+    _write_startup_message(message, frame.f_back if frame is not None else None, logging.INFO)
+
+
+def print_startup_warning(message: str) -> None:
+    """Write a boot-time warning as print_startup_message writes a message, even with suppress_startup_output
+    on, since it names something the operator has to fix. With JSON logs on it is a WARNING record, on
+    stdout where the print put it, so a collector that filters on the level field keeps it."""
+    frame: Final = inspect.currentframe()
+    _write_startup_message(message, frame.f_back if frame is not None else None, logging.WARNING)
+
+
+def _write_startup_message(message: str, caller: FrameType | None, level: int) -> None:
     if not litellm.json_logs:
-        print(message)  # noqa: T201  # plain mode: this is the operator-terminal print it replaces
+        import click
+
+        click.echo(terminal_text(message))
         return
     text: Final = _ANSI_ESCAPE.sub("", message).strip("\n")
     if not text:
         return
-    frame: Final = inspect.currentframe()
-    caller: Final = frame.f_back if frame is not None else None
     record: Final = verbose_proxy_logger.makeRecord(
         verbose_proxy_logger.name,
-        logging.INFO,
+        level,
         caller.f_code.co_filename if caller is not None else __file__,
         caller.f_lineno if caller is not None else 0,
         text,
@@ -934,8 +1008,8 @@ def print_startup_message(message: str) -> None:
 
 
 def _startup_json_handler() -> logging.Handler:
-    """The proxy's JSON handler on stdout, filters included, for one startup message."""
-    handler: Final = LevelRoutingStreamHandler()
+    """The proxy's JSON handler on stdout, filters included, for one startup message or warning."""
+    handler: Final = logging.StreamHandler(sys.stdout)
     handler.setFormatter(JsonFormatter())
     handler.addFilter(_secret_filter)
     handler.addFilter(_correlation_filter)

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/Readme.md

@@ -8,10 +8,9 @@ The following caching mechanisms are supported:
 2. **RedisSemanticCache**
 3. **QdrantSemanticCache**
 4. **InMemoryCache**
-5. **DiskCache**
-6. **S3Cache**
-7. **AzureBlobCache**
-8. **DualCache** (updates both Redis and an in-memory cache simultaneously)
+5. **S3Cache**
+6. **AzureBlobCache**
+7. **DualCache** (updates both Redis and an in-memory cache simultaneously)
 
 ## Folder Structure
 
@@ -20,7 +19,6 @@ litellm/caching/
 ├── base_cache.py
 ├── caching.py
 ├── caching_handler.py
-├── disk_cache.py
 ├── dual_cache.py
 ├── in_memory_cache.py
 ├── qdrant_semantic_cache.py

litellm/caching/__init__.py

@@ -1,6 +1,5 @@
 from .azure_blob_cache import AzureBlobCache
 from .caching import Cache, LiteLLMCacheType
-from .disk_cache import DiskCache
 from .dual_cache import DualCache
 from .gcs_cache import GCSCache
 from .in_memory_cache import InMemoryCache

litellm/caching/_embedding_router.py

@@ -57,7 +57,7 @@ def resolve_embedding_max_input_tokens(
         return configured_max_input_tokens
     if router is None:
         return None
-    deployment_max_input_tokens, _ = router.get_configured_token_limits(embedding_model)
+    deployment_max_input_tokens, _ = router.registry().group(embedding_model, field="token_limits")
     return deployment_max_input_tokens
 
 

litellm/caching/caching.py

@@ -31,7 +31,6 @@ from litellm.types.utils import EmbeddingResponse, is_litellm_owned_kwarg
 
 from .azure_blob_cache import AzureBlobCache
 from .base_cache import BaseCache
-from .disk_cache import DiskCache
 from .dual_cache import DualCache  # noqa: F401  # re-exported, callers import DualCache from litellm.caching.caching
 from .gcs_cache import GCSCache
 from .in_memory_cache import InMemoryCache
@@ -153,7 +152,7 @@ class Cache:
         Initializes the cache based on the given type.
 
         Args:
-            type (str, optional): The type of cache to initialize. Can be "local", "redis", "redis-semantic", "qdrant-semantic", "s3" or "disk". Defaults to "local".
+            type (str, optional): The type of cache to initialize. Can be "local", "redis", "redis-semantic", "qdrant-semantic" or "s3". Defaults to "local".
 
             # Redis Cache Args
             host (str, optional): The host address for the Redis cache. Required if type is "redis".
@@ -174,9 +173,6 @@ class Cache:
             semantic_cache_scope (str, optional): "key" isolates semantic-cache buckets per key/team/org. "end_user" additionally isolates per end user (falls back to the key scope when the request carries no end-user id). Defaults to "key".
             max_messages (int, optional): Requests with more `messages` (or Responses API `input` items) than this are neither looked up nor stored, so long agent conversations never serve or create a cache entry. None disables the limit. Defaults to 4.
 
-            # Disk Cache Args
-            disk_cache_dir (str, optional): The directory for the disk cache. Defaults to None.
-
             # S3 Cache Args
             s3_bucket_name (str, optional): The bucket name for the s3 cache. Defaults to None.
             s3_region_name (str, optional): The region name for the s3 cache. Defaults to None.
@@ -306,8 +302,11 @@ class Cache:
                 account_url=azure_account_url,
                 container=azure_blob_container,
             )
-        elif type == LiteLLMCacheType.DISK:
-            self.cache = DiskCache(disk_cache_dir=disk_cache_dir)
+        else:
+            supported_types: Final = ", ".join(member.value for member in LiteLLMCacheType)
+            raise ValueError(f"Unsupported cache type {type!r}. Supported types: {supported_types}")
+        if disk_cache_dir is not None:
+            verbose_logger.warning("Ignoring disk_cache_dir, the disk cache backend was removed")
         if "cache" not in litellm.input_callback:
             litellm.input_callback.append("cache")
         if "cache" not in litellm.success_callback:
@@ -455,17 +454,33 @@ 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 ""
@@ -1028,7 +1043,7 @@ def enable_cache(
     Enable cache with the specified configuration.
 
     Args:
-        type (Optional[Literal["local", "redis", "s3", "disk"]]): The type of cache to enable. Defaults to "local".
+        type (Optional[Literal["local", "redis", "s3"]]): The type of cache to enable. Defaults to "local".
         host (Optional[str]): The host address of the cache server. Defaults to None.
         port (Optional[str]): The port number of the cache server. Defaults to None.
         password (Optional[str]): The password for the cache server. Defaults to None.
@@ -1040,7 +1055,7 @@ def enable_cache(
         None
 
     Raises:
-        None
+        ValueError: If no cache is set yet and the cache type is unsupported.
     """
     print_verbose("LiteLLM: Enabling Cache")
     if "cache" not in litellm.input_callback:
@@ -1075,7 +1090,7 @@ def update_cache(
     Update the cache for LiteLLM.
 
     Args:
-        type (Optional[Literal["local", "redis", "s3", "disk"]]): The type of cache. Defaults to "local".
+        type (Optional[Literal["local", "redis", "s3"]]): The type of cache. Defaults to "local".
         host (Optional[str]): The host of the cache. Defaults to None.
         port (Optional[str]): The port of the cache. Defaults to None.
         password (Optional[str]): The password for the cache. Defaults to None.

litellm/caching/caching_handler.py

@@ -9,7 +9,7 @@ This file is a wrapper around caching.py
 
 This class is used to handle caching logic specific for LLM API requests (completion / embedding / text_completion / transcription etc)
 
-It utilizes the (RedisCache, s3Cache, RedisSemanticCache, QdrantSemanticCache, InMemoryCache, DiskCache) based on what the user has setup
+It utilizes the (RedisCache, s3Cache, RedisSemanticCache, QdrantSemanticCache, InMemoryCache) based on what the user has setup
 
 In each method it will call the appropriate method from caching.py
 """
@@ -830,6 +830,13 @@ class LLMCachingHandler:
         if is_internal_call.get():
             return
 
+        if logging_obj.defer_async_logging and not logging_obj.stream:
+            from litellm.utils import dispatch_success_logging
+
+            logging_obj.model_call_details["cache_hit"] = cache_hit  # rebind-ok: capture cache status before deferring
+            dispatch_success_logging(logging_obj, cached_result, start_time, end_time, False, False)
+            return
+
         from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
 
         GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(

litellm/caching/disk_cache.py

@@ -1,92 +0,0 @@
-import json
-from typing import TYPE_CHECKING, Any, Final
-
-from pydantic import TypeAdapter
-
-from .base_cache import BaseCache
-
-if TYPE_CHECKING:
-    from opentelemetry.trace import Span as _Span
-
-    Span = _Span
-else:
-    Span = Any
-
-_DECODED_DISK_VALUE: Final = TypeAdapter(object)
-
-
-class DiskCache(BaseCache):
-    def __init__(self, disk_cache_dir: str | None = None):
-        try:
-            import diskcache as dc
-        except ModuleNotFoundError as e:
-            raise ModuleNotFoundError("Please install litellm with `litellm[caching]` to use disk caching.") from e
-
-        # if users don't provider one, use the default litellm cache
-        if disk_cache_dir is None:
-            self.disk_cache = dc.Cache(".litellm_cache")
-        else:
-            self.disk_cache = dc.Cache(disk_cache_dir)
-
-    def set_cache(self, key, value, **kwargs):
-        if "ttl" in kwargs:
-            self.disk_cache.set(key, value, expire=kwargs["ttl"])
-        else:
-            self.disk_cache.set(key, value)
-
-    async def async_set_cache(self, key, value, **kwargs):
-        self.set_cache(key=key, value=value, **kwargs)
-
-    async def async_set_cache_pipeline(self, cache_list, **kwargs):
-        for cache_key, cache_value in cache_list:
-            if "ttl" in kwargs:
-                self.set_cache(key=cache_key, value=cache_value, ttl=kwargs["ttl"])
-            else:
-                self.set_cache(key=cache_key, value=cache_value)
-
-    def get_cache(self, key, **kwargs):
-        original_cached_response: Final = self.disk_cache.get(key)
-        if original_cached_response:
-            try:
-                cached_response = _DECODED_DISK_VALUE.validate_python(json.loads(original_cached_response))
-            except Exception:
-                cached_response = original_cached_response
-            return cached_response
-        return None
-
-    def batch_get_cache(self, keys: list, **kwargs):
-        return_val: Final = []
-        for k in keys:
-            val = self.get_cache(key=k, **kwargs)
-            return_val.append(val)
-        return return_val
-
-    def increment_cache(self, key, value: int, **kwargs) -> int:
-        with self.disk_cache.transact():
-            cached_value: Final = self.get_cache(key=key)
-            init_value: Final = cached_value if isinstance(cached_value, int) else 0
-            new_value: Final = init_value + value
-            self.set_cache(key, new_value, **kwargs)
-            return new_value
-
-    async def async_get_cache(self, key, **kwargs):
-        return self.get_cache(key=key, **kwargs)
-
-    async def async_batch_get_cache(self, keys: list, **kwargs):
-        return_val: Final = []
-        for k in keys:
-            val = self.get_cache(key=k, **kwargs)
-            return_val.append(val)
-        return return_val
-
-    async def async_increment(self, key, value: int, **kwargs) -> int:
-        return self.increment_cache(key=key, value=value, **kwargs)
-
-    def flush_cache(self):
-        self.disk_cache.clear()
-
-    async def disconnect(self):
-        pass
-
-    def delete_cache(self, key):
-        self.disk_cache.pop(key)

litellm/caching/evicted_client_closer.py

@@ -29,6 +29,9 @@ it does not belong to. Queued clients are therefore bucketed by what it takes to
 close them, and each bucket is ordered by deadline, so a reap walks the entries
 that are due rather than the whole queue.
 
+A bucket whose loop has been collected is dropped, and its clients are left to the
+collector, as they are when the queue is full.
+
 The queue holds its clients weakly, so waiting out a grace window never keeps
 alive anything the collector would have reclaimed first.
 """
@@ -36,6 +39,7 @@ alive anything the collector would have reclaimed first.
 import asyncio
 import contextlib
 import inspect
+import itertools
 import threading
 import time
 import weakref
@@ -44,6 +48,8 @@ from collections.abc import Awaitable, Callable, Iterator
 from dataclasses import dataclass, replace
 from typing import Final
 
+import httpx
+
 from litellm.constants import (
     EVICTED_LLM_CLIENT_CLOSE_GRACE_SECONDS,
     EVICTED_LLM_CLIENT_CLOSE_MAX_PENDING,
@@ -53,6 +59,9 @@ _CLOSABLE_ANYWHERE: Final = "closable-anywhere"
 _CLOSABLE_ON_ANY_LOOP: Final = "closable-on-any-loop"
 
 _BucketKey = str | int
+_LOOP_TOKENS: Final[weakref.WeakKeyDictionary[asyncio.AbstractEventLoop, int]] = weakref.WeakKeyDictionary()
+_LOOP_TOKEN_COUNTER: Final = itertools.count()
+_LOOP_TOKEN_LOCK: Final = threading.Lock()
 
 
 @dataclass(frozen=True, slots=True)
@@ -63,12 +72,12 @@ class _PendingClose:
     collector would otherwise have reclaimed first.
 
     ``needs_loop`` is set for a client whose close is a coroutine; those can only
-    be closed from the event loop they were evicted on, recorded in ``loop_id``.
+    be closed from the event loop they were evicted on, recorded in ``loop_token``.
     A client that closes synchronously carries neither constraint.
     """
 
     client_ref: "weakref.ref[object]"
-    loop_id: int | None
+    loop_token: int | None
     needs_loop: bool
     close_after: float
 
@@ -77,16 +86,24 @@ def _bucket_key(pending: _PendingClose) -> _BucketKey:
     """Which reaps can close this entry: any at all, any running a loop, or one loop's."""
     if not pending.needs_loop:
         return _CLOSABLE_ANYWHERE
-    if pending.loop_id is None:
+    if pending.loop_token is None:
         return _CLOSABLE_ON_ANY_LOOP
-    return pending.loop_id
+    return pending.loop_token
 
 
-def _running_loop_id() -> int | None:
+def running_loop_token() -> int | None:
     try:
-        return id(asyncio.get_running_loop())
+        loop: Final = asyncio.get_running_loop()
     except RuntimeError:
         return None
+    try:
+        token: Final[int | None] = _LOOP_TOKENS.get(loop)
+        if token is not None:
+            return token
+        with _LOOP_TOKEN_LOCK:
+            return _LOOP_TOKENS.setdefault(loop, next(_LOOP_TOKEN_COUNTER))
+    except TypeError:
+        return id(loop)
 
 
 def _close_function(client: object) -> Callable[[], object] | None:
@@ -94,13 +111,24 @@ def _close_function(client: object) -> Callable[[], object] | None:
     return close_fn
 
 
-def _transport_of(client: object) -> object:
-    """The httpx transport behind an SDK wrapper, a litellm handler, or a bare client."""
+def _mounted_transports(holder: object) -> tuple[object, ...]:
+    mounts: Final[object] = getattr(holder, "_mounts", None)
+    if not isinstance(mounts, dict):
+        return ()
+    return tuple(
+        mounted
+        for mounted in mounts.values()  # pyright: ignore[reportUnknownVariableType]  # untyped httpx mount map
+        if isinstance(mounted, (httpx.AsyncBaseTransport, httpx.BaseTransport))
+    )
+
+
+def _transports_of(client: object) -> tuple[object, ...]:
+    """The httpx transports behind an SDK wrapper, a litellm handler, or a bare client, proxy mounts included."""
     for holder in (getattr(client, "_client", None), getattr(client, "client", None), client):
         transport: object = getattr(holder, "_transport", None)
         if transport is not None:
-            return transport
-    return None
+            return (transport, *_mounted_transports(holder))
+    return ()
 
 
 def _connection_is_idle(connection: object) -> bool:
@@ -123,6 +151,17 @@ def _pool_has_busy_connection(transport: object) -> bool | None:
     )
 
 
+def _transport_in_flight(transport: object) -> bool:
+    spread_in_flight: Final[object] = getattr(transport, "has_request_in_flight", None)
+    if callable(spread_in_flight):
+        return bool(spread_in_flight())
+    pooled_busy: Final = _pool_has_busy_connection(transport)
+    if pooled_busy is not None:
+        return pooled_busy
+    session: Final[object] = getattr(transport, "client", None)
+    return bool(getattr(getattr(session, "connector", None), "_acquired", None))
+
+
 def _has_connection_in_flight(client: object) -> bool:
     """Whether the client is servicing a request right now.
 
@@ -138,12 +177,7 @@ def _has_connection_in_flight(client: object) -> bool:
     try:
         if getattr(getattr(client, "connection_pool", None), "_in_use_connections", None):
             return True
-        transport: Final = _transport_of(client)
-        pooled_busy: Final = _pool_has_busy_connection(transport)
-        if pooled_busy is not None:
-            return pooled_busy
-        session: Final[object] = getattr(transport, "client", None)
-        return bool(getattr(getattr(session, "connector", None), "_acquired", None))
+        return any(_transport_in_flight(transport) for transport in _transports_of(client))
     except Exception:  # noqa: BLE001 - a client that cannot report its state is treated as idle
         return False
 
@@ -167,6 +201,8 @@ class EvictedClientCloser:
         self._clock = clock
         self._owned: weakref.WeakSet[object] = weakref.WeakSet()
         self._buckets: dict[_BucketKey, deque[_PendingClose]] = {}  # mutable-ok: deadline-ordered queues
+        self._dead_loop_tokens: deque[int] = deque()  # mutable-ok: loop finalizers append without taking the queue lock
+        self._watched_loop_tokens: set[int] = set()  # mutable-ok: tracks tokens until their dead buckets are discarded
         self._pending_count = 0
         self._queue_lock = threading.Lock()  # the cache is reachable from every worker thread's loop
         self._close_tasks: set[asyncio.Task[None]] = set()  # mutable-ok: strong refs to running closes
@@ -197,13 +233,19 @@ class EvictedClientCloser:
         close_fn: Final = _close_function(client)
         if close_fn is None:
             return
+        loop_token: Final = running_loop_token()
+        needs_loop: Final = inspect.iscoroutinefunction(close_fn)
         if self._pending_count >= self._max_pending:
-            return
+            self._discard_dead_loops()
+            if self._pending_count >= self._max_pending:
+                return
+        if needs_loop and loop_token is not None:
+            self._watch_loop(loop_token)
         self._enqueue(
             _PendingClose(
                 client_ref=weakref.ref(client),
-                loop_id=_running_loop_id(),
-                needs_loop=inspect.iscoroutinefunction(close_fn),
+                loop_token=loop_token,
+                needs_loop=needs_loop,
                 close_after=self._clock() + self._grace_seconds,
             )
         )
@@ -217,7 +259,7 @@ class EvictedClientCloser:
         if not self._pending_count:
             return
         now: Final = self._clock()
-        for pending in self._take_due(_running_loop_id(), now):
+        for pending in self._take_due(running_loop_token(), now):
             client = pending.client_ref()
             if client is None:
                 continue
@@ -230,6 +272,29 @@ class EvictedClientCloser:
     def pending_count(self) -> int:
         return self._pending_count
 
+    def _watch_loop(self, loop_token: int) -> None:
+        if loop_token in self._watched_loop_tokens:
+            return
+        try:
+            weakref.finalize(asyncio.get_running_loop(), self._dead_loop_tokens.append, loop_token)
+        except TypeError:
+            return
+        self._watched_loop_tokens.add(loop_token)
+
+    def _discard_dead_loops(self) -> None:
+        with self._queue_lock:
+            self._discard_dead_loops_locked()
+
+    def _discard_dead_loops_locked(self) -> None:
+        while self._dead_loop_tokens:
+            self._discard_dead_loop_token_locked(self._dead_loop_tokens.popleft())
+
+    def _discard_dead_loop_token_locked(self, loop_token: int) -> None:
+        self._watched_loop_tokens.discard(loop_token)
+        bucket: Final = self._buckets.pop(loop_token, None)
+        if bucket is not None:
+            self._pending_count -= len(bucket)
+
     def _enqueue(self, pending: _PendingClose) -> None:
         """Append to the entry's bucket, dropping any dead entries it queues behind.
 
@@ -238,6 +303,7 @@ class EvictedClientCloser:
         the front rather than having to be searched for.
         """
         with self._queue_lock:
+            self._discard_dead_loops_locked()
             bucket: Final = self._buckets.setdefault(_bucket_key(pending), deque())
             while bucket and bucket[0].client_ref() is None:
                 bucket.popleft()
@@ -245,9 +311,12 @@ class EvictedClientCloser:
             bucket.append(pending)
             self._pending_count += 1
 
-    def _take_due(self, loop_id: int | None, now: float) -> tuple[_PendingClose, ...]:
-        buckets = (_CLOSABLE_ANYWHERE,) if loop_id is None else (_CLOSABLE_ANYWHERE, _CLOSABLE_ON_ANY_LOOP, loop_id)
+    def _take_due(self, loop_token: int | None, now: float) -> tuple[_PendingClose, ...]:
+        buckets = (
+            (_CLOSABLE_ANYWHERE,) if loop_token is None else (_CLOSABLE_ANYWHERE, _CLOSABLE_ON_ANY_LOOP, loop_token)
+        )
         with self._queue_lock:
+            self._discard_dead_loops_locked()
             return tuple(pending for key in buckets for pending in self._drain_locked(key, now))
 
     def _drain_locked(self, key: _BucketKey, now: float) -> Iterator[_PendingClose]:

litellm/caching/llm_caching_handler.py

@@ -2,10 +2,9 @@
 Add the event loop to the cache key, to prevent event loop closed errors.
 """
 
-import asyncio
 from typing import Final
 
-from .evicted_client_closer import EvictedClientCloser, default_evicted_client_closer
+from .evicted_client_closer import EvictedClientCloser, default_evicted_client_closer, running_loop_token
 from .in_memory_cache import InMemoryCache
 
 
@@ -43,24 +42,22 @@ class LLMClientCache(InMemoryCache):
         self.evicted_client_closer.schedule(evicted)
         self.evicted_client_closer.reap()
 
-    def update_cache_key_with_event_loop(self, key):
+    def update_cache_key_with_event_loop(self, key: str) -> str:
         """
         Add the event loop to the cache key, to prevent event loop closed errors.
         If none, use the key as is.
         """
-        try:
-            event_loop: Final = asyncio.get_running_loop()
-            stringified_event_loop: Final = str(id(event_loop))
-            return f"{key}-{stringified_event_loop}"
-        except RuntimeError:  # handle no current running event loop
-            return key
+        token: Final = running_loop_token()
+        return f"{key}-{token}" if token is not None else key
 
-    def set_cache(self, key: str, value: object, litellm_owned_client: bool = False, **kwargs):
+    def _scoped_key(self, key: str, loop_bound: bool) -> str:
+        return self.update_cache_key_with_event_loop(key) if loop_bound else key
+
+    def set_cache(self, key: str, value: object, litellm_owned_client: bool = False, loop_bound: bool = True, **kwargs):
         """``litellm_owned_client`` marks a client litellm built, so it may be closed once evicted."""
         if litellm_owned_client:
             self.evicted_client_closer.mark_owned(value)
-        key = self.update_cache_key_with_event_loop(key)
-        return super().set_cache(key, value, **kwargs)
+        return super().set_cache(self._scoped_key(key, loop_bound), value, **kwargs)
 
     async def async_set_cache(self, key: str, value: object, litellm_owned_client: bool = False, **kwargs):
         if litellm_owned_client:
@@ -68,13 +65,13 @@ class LLMClientCache(InMemoryCache):
         key = self.update_cache_key_with_event_loop(key)
         return await super().async_set_cache(key, value, **kwargs)
 
-    def get_cache(self, key, **kwargs):
-        key = self.update_cache_key_with_event_loop(key)
+    def get_cache(self, key: str, loop_bound: bool = True, **kwargs):
+        scoped_key: Final = self._scoped_key(key, loop_bound)
         self.evicted_client_closer.reap()
 
-        return super().get_cache(key, **kwargs)
+        return super().get_cache(scoped_key, **kwargs)
 
-    async def async_get_cache(self, key, **kwargs):
+    async def async_get_cache(self, key: str, **kwargs):
         key = self.update_cache_key_with_event_loop(key)
 
         return await super().async_get_cache(key, **kwargs)

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/completion_extras/litellm_responses_transformation/handler.py

@@ -260,6 +260,7 @@ class ResponsesToCompletionBridgeHandler:
                 structured_output=is_structured_output_request(
                     cast("Mapping[str, object]", optional_params)  # cast-ok: optional_params is an untyped dict
                 ),
+                replays_openai_history=custom_llm_provider == "openai",
             )
             streamwrapper: Final = CustomStreamWrapper(
                 completion_stream=completion_stream,
@@ -363,6 +364,7 @@ class ResponsesToCompletionBridgeHandler:
                 structured_output=is_structured_output_request(
                     cast("Mapping[str, object]", optional_params)  # cast-ok: optional_params is an untyped dict
                 ),
+                replays_openai_history=custom_llm_provider == "openai",
             )
             streamwrapper: Final = CustomStreamWrapper(
                 completion_stream=completion_stream,

litellm/completion_extras/litellm_responses_transformation/history.py

@@ -0,0 +1,432 @@
+import hashlib
+import json
+from collections.abc import Iterator, Mapping, Sequence
+from dataclasses import dataclass
+from itertools import accumulate, chain, pairwise
+from types import MappingProxyType
+from typing import Annotated, Final, Literal, TypeAlias
+
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, ValidationError
+
+REPLAY_FIELD: Final = "openai_responses_replay"
+PHASE_FIELD: Final = "phase"
+
+Phase: TypeAlias = Literal["commentary", "final_answer"]
+
+
+class _Frozen(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+
+class _OutputText(_Frozen):
+    type: Literal["output_text"]
+    text: str
+
+
+class _OutputMessage(_Frozen):
+    type: Literal["message"]
+    role: Literal["assistant"]
+    content: tuple[_OutputText, ...]
+    phase: Phase | None = None
+
+
+class _OutputReasoning(_Frozen):
+    type: Literal["reasoning"]
+    id: str
+
+
+class _OutputToolCall(_Frozen):
+    type: Literal["function_call", "custom_tool_call"]
+    call_id: str
+    id: str | None = None
+
+
+class _OutputToolSearch(_Frozen):
+    type: Literal["tool_search_call", "tool_search_output"]
+
+
+_OutputItem: TypeAlias = Annotated[
+    _OutputMessage | _OutputReasoning | _OutputToolCall | _OutputToolSearch, Field(discriminator="type")
+]
+_OUTPUT_ITEM: Final[TypeAdapter[_OutputItem]] = TypeAdapter(_OutputItem)
+
+
+class _AnyMessage(_Frozen):
+    type: Literal["message"]
+
+
+class _MessageEntry(_Frozen):
+    type: Literal["message"]
+    parts: tuple[Annotated[int, Field(ge=0)], ...]
+    phase: Phase | None = None
+
+
+class _IndexedEntry(_Frozen):
+    type: Literal["reasoning", "function_call", "custom_tool_call", "tool_search_call", "tool_search_output"]
+    index: Annotated[int, Field(ge=0)]
+
+
+_Entry: TypeAlias = Annotated[_MessageEntry | _IndexedEntry, Field(discriminator="type")]
+
+
+class _Replay(_Frozen):
+    version: Literal[1]
+    items: tuple[_Entry, ...]
+    checksum: str
+
+
+class _Namespaced(_Frozen):
+    namespace: str | None = None
+
+
+class _ToolFunction(_Frozen):
+    name: str = ""
+    arguments: str = ""
+    provider_specific_fields: _Namespaced | None = None
+
+
+class _ToolCall(_Frozen):
+    id: str
+    function: _ToolFunction
+    provider_specific_fields: _Namespaced | None = None
+
+
+class _SummaryText(_Frozen):
+    type: str = "summary_text"
+    text: str = ""
+
+
+class _Reasoning(_Frozen):
+    id: str | None = None
+    encrypted_content: str | None = None
+    summary: tuple[_SummaryText, ...] | None = None
+
+
+class _ProviderFields(_Frozen):
+    phase: Phase | None = None
+    tool_search_items: tuple[Mapping[str, object], ...] | None = None
+    openai_responses_replay: object = None
+
+
+class _ChatMessage(_Frozen):
+    role: str | None = None
+    content: str | None = None
+    tool_calls: tuple[_ToolCall, ...] | None = None
+    reasoning_items: tuple[_Reasoning, ...] | None = None
+    phase: Phase | None = None
+    provider_specific_fields: _ProviderFields | None = None
+
+
+class _PhaseFields(_Frozen):
+    phase: Phase | None = None
+
+
+class _RequestedPhaseFields(_Frozen):
+    phase: object = None
+
+
+class _RequestedPhase(_Frozen):
+    phase: object = None
+    provider_specific_fields: _RequestedPhaseFields | None = None
+
+
+@dataclass(frozen=True, slots=True)
+class UnknownPhase:
+    value: object
+
+
+_PHASE: Final[TypeAdapter[Phase]] = TypeAdapter(Phase)
+
+
+class _RetainedFields(_PhaseFields):
+    openai_responses_replay: object = None
+
+
+@dataclass(frozen=True, slots=True)
+class ReplayedTurn:
+    items: tuple[Mapping[str, object], ...]
+    custom_call_ids: frozenset[str]
+
+
+def _as_json(value: object) -> object:
+    return value.model_dump() if isinstance(value, BaseModel) else value
+
+
+def _parsed_output_item(item: object) -> _OutputItem | None:
+    try:
+        return _OUTPUT_ITEM.validate_python(_as_json(item))
+    except ValidationError:
+        return None
+
+
+def _is_message(item: object) -> bool:
+    try:
+        _AnyMessage.model_validate(_as_json(item))
+    except ValidationError:
+        return False
+    return True
+
+
+def _chat_message(message: object) -> _ChatMessage | None:
+    try:
+        return _ChatMessage.model_validate(_as_json(message))
+    except ValidationError:
+        return None
+
+
+def requested_phase(message: object) -> Phase | UnknownPhase | None:
+    try:
+        requested: Final = _RequestedPhase.model_validate(_as_json(message))
+    except ValidationError:
+        return None
+    fields: Final = requested.provider_specific_fields
+    value: Final = requested.phase or (fields.phase if fields else None)
+    if value is None:
+        return None
+    try:
+        return _PHASE.validate_python(value)
+    except ValidationError:
+        return UnknownPhase(value)
+
+
+def reasoning_input_item(
+    item_id: str | None, summary: Sequence[object] | None, encrypted_content: str | None
+) -> Mapping[str, object]:
+    return {
+        "type": "reasoning",
+        **({"id": item_id} if item_id else {}),
+        "summary": list(summary or ()),
+        **({"encrypted_content": encrypted_content} if encrypted_content else {}),
+    }
+
+
+def retained_replay_fields(provider_specific_fields: object) -> Mapping[str, object] | None:
+    try:
+        retained: Final = _RetainedFields.model_validate(provider_specific_fields)
+    except ValidationError:
+        return None
+    return retained.model_dump(exclude_none=True) or None
+
+
+def output_message_text(output_items: Sequence[object], *, first_message_only: bool) -> str:
+    parsed: Final = map(_parsed_output_item, output_items)
+    messages: Final = tuple(item for item in parsed if isinstance(item, _OutputMessage))
+    return "".join(_texts(messages[:1] if first_message_only else messages))
+
+
+def _tool_search_entries(message: _ChatMessage) -> tuple[Mapping[str, object], ...]:
+    fields: Final = message.provider_specific_fields
+    items: Final = (fields.tool_search_items if fields else None) or ()
+    return tuple(item for item in items if item.get("type") in ("tool_search_call", "tool_search_output"))
+
+
+def _checksum(message: _ChatMessage, entries: Sequence[_MessageEntry | _IndexedEntry]) -> str:
+    fields: Final = message.provider_specific_fields
+    bound: Final = {
+        "content": message.content or "",
+        "tool_calls": [[call.id, call.function.name, call.function.arguments] for call in message.tool_calls or ()],
+        "reasoning": [
+            [item.id, item.encrypted_content, [part.text for part in item.summary or ()]]
+            for item in message.reasoning_items or ()
+        ],
+        "tool_search_items": list((fields.tool_search_items if fields else None) or ()),
+        "phase": message.phase or (fields.phase if fields else None),
+        "items": [entry.model_dump(mode="json", exclude_none=True) for entry in entries],
+    }
+    encoded: Final = json.dumps(bound, sort_keys=True, separators=(",", ":"), ensure_ascii=False)
+    return hashlib.sha256(encoded.encode("utf-8", "surrogatepass")).hexdigest()
+
+
+def _kind(item: _OutputItem) -> str:
+    match item:
+        case _OutputToolCall():
+            return "tool_call"
+        case _OutputToolSearch():
+            return "tool_search"
+        case _OutputMessage() | _OutputReasoning():
+            return item.type
+
+
+def _entry(items: Sequence[_OutputItem], position: int) -> _MessageEntry | _IndexedEntry:
+    item: Final = items[position]
+    if isinstance(item, _OutputMessage):
+        return _MessageEntry(type="message", parts=tuple(len(part.text) for part in item.content), phase=item.phase)
+    ordinal: Final = sum(1 for earlier in items[:position] if _kind(earlier) == _kind(item))
+    return _IndexedEntry(type=item.type, index=ordinal)
+
+
+def _texts(items: Sequence[_OutputItem]) -> Iterator[str]:
+    for item in items:
+        if isinstance(item, _OutputMessage):
+            yield from (part.text for part in item.content)
+
+
+def _matches(items: Sequence[_OutputItem], message: _ChatMessage) -> bool:
+    reasoning_ids: Final = tuple(item.id for item in items if isinstance(item, _OutputReasoning))
+    calls: Final = tuple(item for item in items if isinstance(item, _OutputToolCall))
+    tool_calls: Final = message.tool_calls or ()
+    searches: Final = sum(1 for item in items if isinstance(item, _OutputToolSearch))
+    return (
+        "".join(_texts(items)) == (message.content or "")
+        and reasoning_ids == tuple(item.id for item in message.reasoning_items or ())
+        and len(calls) == len(tool_calls)
+        and all(chat.id in (call.call_id, call.id) for call, chat in zip(calls, tool_calls))
+        and searches == len(_tool_search_entries(message))
+    )
+
+
+def _content_phase(
+    output_items: Sequence[object], parsed: Sequence[_OutputItem | None], *, first_message_only: bool
+) -> Phase | None:
+    messages: Final = tuple(item for raw, item in zip(output_items, parsed) if _is_message(raw))
+    if not messages:
+        return None
+    content_message: Final = messages[0] if first_message_only else messages[-1]
+    return content_message.phase if isinstance(content_message, _OutputMessage) else None
+
+
+def _with_phase(message: _ChatMessage, phase: Phase | None) -> _ChatMessage:
+    if phase is None:
+        return message
+    fields: Final = (message.provider_specific_fields or _ProviderFields()).model_copy(update={PHASE_FIELD: phase})
+    return message.model_copy(update={"provider_specific_fields": fields})
+
+
+def replay_fields(
+    output_items: Sequence[object], message: object, *, completed: bool, first_message_only: bool
+) -> Mapping[str, object]:
+    parsed: Final = tuple(map(_parsed_output_item, output_items))
+    phase: Final = _content_phase(output_items, parsed, first_message_only=first_message_only)
+    phase_fields: Final[Mapping[str, object]] = MappingProxyType({PHASE_FIELD: phase} if phase else {})
+    items: Final = tuple(item for item in parsed if item is not None)
+    parsed_message: Final = _chat_message(message)
+    chat: Final = _with_phase(parsed_message, phase) if parsed_message is not None else None
+    if not completed or not items or len(items) != len(parsed) or chat is None or not _matches(items, chat):
+        return phase_fields
+    entries: Final = tuple(_entry(items, position) for position in range(len(items)))
+    replay: Final = _Replay(version=1, items=entries, checksum=_checksum(chat, entries))
+    return MappingProxyType({**phase_fields, REPLAY_FIELD: replay.model_dump(mode="json", exclude_none=True)})
+
+
+def _stored_replay(message: _ChatMessage) -> _Replay | None:
+    fields: Final = message.provider_specific_fields
+    if fields is None or fields.openai_responses_replay is None:
+        return None
+    try:
+        return _Replay.model_validate(fields.openai_responses_replay)
+    except ValidationError:
+        return None
+
+
+def _message_texts(entries: Sequence[_MessageEntry], content: str) -> tuple[tuple[str, ...], ...]:
+    lengths: Final = tuple(chain.from_iterable(entry.parts for entry in entries))
+    bounds: Final = tuple(accumulate(lengths, initial=0))
+    parts: Final = tuple(content[start:end] for start, end in pairwise(bounds))
+    groups: Final = tuple(accumulate((len(entry.parts) for entry in entries), initial=0))
+    return tuple(parts[start:end] for start, end in pairwise(groups))
+
+
+def _message_item(texts: Sequence[str], phase: Phase | None) -> Mapping[str, object]:
+    return {
+        "type": "message",
+        "role": "assistant",
+        "content": [{"type": "output_text", "text": text} for text in texts],
+        **({PHASE_FIELD: phase} if phase else {}),
+    }
+
+
+def _function_call_item(call: _ToolCall) -> Mapping[str, object]:
+    namespaced: Final = call.provider_specific_fields or call.function.provider_specific_fields
+    namespace: Final = namespaced.namespace if namespaced else None
+    return {
+        "type": "function_call",
+        "call_id": call.id,
+        "name": call.function.name,
+        "arguments": call.function.arguments,
+        **({"namespace": namespace} if namespace else {}),
+    }
+
+
+def _custom_tool_call_item(call: _ToolCall) -> Mapping[str, object]:
+    return {
+        "type": "custom_tool_call",
+        "call_id": call.id,
+        "name": call.function.name,
+        "input": call.function.arguments,
+    }
+
+
+def _reasoning_item(item: _Reasoning) -> Mapping[str, object]:
+    summary: Final = tuple(part.model_dump() for part in item.summary or ())
+    return reasoning_input_item(item.id, summary, item.encrypted_content)
+
+
+@dataclass(frozen=True, slots=True)
+class _Resolved:
+    message: _ChatMessage
+    texts: Mapping[int, tuple[str, ...]]
+    tool_searches: tuple[Mapping[str, object], ...]
+
+
+def _input_item(resolved: _Resolved, position: int, entry: _MessageEntry | _IndexedEntry) -> Mapping[str, object]:
+    if isinstance(entry, _MessageEntry):
+        return _message_item(resolved.texts[position], entry.phase)
+    tool_calls: Final = resolved.message.tool_calls or ()
+    match entry.type:
+        case "reasoning":
+            return _reasoning_item((resolved.message.reasoning_items or ())[entry.index])
+        case "function_call":
+            return _function_call_item(tool_calls[entry.index])
+        case "custom_tool_call":
+            return _custom_tool_call_item(tool_calls[entry.index])
+        case "tool_search_call" | "tool_search_output":
+            return dict(resolved.tool_searches[entry.index])
+
+
+def _fits(message: _ChatMessage, entries: Sequence[_MessageEntry | _IndexedEntry]) -> bool:
+    tool_calls: Final = len(message.tool_calls or ())
+    tool_searches: Final = len(_tool_search_entries(message))
+    sizes: Final = MappingProxyType(
+        {
+            "reasoning": len(message.reasoning_items or ()),
+            "function_call": tool_calls,
+            "custom_tool_call": tool_calls,
+            "tool_search_call": tool_searches,
+            "tool_search_output": tool_searches,
+        }
+    )
+    parts: Final = chain.from_iterable(entry.parts for entry in entries if isinstance(entry, _MessageEntry))
+    indexed: Final = (entry for entry in entries if isinstance(entry, _IndexedEntry))
+    return sum(parts) == len(message.content or "") and all(entry.index < sizes[entry.type] for entry in indexed)
+
+
+def _resolve(
+    message: _ChatMessage, entries: Sequence[_MessageEntry | _IndexedEntry], replay_tool_search: bool
+) -> ReplayedTurn | None:
+    indexed: Final = tuple(entry for entry in entries if isinstance(entry, _IndexedEntry))
+    if not replay_tool_search and any(entry.type in ("tool_search_call", "tool_search_output") for entry in indexed):
+        return None
+    messages: Final = tuple(entry for entry in entries if isinstance(entry, _MessageEntry))
+    calls: Final = tuple(entry for entry in indexed if entry.type in ("function_call", "custom_tool_call"))
+    texts: Final = _message_texts(messages, message.content or "")
+    positions: Final = tuple(position for position, entry in enumerate(entries) if isinstance(entry, _MessageEntry))
+    resolved: Final = _Resolved(
+        message=message,
+        texts=MappingProxyType(dict(zip(positions, texts, strict=True))),
+        tool_searches=_tool_search_entries(message),
+    )
+    tool_calls: Final = message.tool_calls or ()
+    return ReplayedTurn(
+        items=tuple(_input_item(resolved, position, entry) for position, entry in enumerate(entries)),
+        custom_call_ids=frozenset(tool_calls[entry.index].id for entry in calls if entry.type == "custom_tool_call"),
+    )
+
+
+def replay_items(message: object, *, replay_tool_search: bool) -> ReplayedTurn | None:
+    chat: Final = _chat_message(message)
+    stored: Final = _stored_replay(chat) if chat is not None and chat.role == "assistant" else None
+    if chat is None or stored is None or stored.checksum != _checksum(chat, stored.items):
+        return None
+    if not _fits(chat, stored.items):
+        return None
+    return _resolve(chat, stored.items, replay_tool_search)

litellm/completion_extras/litellm_responses_transformation/transformation.py

@@ -24,9 +24,19 @@ from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError
 import litellm
 from litellm import ModelResponse
 from litellm._logging import verbose_logger
+from litellm.completion_extras.litellm_responses_transformation.history import (
+    PHASE_FIELD,
+    UnknownPhase,
+    output_message_text,
+    reasoning_input_item,
+    replay_fields,
+    replay_items,
+    requested_phase,
+)
 from litellm.integrations.anthropic_cache_control_hook import supports_openai_prompt_cache_breakpoint
 from litellm.litellm_core_utils.hidden_params import get_hidden_params, get_or_create_hidden_params
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    prompt_cache_breakpoint_for_wire,
     responses_reasoning_items_from_thinking_blocks,
     with_prompt_cache_breakpoint,
 )
@@ -52,7 +62,6 @@ from litellm.types.llms.openai import (
     ChatCompletionToolCallChunk,
     ChatCompletionToolCallFunctionChunk,
     ChatCompletionToolParamFunctionChunk,
-    PromptCacheBreakpoint,
     Reasoning,
     ResponsesAPIOptionalRequestParams,
     ResponsesAPIResponse,
@@ -490,7 +499,7 @@ def _fold_choices_into_one(
     choices: Sequence["Choices"],
     tool_calls: Sequence[Mapping[str, object]],
     trailing_reasoning_content: str | None,
-    trailing_reasoning_item: _BuiltReasoningItem | None,
+    trailing_reasoning_items: Sequence[_BuiltReasoningItem],
     tool_search_items: Sequence[Mapping[str, object]],
     structured_output: bool = False,
 ) -> "Choices":
@@ -509,7 +518,7 @@ def _fold_choices_into_one(
     reasoning_contents: Final = (*(_message_reasoning_content(m) for m in messages), trailing_reasoning_content)
     reasoning_items: Final = (
         *chain.from_iterable(_message_reasoning_items(m) for m in messages),
-        *(() if trailing_reasoning_item is None else (trailing_reasoning_item,)),
+        *trailing_reasoning_items,
     )
     return Choices(
         message=Message(
@@ -526,6 +535,61 @@ def _fold_choices_into_one(
     )
 
 
+def _with_replay_fields(
+    choice: "Choices", output_items: Sequence[object], *, completed: bool, structured_output: bool
+) -> "Choices":
+    fields: Final = {
+        **(choice.message.provider_specific_fields or {}),
+        **replay_fields(output_items, choice.message, completed=completed, first_message_only=structured_output),
+    }
+    message: Final = choice.message.model_copy(update={"provider_specific_fields": fields or None})
+    return choice.model_copy(update={"message": message})
+
+
+_STREAMED_OUTPUT_ITEMS: Final[TypeAdapter[tuple[Mapping[str, object], ...]]] = TypeAdapter(
+    tuple[Mapping[str, object], ...]
+)
+
+
+def completed_stream_history(
+    output_payload: object,
+    tool_search_fields: Mapping[str, object] | None,
+    *,
+    completed: bool,
+    structured_output: bool,
+) -> dict[str, object] | None:  # mutable-ok: Delta.provider_specific_fields is typed as a dict
+    items: Final = _STREAMED_OUTPUT_ITEMS.validate_python(output_payload)
+    calls: Final = tuple(item for item in items if item.get("type") in ("function_call", "custom_tool_call"))
+    streamed_message: Final = {
+        "role": "assistant",
+        "content": output_message_text(items, first_message_only=structured_output),
+        "tool_calls": [tool_call_dict_from_output_item(call, index) for index, call in enumerate(calls)],
+        "reasoning_items": list(_reasoning_items_from_output_items(items)),
+        "provider_specific_fields": tool_search_fields,
+    }
+    fields: Final = {
+        **(tool_search_fields or {}),
+        **replay_fields(items, streamed_message, completed=completed, first_message_only=structured_output),
+    }
+    return fields or None
+
+
+def _assistant_phase_field(msg: object, replay_openai_history: bool, drop_params: bool) -> Mapping[str, object]:
+    match requested_phase(msg) if replay_openai_history else None:
+        case None:
+            return {}
+        case UnknownPhase(value=value) if drop_params:
+            verbose_logger.debug("Chat provider: dropping assistant phase=%r under drop_params", value)
+            return {}
+        case UnknownPhase(value=value):
+            raise litellm.UnsupportedParamsError(
+                message=f"phase must be 'commentary' or 'final_answer', got {value!r}. "
+                "To drop it, set `litellm.drop_params=True` or pass `drop_params=True`."
+            )
+        case phase:
+            return {PHASE_FIELD: phase}
+
+
 def _responses_event_type(chunk: Mapping[str, object]) -> object:
     event_type: Final = chunk.get("type")
     return event_type.value if isinstance(event_type, ResponsesAPIStreamEvents) else event_type
@@ -567,19 +631,6 @@ def _map_incomplete_reason_to_finish_reason(incomplete_reason: str | None) -> Li
     return "length"
 
 
-_PROMPT_CACHE_BREAKPOINT: Final = TypeAdapter(PromptCacheBreakpoint)
-
-
-def _prompt_cache_breakpoint_for_wire(marker: object, drop_params: bool) -> object:
-    if marker is None or not drop_params:
-        return marker
-    try:
-        return _PROMPT_CACHE_BREAKPOINT.validate_python(marker)
-    except ValidationError:
-        verbose_logger.debug("Chat provider: dropping malformed prompt_cache_breakpoint %r under drop_params", marker)
-        return None
-
-
 def _input_file_from_file_value(file_value: object) -> dict[str, object]:
     if not isinstance(file_value, dict):
         return {"type": "input_file"}
@@ -667,15 +718,7 @@ def _reasoning_item_to_response_input(
     An item without an id is sent without one: the Responses API accepts that and
     verifies the encrypted content on its own, while it rejects any id it did not mint.
     """
-    item_id: Final = r_item.get("id")
-    encrypted_content: Final = r_item.get("encrypted_content")
-    return {
-        "type": "reasoning",
-        **({"id": item_id} if item_id else {}),
-        # summary is always required by the Responses API, even when empty
-        "summary": r_item.get("summary") or [],
-        **({"encrypted_content": encrypted_content} if encrypted_content else {}),
-    }
+    return dict(reasoning_input_item(r_item.get("id"), r_item.get("summary"), r_item.get("encrypted_content")))
 
 
 def _requested_defer_loading(tool: Mapping[str, object], function_tool: Mapping[str, object]) -> object:
@@ -804,12 +847,14 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         keep_prompt_cache_breakpoints: bool = False,
         replay_tool_search: bool = False,
         include_namespace: bool = False,
+        replay_openai_history: bool = False,
     ) -> tuple[list[object], str | None]:
         converted_input_items, instructions = self._convert_chat_completion_messages_to_responses_input(
             messages,
             drop_params=drop_params,
             replay_tool_search=replay_tool_search,
             include_namespace=include_namespace,
+            replay_openai_history=replay_openai_history,
         )
         return (
             converted_input_items
@@ -825,10 +870,18 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         drop_params: bool = False,
         replay_tool_search: bool = False,
         include_namespace: bool = False,
+        replay_openai_history: bool = False,
     ) -> tuple[list[object], str | None]:
         input_items: Final[list[object]] = []
         instructions: str | None = None
-        custom_tool_call_ids: Final = frozenset(
+        replayed_turns: Final = tuple(
+            replay_items(msg, replay_tool_search=replay_tool_search) if replay_openai_history else None
+            for msg in messages
+        )
+        replayed_custom_call_ids: Final = frozenset(
+            chain.from_iterable(turn.custom_call_ids for turn in replayed_turns if turn is not None)
+        )
+        custom_tool_call_ids: Final = replayed_custom_call_ids | frozenset(
             tool_call["id"]
             for msg in messages
             if msg.get("role") == "assistant" and isinstance(msg.get("tool_calls"), list)
@@ -849,7 +902,9 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
             tool_calls = msg.get("tool_calls")
             tool_call_id = msg.get("tool_call_id")
 
-            if role == "system":
+            if (replayed_turn := replayed_turns[index]) is not None:
+                input_items.extend(replayed_turn.items)
+            elif role == "system":
                 # Extract system message as instructions
                 if isinstance(content, str) and index < leading_system_count:
                     if instructions:
@@ -915,6 +970,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                             "content": self._convert_content_to_responses_format(
                                 content, "assistant", drop_params=drop_params
                             ),
+                            **_assistant_phase_field(msg, replay_openai_history, drop_params),
                         }
                     )
                 for tool_call in tool_calls:
@@ -955,6 +1011,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                         "content": self._convert_content_to_responses_format(
                             content, cast(str, role), drop_params=drop_params
                         ),
+                        **_assistant_phase_field(msg, replay_openai_history and role == "assistant", drop_params),
                     }
                 )
             elif role == "assistant":
@@ -1081,6 +1138,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
             keep_prompt_cache_breakpoints=supports_prompt_cache_breakpoint,
             replay_tool_search=_requests_tool_search(cast(object, optional_params)),  # cast-ok: upcast
             include_namespace=True,
+            replay_openai_history=provider == "openai",
         )
         # OpenAI's Responses API rejects an empty input. For a system-only
         # request, carry the system message as a system-role input item instead
@@ -1165,6 +1223,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         output_items: Sequence[object],
         handle_raw_dict_callback: Callable[..., tuple["Choices | None", int]] | None = None,
         structured_output: bool = False,
+        replay_openai_history: bool = False,
+        completed: bool = False,
     ) -> list["Choices"]:
         """
         Convert Responses API output items to chat completion choices.
@@ -1174,6 +1234,8 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
             handle_raw_dict_callback: Optional callback for handling raw dict items
             structured_output: The chat request carried a json response_format, so only the
                 first message item becomes content
+            replay_openai_history: Record the output's item sequence for the next turn
+            completed: The response finished, so its item sequence is whole
 
         Returns:
             List of Choices objects
@@ -1196,7 +1258,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         choices: Final[list[Choices]] = []
         index = 0
         reasoning_content: str | None = None
-        pending_reasoning_item: _BuiltReasoningItem | None = None
+        pending_reasoning_items: tuple[_BuiltReasoningItem, ...] = ()
 
         # Collect all tool calls to put them in a single choice
         # (Chat Completions API expects all tool calls in one message)
@@ -1205,12 +1267,19 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
 
         for item in output_items:
             if isinstance(item, ResponseReasoningItem):
-                pending_reasoning_item = _build_reasoning_item(
-                    item_id=item.id,
-                    encrypted_content=getattr(item, "encrypted_content", None),
-                    summary_raw=item.summary,
+                pending_reasoning_items = (
+                    *(pending_reasoning_items if replay_openai_history else ()),
+                    _build_reasoning_item(
+                        item_id=item.id,
+                        encrypted_content=getattr(item, "encrypted_content", None),
+                        summary_raw=item.summary,
+                    ),
+                )
+                reasoning_content = " ".join(
+                    s["text"]
+                    for s in chain.from_iterable(built["summary"] for built in pending_reasoning_items)
+                    if s.get("text")
                 )
-                reasoning_content = " ".join(s["text"] for s in pending_reasoning_item["summary"] if s.get("text"))
 
             elif isinstance(item, ResponseOutputMessage):
                 for content in item.content:
@@ -1227,7 +1296,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                         annotations=annotations,
                         reasoning_items=cast(
                             list[ChatCompletionReasoningItem] | None,
-                            ([pending_reasoning_item] if pending_reasoning_item is not None else None),
+                            (list(pending_reasoning_items) or None),
                         ),
                     )
 
@@ -1240,7 +1309,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                     )
 
                     reasoning_content = None  # flush
-                    pending_reasoning_item = None  # flush
+                    pending_reasoning_items = ()  # flush
                     index += 1
 
             elif isinstance(item, ResponseFunctionToolCall):
@@ -1300,16 +1369,17 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                     output_items, "stop", tool_search_items=tool_search_items
                 )
             ]
-        return [
-            _fold_choices_into_one(
-                choices=choices,
-                tool_calls=accumulated_tool_calls,
-                trailing_reasoning_content=reasoning_content,
-                trailing_reasoning_item=pending_reasoning_item,
-                tool_search_items=tool_search_items,
-                structured_output=structured_output,
-            )
-        ]
+        folded: Final = _fold_choices_into_one(
+            choices=choices,
+            tool_calls=accumulated_tool_calls,
+            trailing_reasoning_content=reasoning_content,
+            trailing_reasoning_items=pending_reasoning_items,
+            tool_search_items=tool_search_items,
+            structured_output=structured_output,
+        )
+        if not replay_openai_history:
+            return [folded]
+        return [_with_replay_fields(folded, output_items, completed=completed, structured_output=structured_output)]
 
     @staticmethod
     def _build_empty_choice(
@@ -1443,6 +1513,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
             structured_output=is_structured_output_request(
                 cast("Mapping[str, object]", optional_params)  # cast-ok: optional_params is an untyped dict
             ),
+            replay_openai_history=_served_provider(
+                cast("Mapping[str, object]", litellm_params)  # cast-ok: litellm_params is an untyped dict
+            )
+            == "openai",
+            completed=raw_response.status == "completed",
         )
 
         response_is_incomplete: Final = raw_response.status == "incomplete" or (
@@ -1506,9 +1581,14 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
         sync_stream: bool,
         json_mode: bool | None = False,
         structured_output: bool = False,
+        replays_openai_history: bool = False,
     ) -> BaseModelResponseIterator:
         return OpenAiResponsesToChatCompletionStreamIterator(
-            streaming_response, sync_stream, json_mode, structured_output=structured_output
+            streaming_response,
+            sync_stream,
+            json_mode,
+            structured_output=structured_output,
+            replays_openai_history=replays_openai_history,
         )
 
     def _convert_content_str_to_input_text(self, content: str, role: str) -> dict[str, object]:
@@ -1586,7 +1666,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                     if original_type == "text":
                         converted = with_prompt_cache_breakpoint(
                             self._convert_content_str_to_input_text(item.get("text", ""), role),
-                            _prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
+                            prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
                         )
                         result.append(converted)
                         verbose_logger.debug("Chat provider:   text -> %s", converted)
@@ -1599,14 +1679,14 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                                     cast(ChatCompletionImageObject, item), role
                                 ),
                             ),
-                            _prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
+                            prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
                         )
                         result.append(converted)
                         verbose_logger.debug("Chat provider:   image_url -> %s", converted)
                     elif original_type == "input_audio":
                         converted = with_prompt_cache_breakpoint(
                             {"type": "input_audio", "input_audio": item.get("input_audio")},
-                            _prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
+                            prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
                         )
                         result.append(converted)
                         verbose_logger.debug("Chat provider:   input_audio -> %s", converted)
@@ -1622,7 +1702,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                                 _input_file_from_file_value(
                                     cast("ChatCompletionFileObject", item).get("file"),  # cast-ok: type tag checked
                                 ),
-                                _prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
+                                prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
                             )
                             result.append(converted)
                             verbose_logger.debug("Chat provider:   file -> %s", converted)
@@ -1646,7 +1726,7 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge):
                             # Default to input_text for unknown types
                             converted = with_prompt_cache_breakpoint(
                                 self._convert_content_str_to_input_text(str(item.get("text", item)), role),
-                                _prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
+                                prompt_cache_breakpoint_for_wire(item.get("prompt_cache_breakpoint"), drop_params),
                             )
                             result.append(converted)
                             verbose_logger.debug("Chat provider:   unknown(%s) -> %s", original_type, converted)
@@ -1890,8 +1970,10 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
         sync_stream: bool,
         json_mode: bool | None = False,
         structured_output: bool = False,
+        replays_openai_history: bool = False,
     ):
         super().__init__(streaming_response, sync_stream, json_mode)
+        self._replays_openai_history: Final = replays_openai_history
         self._chat_completion_id: str | None = None
         self._served_service_tier: str | None = None
         self._tool_call_index_map: dict[int, int] = {}  # mutable-ok: per-stream accumulator state
@@ -1937,6 +2019,8 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
     def translate_responses_chunk_to_openai_stream(
         parsed_chunk: dict | BaseModel,
         tool_call_index_map: dict[int, int] | None = None,  # mutable-ok: per-stream state, remapped in place
+        replays_openai_history: bool = False,
+        structured_output: bool = False,
     ) -> "ModelResponseStream":
         """
         Translate a Responses API streaming chunk to OpenAI chat completion streaming format.
@@ -1944,6 +2028,8 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
         Args:
             parsed_chunk: Dict containing the Responses API event chunk
             tool_call_index_map: Per-stream output_index -> sequential tool_call index map
+            replays_openai_history: Record the completed output's item sequence for the next turn
+            structured_output: Only the first message item became content
 
         Returns:
             ModelResponseStream: OpenAI-formatted streaming chunk
@@ -2154,6 +2240,10 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
             terminal_reasoning_items_typed: Final = _as_chat_reasoning_items(
                 _reasoning_items_from_output_items(output_items)
             )
+            output_payload: Final = cast(object, output_items)  # cast-ok: untyped
+            tool_search_fields: Final = _tool_search_provider_fields(
+                _tool_search_items_from_output_items(output_payload)
+            )
 
             usage = None
             if response_data.get("usage"):
@@ -2167,10 +2257,17 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
                     StreamingChoices(
                         index=0,
                         delta=Delta(
-                            content="",
+                            content=None if replays_openai_history else "",
                             reasoning_items=terminal_reasoning_items_typed,
-                            provider_specific_fields=_tool_search_provider_fields(
-                                _tool_search_items_from_output_items(cast(object, output_items))  # cast-ok: untyped
+                            provider_specific_fields=(
+                                completed_stream_history(
+                                    output_payload,
+                                    tool_search_fields,
+                                    completed=event_type == "response.completed",
+                                    structured_output=structured_output,
+                                )
+                                if replays_openai_history
+                                else tool_search_fields
                             ),
                         ),
                         finish_reason=finish_reason,
@@ -2216,7 +2313,10 @@ class OpenAiResponsesToChatCompletionStreamIterator(BaseModelResponseIterator):
             self._empty_content_chunk()
             if self._is_text_delta_after_structured_answer(chunk)
             else OpenAiResponsesToChatCompletionStreamIterator.translate_responses_chunk_to_openai_stream(
-                chunk, tool_call_index_map=self._tool_call_index_map
+                chunk,
+                tool_call_index_map=self._tool_call_index_map,
+                replays_openai_history=self._replays_openai_history,
+                structured_output=self._structured_output,
             )
         )
         if self._structured_output and _is_message_item_done(chunk):

litellm/constants.py

@@ -158,7 +158,7 @@ DEFAULT_REPLICATE_POLLING_DELAY_SECONDS: Final = get_env_int("DEFAULT_REPLICATE_
 DEFAULT_IMAGE_TOKEN_COUNT: Final = get_env_int("DEFAULT_IMAGE_TOKEN_COUNT", 250)
 HF_CONFIG_FETCH_TIMEOUT_SECONDS: Final = 10.0
 HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS: Final = get_env_int_in_range(
-    "HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS", 300, 1, 86400
+    "HUGGINGFACE_TOKENIZER_LOAD_RETRY_SECONDS", 60, 1, 86400
 )
 
 # Maximum wall-clock seconds a streaming response is allowed to run.
@@ -628,6 +628,7 @@ STS_CREDENTIAL_EXPIRY_SAFETY_MARGIN_SECONDS: Final = 60
 BEDROCK_MIN_THINKING_BUDGET_TOKENS: Final = get_env_int("BEDROCK_MIN_THINKING_BUDGET_TOKENS", 1024)
 # Anthropic's Messages API rejects thinking.budget_tokens < 1024.
 ANTHROPIC_MIN_THINKING_BUDGET_TOKENS: Final = 1024
+ANTHROPIC_BILLING_METADATA_PREFIX: Final = "x-anthropic-billing-header:"
 REPLICATE_POLLING_DELAY_SECONDS: Final = get_env_float("REPLICATE_POLLING_DELAY_SECONDS", 0.5)
 DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS: Final = get_env_int("DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS", 128000)
 DEFAULT_OCI_CHAT_MAX_TOKENS: Final = 4096

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:
@@ -1846,7 +1887,10 @@ def completion_cost(
                         video_resolution=video_resolution,
                     )
                 elif call_type in _SPEECH_CALL_TYPES:
-                    prompt_characters = litellm.utils.count_characters(text=prompt)
+                    prompt_characters = _speech_billed_characters(
+                        completion_response if isinstance(completion_response, HttpxBinaryResponseContent) else None,
+                        prompt,
+                    )
                 elif call_type in _TRANSCRIPTION_CALL_TYPES:
                     # Check _hidden_params first (duration stored there to
                     # avoid polluting the response body), then fall back to
@@ -1984,6 +2028,19 @@ 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"
@@ -2312,6 +2369,11 @@ def response_cost_calculator(
         raise e
 
 
+def _speech_billed_characters(speech_response: HttpxBinaryResponseContent | None, prompt: str) -> int:
+    reported: Final = speech_response.hidden_params.get("billed_characters") if speech_response is not None else None
+    return reported if isinstance(reported, int) else litellm.utils.count_characters(text=prompt)
+
+
 def _video_input_image_cost(
     model: str,
     custom_llm_provider: str | None,

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

@@ -53,7 +53,7 @@ from litellm.main import (
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.images.main import ImageEditOptionalRequestParams
 from litellm.types.llms.openai import ImageGenerationRequestQuality
-from litellm.types.router import GenericLiteLLMParams
+from litellm.types.router import CustomPricingLiteLLMParams, GenericLiteLLMParams
 from litellm.types.utils import (
     LITELLM_IMAGE_VARIATION_PROVIDERS,
     LlmProviders,
@@ -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:
@@ -998,6 +1010,7 @@ def image_edit(
             optional_params=dict(image_edit_request_params),
             litellm_params={
                 **image_edit_request_params,
+                **litellm_params.model_dump(include=set(CustomPricingLiteLLMParams.model_fields), exclude_none=True),
                 "litellm_call_id": litellm_call_id,
                 "model_info": model_info,
                 "vertex_location": litellm_params.vertex_location,

litellm/integrations/SlackAlerting/slack_alerting.py

@@ -349,7 +349,7 @@ class SlackAlerting(CustomBatchLogger):
         except Exception:
             return 0
 
-    async def send_daily_reports(self, router) -> bool:
+    async def send_daily_reports(self, router: Router) -> bool:
         """
         Send a daily report on:
         - Top 5 deployments with most failed requests
@@ -365,7 +365,7 @@ class SlackAlerting(CustomBatchLogger):
             False -> if not sent
         """
 
-        ids: Final = router.get_model_ids()
+        ids: Final = router.registry().names(kind="ids")
 
         # get keys
         failed_request_keys: Final = [f"{id}:{SlackAlertingCacheKeys.failed_requests_key.value}" for id in ids]
@@ -430,7 +430,7 @@ class SlackAlerting(CustomBatchLogger):
             message += "\tNone\n"
         for i in range(len(top_5_failed)):
             key = failed_request_keys[top_5_failed[i]].split(":")[0]
-            _deployment = router.get_model_info(key)
+            _deployment = router.registry().deployment_dict(key)
             if isinstance(_deployment, dict):
                 deployment_name = _deployment["litellm_params"].get("model", "")
             else:
@@ -452,7 +452,7 @@ class SlackAlerting(CustomBatchLogger):
             message += "\tNone\n"
         for i in range(len(top_5_slowest)):
             key = latency_keys[top_5_slowest[i]].split(":")[0]
-            _deployment = router.get_model_info(key)
+            _deployment = router.registry().deployment_dict(key)
             if _deployment is not None:
                 deployment_name = _deployment["litellm_params"].get("model", "")
             else:
@@ -802,7 +802,7 @@ class SlackAlerting(CustomBatchLogger):
         if self.llm_router is None:
             return
 
-        deployment: Final = self.llm_router.get_deployment(model_id=deployment_id)
+        deployment: Final = self.llm_router.registry().deployment(deployment_id)
 
         if deployment is None:
             return
@@ -953,7 +953,7 @@ class SlackAlerting(CustomBatchLogger):
                 return
 
             ### EXTRACT MODEL DETAILS ###
-            deployment: Final = self.llm_router.get_deployment(model_id=deployment_id)
+            deployment: Final = self.llm_router.registry().deployment(deployment_id)
             if deployment is None:
                 return
 

litellm/integrations/agentops/agentops.py

@@ -102,18 +102,14 @@ class AgentOps(OpenTelemetry):
             "Connection": "keep-alive",
         }
 
-        client: Final = get_httpx_client()
-        try:
-            response: Final = client.post(
-                url=auth_endpoint,
-                headers=headers,
-                json={"api_key": api_key},
-                timeout=10,
-            )
+        response: Final = get_httpx_client().post(
+            url=auth_endpoint,
+            headers=headers,
+            json={"api_key": api_key},
+            timeout=10,
+        )
 
-            if response.status_code != 200:
-                raise Exception(f"Failed to fetch auth token: {response.text}")
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch auth token: {response.text}")
 
-            return _DECODED_JSON.validate_python(response.json())
-        finally:
-            client.close()
+        return _DECODED_JSON.validate_python(response.json())

litellm/integrations/anthropic_cache_control_hook.py

@@ -72,6 +72,8 @@ MAX_CACHE_CONTROL_BLOCKS: Final = 4
 CACHE_BREAKPOINT_KEYS: Final = ("cache_control", "prompt_cache_breakpoint")
 OPENAI_PROMPT_CACHE_BREAKPOINT_MIN_GPT_VERSION: Final = (5, 6)
 _GPT_VERSION_PATTERN: Final = re.compile(r"^gpt-(\d+)(?:\.(\d+))?")
+_FINE_TUNED_BASE_PATTERN: Final = re.compile(r"^ft:([^:]+)")
+_O_SERIES_PATTERN: Final = re.compile(r"^o\d+(?:-|$)")
 OPENAI_PROMPT_CACHE_BREAKPOINT_BLOCK_TYPES: Final = frozenset(
     {"text", "image", "image_url", "file", "input_audio", "input_text", "input_image", "input_file"}
 )
@@ -125,6 +127,10 @@ def _served_provider_for_model(model: str) -> str | None:
 
 
 def supports_openai_prompt_cache_breakpoint(model: str, custom_llm_provider: str | None = None) -> bool:
+    return openai_prompt_cache_breakpoint_support(model, custom_llm_provider) is True
+
+
+def openai_prompt_cache_breakpoint_support(model: str, custom_llm_provider: str | None = None) -> bool | None:
     hosted_flag: Final = (
         None
         if custom_llm_provider is None
@@ -135,9 +141,19 @@ def supports_openai_prompt_cache_breakpoint(model: str, custom_llm_provider: str
     model_map_flag: Final = _model_map_prompt_cache_breakpoint_flag(model)
     if model_map_flag is not None:
         return model_map_flag
-    version_match: Final = _GPT_VERSION_PATTERN.match(model.rsplit("/", 1)[-1].lower())
-    if version_match is None:
+    fine_tuned_base: Final = _FINE_TUNED_BASE_PATTERN.match(model.rsplit("/", 1)[-1])
+    if fine_tuned_base is not None:
+        return False if _named_model_breakpoint_support(fine_tuned_base.group(1)) is False else None
+    return _named_model_breakpoint_support(model)
+
+
+def _named_model_breakpoint_support(model: str) -> bool | None:
+    bare_model: Final = model.rsplit("/", 1)[-1].lower()
+    if _O_SERIES_PATTERN.match(bare_model) is not None:
         return False
+    version_match: Final = _GPT_VERSION_PATTERN.match(bare_model)
+    if version_match is None:
+        return None
     version: Final = (int(version_match.group(1)), int(version_match.group(2) or 0))
     return version >= OPENAI_PROMPT_CACHE_BREAKPOINT_MIN_GPT_VERSION
 

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/langfuse/langfuse_otel.py

@@ -35,6 +35,8 @@ from litellm.types.utils import StandardCallbackDynamicParams
 if TYPE_CHECKING:
     from opentelemetry.trace import Span as _Span
 
+    from litellm.proxy._types import UserAPIKeyAuth
+
     Span = _Span
 else:
     Span = Any
@@ -126,6 +128,26 @@ def _starts_its_own_trace(span: object, kwargs: Mapping[str, object]) -> bool:
     )
 
 
+def _a_destination_records_the_call(kwargs: Mapping[str, object]) -> bool:
+    """Whether a key's or team's ``langfuse_otel`` destination gets this call from the OTel v2 fan-out, which records
+    every call but one whose logging payload shows it never reached a provider."""
+    from litellm.integrations.otel.model.metadata import LLMCallEvent
+
+    if not _a_destination_carries_the_trace():
+        return False
+    call: Final = LLMCallEvent.from_dict(kwargs)
+    return call.payload is None or call.reached_a_provider
+
+
+def _a_destination_carries_the_trace() -> bool:
+    """Whether this request resolved a key's or team's ``langfuse_otel`` destination, whose fan-out sends it the whole
+    trace."""
+    from litellm.integrations.otel.model.config import ExporterOwner
+    from litellm.integrations.otel.plumbing.context import destination_backends
+
+    return ExporterOwner.LANGFUSE_OTEL in destination_backends()
+
+
 class LangfuseOtelLogger(OpenTelemetry):
     def __init__(self, config=None, *args, **kwargs):
         # Prevent LangfuseOtelLogger from modifying global environment variables by constructing config manually
@@ -541,6 +563,27 @@ class LangfuseOtelLogger(OpenTelemetry):
         """
         return None
 
+    def _emit_once(
+        self,
+        kwargs: dict[str, object],  # mutable-ok: the v1 dedupe marker is written into the request's kwargs
+        *scope: object,
+    ) -> bool:
+        """Whether to emit this span: once per request, and never for a call a Langfuse destination records."""
+        return not _a_destination_records_the_call(kwargs) and super()._emit_once(kwargs, *scope)  # pyright: ignore[reportUnknownMemberType]  # the v1 hook takes a bare dict
+
+    async def async_post_call_failure_hook(
+        self,
+        request_data: dict[str, object],  # mutable-ok: the v1 hook contract
+        original_exception: Exception,
+        user_api_key_dict: "UserAPIKeyAuth",
+        traceback_str: str | None = None,
+    ) -> None:
+        """Record the proxy failure of a request no Langfuse destination carries."""
+        if not _a_destination_carries_the_trace():
+            await super().async_post_call_failure_hook(  # pyright: ignore[reportUnknownMemberType]  # the v1 hook takes a bare dict
+                request_data, original_exception, user_api_key_dict, traceback_str
+            )
+
     async def async_service_success_hook(self, *args, **kwargs):
         """
         Langfuse should not receive service success logs.

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/otel/emitter.py

@@ -13,9 +13,9 @@ from opentelemetry.trace.status import Status, StatusCode
 
 from litellm.integrations.otel.mappers import resolve_mappers
 from litellm.integrations.otel.mappers.base import AttributeMapper, AttrValue, SpanData
-from litellm.integrations.otel.mappers.langfuse import LANGFUSE_REQUEST_METADATA_ATTRIBUTES
+from litellm.integrations.otel.mappers.langfuse import LANGFUSE_REQUEST_METADATA_ATTRIBUTES, LangfuseMapper
 from litellm.integrations.otel.mappers.openinference import fit_indexed_messages
-from litellm.integrations.otel.model.config import OpenTelemetryV2Config
+from litellm.integrations.otel.model.config import ExporterOwner, OpenTelemetryV2Config
 from litellm.integrations.otel.model.payloads import (
     GuardrailSpanData,
     LLMCallSpanData,
@@ -34,6 +34,7 @@ from litellm.integrations.otel.model.spans import (
     mcp_tool_call_span_name,
     service_span_name,
 )
+from litellm.integrations.otel.plumbing.context import destination_backends
 from litellm.integrations.otel.plumbing.events import GenAIEventRecorder
 from litellm.integrations.otel.plumbing.providers import to_otel_span_kind
 
@@ -168,6 +169,9 @@ class SpanEmitter:
         self._mappers: list[AttributeMapper] = (
             list(mappers) if mappers is not None else resolve_mappers(config.mapper_names)
         )
+        self._langfuse: Final = (
+            None if any(isinstance(mapper, LangfuseMapper) for mapper in self._mappers) else LangfuseMapper()
+        )
         # Bounded LRU (ordered by insertion / most-recent touch). Storing keys
         # only — the value is unused — so it behaves like a capped set.
         self._emitted: OrderedDict[tuple[str, SpanRole], None] = OrderedDict()
@@ -295,17 +299,27 @@ class SpanEmitter:
             )
             else None
         )
-        mapped: Final = MappingProxyType(
-            {key: value for mapper in self._mappers for key, value in mapper.map(data).items()}
+        configured: Final = {key: value for mapper in self._mappers for key, value in mapper.map(data).items()}
+        for_langfuse: Final = (
+            dict(
+                sorted(
+                    self._langfuse.map(data).items(),
+                    key=lambda item: item[0] not in LANGFUSE_REQUEST_METADATA_ATTRIBUTES,
+                )
+            )
+            if self._langfuse is not None and ExporterOwner.LANGFUSE_OTEL in destination_backends()
+            else _NO_ATTRIBUTES
         )
+        mapped: Final = MappingProxyType({**configured, **for_langfuse})
         stamped_later: Final = error_attributes(error) if error else _NO_ATTRIBUTES
         reserved: Final = len(stamped_later.keys() - mapped.keys())
         overwrites: Final = carried_keys(span) & mapped.keys()
-        # The Langfuse request metadata sheds first, so a span at its attribute limit loses nothing else to it.
+        # The Langfuse request metadata, and the Langfuse vocabulary only a destination asked for, shed first,
+        # so a span at its attribute limit loses nothing else to them.
         fitted: Final = fit_indexed_messages(
             MappingProxyType({key: value for key, value in mapped.items() if key not in overwrites}),
             attribute_budget(span, reserved),
-            LANGFUSE_REQUEST_METADATA_ATTRIBUTES,
+            LANGFUSE_REQUEST_METADATA_ATTRIBUTES.union(for_langfuse),
         )
         for key, value in mapped.items():
             if key in overwrites or key in fitted:

litellm/integrations/otel/logger.py

@@ -198,6 +198,7 @@ class OpenTelemetryV2(CustomLogger):
         tracer_provider: TracerProvider | None = None,
         logger_provider: LoggerProvider | None = None,
         meter_provider: "MeterProvider | None" = None,
+        console_default: bool = False,
         **kwargs: Unpack[_CustomLoggerOptions],
     ) -> None:
         super().__init__(**kwargs)
@@ -206,7 +207,7 @@ class OpenTelemetryV2(CustomLogger):
         self._tracer_provider: TracerProvider = (
             tracer_provider
             if tracer_provider is not None
-            else build_tracer_provider(self.config, tenant_overrides=True)
+            else build_tracer_provider(self.config, tenant_overrides=True, console_default=console_default)
         )
         self.tracer: Tracer = get_tracer(self._tracer_provider, LITELLM_TRACER_NAME)
         self._metrics_recorder = self._init_metrics(meter_provider)
@@ -217,7 +218,9 @@ class OpenTelemetryV2(CustomLogger):
             mappers=resolve_mappers(self.config.mapper_names),
             event_recorder=self._init_events(logger_provider),
         )
-        self._tenant_tracers = TenantTracerCache(self.config, callback_name, LITELLM_TRACER_NAME)
+        self._tenant_tracers = TenantTracerCache(
+            self.config, callback_name, LITELLM_TRACER_NAME, console_default=console_default
+        )
         self._open_llm_calls: OrderedDict[str, _LLMCallSpan] = OrderedDict()
         self._init_otel_logger_on_litellm_proxy()
 
@@ -541,9 +544,7 @@ class OpenTelemetryV2(CustomLogger):
         # gate rejection carries ``is_no_upstream_call`` and gets no span. A cache
         # hit never hands off to a provider, so ``cache_hit`` substitutes for the
         # upstream stamp, since the payload proves the call completed.
-        if carrier is None and (
-            call.is_no_upstream_call or call.payload is None or (not call.upstream_started and not call.cache_hit)
-        ):
+        if carrier is None and not call.reached_a_provider:
             return None
         try:
             return self._finish_carrier(carrier, call, start_time, end_time)
@@ -1091,6 +1092,7 @@ def build_otel_v2_logger(
     logger_provider: LoggerProvider | None = None,
     meter_provider: "MeterProvider | None" = None,
     settings: Mapping[str, object] = MappingProxyType({}),
+    console_default: bool = False,
 ) -> OpenTelemetryV2:
     return _logger_class(config)(
         config=config,
@@ -1098,6 +1100,7 @@ def build_otel_v2_logger(
         tracer_provider=tracer_provider,
         logger_provider=logger_provider,
         meter_provider=meter_provider,
+        console_default=console_default,
         **settings,
     )
 

litellm/integrations/otel/mappers/langfuse.py

@@ -103,6 +103,18 @@ class LangfuseMapper:
         ),
     }
 
+    #: Every attribute this mapper can write on a model call.
+    LLM_CALL_ATTRIBUTES: Final[frozenset[str]] = frozenset(
+        {
+            *_LLM_CALL_ATTRS,
+            *_BLOB_ATTRS,
+            LANGFUSE_TRACE_NAME,
+            LANGFUSE_TRACE_USER_ID,
+            LANGFUSE_TRACE_SESSION_ID,
+            LANGFUSE_TRACE_TAGS,
+        }
+    )
+
     def map(self, data: SpanData) -> AttributeMap:
         match data:
             case LLMCallSpanData():

litellm/integrations/otel/model/config.py

@@ -121,6 +121,17 @@ class ExporterSpec(LiteLLMBaseModel):
     )
 
 
+def is_unconfigured_placeholder(spec: ExporterSpec) -> bool:
+    """Whether ``spec`` is the one ``_normalize`` folds in when nothing was configured.
+
+    No field set is what says the operator asked for nothing: an exporter they did
+    configure survives, even ``OTEL_EXPORTER=console`` whose value matches the default,
+    and so does the gated spec ``credential_gated_exporters`` appends, which would
+    otherwise eat itself when one preset layers onto another.
+    """
+    return not spec.model_fields_set
+
+
 class _EnvWithoutBareExcludedServices(PydanticBaseSettingsSource):
     def __init__(self, settings_cls: type[BaseSettings], env_settings: PydanticBaseSettingsSource) -> None:
         super().__init__(settings_cls)

litellm/integrations/otel/model/metadata.py

@@ -272,6 +272,11 @@ class LLMCallEvent:
     trace: TraceControls
     session_id: str | None
 
+    @property
+    def reached_a_provider(self) -> bool:
+        """Whether the call was handed to a provider or answered from the response cache."""
+        return not self.is_no_upstream_call and self.payload is not None and (self.upstream_started or self.cache_hit)
+
     @classmethod
     def from_dict(cls, kwargs: Mapping[str, object]) -> LLMCallEvent:
         raw_payload: Final = kwargs.get("standard_logging_object")

litellm/integrations/otel/plumbing/providers.py

@@ -47,6 +47,7 @@ from litellm.integrations.otel.mappers.langfuse import (
     LANGFUSE_OBSERVATION_OUTPUT,
     LANGFUSE_REQUEST_METADATA_ATTRIBUTES,
     LANGFUSE_TRACE_NAME,
+    LangfuseMapper,
 )
 from litellm.integrations.otel.model.config import (
     CaptureMessageContent,
@@ -55,6 +56,7 @@ from litellm.integrations.otel.model.config import (
     OpenTelemetryV2Config,
     capture_message_content_from_env,
     captures_span_content,
+    is_unconfigured_placeholder,
 )
 from litellm.integrations.otel.model.semconv import (
     DB,
@@ -490,19 +492,25 @@ def _is_litellm_span(span: ReadableSpan) -> bool:
     return scope is not None and scope.name == LITELLM_TRACER_NAME
 
 
-def without_langfuse_request_metadata(span: ReadableSpan) -> ReadableSpan:
-    """``span`` without the request metadata the Langfuse mapper writes for a Langfuse destination.
+def without_destination_langfuse_attributes(span: ReadableSpan, langfuse_mapped: bool) -> ReadableSpan:
+    """``span`` without the Langfuse attributes only a Langfuse destination asked for.
 
-    Only LiteLLM's own spans lose it: any other span keeps every key its own code set.
+    That is the request metadata the Langfuse mapper writes and, on a model call written by
+    a logger whose own mappers leave the Langfuse vocabulary out (``langfuse_mapped`` off),
+    every Langfuse attribute, since that logger wrote them for a key's or team's Langfuse
+    destination alone. Only LiteLLM's own spans lose them: any other span keeps every key
+    its own code set.
     """
     attributes: Final = span.attributes or _NO_ATTRIBUTES
-    if not (_is_litellm_span(span) and any(key in LANGFUSE_REQUEST_METADATA_ATTRIBUTES for key in attributes)):
+    dropped: Final = (
+        LANGFUSE_REQUEST_METADATA_ATTRIBUTES
+        if langfuse_mapped or not is_llm_call_span(span)
+        else LangfuseMapper.LLM_CALL_ATTRIBUTES
+    )
+    if not (_is_litellm_span(span) and any(key in dropped for key in attributes)):
         return span
     return _CountedSpanView(
-        span,
-        MappingProxyType(
-            {key: value for key, value in attributes.items() if key not in LANGFUSE_REQUEST_METADATA_ATTRIBUTES}
-        ),
+        span, MappingProxyType({key: value for key, value in attributes.items() if key not in dropped})
     )
 
 
@@ -658,6 +666,7 @@ def _for_destination(
     span: ReadableSpan,
     destination: "OtelDestination",
     default_capture: str,
+    langfuse_mapped: bool,
 ) -> ReadableSpan:
     """The view of ``span`` a tenant destination receives.
 
@@ -672,13 +681,16 @@ def _for_destination(
     and the query string of the request URL, which can hold the same key. A
     destination whose effective setting disables span content loses the prompt and
     response content too, and a destination for any backend but Langfuse loses the
-    request metadata the Langfuse mapper writes on LiteLLM's own spans. The span itself
-    stays, so the tenant still gets the whole trace tree.
+    Langfuse attributes only a Langfuse destination asked for (see
+    :func:`without_destination_langfuse_attributes`). The span itself stays, so the
+    tenant still gets the whole trace tree.
     """
     extra: Final = destination.resource_attributes
     defaults: Final = destination.resource_defaults
     source: Final = (
-        span if destination.callback_name == ExporterOwner.LANGFUSE_OTEL else without_langfuse_request_metadata(span)
+        span
+        if destination.callback_name == ExporterOwner.LANGFUSE_OTEL
+        else without_destination_langfuse_attributes(span, langfuse_mapped)
     )
     attributes: Final = source.attributes or _NO_ATTRIBUTES
     database: Final = _is_database_span(attributes)
@@ -728,8 +740,10 @@ class TenantFanOutSpanProcessor(SpanProcessor):
         default_capture_by_backend: Mapping[str, str] = MappingProxyType({}),
         pending_drains: int = _MAX_PENDING_DRAINS,
         drain_pool: _DrainPool | None = None,
+        langfuse_mapped: bool = True,
     ) -> None:
         self._operator_sinks: Final = operator_sinks
+        self._langfuse_mapped: Final = langfuse_mapped
         self._excluded_db_systems: Final = excluded_db_systems
         self._default_capture: Final = default_capture
         self._default_capture_by_backend: Final = default_capture_by_backend
@@ -822,7 +836,8 @@ class TenantFanOutSpanProcessor(SpanProcessor):
         try:
             processor.on_end(
                 _scoped(
-                    _for_destination(span, destination, self._default_capture_for(destination)), destination.span_scope
+                    _for_destination(span, destination, self._default_capture_for(destination), self._langfuse_mapped),
+                    destination.span_scope,
                 )
             )
         except Exception as exc:  # noqa: BLE001  # one destination's failure must not cost the others their span
@@ -1198,17 +1213,19 @@ class _OverriddenBackendFilter(SpanProcessor):
         return self._inner.force_flush(timeout_millis)
 
 
-class _LangfuseRequestMetadataFilter(SpanProcessor):
-    """Strip the Langfuse request metadata from LiteLLM's own spans before ``inner`` exports them."""
+class _DestinationLangfuseAttributeFilter(SpanProcessor):
+    """Strip the Langfuse attributes only a Langfuse destination asked for from LiteLLM's own spans before ``inner``
+    exports them."""
 
-    def __init__(self, inner: SpanProcessor) -> None:
+    def __init__(self, inner: SpanProcessor, langfuse_mapped: bool) -> None:
         self._inner: Final = inner
+        self._langfuse_mapped: Final = langfuse_mapped
 
     def on_start(self, span: SDKSpan, parent_context: Context | None = None) -> None:
         self._inner.on_start(span, parent_context)
 
     def on_end(self, span: ReadableSpan) -> None:
-        self._inner.on_end(without_langfuse_request_metadata(span))
+        self._inner.on_end(without_destination_langfuse_attributes(span, self._langfuse_mapped))
 
     def shutdown(self) -> None:
         self._inner.shutdown()
@@ -1491,13 +1508,18 @@ def _spec_processor(
     spec: ExporterSpec,
     use_simple_processor: bool | None,
     strip_message_content: bool,
-    strip_langfuse_request_metadata: bool,
+    strip_destination_langfuse_attributes: bool,
+    langfuse_mapped: bool,
 ) -> SpanProcessor:
     exporting: Final = _processor_for(
         _exporter_from_spec(spec),
         (spec.use_simple_processor if spec.use_simple_processor is not None else use_simple_processor),
     )
-    scoped: Final = _LangfuseRequestMetadataFilter(exporting) if strip_langfuse_request_metadata else exporting
+    scoped: Final = (
+        _DestinationLangfuseAttributeFilter(exporting, langfuse_mapped)
+        if strip_destination_langfuse_attributes
+        else exporting
+    )
     return _MessageContentFilter(scoped) if strip_message_content else scoped
 
 
@@ -1530,6 +1552,7 @@ def build_tracer_provider(
     use_simple_processor: bool | None = None,
     tenant_overrides: bool = False,
     strip_message_content: bool = False,
+    console_default: bool = False,
 ) -> TracerProvider:
     """Build the shared :class:`TracerProvider`.
 
@@ -1548,11 +1571,16 @@ def build_tracer_provider(
 
     ``config.langfuse_span_scope`` narrows the exporter owned by ``langfuse_otel``
     alone; a collector or any other backend in the same config keeps the full tree.
-    In a config that writes the Langfuse vocabulary, every exporter but ``langfuse_otel``'s
-    goes without the request metadata the Langfuse mapper writes on LiteLLM's own spans.
+    Every exporter but ``langfuse_otel``'s goes without the Langfuse attributes only a
+    Langfuse destination asked for (see :func:`without_destination_langfuse_attributes`),
+    which any logger writes for a request with a Langfuse destination.
     ``strip_message_content`` removes prompt and response content before every
     exporter, for a routed provider whose spans can carry content another
     destination asked for.
+
+    The console spec ``config._normalize`` folds in when nothing was configured
+    prints only under ``console_default``, which the ``otel`` callback passes: a
+    vendor preset or the proxy's background global logger was never asked to print.
     """
     provider: Final = TracerProvider(resource=build_resource(config))
     provider.sampler = _RequestExclusionSampler(provider.sampler)
@@ -1564,19 +1592,20 @@ def build_tracer_provider(
         provider.add_span_processor(_processor_for(exporter, use_simple_processor))
         return provider
 
+    langfuse_mapped: Final = _maps_langfuse(config)
     # ``config._normalize`` guarantees at least one spec (it folds the top-level
     # ``exporter``/``endpoint``/``headers`` fields in when ``exporters`` is empty).
-    writes_langfuse: Final = "langfuse" in config.mapper_names or any(
-        spec.owner is ExporterOwner.LANGFUSE_OTEL for spec in config.exporters
-    )
     for spec in config.exporters:
         if spec.requires_headers and not spec.headers:
             continue
+        if is_unconfigured_placeholder(spec) and not console_default:
+            continue
         processor = _spec_processor(
             spec,
             use_simple_processor,
             strip_message_content,
-            strip_langfuse_request_metadata=writes_langfuse and spec.owner is not ExporterOwner.LANGFUSE_OTEL,
+            strip_destination_langfuse_attributes=spec.owner is not ExporterOwner.LANGFUSE_OTEL,
+            langfuse_mapped=langfuse_mapped,
         )
         owner = spec.owner.value if tenant_overrides and spec.owner is not None else None
         scope = _operator_scope(config, spec)
@@ -1613,8 +1642,9 @@ def attach_tenant_fan_out(
     so exactly one fan-out lands. ``configs`` name the operator's own exporters, one
     config per v2 logger since each keeps its own provider and still writes its
     account, so an additive destination pointing at any of them is delivered once
-    rather than twice. ``excluded_db_systems`` only filters what the fan-out
-    delivers, never the operator's own exporters.
+    rather than twice. The first is the config of the logger that owns ``provider``,
+    whose model calls the fan-out carries. ``excluded_db_systems`` only filters what
+    the fan-out delivers, never the operator's own exporters.
     """
     with _FAN_OUT_ATTACH_LOCK:
         if any(isinstance(processor, TenantFanOutSpanProcessor) for processor in _attached_processors(provider)):
@@ -1625,10 +1655,16 @@ def attach_tenant_fan_out(
                 excluded_db_systems=excluded_db_systems,
                 default_capture=_operator_capture_mode(configs),
                 default_capture_by_backend=MappingProxyType(dict(_owned_capture_modes(configs))),
+                langfuse_mapped=any(_maps_langfuse(config) for config in configs[:1]),
             )
         )
 
 
+def _maps_langfuse(config: OpenTelemetryV2Config) -> bool:
+    """Whether a logger built from ``config`` writes the Langfuse vocabulary on every model call."""
+    return "langfuse" in config.mapper_names
+
+
 def _operator_capture_mode(configs: Sequence[OpenTelemetryV2Config]) -> str:
     """The mode of the operator's generic ``otel`` callback, so ``callback_settings.otel`` counts as the global default"""
     return next(

litellm/integrations/otel/plumbing/routing.py

@@ -163,10 +163,12 @@ class TenantTracerCache:
         config: OpenTelemetryV2Config,
         callback_name: str | None,
         tracer_name: str,
+        console_default: bool = False,
     ) -> None:
         self._config = config
         self._callback_name = callback_name
         self._tracer_name = tracer_name
+        self._console_default = console_default
         # Guards the three mutable structures below: ``pre_call`` can run on
         # thread-pool workers concurrently with the event loop, so cache
         # updates, span counts, and retirement must be atomic.
@@ -284,6 +286,7 @@ class TenantTracerCache:
         built: Final = build_tracer_provider(
             self._routed_config(credential_headers, project_headers, endpoint, service_name),
             strip_message_content=not self._config.capture_span_content,
+            console_default=self._console_default,
         )
         self._providers[cache_key] = built
         return built

litellm/integrations/otel/presets/destinations.py

@@ -111,7 +111,7 @@ _REQUIRED_HEADERS_BY_CALLBACK: Final[Mapping[str, frozenset[str]]] = MappingProx
 _NO_ATTRS: Final[Mapping[str, str]] = MappingProxyType({})
 
 
-def _arize_project() -> Mapping[str, str]:
+def _arize_project(_params: StandardCallbackDynamicParams) -> Mapping[str, str]:
     from litellm.integrations.arize.arize import ArizeLogger
 
     project: Final = ArizeLogger.get_arize_config().project_name
@@ -124,10 +124,27 @@ def _arize_default_project() -> Mapping[str, str]:
     return MappingProxyType({"model_id": ARIZE_DEFAULT_PROJECT})
 
 
-#: Resource attributes the operator chose for a backend, applied to every export the
-#: team receives over the span's own resource: ``ARIZE_PROJECT_NAME`` for Arize.
-_RESOURCE_ATTRIBUTES_BY_CALLBACK: Final[Mapping[str, Callable[[], Mapping[str, str]]]] = MappingProxyType(
-    {"arize": _arize_project}
+def _stands_in_for_the_legacy_langfuse_logger() -> bool:
+    """Whether a Langfuse destination replaces the older Langfuse logger's copy, which a proxy without Langfuse keys
+    of its own sends for a key's or team's ``langfuse_otel``."""
+    return not (os.environ.get("LANGFUSE_PUBLIC_KEY") and os.environ.get("LANGFUSE_SECRET_KEY"))
+
+
+def _langfuse_environment(params: StandardCallbackDynamicParams) -> Mapping[str, str]:
+    """The key's or team's ``langfuse_environment``, else ``LANGFUSE_TRACING_ENVIRONMENT``, where the older Langfuse
+    logger wrote it."""
+    from litellm.types.integrations.langfuse_otel import LangfuseSpanAttributes
+
+    if not _stands_in_for_the_legacy_langfuse_logger():
+        return _NO_ATTRS
+    environment: Final = params.get("langfuse_environment") or os.environ.get("LANGFUSE_TRACING_ENVIRONMENT")
+    return MappingProxyType({LangfuseSpanAttributes.LANGFUSE_ENVIRONMENT.value: environment} if environment else {})
+
+
+#: Resource attributes a backend gets on every export the team receives, over the
+#: span's own resource: ``ARIZE_PROJECT_NAME`` for Arize, the Langfuse environment.
+_RESOURCE_ATTRIBUTES_BY_CALLBACK: Final[Mapping[str, Callable[[StandardCallbackDynamicParams], Mapping[str, str]]]] = (
+    MappingProxyType({"arize": _arize_project, "langfuse_otel": _langfuse_environment})
 )
 
 #: Resource attributes a backend needs on every export to accept it, filled only where
@@ -137,12 +154,14 @@ _RESOURCE_DEFAULTS_BY_CALLBACK: Final[Mapping[str, Callable[[], Mapping[str, str
 )
 
 
-def _resource_attributes(callback_name: str, service_name: str | None) -> Mapping[str, str]:
+def _resource_attributes(
+    callback_name: str, service_name: str | None, params: StandardCallbackDynamicParams
+) -> Mapping[str, str]:
     backend: Final = _RESOURCE_ATTRIBUTES_BY_CALLBACK.get(callback_name)
     return MappingProxyType(
         {
             **({"service.name": service_name} if service_name else {}),
-            **(backend() if backend is not None else {}),
+            **(backend(params) if backend is not None else {}),
         }
     )
 
@@ -214,7 +233,7 @@ def destination_for(
     return OtelDestination(
         endpoint=endpoint,
         headers=MappingProxyType(dict(headers)),
-        resource_attributes=_resource_attributes(callback_name, service_name),
+        resource_attributes=_resource_attributes(callback_name, service_name, params),
         resource_defaults=_resource_defaults(callback_name),
         callback_name=callback_name,
         protocol=protocol,

litellm/integrations/otel/presets/utils.py

@@ -3,7 +3,7 @@
 from collections.abc import Iterable
 from typing import Final
 
-from litellm.integrations.otel.model.config import ExporterOwner, ExporterSpec
+from litellm.integrations.otel.model.config import ExporterOwner, ExporterSpec, is_unconfigured_placeholder
 
 
 def ensure_mappers(mapper_names: Iterable[str], *names: str) -> list[str]:
@@ -35,14 +35,3 @@ def credential_gated_exporters(
         *(spec for spec in exporters if not is_unconfigured_placeholder(spec)),
         ExporterSpec(owner=owner, requires_headers=True),
     )
-
-
-def is_unconfigured_placeholder(spec: "ExporterSpec") -> bool:
-    """Whether ``spec`` is the one ``_normalize`` folds in when nothing was configured.
-
-    No field set is what says the operator asked for nothing: an exporter they did
-    configure survives, even ``OTEL_EXPORTER=console`` whose value matches the default,
-    and so does the gated spec this module appends, which would otherwise eat itself
-    when one preset layers onto another.
-    """
-    return not spec.model_fields_set

litellm/integrations/prometheus.py

@@ -468,15 +468,16 @@ def _bounded_requested_model_label(requested_model: object, router_originated: b
     llm_router: Final = _get_proxy_llm_router()
     if llm_router is None:
         return requested_model if router_originated else UNRECOGNIZED_REQUESTED_MODEL_LABEL
-    if llm_router.is_recognized_model(requested_model):
+    registry: Final = llm_router.registry()
+    if registry.resolve(requested_model, kind="recognized"):
         return requested_model
-    if requested_model in llm_router.team_public_model_names:
+    if requested_model in registry.names(kind="team_public"):
         return requested_model
-    if llm_router.pattern_router.route(requested_model) is not None:
+    if registry.patterns().route(requested_model) is not None:
         return requested_model
     if any(
         team_pattern_router.route(requested_model) is not None
-        for team_pattern_router in llm_router.team_pattern_routers.values()
+        for team_pattern_router in registry.patterns(team=True).values()
     ):
         return requested_model
     return UNRECOGNIZED_REQUESTED_MODEL_LABEL
@@ -1173,8 +1174,8 @@ class PrometheusLogger(CustomLogger):
         # Build label filters from valid configurations
         label_filters: Final = self._build_label_filters(parsed_configs)
 
-        # Pretty print the processed configuration
-        self._pretty_print_prometheus_config(label_filters)
+        if litellm.suppress_startup_output is not True:
+            self._pretty_print_prometheus_config(label_filters)
         return label_filters
 
     def _parse_exclude_config(self) -> tuple[frozenset[str], frozenset[str]]:
@@ -3410,7 +3411,7 @@ class PrometheusLogger(CustomLogger):
         OpenAI / Anthropic / Azure return remaining tokens/requests in response
         headers, but Bedrock and Vertex AI do not. This fallback computes
         ``configured_limit - current_usage`` via
-        ``Router.get_remaining_model_group_usage`` so the same gauges are
+        ``Router.health().usage(model_group, kind="remaining")`` so the same gauges are
         emitted for every provider when tpm/rpm is configured on the
         deployment.
         """
@@ -3437,7 +3438,7 @@ class PrometheusLogger(CustomLogger):
                 return
 
             try:
-                remaining_usage: Final = await llm_router.get_remaining_model_group_usage(model_group)
+                remaining_usage: Final = await llm_router.health().usage(model_group, kind="remaining")
             except Exception as e:
                 verbose_logger.exception(
                     "Prometheus: get_remaining_model_group_usage failed for model_group=%s: %s",
@@ -5148,6 +5149,12 @@ 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/shadow_eval_logger.py

@@ -40,7 +40,6 @@ from litellm.litellm_core_utils.llm_judge import (
 )
 from litellm.litellm_core_utils.redact_messages import should_redact_message_logging
 from litellm.llms.base_llm.base_utils import type_to_response_format_param
-from litellm.router_utils.common_utils import resolve_model_group_alias
 from litellm.types.llms.base import LiteLLMBaseModel
 from litellm.types.management_endpoints.auto_router_endpoints import ShadowEvalDirection
 from litellm.types.utils import SHADOW_EVAL_JUDGE_CALL_ORIGIN, SHADOW_EVAL_ROUTER_CALL_ORIGIN
@@ -790,9 +789,7 @@ class ActiveShadowEvalJob(LiteLLMBaseModel):
 def _canonical_group(router: "Router | None", model_group: str) -> str:
     """A model group in the one spelling both a job's scope and a request's model compare
     under: an alias resolves to its target so the two never fail to match on spelling."""
-    return (
-        resolve_model_group_alias(router.model_group_alias, model_group) if router is not None else None
-    ) or model_group
+    return (router.registry().resolve(model_group, kind="alias") if router is not None else None) or model_group
 
 
 def _scope_admits(router: "Router | None", job: "ActiveShadowEvalJob", model_group: str) -> bool:

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,
@@ -78,6 +79,7 @@ if TYPE_CHECKING:
     from litellm.llms.base_llm.anthropic_messages.transformation import (
         BaseAnthropicMessagesConfig,
     )
+    from litellm.router import Router
     from litellm.types.llms.anthropic_messages.anthropic_response import (
         AnthropicMessagesResponse,
     )
@@ -114,12 +116,24 @@ 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]
 
 
 class _SearchToolConfig(TypedDict, total=False):
-    search_tool_name: str
+    search_tool_name: ReadOnly[str]
     litellm_params: ReadOnly[_SearchToolLitellmParams | None]
 
 
@@ -1155,6 +1169,7 @@ 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 +1196,7 @@ 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 +1211,7 @@ 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 +1271,7 @@ 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",
@@ -1754,10 +1769,13 @@ class WebSearchInterceptionLogger(CustomLogger):
 
         return None
 
-    def _select_search_tool_from_router(self, llm_router: object) -> "_SearchToolConfig | None":
-        if llm_router is None or not hasattr(llm_router, "search_tools"):
+    def _select_search_tool_from_router(self, llm_router: "Router | None") -> "_SearchToolConfig | None":
+        if llm_router is None:
             return None
-        search_tools: Final = tuple(getattr(llm_router, "search_tools", None) or ())
+        settings: Final = llm_router.settings()
+        if not settings.is_set("search_tools"):
+            return None
+        search_tools: Final = tuple(settings.search_tools or ())
         return self._select_search_tool_from_list(search_tools=search_tools, source="router")
 
     def _select_search_tool_from_list(
@@ -1814,6 +1832,7 @@ 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 +1855,8 @@ 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 +1942,7 @@ 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

@@ -18,6 +18,7 @@ from litellm.litellm_core_utils.bug_report import (
 )
 from litellm.litellm_core_utils.core_helpers import as_str_mapping, attribute_or_none
 from litellm.litellm_core_utils.secret_redaction import redact_string as redact_secret_string
+from litellm.llms.bigmodel.common_utils import raise_bigmodel_business_error
 from litellm.types.utils import LlmProviders
 
 from ..exceptions import (
@@ -412,6 +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,
@@ -2762,6 +2782,8 @@ def exception_type(
                     exception_provider=exception_provider,
                     extra_information=extra_information,
                 )
+            if custom_llm_provider == "bigmodel":
+                raise_bigmodel_business_error(model, f"{exception_provider} - {error_str}", original_exception)  # pyright: ignore[reportUnknownArgumentType]  # exception_type takes untyped parameters
             if custom_llm_provider == "openrouter":
                 _map_openrouter_exception(
                     model=model,

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -794,6 +794,13 @@ def _get_openai_compatible_provider_info(
             api_base,
             dynamic_api_key,
         ) = litellm.XAIChatConfig().get_openai_compatible_provider_info(api_base, api_key)
+    elif custom_llm_provider == "bigmodel":
+        from litellm.llms.bigmodel.common_utils import get_bigmodel_api_key, get_bigmodel_url
+
+        (
+            api_base,  # rebind-ok: existing provider-resolution pipeline resolves the API base in place
+            dynamic_api_key,  # rebind-ok: existing provider-resolution pipeline resolves the API key in place
+        ) = get_bigmodel_url(api_base, "chat"), get_bigmodel_api_key(api_key)
     elif custom_llm_provider == "zai":
         (
             api_base,

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/internal_call_metadata.py

@@ -120,7 +120,7 @@ def is_unbilled_non_inference_call(
     origin: Final = metadata.get(INTERNAL_CALL_ORIGIN_METADATA_KEY) if metadata is not None else None
     if origin == BACKGROUND_RESPONSE_COST_POLL_CALL_ORIGIN:
         return False
-    if not is_background_response(response):
+    if not is_background_response(_finished_response(response)):
         return True
     return origin == BACKGROUND_RESPONSE_STATUS_POLL_CALL_ORIGIN or _is_managed_background_call(metadata)
 

litellm/litellm_core_utils/litellm_logging.py

@@ -26,7 +26,7 @@ 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,
@@ -101,6 +101,7 @@ from litellm.litellm_core_utils.internal_call_metadata import (
     MODEL_ACCESS_GROUP_METADATA_KEY,
     is_unbilled_non_inference_call,
 )
+from litellm.litellm_core_utils.llm_cost_calc.finalization import FinalizedCost, PreparedCost
 from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import (
     bedrock_guardrail_trace,
     cost_breakdown_with_guardrail,
@@ -169,9 +170,10 @@ 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,
@@ -321,7 +323,7 @@ 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
@@ -875,9 +877,15 @@ 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
+        self.finalized_cost: FinalizedCost | None = None
+        self.prepared_cost: PreparedCost | None = None
+        self.cost_allowance_discount: float | None = None
+        self.cost_finalization_lock = asyncio.Lock()
+        self.deferred_success_result: tuple[object, datetime.datetime, datetime.datetime] | None = None
         self.billed_token_rates: BilledTokenRates | None = None
         self._provider_reported_cost: float | None = None
 
@@ -939,6 +947,11 @@ class Logging(LiteLLMLoggingBaseClass):
             "current_attempt_api_call_start_time": None,
             "litellm_params": litellm_params,
         }
+        new_call.finalized_cost = None
+        new_call.prepared_cost = None
+        new_call.cost_allowance_discount = None
+        new_call.cost_finalization_lock = asyncio.Lock()
+        new_call.deferred_success_result = None
         new_call.streaming_chunks = []
         new_call.sync_streaming_chunks = []
         return new_call
@@ -1225,6 +1238,9 @@ 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
@@ -2004,10 +2020,10 @@ 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,
@@ -2046,14 +2062,17 @@ 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:
@@ -2521,6 +2540,55 @@ class Logging(LiteLLMLoggingBaseClass):
             **kwargs,
         )
 
+    def prepare_response_cost(self, response: object, now: datetime.datetime) -> PreparedCost | None:
+        if self.prepared_cost is not None:
+            return self.prepared_cost
+        recorded_cache_hit: Final[object] = self.model_call_details.get("cache_hit")
+        deferred: Final = self.deferred_success_result
+        prepared: Final = self.prepare_success_logging(
+            result=deferred[0] if deferred is not None else response,
+            start_time=deferred[1] if deferred is not None else self.start_time,
+            end_time=deferred[2] if deferred is not None else now.replace(tzinfo=None),
+            cache_hit=recorded_cache_hit if isinstance(recorded_cache_hit, bool) else None,
+            standard_logging_object=None,
+            build_logging_payload=True,
+            publish_logging_payload=False,
+        )
+        payload: Final = prepared[3]
+        raw_cost: Final[object] = self.model_call_details.get("response_cost")
+        if payload is None or not isinstance(raw_cost, (int, float)):
+            return None
+        recorded_usage: Final[object] = self.model_call_details.get("combined_usage_object")
+        response_usage: Final[object] = getattr(prepared[2], "usage", None)
+        normalized_usage: Final = (
+            recorded_usage
+            if isinstance(recorded_usage, Usage)
+            else response_usage
+            if isinstance(response_usage, Usage)
+            else None
+        )
+        usage: Final = StandardLoggingPayloadSetup.get_usage_from_response_obj(
+            _extract_response_obj_and_hidden_params(prepared[2], None)[0],
+            combined_usage_object=normalized_usage,
+        ).model_copy(deep=True)
+        self.prepared_cost = PreparedCost(
+            raw_cost,
+            {
+                **payload,
+                "response": None,
+                "messages": None,
+                "model_map_information": copy.deepcopy(payload["model_map_information"]),
+                "cost_breakdown": copy.deepcopy(payload["cost_breakdown"]),
+            },
+            usage,
+        )
+        self.record_partial_usage_for_failure(usage, raw_cost)
+        return self.prepared_cost
+
+    def record_finalized_cost(self, finalized: FinalizedCost) -> None:
+        self.finalized_cost = finalized
+        self.model_call_details["response_cost"] = finalized.amount
+
     def record_partial_usage_for_failure(self, usage: Usage, response_cost: float) -> None:
         """Stash what an interrupted stream already consumed so the failure log bills it instead of zero."""
         self.model_call_details["combined_usage_object"] = usage
@@ -2873,12 +2941,16 @@ class Logging(LiteLLMLoggingBaseClass):
                     logging_result, "_hidden_params", {}
                 )
 
-        if self.model_call_details.get("cache_hit") is True:
+        if self.finalized_cost is not None:
+            self.model_call_details["response_cost"] = self.finalized_cost.amount
+        elif self.model_call_details.get("cache_hit") is True:
             self.model_call_details["response_cost"] = 0.0
         elif "response_cost" in hidden_params:
             self.model_call_details["response_cost"] = hidden_params["response_cost"]
             self._record_zero_cost_diagnostic(logging_result, hidden_params["response_cost"])
-        elif (existing_cost := self.model_call_details.get("response_cost")) is not None and existing_cost != 0:
+        elif (existing_cost := self.model_call_details.get("response_cost")) is not None and (
+            existing_cost != 0 or self.call_type == CallTypes.pass_through.value  # pyright: ignore[reportUnknownMemberType]  # Logging.call_type is untyped
+        ):
             # Preserve response_cost if already calculated (e.g., by pass-through
             # handlers like Gemini/Vertex which call completion_cost directly).
             # Do not preserve 0 from failure_handler on intermediate router retries.
@@ -2997,7 +3069,7 @@ class Logging(LiteLLMLoggingBaseClass):
         standard_logging_object: StandardLoggingPayload | None = None,
         build_logging_payload: bool = True,
     ) -> tuple[datetime.datetime, datetime.datetime, object]:
-        prepared: Final = self._prepare_success_logging(
+        prepared: Final = self.prepare_success_logging(
             result=result,
             start_time=start_time,
             end_time=end_time,
@@ -3008,7 +3080,7 @@ class Logging(LiteLLMLoggingBaseClass):
         )
         return prepared[0], prepared[1], prepared[2]
 
-    def _prepare_success_logging(
+    def prepare_success_logging(
         self,
         result: object,
         start_time: datetime.datetime | None,
@@ -3029,7 +3101,11 @@ class Logging(LiteLLMLoggingBaseClass):
 
             self.model_call_details["log_event_type"] = "successful_api_call"
             self.model_call_details["end_time"] = end_time
-            self.model_call_details["cache_hit"] = cache_hit
+            self.model_call_details["cache_hit"] = (
+                self.prepared_cost.payload["cache_hit"]
+                if cache_hit is None and self.prepared_cost is not None
+                else cache_hit
+            )
 
             if self.call_type == CallTypes.anthropic_messages.value:
                 result = self._anthropic_messages_logged_response(result=result)
@@ -3068,6 +3144,7 @@ class Logging(LiteLLMLoggingBaseClass):
 
             if (
                 litellm.max_budget
+                and publish_logging_payload
                 and self.stream is False
                 and result is not None
                 and isinstance(result, dict)
@@ -3098,10 +3175,17 @@ class Logging(LiteLLMLoggingBaseClass):
         publish_logging_payload: bool,
     ) -> StandardLoggingPayload | None:
         if standard_logging_object is not None:
+            effective_payload: Final = (
+                self.finalized_cost.logging_payload(standard_logging_object)
+                if self.finalized_cost is not None
+                else standard_logging_object
+            )
+            if self.finalized_cost is not None:
+                self.model_call_details["response_cost"] = self.finalized_cost.amount
             if not publish_logging_payload:
-                return standard_logging_object.copy()
-            self.model_call_details["standard_logging_object"] = standard_logging_object
-            return standard_logging_object
+                return effective_payload.copy()
+            self.model_call_details["standard_logging_object"] = effective_payload
+            return effective_payload
         if result is None or self.stream is True:
             # Streaming reaches here before its cost is known, so the cost
             # is seeded to None, but only when nothing has already
@@ -3318,7 +3402,7 @@ 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,
@@ -3342,7 +3426,7 @@ class Logging(LiteLLMLoggingBaseClass):
         verbose_logger.debug("Logging Details LiteLLM-Success Call: Cache_hit=%s", cache_hit)
         if not self.should_run_logging(event_type="sync_success"):  # prevent double logging
             return
-        start_time, end_time, result, request_logging_payload = self._prepare_success_logging(
+        start_time, end_time, result, request_logging_payload = self.prepare_success_logging(
             start_time=start_time,
             end_time=end_time,
             result=result,
@@ -3810,7 +3894,7 @@ 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
                 )
@@ -4388,7 +4472,9 @@ class Logging(LiteLLMLoggingBaseClass):
         # chunks already delivered; the router stashes that recovered usage as
         # ``combined_usage_object`` and pre-computes its cost, so preserve it
         # here instead of zeroing the spend on an otherwise-failed request.
-        if self.model_call_details.get("combined_usage_object") is None:
+        if self.finalized_cost is not None:
+            self.model_call_details["response_cost"] = self.finalized_cost.amount
+        elif self.model_call_details.get("combined_usage_object") is None:
             self.model_call_details["response_cost"] = 0
 
         if hasattr(exception, "headers") and isinstance(exception.headers, dict):
@@ -4458,13 +4544,14 @@ 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()
 
@@ -4658,12 +4745,13 @@ 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()
 
@@ -5725,7 +5813,7 @@ def build_custom_logger_compatible_class(
                     return callback
             otel_settings: Final = _get_custom_logger_settings_from_proxy_server(callback_name=logging_integration)
             otel_logger_v2: Final = build_otel_v2_logger(
-                config=OpenTelemetryV2Config(**otel_settings), settings=otel_settings
+                config=OpenTelemetryV2Config(**otel_settings), settings=otel_settings, console_default=True
             )
             _in_memory_loggers.append(otel_logger_v2)
             _maybe_auto_initialize_arize_phoenix(_in_memory_loggers)
@@ -6750,6 +6838,7 @@ 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.
@@ -6823,6 +6912,7 @@ 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:
@@ -7546,6 +7636,12 @@ def _cache_hit_cost_breakdown(served: CostBreakdown | None) -> CostBreakdown:
     )
 
 
+def _finalized_cost_of(logging_obj: object) -> FinalizedCost | None:
+    """Duck-typed logging objects (and test doubles) carry no finalized cost."""
+    finalized: Final = getattr(logging_obj, "finalized_cost", None)
+    return finalized if isinstance(finalized, FinalizedCost) else None
+
+
 def get_standard_logging_object_payload(
     kwargs: dict | None,
     init_response_obj: object,
@@ -7623,6 +7719,7 @@ 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(
@@ -7648,7 +7745,10 @@ def get_standard_logging_object_payload(
         raw_response_cost: Final = kwargs.get("response_cost")
         llm_response_cost: Final[float] = raw_response_cost or 0.0
         guardrail_cost: Final = guardrail_information_cost(metadata.get("standard_logging_guardrail_information"))
-        response_cost: Final[float] = llm_response_cost + guardrail_cost
+        finalized_cost: Final = _finalized_cost_of(logging_obj)
+        response_cost: Final[float] = (
+            finalized_cost.amount if finalized_cost is not None else llm_response_cost + guardrail_cost
+        )
 
         # clean up litellm hidden params
         clean_hidden_params: Final = StandardLoggingPayloadSetup.get_hidden_params(hidden_params)
@@ -7715,13 +7815,17 @@ def get_standard_logging_object_payload(
         ):
             model_name = response_model_name
 
-        request_cost_breakdown: Final = cost_breakdown_with_guardrail(
-            (
-                _cache_hit_cost_breakdown(uncached_price.cost_breakdown)
-                if uncached_price is not None
-                else logging_obj.cost_breakdown
-            ),
-            guardrail_cost,
+        request_cost_breakdown: Final = (
+            finalized_cost.logging_breakdown()
+            if finalized_cost is not None
+            else cost_breakdown_with_guardrail(
+                (
+                    _cache_hit_cost_breakdown(uncached_price.cost_breakdown)
+                    if uncached_price is not None
+                    else logging_obj.cost_breakdown
+                ),
+                guardrail_cost,
+            )
         )
         captured_baseline: Final = logging_obj.baseline_observation
         autorouter_savings: Final = (
@@ -7778,6 +7882,7 @@ def get_standard_logging_object_payload(
             model=model_name,
             metadata=clean_metadata,
             cache_key=clean_hidden_params["cache_key"],
+            cost_finalized=finalized_cost is not None,
             response_cost=response_cost,
             cost_breakdown=request_cost_breakdown,
             autorouter_savings=autorouter_savings,
@@ -7907,9 +8012,8 @@ 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_cost_calc/finalization.py

@@ -0,0 +1,61 @@
+from collections.abc import Mapping
+from dataclasses import dataclass
+from types import MappingProxyType
+from typing import Final, TypeAlias
+
+from pydantic import TypeAdapter
+
+from litellm.types.utils import CostBreakdown, StandardLoggingPayload, Usage
+
+BreakdownValue: TypeAlias = float | str | None | Mapping[str, float]
+_BREAKDOWN_FIELDS: Final = TypeAdapter(Mapping[str, BreakdownValue])
+_COST_BREAKDOWN: Final = TypeAdapter(CostBreakdown)
+
+
+@dataclass(frozen=True, slots=True)
+class PreparedCost:
+    amount: float
+    payload: StandardLoggingPayload
+    usage: Usage
+
+
+@dataclass(frozen=True, slots=True)
+class FinalizedCost:
+    amount: float
+    breakdown: Mapping[str, BreakdownValue] | None
+    allowance_discount: float
+
+    def logging_payload(self, payload: StandardLoggingPayload) -> StandardLoggingPayload:
+        return {
+            **payload,
+            "response_cost": self.amount,
+            "cost_breakdown": self.logging_breakdown(),
+            "cost_finalized": True,
+        }
+
+    def logging_breakdown(self) -> CostBreakdown | None:
+        return _COST_BREAKDOWN.validate_python(self.breakdown) if self.breakdown is not None else None
+
+
+def finalize_cost(amount: float, breakdown: CostBreakdown | None, allowance_discount: float = 0.0) -> FinalizedCost:
+    discount: Final = min(max(amount, 0.0), max(allowance_discount, 0.0))
+    total: Final = amount - discount
+    existing: Final = _BREAKDOWN_FIELDS.validate_python(breakdown) if breakdown is not None else None
+    adjusted: Final = (
+        {
+            **(existing or {}),
+            "total_cost": total,
+            "original_cost": (breakdown or {}).get("original_cost", amount),
+            "discount_amount": (breakdown or {}).get("discount_amount", 0.0) + discount,
+        }
+        if discount > 0
+        else existing
+    )
+    frozen: Final = (
+        MappingProxyType(
+            {key: MappingProxyType(value) if isinstance(value, Mapping) else value for key, value in adjusted.items()}
+        )
+        if adjusted is not None
+        else None
+    )
+    return FinalizedCost(amount=total, breakdown=frozen, allowance_discount=discount)

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -75,6 +75,14 @@ _IMAGE_TOKEN_RATE_KEYS: Final[tuple[str, ...]] = (
     "output_cost_per_image_token",
 )
 
+
+def _deployment_prices_images_without_an_output_price(pricing: ModelInfo | None) -> bool:
+    return pricing is not None and (
+        "input_cost_per_pixel" in pricing
+        or ("input_cost_per_image" in pricing and "output_cost_per_image" not in pricing)
+    )
+
+
 # Pre-resolved service-tier cost-key suffixes (e.g. "_priority"). Used per
 # request in the cost-calc path, so the f-strings are built once here instead
 # of being rebuilt for every model_info key on every call. Longest-first so a
@@ -127,7 +135,7 @@ def _get_token_detail_value(details: object, key: str) -> int | None:
     return value if isinstance(value, int) else None
 
 
-_IMAGE_SIZE_PATTERN: Final = re.compile(r"\d+(?:x|-x-)\d+")
+_IMAGE_SIZE_PATTERN: Final = re.compile(r"\d+(?:x|-x-|\*)\d+")
 
 
 def _requested_image_param(optional_params: Mapping[str, object] | None, key: str) -> str | None:
@@ -137,7 +145,7 @@ def _requested_image_param(optional_params: Mapping[str, object] | None, key: st
 
 def _requested_image_size(optional_params: Mapping[str, object] | None) -> str | None:
     value: Final = _requested_image_param(optional_params, "size")
-    return value if value is not None and _IMAGE_SIZE_PATTERN.fullmatch(value) else None
+    return value.replace("*", "x") if value is not None and _IMAGE_SIZE_PATTERN.fullmatch(value) else None
 
 
 def get_web_search_requests(server_tool_use: object) -> int | None:
@@ -2090,7 +2098,7 @@ class _ModelPricedImageCostCalculator(Protocol):
 
 
 def _model_priced_image_cost_calculator(
-    custom_llm_provider: str | None, model: str
+    custom_llm_provider: str | None, model: str, pricing: ModelInfo | None
 ) -> _ModelPricedImageCostCalculator | None:
     match custom_llm_provider:
         case litellm.LlmProviders.RECRAFT.value:
@@ -2116,6 +2124,14 @@ def _model_priced_image_cost_calculator(
             from litellm.llms.tencent.image_generation.transformation import is_tokenhub_image_model
 
             return tencent_image_cost_calculator if is_tokenhub_image_model(model) else None
+        case litellm.LlmProviders.DASHSCOPE.value:
+            if _deployment_prices_images_without_an_output_price(pricing):
+                return None
+            from litellm.llms.dashscope.image_generation.cost_calculator import (
+                cost_calculator as dashscope_image_cost_calculator,
+            )
+
+            return dashscope_image_cost_calculator
         case _:
             return None
 
@@ -2178,7 +2194,7 @@ class CostCalculatorUtils:
         )
         resolved_n: Final = n if n is not None else (len(completion_response.data) if completion_response.data else 0)
         pricing: Final = deployment_pricing(model_info)
-        model_priced_calculator: Final = _model_priced_image_cost_calculator(custom_llm_provider, model)
+        model_priced_calculator: Final = _model_priced_image_cost_calculator(custom_llm_provider, model, pricing)
 
         if model_priced_calculator is not None:
             return model_priced_calculator(model=model, image_response=completion_response, model_info=pricing)

litellm/litellm_core_utils/llm_judge.py

@@ -108,7 +108,7 @@ def judge_target(router: Router | None, model: str, team_id: str | None = None)
     only for its own team and a team's own deployment resolves for nobody else, so asking
     without it answers for a caller who does not exist.
     """
-    served: Final = router.resolved_litellm_models(model, team_id=team_id) if router is not None else ()
+    served: Final = router.registry().names(model, kind="litellm_models", team_id=team_id) if router is not None else ()
     if served:
         return JudgeTarget("router", frozenset(_provider_qualified(m) or m for m in served))
     qualified: Final = _provider_qualified(model)
@@ -148,7 +148,7 @@ def attached_auth(request_data: object) -> UserAPIKeyAuth | None:
 
 
 def _named_route(router: Router, deployment_id: str) -> bool:
-    deployment: Final = router.get_deployment(model_id=deployment_id)
+    deployment: Final = router.registry().deployment(deployment_id)
     if deployment is None:
         return False
     public_names: Final = (deployment.model_name, deployment.model_info.team_public_model_name or "")
@@ -167,7 +167,7 @@ def caller_team_for(router: Router | None, model: str, auth: UserAPIKeyAuth | No
     team_id: Final = auth.team_id if auth is not None else None
     if team_id is None or router is None or judge_target(router, model).via != "nothing":
         return None
-    team_route: Final = router.get_candidate_model_ids_for_route(model, team_id)
+    team_route: Final = router.registry().names(model, kind="route_candidates", team_id=team_id)
     return team_id if team_route and all(_named_route(router, deployment_id) for deployment_id in team_route) else None
 
 

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

@@ -17,7 +17,7 @@ from pathlib import Path
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, Literal, TypeAlias, TypeVar, cast
 
-from pydantic import TypeAdapter
+from pydantic import TypeAdapter, ValidationError
 
 import litellm
 from litellm import verbose_logger
@@ -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")
@@ -2073,6 +2176,19 @@ def with_prompt_cache_breakpoint(target: _MarkedT, marker: object) -> _MarkedT:
     return cast(_MarkedT, marked)  # cast-ok: same block shape as the input plus the marker key
 
 
+_PROMPT_CACHE_BREAKPOINT: Final = TypeAdapter(PromptCacheBreakpoint)
+
+
+def prompt_cache_breakpoint_for_wire(marker: object, drop_params: bool) -> object:
+    if marker is None or not drop_params:
+        return marker
+    try:
+        return _PROMPT_CACHE_BREAKPOINT.validate_python(marker)
+    except ValidationError:
+        verbose_logger.debug("Dropping malformed prompt_cache_breakpoint %r under drop_params", marker)
+        return None
+
+
 def cache_control_as_prompt_cache_breakpoint(block: _MarkedT) -> _MarkedT:
     """Anthropic's ``cache_control`` mark rewritten as OpenAI's ``prompt_cache_breakpoint`` on the same block."""
     if block.get("cache_control") is None: