Merge origin/main into litellm_nvidia_nim_logprobs

.circleci/AGENTS.md

@@ -0,0 +1,7 @@
+# .circleci
+
+The `integration` workflow on `main` runs from a scheduled pipeline in the CircleCI project settings, cron `9 0-8,14-23 * * *` (UTC) on `main` with no pipeline parameters. It has no `triggers:` block in this file because a workflow with one runs only on its schedule, so the API pipelines that set `run_migration_tests` or `routing_parity_base` would stop running it. If main stops getting integration runs, check that the schedule still exists and recreate it with these values
+
+Jobs clone with the `checkout_over_https` command, never the built-in `checkout` step, because CircleCI falls back to the project's SSH checkout key without its GitHub App access, GitHub rejects that key, and the public repo needs no key. It fetches the job's commit with full history and no file contents (`--filter=blob:none`), like the built-in step did, so `scripts/path_filter.sh` can find a merge base
+
+A step that checks out files from another commit fetches that commit with `--no-filter`, as `routing_parity` does, because the clone has no file contents and git would otherwise download each missing file in its own request, which took 210 to 896 seconds for the routing parity base

.circleci/config.yml

@@ -21,19 +21,30 @@ orbs:
   win: circleci/windows@5.0 # Add Windows orb
 
 commands:
-  checkout_migration_source:
+  checkout_over_https:
+    parameters:
+      revision:
+        type: string
+        default: ""
+      shell:
+        type: string
+        default: /bin/bash -eo pipefail
     steps:
       - run:
-          name: Select the requested migration test revision
+          name: Check out the public repository over HTTPS
+          shell: << parameters.shell >>
           environment:
-            MIGRATION_SOURCE_SHA: << pipeline.parameters.migration_source_sha >>
+            REQUESTED_REVISION: << parameters.revision >>
           command: |
-            revision="${MIGRATION_SOURCE_SHA:-$CIRCLE_SHA1}"
+            revision="${REQUESTED_REVISION:-$CIRCLE_SHA1}"
             [[ "$revision" =~ ^[0-9a-f]{40}$ ]] || exit 1
             git init
             git remote add origin https://github.com/BerriAI/litellm.git
-            git fetch --depth 1 origin "$revision"
-            git checkout --detach FETCH_HEAD
+            for attempt in 1 2 3; do
+              git fetch --filter=blob:none origin "$revision" && git checkout --detach FETCH_HEAD && break
+              [[ $attempt -lt 3 ]] || exit 1
+              sleep 5
+            done
   skip_if_unrelated_changes:
     parameters:
       category:
@@ -347,7 +358,7 @@ commands:
       - when:
           condition: << parameters.checkout >>
           steps:
-            - checkout
+            - checkout_over_https
       - setup_google_dns
       - install_uv
       - install_rust
@@ -377,7 +388,8 @@ jobs:
       CARGO_HTTP_MULTIPLEXING: "false"
       CARGO_NET_RETRY: "5"
     steps:
-      - checkout
+      - checkout_over_https:
+          shell: bash.exe
       - run:
           name: Skip job when no windows-release-relevant files changed
           shell: bash.exe
@@ -451,7 +463,7 @@ jobs:
           password: ${DOCKERHUB_PASSWORD}
     working_directory: ~/project
     steps:
-      - checkout
+      - checkout_over_https
       - setup_google_dns
       - install_uv
       - install_rust
@@ -482,7 +494,7 @@ jobs:
     working_directory: ~/project
 
     steps:
-      - checkout
+      - checkout_over_https
       - skip_if_unrelated_changes
       - attach_workspace:
           at: ~/project
@@ -559,7 +571,7 @@ jobs:
     resource_class: medium
     working_directory: ~/project
     steps:
-      - checkout
+      - checkout_over_https
       - run:
           name: Generate LiteLLM master key
           command: |
@@ -659,7 +671,7 @@ jobs:
     resource_class: large
     working_directory: ~/project
     steps:
-      - checkout
+      - checkout_over_https
       - run:
           name: Generate LiteLLM master key
           command: |
@@ -740,7 +752,7 @@ jobs:
     resource_class: large
     working_directory: ~/project
     steps:
-      - checkout
+      - checkout_over_https
       - skip_if_unrelated_changes:
           category: client
       - setup_google_dns
@@ -794,7 +806,7 @@ jobs:
       # the rest of the suite: nothing else performs a logout.
       PROXY_LOGOUT_URL: "https://www.example.com"
     steps:
-      - checkout
+      - checkout_over_https
       - run:
           name: Generate LiteLLM master key
           command: |
@@ -940,7 +952,7 @@ jobs:
       # and by migration.serverRootPath.config.ts, which refuses to run without it.
       SERVER_ROOT_PATH: "/litellm"
     steps:
-      - checkout
+      - checkout_over_https
       - run:
           name: Generate LiteLLM master key
           command: |
@@ -1066,11 +1078,12 @@ jobs:
       - when:
           condition: << parameters.migration_qualification >>
           steps:
-            - checkout_migration_source
+            - checkout_over_https:
+                revision: << pipeline.parameters.migration_source_sha >>
       - unless:
           condition: << parameters.migration_qualification >>
           steps:
-            - checkout
+            - checkout_over_https
             - skip_if_unrelated_changes
 
       - run:
@@ -1122,7 +1135,8 @@ jobs:
       MIGRATION_TEST_OUTPUT: /tmp/migration-results
       PYTHONPATH: tests/e2e
     steps:
-      - checkout_migration_source
+      - checkout_over_https:
+          revision: << pipeline.parameters.migration_source_sha >>
       - run:
           name: Generate LiteLLM master key
           command: |
@@ -1197,7 +1211,7 @@ jobs:
     resource_class: medium
     working_directory: ~/project
     steps:
-      - checkout
+      - checkout_over_https
       - skip_if_unrelated_changes
       - attach_workspace:
           at: ~/project
@@ -1371,7 +1385,7 @@ jobs:
     environment:
       UV_PYTHON_DOWNLOADS: never
     steps:
-      - checkout
+      - checkout_over_https
       - install_uv
       - run:
           name: Install the locked shard verifier and coverage dependencies
@@ -1419,7 +1433,7 @@ jobs:
             ROUTING_PARITY_BASE: << pipeline.parameters.routing_parity_base >>
           command: |
             [[ "$ROUTING_PARITY_BASE" =~ ^[0-9a-f]{40}$ ]] || exit 1
-            git fetch --depth 1 origin "$ROUTING_PARITY_BASE"
+            git fetch --depth 1 --no-filter origin "$ROUTING_PARITY_BASE"
             git rm -r -f --quiet litellm enterprise litellm-proxy-extras
             git checkout "$ROUTING_PARITY_BASE" -- litellm enterprise litellm-proxy-extras
             git reset --quiet
@@ -1494,7 +1508,7 @@ jobs:
     environment:
       DATABASE_URL: postgresql://postgres:postgres@localhost:5432/litellm_test
     steps:
-      - checkout
+      - checkout_over_https
       - skip_if_unrelated_changes
       - setup_litellm_test_deps:
           checkout: false

.github/PULL_REQUEST_TEMPLATE/general.md

@@ -4,9 +4,20 @@
      Drop every section you have nothing to put in, heading included: a bare "## Relevant issues" or
      "## Affected release" with nothing under it must not appear in the final description -->
 
-<!-- Two or three plain sentences a teammate can read in 10 seconds, written the way you'd explain it in Slack
+## Handwritten 1-3 sentence description
+
+<!-- The PR author rewrites this in their own words before asking for review. Being able to sum up the PR in a few
+     plain sentences proves the author understands what it does, and human-written technical English is far easier
+     for a reviewer to parse than AI-written English
+     An AI agent may draft a starting point here, but always wraps its draft in the make-me-human-readable tags below,
+     backticks included so the tags stay visible on GitHub, so nobody mistakes it for the author's words. The author rewrites it in their own words and deletes the tags.
+     Tags still present at review time mean the summary is AI-written and has not been rewritten yet
      Say what the PR does for users and why, then anything that keeps it working over time (a sync job, a config someone owns)
-     No headings, bullets, tables, or code names here. Everything else goes inside the Details block below -->
+     No bullets, tables, or code names here. Everything else goes inside the Details block below -->
+
+`<make-me-human-readable>`
+<blah>
+`</make-me-human-readable>`
 
 <details>
 <summary>Details</summary>

.github/workflows/create-rc-branch.yml

@@ -2,7 +2,7 @@ name: Create RC Branch
 
 on:
   schedule:
-    - cron: "0 3 * * 5"
+    - cron: "0 3 * * 1"
       timezone: "America/Los_Angeles"
   workflow_dispatch:
 

AGENTS.md

@@ -41,6 +41,8 @@ Same applies for filing bug reports and feature requests, with .github/ISSUE_TEM
 
 If you're resolving a linear ticket, in the "## Linear ticket" section of the PR, say "Resolves LIT-1234", replacing "LIT-1234" with the actual ticket id that you're resolving. If you don't have the ticket id, don't make one up or search for it. Just drop the section
 
+Run QA proxies with `python litellm/proxy/proxy_cli.py --config litellm/proxy/dev_config.yaml --num_workers 2 --detailed_debug --reload --use_v2_migration_resolver 2>&1 | tee litellm.log`, and swap its `--config` for a copy of `litellm/proxy/dev_config.yaml` that points both `router_settings` (`redis_host`, `redis_port`) and `general_settings.coordination_redis` (`host`, `port`) at one dedicated Redis instance, with credentials when required, so the workers share router state and spend, budget, and rate-limit counters. Check that two worker PIDs are serving, and use the same worker count and Redis setup for the before and after proof. When the PR is about `--run_gunicorn`, `--run_hypercorn`, or `--run_granian`, which `--reload` does not support, drop `--reload`. When it is about behavior without Redis, drop Redis. When the proxy prints `"workers" flag is ignored when reloading is enabled`, that checkout or its uvicorn reloads only one worker, so drop the two-worker check for it. Say in the proof which part you dropped and why
+
 Simple PRs may skip before/after e2e when the full diff is mechanical and focused checks completely prove it, such as #49030 restoring an existing helper name. Trace affected callers and run relevant tests/static checks at the tip. For a bug fix, establish that the regression catches the original defect. Size alone never qualifies. New behavior, API/provider semantics, auth, billing, persistence, concurrency or uncertain integration risk need live e2e. In Screenshots / Proof of Fix, record `E2E exception at <tip SHA>`, scope, rationale, commands and results. Tests count as proof only for this exception. Reassess changed diffs, retain CI/bot gates, and honor explicit e2e requests (2026-10-28, unnecessary live QA for #49030)
 
 For other PRs, proof must show the end user's real flow before and after, with commands and output against a live proxy and real provider calls where applicable. Pytest alone is insufficient. For UI or interactive coding-tool flows, drive that surface and embed before/after screenshots, with URLs, clicks and input fields a reviewer can reproduce

README.md

@@ -696,7 +696,7 @@ For companies that need better security, user management and professional suppor
 [Talk to founders](https://enterprise.litellm.ai/demo)
 
 This covers:
-- ✅ **Features under the [LiteLLM Commercial License](https://docs.litellm.ai/docs/proxy/enterprise):**
+- ✅ **Features under the [LiteLLM Commercial License](https://docs.litellm.ai/docs/enterprise):**
 - ✅ **Feature Prioritization**
 - ✅ **Custom Integrations**
 - ✅ **Professional Support - Dedicated discord + slack**

enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py

@@ -200,6 +200,7 @@ async def log_billed_background_response(billed: BilledBackgroundResponse) -> Bi
     unpriced: Final = _without_read_price(billed.response)
     cost: Final = logging_obj.response_cost_calculator(result=unpriced)
     end_time: Final = datetime.now()
+    logging_obj.capture_usage_before_logging(unpriced, logging_obj.start_time, end_time)
     GLOBAL_LOGGING_WORKER.ensure_initialized_and_enqueue(
         async_coroutine=logging_obj.async_success_handler(
             result=unpriced, start_time=logging_obj.start_time, end_time=end_time

litellm-rust/crates/core-utils/src/core_helpers.rs

@@ -13,6 +13,7 @@ const FINISH_REASONS: &[(&str, &str)] = &[
     ("max_tokens", "length"),
     ("refusal", "content_filter"),
     ("compaction", "length"),
+    ("pause_turn", "length"),
     ("model_context_window_exceeded", "length"),
     ("guardrail_intervened", "content_filter"),
     ("content_filtered", "content_filter"),
@@ -75,6 +76,7 @@ mod tests {
     #[case::end_turn("end_turn", "stop")]
     #[case::stop_sequence("stop_sequence", "stop")]
     #[case::max_tokens("max_tokens", "length")]
+    #[case::pause_turn("pause_turn", "length")]
     #[case::model_context_window_exceeded("model_context_window_exceeded", "length")]
     #[case::refusal("refusal", "content_filter")]
     #[case::guardrail_intervened("guardrail_intervened", "content_filter")]

litellm-rust/crates/cost/AGENTS.md

@@ -0,0 +1 @@
+The price vocabulary (meter, band, channel, `When`, `Billing`) is explained in [../model-catalog/AGENTS.md](../model-catalog/AGENTS.md#concepts), the only crate that maps it to JSON keys

litellm-rust/crates/cost/src/price.rs

@@ -19,6 +19,7 @@ pub enum ServiceTier {
     Balanced,
 }
 
+/// A prompt-size range. `OverPromptTokens(n)` is spelled `_above_<n>_tokens` or `_above_<n / 1000>k_tokens`
 #[derive(Clone, Copy, Debug, Default, PartialEq, Eq, PartialOrd, Ord, Hash)]
 pub enum Band {
     #[default]
@@ -26,12 +27,15 @@ pub enum Band {
     OverPromptTokens(NonZeroU64),
 }
 
+/// The request condition a price applies under, its service tier and prompt band
 #[derive(Clone, Copy, Debug, Default, PartialEq, Eq, PartialOrd, Ord, Hash)]
 pub struct When {
     pub tier: ServiceTier,
     pub band: Band,
 }
 
+/// The route whose price list a call reads, from the `_batches` and `_decisions` suffixes. Not part of `When`,
+/// since a missing batch price is half the online rate and a missing decisions price is the full online rate
 #[derive(Clone, Copy, Debug, Default, PartialEq, Eq, PartialOrd, Ord, Hash)]
 pub enum Channel {
     #[default]
@@ -40,6 +44,7 @@ pub enum Channel {
     Decisions,
 }
 
+/// The key of one rate, what is counted, through which channel and under which condition
 #[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord, Hash)]
 pub struct Price {
     pub meter: Meter,
@@ -47,6 +52,7 @@ pub struct Price {
     pub when: When,
 }
 
+/// Every rate a row lists, its off-peak overlay and the row attributes that say how the rates apply
 #[derive(Clone, Debug, Default, PartialEq)]
 pub struct Prices {
     rates: BTreeMap<Price, UnitPrice>,
@@ -117,6 +123,7 @@ pub enum TierScope {
     WholeCall,
 }
 
+/// Rates that replace the row's token rates while a UTC window in the schedule is open
 #[derive(Clone, Debug, PartialEq)]
 pub struct OffPeak {
     schedule: Schedule,
@@ -133,6 +140,7 @@ pub struct OffPeakRates {
     pub reasoning: Option<UnitPrice>,
 }
 
+/// Multipliers applied on top of a priced call, never prices themselves
 #[derive(Clone, Debug, Default, PartialEq)]
 pub struct Adjustments(BTreeMap<Uplift, Multiplier>);
 

litellm-rust/crates/cost/src/price/meter.rs

@@ -1,5 +1,6 @@
 use std::num::NonZeroU64;
 
+/// What a price counts. A variation only one meter has, such as cache TTL or image size, lives inside its variant
 #[derive(Clone, Debug, PartialEq, Eq, PartialOrd, Ord, Hash)]
 pub enum Meter {
     Input(InputUnit),
@@ -51,12 +52,14 @@ pub enum OutputUnit {
     VideoSecond,
 }
 
+/// `Explicit` is a cache the caller created ahead of time, spelled `explicit_cache_read_*`. `Implicit` is every other read
 #[derive(Clone, Copy, Debug, PartialEq, Eq, PartialOrd, Ord, Hash)]
 pub enum CacheReadMode {
     Implicit,
     Explicit,
 }
 
+/// How long a cache write lives. `OneHour` and `TwelveHours` are spelled `_above_1hr` and `_above_12hr`
 #[derive(Clone, Copy, Debug, PartialEq, Eq, PartialOrd, Ord, Hash)]
 pub enum CacheTtl {
     Default,

litellm-rust/crates/llms-types/src/providers/anthropic/beta.rs

@@ -124,12 +124,86 @@ pub enum AnthropicBeta {
     ToolSearchTool20251019,
     #[strum(serialize = "inline-tools-2026-09-15")]
     InlineTools20260915,
+    #[strum(serialize = "afk-mode-2026-01-31")]
+    AfkMode20260131,
+    #[strum(serialize = "agent-memory-2026-07-22")]
+    AgentMemory20260722,
+    #[strum(serialize = "analytics-usage-cost-2026-08-24")]
+    AnalyticsUsageCost20260824,
+    #[strum(serialize = "cache-diagnosis-2026-04-07")]
+    CacheDiagnosis20260407,
+    #[strum(serialize = "ce-plugins-2026-09-01")]
+    CePlugins20260901,
+    #[strum(serialize = "ce-user-management-2026-07-13")]
+    CeUserManagement20260713,
+    #[strum(serialize = "code-execution-2025-05-22")]
+    CodeExecution20250522,
+    #[strum(serialize = "computer-use-2024-10-22")]
+    ComputerUse20241022,
+    #[strum(serialize = "dev-full-thinking-2025-05-14")]
+    DevFullThinking20250514,
+    #[strum(serialize = "dreaming-2026-04-21")]
+    Dreaming20260421,
+    #[strum(serialize = "experimental-claude-tag-2026-10-21")]
+    ExperimentalClaudeTag20261021,
+    #[strum(serialize = "extended-cache-ttl-2025-04-11")]
+    ExtendedCacheTtl20250411,
+    #[strum(serialize = "fallback-credit-2026-06-01")]
+    FallbackCredit20260601,
+    #[strum(serialize = "fallback-credit-2026-07-01")]
+    FallbackCredit20260701,
+    #[strum(serialize = "managed-agents-2026-04-01")]
+    ManagedAgents20260401,
+    #[strum(serialize = "mcp-client-2026-09-15")]
+    McpClient20260915,
+    #[strum(serialize = "mcp-tunnels-2026-06-22")]
+    McpTunnels20260622,
+    #[strum(serialize = "message-batches-2024-09-24")]
+    MessageBatches20240924,
+    #[strum(serialize = "message-threads-2026-08-12")]
+    MessageThreads20260812,
+    #[strum(serialize = "mid-conversation-system-clear-at-2026-08-21")]
+    MidConversationSystemClearAt20260821,
+    #[strum(serialize = "model-context-window-exceeded-2025-08-26")]
+    ModelContextWindowExceeded20250826,
+    #[strum(serialize = "output-300k-2026-03-24")]
+    Output300k20260324,
+    #[strum(serialize = "pdfs-2024-09-25")]
+    Pdfs20240925,
+    #[strum(serialize = "prompt-caching-2024-07-31")]
+    PromptCaching20240731,
+    #[strum(serialize = "redact-thinking-2026-02-12")]
+    RedactThinking20260212,
+    #[strum(serialize = "server-side-fallback-2026-06-01")]
+    ServerSideFallback20260601,
+    #[strum(serialize = "server-side-fallback-2026-07-01")]
+    ServerSideFallback20260701,
+    #[strum(serialize = "spend-limit-reads-2026-09-26")]
+    SpendLimitReads20260926,
+    #[strum(serialize = "structured-outputs-2025-12-15")]
+    StructuredOutputs20251215,
+    #[strum(serialize = "task-budgets-2026-03-13")]
+    TaskBudgets20260313,
+    #[strum(serialize = "telemetry-destinations-2026-08-11")]
+    TelemetryDestinations20260811,
+    #[strum(serialize = "thinking-token-count-2026-05-13")]
+    ThinkingTokenCount20260513,
+    #[strum(serialize = "token-counting-2024-11-01")]
+    TokenCounting20241101,
+    #[strum(serialize = "user-profiles-2026-03-24")]
+    UserProfiles20260324,
+    #[strum(serialize = "user-profiles-2026-08-18")]
+    UserProfiles20260818,
+    #[strum(serialize = "user-profiles-2026-09-04")]
+    UserProfiles20260904,
+    #[strum(serialize = "webhooks-2026-10-07")]
+    Webhooks20261007,
     #[strum(default, transparent)]
     Other(String),
 }
 
 impl AnthropicBeta {
-    pub const KNOWN: [Self; 40] = [
+    pub const KNOWN: [Self; 77] = [
         Self::Oauth20250420,
         Self::WebFetch20250910,
         Self::WebSearch20250305,
@@ -170,6 +244,43 @@ impl AnthropicBeta {
         Self::ToolExamples20251029,
         Self::ToolSearchTool20251019,
         Self::InlineTools20260915,
+        Self::AfkMode20260131,
+        Self::AgentMemory20260722,
+        Self::AnalyticsUsageCost20260824,
+        Self::CacheDiagnosis20260407,
+        Self::CePlugins20260901,
+        Self::CeUserManagement20260713,
+        Self::CodeExecution20250522,
+        Self::ComputerUse20241022,
+        Self::DevFullThinking20250514,
+        Self::Dreaming20260421,
+        Self::ExperimentalClaudeTag20261021,
+        Self::ExtendedCacheTtl20250411,
+        Self::FallbackCredit20260601,
+        Self::FallbackCredit20260701,
+        Self::ManagedAgents20260401,
+        Self::McpClient20260915,
+        Self::McpTunnels20260622,
+        Self::MessageBatches20240924,
+        Self::MessageThreads20260812,
+        Self::MidConversationSystemClearAt20260821,
+        Self::ModelContextWindowExceeded20250826,
+        Self::Output300k20260324,
+        Self::Pdfs20240925,
+        Self::PromptCaching20240731,
+        Self::RedactThinking20260212,
+        Self::ServerSideFallback20260601,
+        Self::ServerSideFallback20260701,
+        Self::SpendLimitReads20260926,
+        Self::StructuredOutputs20251215,
+        Self::TaskBudgets20260313,
+        Self::TelemetryDestinations20260811,
+        Self::ThinkingTokenCount20260513,
+        Self::TokenCounting20241101,
+        Self::UserProfiles20260324,
+        Self::UserProfiles20260818,
+        Self::UserProfiles20260904,
+        Self::Webhooks20261007,
     ];
 
     pub fn as_str(&self) -> &str {
@@ -195,10 +306,9 @@ impl AnthropicBeta {
 
     fn rejected_by(&self) -> &'static [BetaProvider] {
         match self {
-            Self::AdvancedToolUse20251120 => {
+            Self::AdvancedToolUse20251120 | Self::ToolSearchTool20251019 => {
                 &[BetaProvider::BedrockConverse, BetaProvider::Databricks]
             }
-            Self::ContextManagement20250627 => &[BetaProvider::BedrockConverse],
             Self::AdvisorTool20260301 => &[
                 BetaProvider::AzureAi,
                 BetaProvider::BedrockConverse,
@@ -207,98 +317,99 @@ impl AnthropicBeta {
                 BetaProvider::VertexAi,
                 BetaProvider::Databricks,
             ],
-            Self::Compact20260904 => &[
+            Self::AfkMode20260131
+            | Self::CePlugins20260901
+            | Self::CeUserManagement20260713
+            | Self::Compact20260904
+            | Self::Dreaming20260421
+            | Self::ExperimentalClaudeTag20261021
+            | Self::ExtendedCacheTtl20250411
+            | Self::FallbackCredit20260601
+            | Self::FallbackCredit20260701
+            | Self::InlineTools20260915
+            | Self::ManagedAgents20260401
+            | Self::McpTunnels20260622
+            | Self::MidConversationSystemClearAt20260821
+            | Self::ModelContextWindowExceeded20250826
+            | Self::Oauth20250420
+            | Self::Pdfs20240925
+            | Self::PromptCaching20240731
+            | Self::PromptCachingScope20260105
+            | Self::RedactThinking20260212
+            | Self::ServerSideFallback20260601
+            | Self::SpendLimitReads20260926
+            | Self::StructuredOutputs20251215
+            | Self::TaskBudgets20260313
+            | Self::TelemetryDestinations20260811
+            | Self::ThinkingTokenCount20260513
+            | Self::TokenCounting20241101
+            | Self::UserProfiles20260324
+            | Self::UserProfiles20260818
+            | Self::UserProfiles20260904 => &[
                 BetaProvider::BedrockConverse,
                 BetaProvider::Bedrock,
                 BetaProvider::BedrockMantle,
                 BetaProvider::Databricks,
             ],
-            Self::Bash20241022
+            Self::AgentMemory20260722
+            | Self::Bash20241022
             | Self::Bash20250124
+            | Self::DevFullThinking20250514
             | Self::McpServers20251204
             | Self::StructuredOutput20240301
             | Self::TextEditor20241022
             | Self::TextEditor20250124 => BetaProvider::VARIANTS,
-            Self::ClaudeCode20250219 | Self::ToolExamples20251029 => &[
-                BetaProvider::Anthropic,
-                BetaProvider::AzureAi,
-                BetaProvider::BedrockConverse,
-                BetaProvider::Bedrock,
-                BetaProvider::VertexAi,
-                BetaProvider::Databricks,
-            ],
-            Self::CodeExecution20250825
+            Self::AnalyticsUsageCost20260824
+            | Self::CacheDiagnosis20260407
+            | Self::CodeExecution20250522
+            | Self::CodeExecution20250825
+            | Self::ComputerUse20241022
+            | Self::FastMode20260201
             | Self::FilesApi20250414
             | Self::McpClient20250404
             | Self::McpClient20251120
-            | Self::PromptCachingScope20260105
+            | Self::McpClient20260915
+            | Self::MessageBatches20240924
+            | Self::MessageThreads20260812
+            | Self::Output300k20260324
+            | Self::ServerSideFallback20260701
             | Self::Skills20251002
-            | Self::WebFetch20250910 => &[
+            | Self::WebFetch20250910
+            | Self::Webhooks20261007 => &[
                 BetaProvider::BedrockConverse,
                 BetaProvider::Bedrock,
                 BetaProvider::BedrockMantle,
                 BetaProvider::VertexAi,
                 BetaProvider::Databricks,
             ],
-            Self::Compact20260112 => &[BetaProvider::AzureAi, BetaProvider::BedrockConverse],
+            Self::ClaudeCode20250219
+            | Self::PerTurnControl20260701
+            | Self::ToolExamples20251029 => &[
+                BetaProvider::BedrockConverse,
+                BetaProvider::Bedrock,
+                BetaProvider::Databricks,
+            ],
+            Self::Compact20260112
+            | Self::ContextManagement20250627
+            | Self::DangerousToolUse20260903 => &[BetaProvider::BedrockConverse],
             Self::ComputerUse20250124
             | Self::ComputerUse20251124
             | Self::Context1m20250807
+            | Self::Effort20251124
+            | Self::FineGrainedToolStreaming20250514
             | Self::ThinkingBindingControls20260801
             | Self::ThinkingDisplayUpdates20260818 => &[],
-            Self::DangerousToolUse20260903 => &[BetaProvider::BedrockConverse],
-            Self::Effort20251124 => &[BetaProvider::VertexAi],
-            Self::FastMode20260201 | Self::Oauth20250420 => &[
-                BetaProvider::AzureAi,
-                BetaProvider::BedrockConverse,
-                BetaProvider::Bedrock,
-                BetaProvider::BedrockMantle,
-                BetaProvider::VertexAi,
-                BetaProvider::Databricks,
-            ],
-            Self::FineGrainedToolStreaming20250514 => {
-                &[BetaProvider::AzureAi, BetaProvider::VertexAi]
-            }
-            Self::InterleavedThinking20250514 | Self::WebSearch20250305 => {
-                &[BetaProvider::BedrockConverse, BetaProvider::Bedrock]
-            }
-            Self::MidConversationOutputConfig20260701 => &[
-                BetaProvider::AzureAi,
-                BetaProvider::BedrockConverse,
-                BetaProvider::BedrockMantle,
-                BetaProvider::VertexAi,
-                BetaProvider::Databricks,
-            ],
-            Self::MidConversationToolChanges20260701 => &[
+            Self::InterleavedThinking20250514
+            | Self::Output128k20250219
+            | Self::TokenEfficientTools20250219
+            | Self::WebSearch20250305 => &[BetaProvider::BedrockConverse, BetaProvider::Bedrock],
+            Self::MidConversationOutputConfig20260701
+            | Self::MidConversationToolChanges20260701 => &[
                 BetaProvider::BedrockConverse,
                 BetaProvider::BedrockMantle,
                 BetaProvider::Databricks,
             ],
-            Self::Output128k20250219 | Self::TokenEfficientTools20250219 => &[
-                BetaProvider::AzureAi,
-                BetaProvider::BedrockConverse,
-                BetaProvider::Bedrock,
-                BetaProvider::VertexAi,
-            ],
-            Self::PerTurnControl20260701 => &[
-                BetaProvider::BedrockConverse,
-                BetaProvider::Bedrock,
-                BetaProvider::Databricks,
-            ],
-            Self::StructuredOutputs20251113 => &[BetaProvider::Bedrock, BetaProvider::VertexAi],
-            Self::ToolSearchTool20251019 => &[
-                BetaProvider::Anthropic,
-                BetaProvider::AzureAi,
-                BetaProvider::BedrockConverse,
-                BetaProvider::Databricks,
-            ],
-            Self::InlineTools20260915 => &[
-                BetaProvider::AzureAi,
-                BetaProvider::BedrockConverse,
-                BetaProvider::Bedrock,
-                BetaProvider::BedrockMantle,
-                BetaProvider::Databricks,
-            ],
+            Self::StructuredOutputs20251113 => &[BetaProvider::Bedrock],
             Self::Other(_) => BetaProvider::VARIANTS,
         }
     }
@@ -451,7 +562,44 @@ mod tests {
             AnthropicBeta::TokenEfficientTools20250219,
             AnthropicBeta::ToolExamples20251029,
             AnthropicBeta::ToolSearchTool20251019,
-            AnthropicBeta::InlineTools20260915
+            AnthropicBeta::InlineTools20260915,
+            AnthropicBeta::AfkMode20260131,
+            AnthropicBeta::AgentMemory20260722,
+            AnthropicBeta::AnalyticsUsageCost20260824,
+            AnthropicBeta::CacheDiagnosis20260407,
+            AnthropicBeta::CePlugins20260901,
+            AnthropicBeta::CeUserManagement20260713,
+            AnthropicBeta::CodeExecution20250522,
+            AnthropicBeta::ComputerUse20241022,
+            AnthropicBeta::DevFullThinking20250514,
+            AnthropicBeta::Dreaming20260421,
+            AnthropicBeta::ExperimentalClaudeTag20261021,
+            AnthropicBeta::ExtendedCacheTtl20250411,
+            AnthropicBeta::FallbackCredit20260601,
+            AnthropicBeta::FallbackCredit20260701,
+            AnthropicBeta::ManagedAgents20260401,
+            AnthropicBeta::McpClient20260915,
+            AnthropicBeta::McpTunnels20260622,
+            AnthropicBeta::MessageBatches20240924,
+            AnthropicBeta::MessageThreads20260812,
+            AnthropicBeta::MidConversationSystemClearAt20260821,
+            AnthropicBeta::ModelContextWindowExceeded20250826,
+            AnthropicBeta::Output300k20260324,
+            AnthropicBeta::Pdfs20240925,
+            AnthropicBeta::PromptCaching20240731,
+            AnthropicBeta::RedactThinking20260212,
+            AnthropicBeta::ServerSideFallback20260601,
+            AnthropicBeta::ServerSideFallback20260701,
+            AnthropicBeta::SpendLimitReads20260926,
+            AnthropicBeta::StructuredOutputs20251215,
+            AnthropicBeta::TaskBudgets20260313,
+            AnthropicBeta::TelemetryDestinations20260811,
+            AnthropicBeta::ThinkingTokenCount20260513,
+            AnthropicBeta::TokenCounting20241101,
+            AnthropicBeta::UserProfiles20260324,
+            AnthropicBeta::UserProfiles20260818,
+            AnthropicBeta::UserProfiles20260904,
+            AnthropicBeta::Webhooks20261007
         )]
         beta: AnthropicBeta,
     ) {
@@ -551,7 +699,7 @@ mod tests {
         BetaProvider::Anthropic,
         Some(AnthropicBeta::Oauth20250420)
     )]
-    #[case::rejected("effort-2025-11-24", BetaProvider::VertexAi, None)]
+    #[case::rejected("skills-2025-10-02", BetaProvider::VertexAi, None)]
     #[case::unknown("example-beta-2099-01-01", BetaProvider::Anthropic, None)]
     fn on_resolves_known_spellings_held_as_other(
         #[case] raw: &str,
@@ -580,12 +728,11 @@ mod tests {
 
     #[test]
     fn unknown_values_are_kept_verbatim() {
-        let parsed: AnthropicBeta = "claude-code-20250219".parse().unwrap();
-        assert_eq!(
-            parsed,
-            AnthropicBeta::Other("claude-code-20250219".to_string())
+        let parsed: AnthropicBeta = "example-beta-2099-01-01".parse().unwrap();
+        assert!(
+            matches!(&parsed, AnthropicBeta::Other(value) if value == "example-beta-2099-01-01")
         );
-        assert_eq!(parsed.to_string(), "claude-code-20250219");
+        assert_eq!(parsed.to_string(), "example-beta-2099-01-01");
     }
 
     #[test]

litellm-rust/crates/llms/src/anthropic/beta_headers.rs

@@ -96,7 +96,10 @@ mod tests {
     }
 
     #[rstest]
-    #[case::azure_ai(BetaProvider::AzureAi, "fast-mode-2026-02-01,example-beta-2099-01-01")]
+    #[case::azure_ai(
+        BetaProvider::AzureAi,
+        "advisor-tool-2026-03-01,example-beta-2099-01-01"
+    )]
     #[case::anthropic(BetaProvider::Anthropic, "bash_20241022")]
     #[case::vertex_ai(BetaProvider::VertexAi, " , ")]
     fn filter_removes_header_when_nothing_survives(

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

@@ -1,3 +1,41 @@
+## Concepts
+
+Every price in a row lowers to one `Price { meter, channel, when }` and its `UnitPrice`. These keys come from the `FlatKey::parse` tests
+
+- `input_cost_per_token`
+  - meter `Input(Token(Text))`, channel `Online`, tier `Standard`, band `Base`
+- `output_cost_per_token_above_272k_tokens_priority`
+  - meter `Output(Token(Text))`, channel `Online`
+  - `_priority` sets tier `Priority`
+  - `_above_272k_tokens` sets band `OverPromptTokens(272000)`
+- `cache_creation_input_token_cost_above_1hr_above_200k_tokens`
+  - meter `CacheWrite { Text, OneHour }`, where `_above_1hr` is the cache TTL, not a band
+  - `_above_200k_tokens` sets band `OverPromptTokens(200000)`
+  - channel `Online`, tier `Standard`
+- `ocr_cost_per_page_batches`
+  - meter `Feature(OcrPage)`, tier `Standard`, band `Base`
+  - `_batches` sets channel `Batch`
+
+- Meter is what gets counted, such as text input tokens, reasoning tokens, an image of a given size or a web search
+  - A variation only one meter has stays inside it (cache TTL, image size, resolution, `_above_Ns_interval`, `max_results_range`)
+- `UnitPrice` is USD for one unit of the meter, such as one token, image, page or second
+- Band is a range of prompt sizes, starting where `_above_<N>_tokens` or `_above_<N>k_tokens` says
+  - `Prices::band` picks one band from prompt tokens alone, the highest edge passed wins, and `Band::Base` is below every edge
+  - The whole call moves, so every token meter, output included, bills all its tokens at that band's rate
+  - `Billing::band_edge` says whether a prompt exactly at the edge is in the band (exclusive by default, inclusive for xAI)
+  - A band only another tier or the batch channel prices never moves the call
+- `When` is the request's condition, its service tier and band
+  - A missing price falls back from `(tier, band)` to `(standard, band)`, `(tier, base)`, then `(standard, base)`
+- `Channel` is the route whose price list the call reads, and each falls back differently
+  - `Online` falls back across `When` as above
+  - `Batch` (`_batches`) uses bands but no service tier, and a missing price is half the online rate
+  - `Decisions` (`_decisions`, OpenAI `/v1/decisions`) overrides single online fields, and a missing price is the full online rate
+- Off-peak and `Adjustments` are never prices
+  - Off-peak swaps in other token rates while a UTC window is open
+  - `Adjustments` multiplies a priced call for region, residency, geo or `speed: fast`
+- `Billing` on `Prices` holds row attributes that change how its prices apply (text unit, band edge, cache-read fallback, wall clock, web search unit, tier scope)
+  - `lower.rs` derives it from the row and its `litellm_provider`, so `litellm-cost` holds no provider list
+
 ## Layout
 
 `wire/` reads one entry as written. `EntryWire::read` makes one pass over the entry's keys and puts every non-null key in exactly one place, which is an attribute, a flat price parsed by `FlatKey::parse`, a price block, a `supports_*` capability or `unknown`. A known key with a bad value becomes an `Error` that names the key, and the rest of the entry is still read
@@ -6,8 +44,6 @@
 
 `catalog.rs` reads the document, indexes aliases and runs the checks across entries. A bad entry keeps its raw fields and its errors are listed in `Loaded.errors`. Deciding what to do with them is the caller's job. `Catalog::written` gives back every top-level key and value as the document wrote them, in document order. `Catalog::served` gives back every model and alias as Python serves it, without `sample_spec` and `fallback_generalizations`
 
-`Billing` on `Prices` holds the row attributes pricing reads (text unit, band edge, cache-read fallback, wall clock, web search unit, tier scope). `lower.rs` derives it from the row and its `litellm_provider`, so `litellm-cost` holds no provider list
-
 `store/` is the catalog's in-memory store and the only place that indexes model names. Its rules are in [src/store/AGENTS.md](src/store/AGENTS.md)
 
 `layers.rs`, `resolve/`, `model_info.rs` and `register.rs` hold types only. The functions over them land with their first caller
@@ -16,7 +52,7 @@ Catalog errors use one opaque `Error` with typed causes and JSON Pointer accesso
 
 ## Prices
 
-The price vocabulary lives in `litellm-cost`, and only this crate knows JSON key names. Every price is keyed by `Meter`, `Channel` and `When`. A variation only one meter has stays in the meter (cache TTL, image size, resolution, `_above_Ns_interval`, `max_results_range`). Service tier and prompt band make up `When`. Batch and decisions are a `Channel` because Python bills them with their own rules. Off-peak is an overlay on `Prices`, and regional uplifts are `Adjustments`, never prices
+The price vocabulary lives in `litellm-cost`, and only this crate knows JSON key names
 
 A new `N`, image size or resolution in an existing key shape parses with no Rust change. A new unit, token kind, feature or tier needs a variant in `litellm-cost` plus its spelling in `wire/flat_key.rs` and `FLAT_KEY_PATTERN` in `schema.rs`
 

litellm/__init__.py

@@ -1647,6 +1647,7 @@ def set_global_gitlab_config(config: Mapping[str, object]) -> None:
 # Lazy loading system for heavy modules to reduce initial import time and memory usage
 
 if TYPE_CHECKING:
+    from . import guardrails as guardrails
     from litellm.types.utils import ModelInfo as _ModelInfoType
     from litellm.types.utils import PriorityReservationSettings
     from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
@@ -2499,10 +2500,10 @@ def __getattr__(name: str) -> Any:
         handler_func: Final = registry[name]
         return handler_func(name)
 
-    if name == "proxy":
+    if name in ("proxy", "guardrails"):
         import importlib
 
-        return importlib.import_module("litellm.proxy")
+        return importlib.import_module(f"litellm.{name}")
 
     if name == "harness" or name in _AGENT_EXPORTS:
         import importlib

litellm/_internal_context.py

@@ -21,6 +21,7 @@ _T = TypeVar("_T")
 # When True, suppresses async logging and billing for internal sub-calls
 # (e.g., emulated file-search steps that make nested LLM calls).
 is_internal_call: Final[ContextVar[bool]] = ContextVar("is_internal_call", default=False)
+internal_parent_request_id: Final[ContextVar[str | None]] = ContextVar("internal_parent_request_id", default=None)
 
 # One request prices its totals, its per-token-type lines and the rates it reports on
 # separate code paths. Each reads the clock for off-peak pricing, so without a pinned
@@ -40,13 +41,15 @@ _mirrored_call: Final[ContextVar[bool]] = ContextVar("mirrored_call", default=Fa
 
 
 @contextmanager
-def internal_sub_call() -> Generator[None]:
+def internal_sub_call(parent_request_id: str | None = None) -> Generator[None]:
     """A nested call the parent call logs and bills, so the parent must fold this call's cost into its own."""
     token: Final = is_internal_call.set(True)
+    parent_token: Final = internal_parent_request_id.set(parent_request_id or internal_parent_request_id.get())
     try:
         yield
     finally:
         is_internal_call.reset(token)
+        internal_parent_request_id.reset(parent_token)
 
 
 @contextmanager

litellm/_lazy_imports_registry.py

@@ -1469,11 +1469,11 @@ _UTILS_MODULE_IMPORT_MAP: Final = {
     "AsyncHTTPHandler": ("litellm.llms.custom_httpx.http_handler", "AsyncHTTPHandler"),
     "HTTPHandler": ("litellm.llms.custom_httpx.http_handler", "HTTPHandler"),
     "get_num_retries_from_retry_policy": (
-        "litellm.router_utils.get_retry_from_policy",
+        "litellm.litellm_core_utils.get_retry_from_policy",
         "get_num_retries_from_retry_policy",
     ),
     "reset_retry_policy": (
-        "litellm.router_utils.get_retry_from_policy",
+        "litellm.litellm_core_utils.get_retry_from_policy",
         "reset_retry_policy",
     ),
     "get_coroutine_checker": (

litellm/a2a_protocol/litellm_completion_bridge/handler.py

@@ -22,7 +22,7 @@ from litellm.a2a_protocol.litellm_completion_bridge.transformation import (
     A2AStreamingContext,
 )
 from litellm.a2a_protocol.providers.config_manager import A2AProviderConfigManager
-from litellm.constants import A2A_CHAT_COMPLETION_BRIDGE_PROVIDERS
+from litellm.constants import A2A_CHAT_COMPLETION_BRIDGE_PROVIDERS, A2A_COMPLETION_BRIDGE_WORKING_MESSAGE
 from litellm.interactions.agents.utils import merge_agent_headers
 from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm.types.utils import ModelResponse
@@ -268,7 +268,7 @@ class A2ACompletionBridgeHandler:
             ctx=ctx,
             state="working",
             final=False,
-            message_text="Processing request...",
+            message_text=A2A_COMPLETION_BRIDGE_WORKING_MESSAGE,
         )
         yield working_event
 

litellm/a2a_protocol/main.py

@@ -191,6 +191,23 @@ def _set_litellm_params_on_logging_obj(
     }
 
 
+async def _record_send_message_billing(
+    kwargs: Mapping[str, object],
+    request: "SendMessageRequest",
+    response_dict: Mapping[str, object],
+    litellm_params: Mapping[str, object],
+    agent_id: str | None,
+) -> None:
+    prompt_tokens, completion_tokens, _ = await asyncify(A2ARequestUtils.calculate_usage_from_request_response)(
+        request=request,
+        response_dict=response_dict,
+    )
+    _set_usage_on_logging_obj(kwargs=kwargs, prompt_tokens=prompt_tokens, completion_tokens=completion_tokens)
+    if "error" not in response_dict:
+        _set_litellm_params_on_logging_obj(kwargs=kwargs, litellm_params=litellm_params)
+    _set_agent_id_on_logging_obj(kwargs=kwargs, agent_id=agent_id)
+
+
 def _get_a2a_model_info(a2a_client: "A2AClientType", kwargs: Mapping[str, object]) -> str:
     """
     Extract agent info and set model/custom_llm_provider for cost tracking.
@@ -483,13 +500,21 @@ async def asend_message(
     if custom_llm_provider:
         if request is None:
             raise ValueError("request is required for completion bridge")
-        return await _send_message_via_completion_bridge(
+        bridge_response: Final = await _send_message_via_completion_bridge(
             request=request,
             custom_llm_provider=custom_llm_provider,
             api_base=api_base,
             litellm_params=litellm_params,
             agent_extra_headers=agent_extra_headers,
         )
+        await _record_send_message_billing(
+            kwargs=kwargs,
+            request=request,
+            response_dict=bridge_response.model_dump(mode="json", exclude_none=True),
+            litellm_params=litellm_params,
+            agent_id=agent_id,
+        )
+        return bridge_response
 
     # Standard A2A client flow
     if request is None:
@@ -538,30 +563,14 @@ async def asend_message(
     # Wrap in LiteLLM response type for _hidden_params support
     response: Final = LiteLLMSendMessageResponse.from_a2a_response(a2a_response, request_id=str(request.id))
 
-    # Calculate token usage from request and response
-    response_dict: Final[dict[str, object]] = a2a_response.root.model_dump(mode="json", exclude_none=True)
-    (
-        prompt_tokens,
-        completion_tokens,
-        _,
-    ) = await asyncify(A2ARequestUtils.calculate_usage_from_request_response)(
-        request=request,
-        response_dict=response_dict,
-    )
-
-    # Set usage on logging obj for standard logging payload
-    _set_usage_on_logging_obj(
+    await _record_send_message_billing(
         kwargs=kwargs,
-        prompt_tokens=prompt_tokens,
-        completion_tokens=completion_tokens,
+        request=request,
+        response_dict=a2a_response.root.model_dump(mode="json", exclude_none=True),
+        litellm_params=litellm_params,
+        agent_id=agent_id,
     )
 
-    # Merge agent pricing params into the logging obj so cost is calculated
-    _set_litellm_params_on_logging_obj(kwargs=kwargs, litellm_params=litellm_params)
-
-    # Set agent_id on logging obj for SpendLogs tracking
-    _set_agent_id_on_logging_obj(kwargs=kwargs, agent_id=agent_id)
-
     return response
 
 
@@ -646,6 +655,7 @@ async def asend_message_streaming(
     metadata: dict[str, object] | None = None,
     proxy_server_request: dict[str, object] | None = None,
     agent_extra_headers: dict[str, str] | None = None,
+    agent_name: str | None = None,
     **kwargs: object,
 ) -> AsyncIterator["SendStreamingMessageResponse | Mapping[str, object]"]:
     """
@@ -661,6 +671,7 @@ async def asend_message_streaming(
         agent_id: Optional agent ID for tracking in SpendLogs
         metadata: Optional metadata dict (contains user_api_key, user_id, team_id, etc.)
         proxy_server_request: Optional proxy server request data
+        agent_name: Agent name logged as a2a_agent/<agent_name> for completion bridge agents
 
     Yields:
         SendStreamingMessageResponse chunks from the agent
@@ -685,6 +696,7 @@ async def asend_message_streaming(
             print(chunk)
         ```
     """
+    called_at: Final = datetime.datetime.now()
     litellm_params = litellm_params or {}
     custom_llm_provider: Final = litellm_params.get("custom_llm_provider")
 
@@ -705,20 +717,47 @@ async def asend_message_streaming(
             request.params.model_dump(mode="json") if hasattr(request.params, "model_dump") else dict(request.params)
         )
 
-        async for chunk in A2ACompletionBridgeHandler.handle_streaming(
+        bridge_stream: Final = A2ACompletionBridgeHandler.handle_streaming(
             request_id=str(request.id),
             params=params,
             litellm_params=litellm_params,
             api_base=api_base,
             agent_extra_headers=agent_extra_headers,
-        ):
-            yield chunk
+        )
+        caller_logging_obj: Final = kwargs.get("litellm_logging_obj")
+        bridge_logging_obj: Final = (
+            caller_logging_obj
+            if isinstance(caller_logging_obj, Logging)
+            else _build_streaming_logging_obj(
+                request=request,
+                agent_name=agent_name or "unknown",
+                agent_id=agent_id,
+                litellm_params=litellm_params,
+                metadata=metadata,
+                proxy_server_request=proxy_server_request,
+                start_time=called_at,
+            )
+        )
+        _set_litellm_params_on_logging_obj(kwargs=kwargs, litellm_params=litellm_params)
+        _set_agent_id_on_logging_obj(kwargs=kwargs, agent_id=agent_id)
+
+        bridge_iterator: Final = A2AStreamingIterator(
+            stream=bridge_stream,
+            request=request,
+            logging_obj=bridge_logging_obj,
+            agent_name=agent_name or "unknown",
+        )
+        try:
+            async for chunk in bridge_iterator:
+                yield chunk
+        except (GeneratorExit, asyncio.CancelledError):
+            await bridge_iterator.abill_on_disconnect()
+            raise
         return
 
     if request is None:
         raise ValueError("request is required")
 
-    called_at: Final = datetime.datetime.now()
     _raw_logging_obj: Final = kwargs.get("litellm_logging_obj")
     logging_obj: Logging | None = _raw_logging_obj if isinstance(_raw_logging_obj, Logging) else None
 
@@ -742,12 +781,12 @@ async def asend_message_streaming(
 
     assert a2a_client is not None
 
-    agent_name: Final = _get_a2a_model_info(a2a_client, kwargs)
+    card_agent_name: Final = _get_a2a_model_info(a2a_client, kwargs)
 
     if logging_obj is None:
         logging_obj = _build_streaming_logging_obj(
             request=request,
-            agent_name=agent_name,
+            agent_name=card_agent_name,
             agent_id=agent_id,
             litellm_params=litellm_params,
             metadata=metadata,
@@ -755,7 +794,7 @@ async def asend_message_streaming(
             start_time=called_at,
         )
 
-    verbose_logger.info("A2A send_message_streaming request_id=%s, agent=%s", request.id, agent_name)
+    verbose_logger.info("A2A send_message_streaming request_id=%s, agent=%s", request.id, card_agent_name)
 
     agent_card: Final = _get_a2a_client_agent_card(a2a_client)
     card_url: Final = get_agent_card_url(agent_card) if agent_card else None
@@ -766,18 +805,23 @@ async def asend_message_streaming(
         agent_card=agent_card,
         card_url=card_url,
         api_base=api_base,
-        agent_name=agent_name,
+        agent_name=card_agent_name,
     )
 
     _set_agent_id_on_logging_obj(kwargs=kwargs, agent_id=agent_id)
 
-    async for chunk in A2AStreamingIterator(
+    url_agent_iterator: Final = A2AStreamingIterator(
         stream=stream,
         request=request,
         logging_obj=logging_obj,
-        agent_name=agent_name,
-    ):
-        yield chunk
+        agent_name=card_agent_name,
+    )
+    try:
+        async for chunk in url_agent_iterator:
+            yield chunk
+    except (GeneratorExit, asyncio.CancelledError):
+        await url_agent_iterator.abill_on_disconnect()
+        raise
 
 
 async def create_a2a_client(

litellm/a2a_protocol/providers/pydantic_ai_agents/transformation.py

@@ -515,6 +515,8 @@ class PydanticAITransformation:
                             "contextId": context_id,
                             "kind": "artifact-update",
                             "taskId": task_id,
+                            "append": i > 0,
+                            "lastChunk": is_last_chunk,
                             "artifact": {
                                 "artifactId": artifact_id,
                                 "parts": [

litellm/a2a_protocol/providers/watsonx_orchestrate/transformation.py

@@ -206,6 +206,8 @@ class WatsonxOrchestrateTransformation:
                     "contextId": context_id,
                     "kind": "artifact-update",
                     "taskId": task_id,
+                    "append": i > 0,
+                    "lastChunk": is_last,
                     "artifact": {
                         "artifactId": artifact_id,
                         "parts": [{"kind": "text", "text": chunk_text}],

litellm/a2a_protocol/streaming_iterator.py

@@ -3,16 +3,18 @@ A2A Streaming Iterator with token tracking and logging support.
 """
 
 import asyncio
-from collections.abc import AsyncIterator
+from collections.abc import AsyncIterator, Mapping
 from datetime import datetime
 from typing import TYPE_CHECKING, Final
 
+import anyio
 from pydantic import TypeAdapter
 
 import litellm
 from litellm._logging import verbose_logger
 from litellm.a2a_protocol.cost_calculator import A2ACostCalculator
 from litellm.a2a_protocol.utils import A2ARequestUtils
+from litellm.constants import A2A_COMPLETION_BRIDGE_WORKING_MESSAGE
 from litellm.litellm_core_utils.asyncify import asyncify
 from litellm.litellm_core_utils.core_helpers import as_str_mapping
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
@@ -23,6 +25,23 @@ if TYPE_CHECKING:
 _OPAQUE_PAYLOAD: Final = TypeAdapter(object)
 
 
+def _chunk_payload(chunk: "SendStreamingMessageResponse | Mapping[str, object]") -> Mapping[str, object]:
+    if isinstance(chunk, Mapping):
+        return chunk
+    return chunk.model_dump(mode="json", exclude_none=True) if hasattr(chunk, "model_dump") else {}
+
+
+def _artifact_update(payload: Mapping[str, object]) -> tuple[str, bool] | None:
+    result: Final = as_str_mapping(payload.get("result"))
+    if result is None or result.get("kind") != "artifact-update":
+        return None
+    artifact: Final = as_str_mapping(result.get("artifact"))
+    artifact_id: Final = artifact.get("artifactId") if artifact is not None else None
+    if not isinstance(artifact_id, str):
+        return None
+    return artifact_id, result.get("append") is True
+
+
 class A2AStreamingIterator:
     """
     Async iterator for A2A streaming responses with token tracking.
@@ -32,7 +51,7 @@ class A2AStreamingIterator:
 
     def __init__(
         self,
-        stream: AsyncIterator["SendStreamingMessageResponse"],
+        stream: AsyncIterator["SendStreamingMessageResponse | Mapping[str, object]"],
         request: "SendStreamingMessageRequest",
         logging_obj: LiteLLMLoggingObj,
         agent_name: str = "unknown",
@@ -44,14 +63,16 @@ class A2AStreamingIterator:
         self.start_time = logging_obj.start_time
 
         # Collect chunks for token counting
-        self.chunks: list[SendStreamingMessageResponse] = []
-        self.collected_text_parts: list[str] = []
-        self.final_chunk: SendStreamingMessageResponse | None = None
+        self.chunks: list[SendStreamingMessageResponse | Mapping[str, object]] = []
+        self.latest_message_text = ""
+        self.artifact_texts: Mapping[str, str] = {}
+        self.saw_error = False
+        self.final_chunk: SendStreamingMessageResponse | Mapping[str, object] | None = None
 
     def __aiter__(self):
         return self
 
-    async def __anext__(self) -> "SendStreamingMessageResponse":
+    async def __anext__(self) -> "SendStreamingMessageResponse | Mapping[str, object]":
         try:
             chunk: Final = await self.stream.__anext__()
             if self.logging_obj.completion_start_time is None:
@@ -59,38 +80,54 @@ class A2AStreamingIterator:
 
             # Store chunk
             self.chunks.append(chunk)
+            payload: Final = _chunk_payload(chunk)
+            self.saw_error = self.saw_error or payload.get("error") is not None
 
             # Extract text from chunk for token counting
-            self._collect_text_from_chunk(chunk)
+            self._collect_text_from_chunk(payload)
 
             # Check if this is the final chunk (completed status)
-            if self._is_completed_chunk(chunk):
+            if self._is_completed_chunk(payload):
                 self.final_chunk = chunk
 
             return chunk
 
         except StopAsyncIteration:
-            # Stream ended - handle logging
-            if self.final_chunk is None and self.chunks:
-                self.final_chunk = self.chunks[-1]
             await self._handle_stream_complete()
             raise
 
-    def _collect_text_from_chunk(self, chunk: "SendStreamingMessageResponse") -> None:
-        """Extract text from a streaming chunk and add to collected parts."""
+    async def abill_on_disconnect(self) -> None:
+        if not self.chunks:
+            return
+        with anyio.CancelScope(shield=True):
+            await self._handle_stream_complete()
+
+    def _collect_text_from_chunk(self, payload: Mapping[str, object]) -> None:
+        """Record the reply text a streaming chunk carries."""
         try:
-            chunk_dict: Final = chunk.model_dump(mode="json", exclude_none=True) if hasattr(chunk, "model_dump") else {}
-            text: Final = A2ARequestUtils.extract_text_from_response(chunk_dict)
-            if text:
-                self.collected_text_parts.append(text)
+            text: Final = A2ARequestUtils.extract_text_from_stream_event(payload)
+            if not text:
+                return
+            artifact_update: Final = _artifact_update(payload)
+            if artifact_update is None and text == A2A_COMPLETION_BRIDGE_WORKING_MESSAGE:
+                return
+            if artifact_update is None:
+                self.latest_message_text = text
+                return
+            artifact_id, append = artifact_update
+            artifact_text: Final = self.artifact_texts.get(artifact_id, "") + text if append else text
+            self.artifact_texts = {**self.artifact_texts, artifact_id: artifact_text}
         except Exception:
             verbose_logger.debug("Failed to extract text from A2A streaming chunk")
 
-    def _is_completed_chunk(self, chunk: "SendStreamingMessageResponse") -> bool:
+    def _reply_text(self) -> str:
+        artifact_text: Final = " ".join(text for text in self.artifact_texts.values() if text)
+        return artifact_text or self.latest_message_text
+
+    def _is_completed_chunk(self, payload: Mapping[str, object]) -> bool:
         """Check if chunk indicates stream completion."""
         try:
-            chunk_dict: Final = chunk.model_dump(mode="json", exclude_none=True) if hasattr(chunk, "model_dump") else {}
-            result: Final = chunk_dict.get("result", {})
+            result: Final = payload.get("result", {})
             if isinstance(result, dict):
                 status: Final = result.get("status", {})
                 if isinstance(status, dict):
@@ -101,6 +138,8 @@ class A2AStreamingIterator:
 
     async def _handle_stream_complete(self) -> None:
         """Handle logging and token counting when stream completes."""
+        if self.final_chunk is None and self.chunks:
+            self.final_chunk = self.chunks[-1]
         try:
             end_time: Final = datetime.now()
 
@@ -109,9 +148,7 @@ class A2AStreamingIterator:
             input_text: Final = A2ARequestUtils.extract_text_from_message(input_message)
             prompt_tokens: Final = await asyncify(A2ARequestUtils.count_tokens)(input_text)
 
-            # Use the last (most complete) text from chunks
-            output_text: Final = self.collected_text_parts[-1] if self.collected_text_parts else ""
-            completion_tokens: Final = await asyncify(A2ARequestUtils.count_tokens)(output_text)
+            completion_tokens: Final = await asyncify(A2ARequestUtils.count_tokens)(self._reply_text())
 
             total_tokens: Final = prompt_tokens + completion_tokens
 
@@ -128,7 +165,7 @@ class A2AStreamingIterator:
             self.logging_obj.model_call_details["stream"] = False
 
             # Calculate cost using A2ACostCalculator
-            response_cost: Final = A2ACostCalculator.calculate_a2a_cost(self.logging_obj)
+            response_cost: Final = 0.0 if self.saw_error else A2ACostCalculator.calculate_a2a_cost(self.logging_obj)
             self.logging_obj.model_call_details["response_cost"] = response_cost
 
             # Build result for logging
@@ -167,9 +204,7 @@ class A2AStreamingIterator:
         # Add final chunk result if available
         if self.final_chunk:
             try:
-                chunk_dict: Final = as_str_mapping(
-                    _OPAQUE_PAYLOAD.validate_python(self.final_chunk.model_dump(mode="json", exclude_none=True))
-                )
+                chunk_dict: Final = as_str_mapping(_OPAQUE_PAYLOAD.validate_python(_chunk_payload(self.final_chunk)))
                 if chunk_dict is not None:
                     result["result"] = chunk_dict.get("result", {})
             except Exception:

litellm/a2a_protocol/utils.py

@@ -6,13 +6,17 @@ import hashlib
 from collections.abc import Mapping
 from typing import TYPE_CHECKING, Final, Protocol
 
+from pydantic import TypeAdapter
+
 import litellm
 from litellm._logging import verbose_logger
-from litellm.litellm_core_utils.core_helpers import has_attributes
+from litellm.litellm_core_utils.core_helpers import as_str_mapping, has_attributes
 
 if TYPE_CHECKING:
     from a2a.types import SendMessageRequest, SendStreamingMessageRequest
 
+_OBJECT_ITEMS: Final = TypeAdapter(tuple[object, ...])
+
 
 class _SupportsModelDump(Protocol):
     def model_dump(self, *, mode: str) -> object: ...
@@ -34,6 +38,9 @@ class A2ARequestUtils:
         """
         if message is None:
             return ""
+        # a2a-sdk Part RootModels keep kind/text on part.root, so read pydantic messages as dicts
+        if has_attributes(message, _SupportsModelDump, "model_dump"):
+            return A2ARequestUtils.extract_text_from_message(message.model_dump(mode="json"))
 
         # Handle both dict and object access
         if isinstance(message, dict):
@@ -63,8 +70,8 @@ class A2ARequestUtils:
         Returns:
             Text from response message parts
         """
-        result: Final = response_dict.get("result", {})
-        if not isinstance(result, dict):
+        result: Final = as_str_mapping(response_dict.get("result"))
+        if result is None:
             return ""
 
         # Direct message format (A2A spec): detect by explicit kind tag only.
@@ -72,10 +79,36 @@ class A2ARequestUtils:
         # type that happens to include a "parts" field.
         if result.get("kind") == "message":
             return A2ARequestUtils.extract_text_from_message(result)
+        if result.get("kind") == "task":
+            return A2ARequestUtils.extract_text_from_task(result)
 
         message: Final = result.get("message", {})
         return A2ARequestUtils.extract_text_from_message(message)
 
+    @staticmethod
+    def extract_text_from_task(task: Mapping[str, object]) -> str:
+        artifacts: Final = task.get("artifacts")
+        artifact_items: Final = _OBJECT_ITEMS.validate_python(artifacts) if isinstance(artifacts, list) else ()
+        artifact_texts: Final = tuple(
+            A2ARequestUtils.extract_text_from_message(artifact) for artifact in artifact_items
+        )
+        if any(artifact_texts):
+            return " ".join(text for text in artifact_texts if text)
+        status: Final = as_str_mapping(task.get("status"))
+        return A2ARequestUtils.extract_text_from_message(status.get("message")) if status is not None else ""
+
+    @staticmethod
+    def extract_text_from_stream_event(event: Mapping[str, object]) -> str:
+        result: Final = as_str_mapping(event.get("result"))
+        if result is None:
+            return ""
+        if result.get("kind") == "artifact-update":
+            return A2ARequestUtils.extract_text_from_message(result.get("artifact"))
+        if result.get("kind") == "status-update":
+            status: Final = as_str_mapping(result.get("status"))
+            return A2ARequestUtils.extract_text_from_message(status.get("message")) if status is not None else ""
+        return A2ARequestUtils.extract_text_from_response(event)
+
     @staticmethod
     def get_input_message_from_request(
         request: "SendMessageRequest | SendStreamingMessageRequest",
@@ -128,17 +161,8 @@ class A2ARequestUtils:
         Returns:
             Tuple of (prompt_tokens, completion_tokens, total_tokens)
         """
-        # Count input tokens. Dump the message to a dict first so extraction hits
-        # the dict branch — request-side parts are a2a-sdk Part RootModels whose
-        # kind/text live on part.root, which the object branch cannot read. This
-        # mirrors how the response side already works (it operates on model_dump).
         input_message: Final = A2ARequestUtils.get_input_message_from_request(request)
-        input_text: Final = A2ARequestUtils.extract_text_from_message(
-            input_message.model_dump(mode="json")
-            if has_attributes(input_message, _SupportsModelDump, "model_dump")
-            else input_message
-        )
-        prompt_tokens: Final = A2ARequestUtils.count_tokens(input_text)
+        prompt_tokens: Final = A2ARequestUtils.count_tokens(A2ARequestUtils.extract_text_from_message(input_message))
 
         # Count output tokens
         output_text: Final = A2ARequestUtils.extract_text_from_response(response_dict)

litellm/accounting/protocols.py

@@ -3,7 +3,7 @@
 The event row's ``payload`` is a private envelope versioned by the row's ``schema_version``.
 Usage envelopes retain the public event and request-scoped pricing context: quantities with units,
 model/provider, requested and billed tier/region when known, and per-request/deployment overrides.
-These inputs must survive request teardown. Cost-map rates are resolved at compilation, not capture.
+These inputs, including the effective cost-map rates, must survive request teardown.
 Cost envelopes retain the public CostEvent, including its required finalized pricing snapshot.
 T5/T6 own envelope serialization; this public API does not introduce a second persisted schema.
 """
@@ -38,8 +38,8 @@ class CostCompiler(Protocol):
     async def compile(self, event: OutboundUsageEvent) -> CostEvent | Unpriced:
         """Reuse the committed charge or finalize one charge and its delivery intent.
 
-        Resolve captured request-scoped context by event identity and cost-map rates
-        at compilation. Persist the resolved inputs in the required snapshot. Replay
+        Resolve captured request-scoped context and rates by event identity.
+        Persist the resolved inputs in the required snapshot. Replay
         reuses the committed result and never consumes free allowance again.
         Missing pricing is Unpriced, never an inferred zero.
         """

litellm/accounting/types.py

@@ -276,8 +276,8 @@ class NamedCost(BaseModel):
 class PricingSnapshot(BaseModel):
     """Retained inputs and result of authoritative compilation, reproducible after cost-map rates change.
 
-    Request-scoped inputs and overrides come from the usage row's versioned payload. Cost-map rates are
-    resolved at compilation, not frozen at capture. Retries reuse the committed charge and snapshot.
+    Request-scoped inputs, overrides and effective rates come from the usage row's versioned payload.
+    Retries reuse the committed charge and snapshot.
     Component names match ``CostBreakdown``: reasoning_cost is part of output_cost, not an additional charge.
     Component sums and float tolerances belong to reconciliation, not this structural contract.
     """

litellm/anthropic_beta_headers_config.json

@@ -37,7 +37,47 @@
     "mid-conversation-output-config-2026-07-01": "mid-conversation-output-config-2026-07-01",
     "thinking-display-updates-2026-08-18": "thinking-display-updates-2026-08-18",
     "mid-conversation-tool-changes-2026-07-01": "mid-conversation-tool-changes-2026-07-01",
-    "inline-tools-2026-09-15": "inline-tools-2026-09-15"
+    "inline-tools-2026-09-15": "inline-tools-2026-09-15",
+    "agent-memory-2026-07-22": null,
+    "analytics-usage-cost-2026-08-24": "analytics-usage-cost-2026-08-24",
+    "cache-diagnosis-2026-04-07": "cache-diagnosis-2026-04-07",
+    "ce-plugins-2026-09-01": "ce-plugins-2026-09-01",
+    "ce-user-management-2026-07-13": "ce-user-management-2026-07-13",
+    "claude-code-20250219": "claude-code-20250219",
+    "code-execution-2025-05-22": "code-execution-2025-05-22",
+    "computer-use-2024-10-22": "computer-use-2024-10-22",
+    "dev-full-thinking-2025-05-14": null,
+    "dreaming-2026-04-21": "dreaming-2026-04-21",
+    "experimental-claude-tag-2026-10-21": "experimental-claude-tag-2026-10-21",
+    "extended-cache-ttl-2025-04-11": "extended-cache-ttl-2025-04-11",
+    "fallback-credit-2026-06-01": "fallback-credit-2026-06-01",
+    "fallback-credit-2026-07-01": "fallback-credit-2026-07-01",
+    "managed-agents-2026-04-01": "managed-agents-2026-04-01",
+    "mcp-client-2026-09-15": "mcp-client-2026-09-15",
+    "mcp-tunnels-2026-06-22": "mcp-tunnels-2026-06-22",
+    "message-batches-2024-09-24": "message-batches-2024-09-24",
+    "message-threads-2026-08-12": "message-threads-2026-08-12",
+    "mid-conversation-system-clear-at-2026-08-21": "mid-conversation-system-clear-at-2026-08-21",
+    "model-context-window-exceeded-2025-08-26": "model-context-window-exceeded-2025-08-26",
+    "output-300k-2026-03-24": "output-300k-2026-03-24",
+    "pdfs-2024-09-25": "pdfs-2024-09-25",
+    "prompt-caching-2024-07-31": "prompt-caching-2024-07-31",
+    "server-side-fallback-2026-06-01": "server-side-fallback-2026-06-01",
+    "server-side-fallback-2026-07-01": "server-side-fallback-2026-07-01",
+    "spend-limit-reads-2026-09-26": "spend-limit-reads-2026-09-26",
+    "task-budgets-2026-03-13": "task-budgets-2026-03-13",
+    "telemetry-destinations-2026-08-11": "telemetry-destinations-2026-08-11",
+    "thinking-token-count-2026-05-13": "thinking-token-count-2026-05-13",
+    "token-counting-2024-11-01": "token-counting-2024-11-01",
+    "tool-examples-2025-10-29": "tool-examples-2025-10-29",
+    "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19",
+    "user-profiles-2026-03-24": "user-profiles-2026-03-24",
+    "user-profiles-2026-08-18": "user-profiles-2026-08-18",
+    "user-profiles-2026-09-04": "user-profiles-2026-09-04",
+    "webhooks-2026-10-07": "webhooks-2026-10-07",
+    "afk-mode-2026-01-31": "afk-mode-2026-01-31",
+    "redact-thinking-2026-02-12": "redact-thinking-2026-02-12",
+    "structured-outputs-2025-12-15": "structured-outputs-2025-12-15"
   },
   "azure_ai": {
     "advisor-tool-2026-03-01": null,
@@ -45,23 +85,21 @@
     "bash_20241022": null,
     "bash_20250124": null,
     "code-execution-2025-08-25": "code-execution-2025-08-25",
-    "compact-2026-01-12": null,
-    "compact-2026-09-04": "compact-2026-09-04",
+    "compact-2026-01-12": "compact-2026-01-12",
     "computer-use-2025-01-24": "computer-use-2025-01-24",
     "computer-use-2025-11-24": "computer-use-2025-11-24",
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
     "dangerous-tool-use-2026-09-03": "dangerous-tool-use-2026-09-03",
     "effort-2025-11-24": "effort-2025-11-24",
-    "fast-mode-2026-02-01": null,
+    "fast-mode-2026-02-01": "fast-mode-2026-02-01",
     "files-api-2025-04-14": "files-api-2025-04-14",
-    "fine-grained-tool-streaming-2025-05-14": null,
+    "fine-grained-tool-streaming-2025-05-14": "fine-grained-tool-streaming-2025-05-14",
     "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14",
     "mcp-client-2025-11-20": "mcp-client-2025-11-20",
     "mcp-client-2025-04-04": "mcp-client-2025-04-04",
     "mcp-servers-2025-12-04": null,
-    "mid-conversation-tool-changes-2026-07-01": "mid-conversation-tool-changes-2026-07-01",
-    "output-128k-2025-02-19": null,
+    "output-128k-2025-02-19": "output-128k-2025-02-19",
     "structured-output-2024-03-01": null,
     "per-turn-control-2026-07-01": "per-turn-control-2026-07-01",
     "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05",
@@ -71,9 +109,54 @@
     "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": null,
+    "token-efficient-tools-2025-02-19": "token-efficient-tools-2025-02-19",
     "web-fetch-2025-09-10": "web-fetch-2025-09-10",
-    "web-search-2025-03-05": "web-search-2025-03-05"
+    "web-search-2025-03-05": "web-search-2025-03-05",
+    "agent-memory-2026-07-22": null,
+    "analytics-usage-cost-2026-08-24": "analytics-usage-cost-2026-08-24",
+    "cache-diagnosis-2026-04-07": "cache-diagnosis-2026-04-07",
+    "ce-plugins-2026-09-01": "ce-plugins-2026-09-01",
+    "ce-user-management-2026-07-13": "ce-user-management-2026-07-13",
+    "claude-code-20250219": "claude-code-20250219",
+    "code-execution-2025-05-22": "code-execution-2025-05-22",
+    "compact-2026-09-04": "compact-2026-09-04",
+    "computer-use-2024-10-22": "computer-use-2024-10-22",
+    "dev-full-thinking-2025-05-14": null,
+    "dreaming-2026-04-21": "dreaming-2026-04-21",
+    "experimental-claude-tag-2026-10-21": "experimental-claude-tag-2026-10-21",
+    "extended-cache-ttl-2025-04-11": "extended-cache-ttl-2025-04-11",
+    "fallback-credit-2026-06-01": "fallback-credit-2026-06-01",
+    "fallback-credit-2026-07-01": "fallback-credit-2026-07-01",
+    "inline-tools-2026-09-15": "inline-tools-2026-09-15",
+    "managed-agents-2026-04-01": "managed-agents-2026-04-01",
+    "mcp-client-2026-09-15": "mcp-client-2026-09-15",
+    "mcp-tunnels-2026-06-22": "mcp-tunnels-2026-06-22",
+    "message-batches-2024-09-24": "message-batches-2024-09-24",
+    "message-threads-2026-08-12": "message-threads-2026-08-12",
+    "mid-conversation-output-config-2026-07-01": "mid-conversation-output-config-2026-07-01",
+    "mid-conversation-system-clear-at-2026-08-21": "mid-conversation-system-clear-at-2026-08-21",
+    "mid-conversation-tool-changes-2026-07-01": "mid-conversation-tool-changes-2026-07-01",
+    "model-context-window-exceeded-2025-08-26": "model-context-window-exceeded-2025-08-26",
+    "oauth-2025-04-20": "oauth-2025-04-20",
+    "output-300k-2026-03-24": "output-300k-2026-03-24",
+    "pdfs-2024-09-25": "pdfs-2024-09-25",
+    "prompt-caching-2024-07-31": "prompt-caching-2024-07-31",
+    "server-side-fallback-2026-06-01": "server-side-fallback-2026-06-01",
+    "server-side-fallback-2026-07-01": "server-side-fallback-2026-07-01",
+    "spend-limit-reads-2026-09-26": "spend-limit-reads-2026-09-26",
+    "task-budgets-2026-03-13": "task-budgets-2026-03-13",
+    "telemetry-destinations-2026-08-11": "telemetry-destinations-2026-08-11",
+    "thinking-token-count-2026-05-13": "thinking-token-count-2026-05-13",
+    "token-counting-2024-11-01": "token-counting-2024-11-01",
+    "tool-examples-2025-10-29": "tool-examples-2025-10-29",
+    "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19",
+    "user-profiles-2026-03-24": "user-profiles-2026-03-24",
+    "user-profiles-2026-08-18": "user-profiles-2026-08-18",
+    "user-profiles-2026-09-04": "user-profiles-2026-09-04",
+    "webhooks-2026-10-07": "webhooks-2026-10-07",
+    "afk-mode-2026-01-31": "afk-mode-2026-01-31",
+    "redact-thinking-2026-02-12": "redact-thinking-2026-02-12",
+    "structured-outputs-2025-12-15": "structured-outputs-2025-12-15"
   },
   "bedrock_converse": {
     "advisor-tool-2026-03-01": null,
@@ -196,34 +279,92 @@
     "context-1m-2025-08-07": "context-1m-2025-08-07",
     "context-management-2025-06-27": "context-management-2025-06-27",
     "dangerous-tool-use-2026-09-03": "dangerous-tool-use-2026-09-03",
-    "effort-2025-11-24": null,
+    "effort-2025-11-24": "effort-2025-11-24",
     "fast-mode-2026-02-01": null,
     "files-api-2025-04-14": null,
-    "fine-grained-tool-streaming-2025-05-14": null,
+    "fine-grained-tool-streaming-2025-05-14": "fine-grained-tool-streaming-2025-05-14",
     "interleaved-thinking-2025-05-14": "interleaved-thinking-2025-05-14",
     "mcp-client-2025-11-20": null,
     "mcp-client-2025-04-04": null,
     "mcp-servers-2025-12-04": null,
-    "mid-conversation-tool-changes-2026-07-01": "mid-conversation-tool-changes-2026-07-01",
-    "output-128k-2025-02-19": null,
+    "output-128k-2025-02-19": "output-128k-2025-02-19",
     "structured-output-2024-03-01": null,
     "per-turn-control-2026-07-01": "per-turn-control-2026-07-01",
     "prompt-caching-scope-2026-01-05": null,
     "skills-2025-10-02": null,
-    "structured-outputs-2025-11-13": null,
+    "structured-outputs-2025-11-13": "structured-outputs-2025-11-13",
     "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": null,
+    "token-efficient-tools-2025-02-19": "token-efficient-tools-2025-02-19",
     "tool-search-tool-2025-10-19": "tool-search-tool-2025-10-19",
     "web-fetch-2025-09-10": null,
     "web-search-2025-03-05": "web-search-2025-03-05",
-    "inline-tools-2026-09-15": "inline-tools-2026-09-15"
+    "inline-tools-2026-09-15": "inline-tools-2026-09-15",
+    "agent-memory-2026-07-22": null,
+    "analytics-usage-cost-2026-08-24": null,
+    "cache-diagnosis-2026-04-07": null,
+    "ce-plugins-2026-09-01": null,
+    "ce-user-management-2026-07-13": null,
+    "claude-code-20250219": "claude-code-20250219",
+    "code-execution-2025-05-22": null,
+    "compact-2026-09-04": null,
+    "computer-use-2024-10-22": null,
+    "dev-full-thinking-2025-05-14": null,
+    "dreaming-2026-04-21": null,
+    "experimental-claude-tag-2026-10-21": null,
+    "extended-cache-ttl-2025-04-11": null,
+    "fallback-credit-2026-06-01": "fallback-credit-2026-06-01",
+    "fallback-credit-2026-07-01": "fallback-credit-2026-07-01",
+    "managed-agents-2026-04-01": null,
+    "mcp-client-2026-09-15": null,
+    "mcp-tunnels-2026-06-22": null,
+    "message-batches-2024-09-24": null,
+    "message-threads-2026-08-12": null,
+    "mid-conversation-output-config-2026-07-01": "mid-conversation-output-config-2026-07-01",
+    "mid-conversation-system-clear-at-2026-08-21": "mid-conversation-system-clear-at-2026-08-21",
+    "mid-conversation-tool-changes-2026-07-01": "mid-conversation-tool-changes-2026-07-01",
+    "model-context-window-exceeded-2025-08-26": "model-context-window-exceeded-2025-08-26",
+    "oauth-2025-04-20": null,
+    "output-300k-2026-03-24": null,
+    "pdfs-2024-09-25": "pdfs-2024-09-25",
+    "prompt-caching-2024-07-31": null,
+    "server-side-fallback-2026-06-01": "server-side-fallback-2026-06-01",
+    "server-side-fallback-2026-07-01": null,
+    "spend-limit-reads-2026-09-26": null,
+    "task-budgets-2026-03-13": "task-budgets-2026-03-13",
+    "telemetry-destinations-2026-08-11": null,
+    "thinking-token-count-2026-05-13": "thinking-token-count-2026-05-13",
+    "token-counting-2024-11-01": "token-counting-2024-11-01",
+    "tool-examples-2025-10-29": "tool-examples-2025-10-29",
+    "user-profiles-2026-03-24": null,
+    "user-profiles-2026-08-18": null,
+    "user-profiles-2026-09-04": null,
+    "webhooks-2026-10-07": null,
+    "afk-mode-2026-01-31": "afk-mode-2026-01-31",
+    "redact-thinking-2026-02-12": null,
+    "structured-outputs-2025-12-15": "structured-outputs-2025-12-15"
   },
   "vertex_ai_header_only": {
+    "ce-plugins-2026-09-01": "ce-plugins-2026-09-01",
+    "ce-user-management-2026-07-13": "ce-user-management-2026-07-13",
     "compact-2026-09-04": "compact-2026-09-04",
-    "inline-tools-2026-09-15": "inline-tools-2026-09-15"
+    "dreaming-2026-04-21": "dreaming-2026-04-21",
+    "experimental-claude-tag-2026-10-21": "experimental-claude-tag-2026-10-21",
+    "extended-cache-ttl-2025-04-11": "extended-cache-ttl-2025-04-11",
+    "inline-tools-2026-09-15": "inline-tools-2026-09-15",
+    "managed-agents-2026-04-01": "managed-agents-2026-04-01",
+    "mcp-tunnels-2026-06-22": "mcp-tunnels-2026-06-22",
+    "oauth-2025-04-20": "oauth-2025-04-20",
+    "prompt-caching-2024-07-31": "prompt-caching-2024-07-31",
+    "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05",
+    "redact-thinking-2026-02-12": "redact-thinking-2026-02-12",
+    "spend-limit-reads-2026-09-26": "spend-limit-reads-2026-09-26",
+    "telemetry-destinations-2026-08-11": "telemetry-destinations-2026-08-11",
+    "user-profiles-2026-03-24": "user-profiles-2026-03-24",
+    "user-profiles-2026-08-18": "user-profiles-2026-08-18",
+    "user-profiles-2026-09-04": "user-profiles-2026-09-04"
   },
   "databricks": {
     "advisor-tool-2026-03-01": null,

litellm/batches/batch_utils.py

@@ -3,9 +3,10 @@ from collections.abc import Iterable, Iterator, Mapping, Sequence
 from dataclasses import dataclass
 from dataclasses import replace as dataclasses_replace
 from enum import Enum
+from itertools import groupby
 from typing import Any, Final, Literal, cast
 
-from pydantic import TypeAdapter
+from pydantic import JsonValue, TypeAdapter
 
 import litellm
 from litellm._logging import verbose_logger
@@ -15,6 +16,7 @@ from litellm.llms.base_llm.ocr.transformation import OCRUsageInfo
 from litellm.llms.bedrock.batches.transformation import titan_embedding_usage_from_batch_output
 from litellm.llms.vertex_ai.batches.transformation import (
     is_native_vertex_batch_output_row,
+    native_vertex_batch_row_model,
     native_vertex_batch_row_stats,
 )
 from litellm.types.llms.openai import Batch
@@ -210,6 +212,7 @@ class _BatchOutputLineStats:
     cache_creation_tokens: int
     reasoning_tokens: int
     model: str | None
+    usage: Usage | None
 
 
 def _classify_output_line_stats(
@@ -268,7 +271,7 @@ def _compute_output_line_stats(
     raw_model: Final = response_body.get("model")
     response_model: Final = raw_model if isinstance(raw_model, str) and raw_model else None
     completion_details: Final = usage.completion_tokens_details
-    line_prompt_cost, line_completion_cost = _output_line_cost(
+    line_prompt_cost, line_completion_cost = _output_line_cost_or_pending(
         response_body=response_body,
         usage=usage,
         custom_llm_provider=custom_llm_provider,
@@ -286,7 +289,15 @@ def _compute_output_line_stats(
         cache_read_tokens=prompt_details["cache_hit_tokens"],
         cache_creation_tokens=prompt_details["cache_creation_tokens"],
         reasoning_tokens=(completion_details.reasoning_tokens if completion_details else None) or 0,
-        model=response_model,
+        model=response_model or model_name,
+        usage=(
+            usage.model_copy(update=ocr_usage.model_dump(exclude_none=True))
+            if (ocr_usage := _ocr_usage_info_from_response_body(response_body)) is not None
+            and ocr_usage.model_dump(exclude_none=True)
+            else usage
+            if response_body.get("usage") is not None or usage.total_tokens > 0
+            else None
+        ),
     )
 
 
@@ -298,6 +309,54 @@ def _ocr_usage_info_from_response_body(response_body: Mapping[str, object]) -> O
     return OCRUsageInfo.model_validate(raw_usage_info)
 
 
+def _output_line_cost_or_pending(
+    response_body: Mapping[str, object],
+    usage: Usage,
+    custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm", "anthropic", "bedrock", "mistral"],
+    model_name: str | None,
+    response_model: str | None,
+    model_info: ModelInfo | None,
+    vertex_location: str | None,
+) -> tuple[float, float]:
+    try:
+        return _output_line_cost(
+            response_body, usage, custom_llm_provider, model_name, response_model, model_info, vertex_location
+        )
+    except Exception:  # noqa: BLE001  # retain measured usage when existing batch pricing fails
+        verbose_logger.warning("Batch line pricing unavailable; retaining usage with the existing zero-cost fallback")
+        return 0.0, 0.0
+
+
+def _combined_reported_batch_usage(usages: tuple[Usage | None, ...]) -> Mapping[str, object] | None:
+    from litellm.cost_calculator import BaseTokenUsageProcessor
+
+    reported: Final = [usage for usage in usages if usage is not None]
+    if not reported:
+        return None
+    fields: Final = tuple(TypeAdapter(dict[str, JsonValue]).validate_python(usage.model_dump()) for usage in reported)
+    quantities: Final = {
+        key: sum(value for usage in fields if isinstance(value := usage.get(key), (int, float)))
+        for key in OCRUsageInfo.model_fields
+        if any(isinstance(usage.get(key), (int, float)) for usage in fields)
+    }
+    return {**BaseTokenUsageProcessor.combine_usage_objects(usage_objects=reported).model_dump(), **quantities}
+
+
+def _batch_usage_by_request(usages: tuple[tuple[str | None, Usage | None], ...]) -> tuple[Mapping[str, object], ...]:
+    rows: Final = tuple(
+        {"model": model, "usage": usage.model_dump() if usage is not None else None} for model, usage in usages
+    )
+    return tuple(sorted(rows, key=lambda row: json.dumps(row, sort_keys=True)))
+
+
+def _batch_usage_by_model(usages: tuple[tuple[str | None, Usage | None], ...]) -> Mapping[str, object]:
+    ordered: Final = sorted(usages, key=lambda item: item[0] or "")
+    return {
+        model: _combined_reported_batch_usage(tuple(item[1] for item in group))
+        for model, group in groupby(ordered, key=lambda item: item[0] or "")
+    }
+
+
 def _output_line_cost(
     response_body: Mapping[str, object],
     usage: Usage,
@@ -361,6 +420,9 @@ def _aggregate_batch_cost_usage_models(
         completion_tokens=sum(stats.completion_tokens for stats in line_stats),
         reasoning_tokens=sum(stats.reasoning_tokens for stats in line_stats),
         **cache_token_params,
+        batch_usage_by_model=_batch_usage_by_model(tuple((stats.model, stats.usage) for stats in line_stats)),
+        batch_usage_by_request=_batch_usage_by_request(tuple((stats.model, stats.usage) for stats in line_stats)),
+        batch_unknown_usage_requests=sum(stats.usage is None for stats in line_stats),
     )
     batch_models: Final = [model_name] if model_name else [stats.model for stats in line_stats if stats.model]
     total_prompt_cost: Final = sum((stats.prompt_cost for stats in line_stats), 0.0)
@@ -401,17 +463,21 @@ def calculate_vertex_ai_batch_cost_and_usage(
     from litellm.cost_calculator import batch_cost_calculator
     from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig
 
-    row_stats: Final = tuple(
-        native_vertex_batch_row_stats(
-            row,
-            model_name,
-            model_info=model_info,
-            calculate_usage=VertexGeminiConfig.calculate_usage,
-            cost_calculator=batch_cost_calculator,
-            vertex_location=vertex_location,
+    observations: Final = tuple(
+        (
+            native_vertex_batch_row_model(row, model_name),
+            native_vertex_batch_row_stats(
+                row,
+                model_name,
+                model_info=model_info,
+                calculate_usage=VertexGeminiConfig.calculate_usage,
+                cost_calculator=batch_cost_calculator,
+                vertex_location=vertex_location,
+            ),
         )
         for row in vertex_ai_batch_responses
     )
+    row_stats: Final = tuple(stats for _, stats in observations)
     priced: Final = tuple(stats for stats in row_stats if stats is not None)
     total_prompt_cost: Final = sum(stats.prompt_cost for stats in priced)
     total_completion_cost: Final = sum(stats.completion_cost for stats in priced)
@@ -435,6 +501,13 @@ def calculate_vertex_ai_batch_cost_and_usage(
             total_tokens=total_tokens,
             prompt_tokens=prompt_tokens,
             completion_tokens=completion_tokens,
+            batch_usage_by_model=_batch_usage_by_model(
+                tuple((model, stats.usage if stats is not None else None) for model, stats in observations)
+            ),
+            batch_usage_by_request=_batch_usage_by_request(
+                tuple((model, stats.usage if stats is not None else None) for model, stats in observations)
+            ),
+            batch_unknown_usage_requests=sum(stats is None for stats in row_stats),
         ),
         models=(
             [model_name]

litellm/caching/caching.py

@@ -25,7 +25,11 @@ from litellm._internal_context import current_service_target, service_target
 from litellm._logging import verbose_logger
 from litellm.constants import CACHED_STREAMING_CHUNK_DELAY
 from litellm.integrations.otel.runtime import phase_span
-from litellm.litellm_core_utils.model_param_helper import ModelParamHelper, peek_reasoning_summary_aliases
+from litellm.litellm_core_utils.model_param_helper import (
+    ModelParamHelper,
+    embeds_thought_signature_in_tool_call_id,
+    peek_reasoning_summary_aliases,
+)
 from litellm.types.caching import *
 from litellm.types.utils import EmbeddingResponse, is_litellm_owned_kwarg
 
@@ -421,7 +425,12 @@ class Cache:
         reasoning_summary_material: Final = (
             "" if reasoning_summary is None else f"reasoning_summary: {reasoning_summary}"
         )
-        hashed_cache_key = Cache._get_hashed_cache_key(cache_key + reasoning_summary_material)
+        tool_call_id_shape_material: Final = (
+            "" if embeds_thought_signature_in_tool_call_id(kwargs) else "thought_signature_in_tool_call_id: False"
+        )
+        hashed_cache_key = Cache._get_hashed_cache_key(
+            cache_key + reasoning_summary_material + tool_call_id_shape_material
+        )
         hashed_cache_key = self._add_namespace_to_cache_key(hashed_cache_key, **kwargs)
         verbose_logger.debug(
             "\nCreated cache key: %s (source material length: %d)",

litellm/constants.py

@@ -177,6 +177,8 @@ MID_STREAM_FAILURE_LOG_WAIT_SECONDS: Final = 1.0
 # Set to 0 to disable truncation.
 MAX_BASE64_LENGTH_FOR_LOGGING: Final = get_env_int("MAX_BASE64_LENGTH_FOR_LOGGING", 64)
 BASE64_TRUNCATION_OFFLOAD_THRESHOLD_CHARS: Final = 256 * 1024
+BASE64_TRUNCATION_OFFLOAD_THRESHOLD_STRINGS: Final = 256
+BASE64_TRUNCATION_INLINE_COPY_MAX_CHARS: Final = 8 * 1024 * 1024
 REDACTED_BY_LITELLM: Final = "redacted-by-litellm"
 # in-memory stand-in handed to provider converters for redacted arguments; never stored
 REDACTED_TOOL_CALL_ARGUMENTS_PLACEHOLDER: Final = "{}"
@@ -430,6 +432,7 @@ WEBSOCKET_CLOSE_REASON_MAX_BYTES: Final = 123
 DEEPGRAM_DEFAULT_API_BASE: Final = "https://api.deepgram.com/v1"
 NADIR_DEFAULT_API_BASE: Final = "https://api.getnadir.com/v1"
 DEEPGRAM_LISTEN_DEFAULT_MODEL: Final = "nova-3"
+DEEPGRAM_SPEAK_DEFAULT_MODEL: Final = "aura-asteria-en"
 
 BEDROCK_REALTIME_PENDING_OPENING_MESSAGE_SCOPE_KEY: Final = "litellm.bedrock_realtime.pending_opening_message"
 BEDROCK_REALTIME_SESSION_COMMITTED_SCOPE_KEY: Final = "litellm.bedrock_realtime.session_committed"
@@ -634,6 +637,8 @@ 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_MAX_USER_ID_LENGTH: Final = 512
+ANTHROPIC_COMPATIBLE_MAX_USER_ID_LENGTH: Final = 255
 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)
@@ -671,7 +676,7 @@ request_timeout: float = (
     DEFAULT_REQUEST_TIMEOUT_SECONDS if _request_timeout_from_env is None else _request_timeout_from_env
 )
 request_timeout_explicitly_set: bool = _request_timeout_from_env is not None
-DEFAULT_A2A_AGENT_TIMEOUT: Final[float] = get_env_float("DEFAULT_A2A_AGENT_TIMEOUT", 6000.0)  # 10 minutes
+DEFAULT_A2A_AGENT_TIMEOUT: Final[float] = get_env_float("DEFAULT_A2A_AGENT_TIMEOUT", 6000.0)
 AGENT_KILL_SWITCH_TIMEOUT_SECONDS: Final = 10.0
 AGENT_KILL_SWITCH_RESPONSE_BODY_MAX_CHARS: Final = 2000
 # Patterns that indicate a localhost/internal URL in A2A agent cards that should be
@@ -902,6 +907,7 @@ PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO: Final = frozenset(
 
 SUBSCRIPTION_BACKED_PROVIDERS: Final = frozenset({"chatgpt"})
 A2A_CHAT_COMPLETION_BRIDGE_PROVIDERS: Final = frozenset({"databricks_agent"})
+A2A_COMPLETION_BRIDGE_WORKING_MESSAGE: Final = "Processing request..."
 
 LITELLM_EMBEDDING_PROVIDERS_SUPPORTING_INPUT_ARRAY_OF_TOKENS: Final = [
     "openai",
@@ -1162,6 +1168,7 @@ openai_compatible_providers: Final[list[str]] = [
     "cortecs",
     "scx-ai",
     "prism",
+    "hostinger_router",
     "sail",
     "reka",
     "streamlake",
@@ -1685,6 +1692,7 @@ BAGGAGE_MAPPED_TAGS_METADATA_KEY: Final = "_baggage_mapped_tags"
 VECTOR_STORE_CALLER_METADATA_KWARG: Final = "litellm_vector_store_caller_metadata"
 ROUTER_USAGE_COUNTED_TOKENS_METADATA_KEY: Final = "_litellm_router_usage_counted_tokens"
 INTERNAL_CALL_ORIGIN_METADATA_KEY: Final = "internal_call_origin"
+RESPONSES_WEBSOCKET_CALL_ID_METADATA_KEY: Final = "responses_websocket_call_id"
 BILLED_RETRY_ATTEMPT_METADATA_KEY: Final = "billed_retry_attempt"
 MANAGED_BACKGROUND_RESPONSE_METADATA_KEY: Final = "_managed_background_response"
 SESSION_ID_GENERATED_METADATA_KEY: Final = "litellm_session_id_generated"
@@ -2046,6 +2054,7 @@ LITELLM_SETTINGS_SAFE_DB_OVERRIDES: Final = [
 SPECIAL_LITELLM_AUTH_TOKEN: Final = ["ui-token"]
 DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL = get_env_int("DEFAULT_MANAGEMENT_OBJECT_IN_MEMORY_CACHE_TTL", 60)
 AUTH_OBJECTS_TARGET: Final = "auth_objects"
+HEALTH_CHECKS_TARGET: Final = "health_checks"
 DEFAULT_ACCESS_GROUP_CACHE_TTL: Final = get_env_int("DEFAULT_ACCESS_GROUP_CACHE_TTL", 600)
 SPEND_LOG_KEY_METADATA_CACHE_TTL: Final = 600
 SPEND_LOG_KEY_METADATA_MISS_CACHE_TTL: Final = 30

litellm/cost_calculator.py

@@ -6,6 +6,7 @@ import time
 from collections.abc import Mapping, Sequence
 from datetime import datetime
 from functools import lru_cache
+from itertools import accumulate
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, Literal, TypeAlias, cast
 
@@ -1554,12 +1555,66 @@ def _breakdown_total(litellm_logging_obj: LitellmLoggingObject | None) -> float
     return float(total) if isinstance(total, (int, float)) and not isinstance(total, bool) else None
 
 
+def _cost_map_priced_input_output(
+    model: str | None,
+    custom_llm_provider: str | None,
+    usage: Usage | None,
+    call_type: CallTypesLiteral | None,
+    service_tier: str | None,
+    data_residency: str | None,
+) -> tuple[float, float]:
+    if not model or usage is None:
+        return 0.0, 0.0
+    try:
+        return cost_per_token(
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            usage_object=usage,
+            call_type=call_type or "completion",
+            service_tier=service_tier,
+            data_residency=data_residency,
+        )
+    except Exception as pricing_error:  # noqa: BLE001  # cost_per_token raises plain Exception for an unmapped model
+        verbose_logger.debug("No cost map price for reported-cost split of %s: %s", model, pricing_error)
+        return 0.0, 0.0
+
+
+def split_reported_cost_by_cost_map_ratio(
+    reported_total: float,
+    model: str | None,
+    custom_llm_provider: str | None,
+    usage: Usage | None,
+    call_type: CallTypesLiteral | None,
+    service_tier: str | None,
+    data_residency: str | None,
+) -> tuple[float, float]:
+    """Apportions a provider-reported total between input and output in the ratio litellm's own cost map would
+    price the same usage, so a per-input-token model books the whole total on input. Unmapped or zero-priced
+    usage keeps the whole total on output."""
+    priced_input, priced_output = _cost_map_priced_input_output(
+        model=model,
+        custom_llm_provider=custom_llm_provider,
+        usage=usage,
+        call_type=call_type,
+        service_tier=service_tier,
+        data_residency=data_residency,
+    )
+    priced_total: Final = priced_input + priced_output
+    if priced_total <= 0:
+        return 0.0, reported_total
+    input_cost: Final = reported_total * (priced_input / priced_total)
+    return input_cost, reported_total - input_cost
+
+
 def _adjusted_provider_billed_cost(
     reported_cost: float,
     custom_llm_provider: str | None,
+    pricing_provider: str | None,
     model: str | None,
     region_name: str | None,
     litellm_logging_obj: LitellmLoggingObject | None,
+    usage: Usage | None,
+    call_type: CallTypesLiteral | None,
     service_tier: str | None,
     data_residency: str | None,
 ) -> float:
@@ -1569,10 +1624,19 @@ def _adjusted_provider_billed_cost(
     adjusted_cost, margin_percent, margin_fixed_amount, margin_total_amount = _apply_cost_margin(
         base_cost=discounted_cost, custom_llm_provider=custom_llm_provider
     )
+    input_cost, output_cost = split_reported_cost_by_cost_map_ratio(
+        reported_total=adjusted_cost,
+        model=model,
+        custom_llm_provider=pricing_provider,
+        usage=usage,
+        call_type=call_type,
+        service_tier=service_tier,
+        data_residency=data_residency,
+    )
     _store_cost_breakdown_in_logging_obj(
         litellm_logging_obj=litellm_logging_obj,
-        prompt_tokens_cost_usd_dollar=0.0,
-        completion_tokens_cost_usd_dollar=adjusted_cost,
+        prompt_tokens_cost_usd_dollar=input_cost,
+        completion_tokens_cost_usd_dollar=output_cost,
         cost_for_built_in_tools_cost_usd_dollar=0.0,
         total_cost_usd_dollar=adjusted_cost,
         original_cost=reported_cost,
@@ -2080,9 +2144,16 @@ def completion_cost(
                     return _adjusted_provider_billed_cost(
                         reported_cost=reported_cost,
                         custom_llm_provider=custom_llm_provider,
+                        pricing_provider=_pricing_provider_for_cost_candidate(
+                            candidate_provider=candidate_pricing_provider,
+                            request_provider=request_pricing_provider,
+                            current_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,
+                        usage=cost_per_token_usage_object,
+                        call_type=call_type,
                         service_tier=service_tier,
                         data_residency=data_residency,
                     )
@@ -2387,10 +2458,28 @@ def response_cost_calculator(
                     provider_response_cost: Final = get_response_cost_from_hidden_params(hidden_params)
                     if provider_response_cost is not None and not custom_pricing:
                         if _breakdown_total(litellm_logging_obj) != provider_response_cost:
+                            header_priced_model: Final = select_model_name_for_cost_calc(
+                                model=model,
+                                completion_response=response_object,
+                                custom_llm_provider=custom_llm_provider,
+                                custom_pricing=custom_pricing,
+                                base_model=base_model,
+                                router_model_id=router_model_id,
+                                region_name=region_name,
+                            )
+                            header_input_cost, header_output_cost = split_reported_cost_by_cost_map_ratio(
+                                reported_total=provider_response_cost,
+                                model=header_priced_model or model,
+                                custom_llm_provider=pricing_provider or custom_llm_provider,
+                                usage=get_usage_object(response_object),
+                                call_type=call_type,
+                                service_tier=service_tier,
+                                data_residency=data_residency,
+                            )
                             _store_cost_breakdown_in_logging_obj(
                                 litellm_logging_obj=litellm_logging_obj,
-                                prompt_tokens_cost_usd_dollar=0.0,
-                                completion_tokens_cost_usd_dollar=provider_response_cost,
+                                prompt_tokens_cost_usd_dollar=header_input_cost,
+                                completion_tokens_cost_usd_dollar=header_output_cost,
                                 cost_for_built_in_tools_cost_usd_dollar=0.0,
                                 total_cost_usd_dollar=provider_response_cost,
                                 original_cost=provider_response_cost,
@@ -3319,7 +3408,7 @@ class RealtimeAPITokenUsageProcessor(BaseTokenUsageProcessor):
         )
 
 
-_RESPONSES_WS_BILLABLE_EVENT_TYPES: Final = frozenset({"response.completed", "response.incomplete"})
+_RESPONSES_WS_BILLABLE_EVENT_TYPES: Final = frozenset({"response.completed", "response.incomplete", "response.failed"})
 
 
 class _ResponsesWsEventResponse(LiteLLMBaseModel):
@@ -3762,44 +3851,65 @@ def handle_realtime_transcription_cost_calculation(
       - {"type": "duration", "seconds": <float>}  → priced via input_cost_per_second
       - {"type": "tokens", "input_tokens": ...}    → priced via input/audio token cost
     """
-    completed_events: Final = [
-        cast(dict, result) for result in results if result.get("type") == _TRANSCRIPTION_COMPLETED_EVENT_TYPE
-    ]
-    if not completed_events:
+    configured_models: Final = tuple(accumulate(map(_session_transcription_model, results), _newer_model))
+    clips: Final = tuple(
+        (as_str_mapping(result.get("usage")) or {}, configured)
+        for result, configured in zip(results, configured_models, strict=True)
+        if result.get("type") == _TRANSCRIPTION_COMPLETED_EVENT_TYPE
+    )
+    if not clips:
         return 0.0
 
-    model_name: Final = _get_transcription_model_name_from_results(results) or litellm_model_name
-    model_info: Final = _cost_map_model_info(model_name, custom_llm_provider)
+    fallback_model: Final = _get_transcription_model_name_from_results(results) or litellm_model_name
+    clip_models: Final = tuple((usage, configured or fallback_model) for usage, configured in clips)
+    model_infos: Final = MappingProxyType(
+        {model: _cost_map_model_info(model, custom_llm_provider) for model in frozenset(m for _, m in clip_models)}
+    )
     override_info: Final = (
         _cost_map_model_info(custom_pricing_model, custom_llm_provider) if custom_pricing_model is not None else None
     )
+    return sum(
+        (_transcription_usage_cost(dict(usage), model_infos[model], override_info) for usage, model in clip_models),
+        0.0,
+    )
 
-    total_cost = 0.0
-    for event in completed_events:
-        usage = event.get("usage") or {}
-        total_cost += _transcription_usage_cost(usage, model_info, override_info)
-    return total_cost
+
+_TRANSCRIPTION_SESSION_EVENT_TYPES: Final = frozenset(
+    {"transcription_session.created", "transcription_session.updated", "session.created", "session.updated"}
+)
+
+
+def _str_field(mapping: Mapping[str, object] | None, key: str) -> str | None:
+    value: Final = None if mapping is None else mapping.get(key)
+    return value if isinstance(value, str) and value else None
+
+
+def _configured_transcription_model(session: Mapping[str, object]) -> str | None:
+    audio_input: Final = as_str_mapping((as_str_mapping(session.get("audio")) or {}).get("input"))
+    nested: Final = _str_field(as_str_mapping((audio_input or {}).get("transcription")), "model")
+    return nested or _str_field(as_str_mapping(session.get("input_audio_transcription")), "model")
+
+
+def _session_of(result: Mapping[str, object]) -> Mapping[str, object] | None:
+    return as_str_mapping(result.get("session")) if result.get("type") in _TRANSCRIPTION_SESSION_EVENT_TYPES else None
+
+
+def _session_transcription_model(result: Mapping[str, object]) -> str | None:
+    session: Final = _session_of(result)
+    return None if session is None else _configured_transcription_model(session)
+
+
+def _newer_model(current: str | None, newer: str | None) -> str | None:
+    return newer or current
 
 
 def _get_transcription_model_name_from_results(
     results: OpenAIRealtimeStreamList,
 ) -> str | None:
-    """Resolve the ASR model from a transcription_session.* / session.* event."""
-    for result in results:
-        if result.get("type") in (
-            "transcription_session.created",
-            "transcription_session.updated",
-            "session.created",
-            "session.updated",
-        ):
-            session = cast(dict, result).get("session", {}) or {}
-            transcription = ((session.get("audio", {}) or {}).get("input", {}) or {}).get(
-                "transcription", {}
-            ) or session.get("input_audio_transcription", {})
-            model = (transcription or {}).get("model") or session.get("model")
-            if model:
-                return model
-    return None
+    return next(
+        (model for result in results if (model := _str_field(_session_of(result), "model"))),
+        None,
+    )
 
 
 _TranscriptionRateKey: TypeAlias = Literal[

litellm/decisions/call.py

@@ -3,10 +3,10 @@ from collections.abc import Mapping, Sequence
 from dataclasses import dataclass, field, replace
 from functools import partial
 from types import MappingProxyType
-from typing import Final, Literal, TypeAlias
+from typing import Annotated, Final, Literal, TypeAlias
 
 import httpx
-from pydantic import BaseModel, ConfigDict, StrictBool, TypeAdapter, ValidationError
+from pydantic import BaseModel, BeforeValidator, ConfigDict, TypeAdapter, ValidationError
 from pydantic_core import to_jsonable_python
 from typing_extensions import assert_never
 
@@ -23,6 +23,7 @@ from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.decisions.transformation import (
     BaseDecisionsConfig,
     DecisionsResponseValidationError,
+    catalog_mode,
     image_media_type,
     image_param,
     input_images,
@@ -61,7 +62,7 @@ from litellm.types.decisions import (
     UnsupportedDecisionsRequest,
 )
 from litellm.types.llms.openai import ChatCompletionUserMessage
-from litellm.types.utils import LlmProviders, ProviderSpecificHeader
+from litellm.types.utils import LlmProviders, ProviderSpecificHeader, is_decisions_model_mode
 from litellm.utils import ProviderConfigManager, get_model_cost_key
 
 SystemOneQuestions: TypeAlias = Mapping[str, DecisionQuestion | Mapping[str, object]]
@@ -257,19 +258,105 @@ def _request_safety_identifier(
     return None
 
 
-def _fields_without_unsupported_media(
-    fields: RequestFields,
+def _bool_or_none(value: object) -> bool | None:
+    return value if isinstance(value, bool) else None
+
+
+def _str_or_none(value: object) -> str | None:
+    return value if isinstance(value, str) else None
+
+
+_DeploymentFlag: TypeAlias = Annotated[bool | None, BeforeValidator(_bool_or_none)]
+
+
+class _DeploymentCapabilities(BaseModel):
+    model_config = ConfigDict(frozen=True, from_attributes=True)
+
+    mode: Annotated[str | None, BeforeValidator(_str_or_none)] = None
+    supports_box_questions: _DeploymentFlag = None
+    supports_audio_input: _DeploymentFlag = None
+    supports_video_input: _DeploymentFlag = None
+
+    def decisions_media_flag(self, flag: bool | None, model: str, custom_llm_provider: str) -> bool | None:
+        if flag is None:
+            return None
+        mode: Final = self.mode if self.mode is not None else catalog_mode(model, custom_llm_provider)
+        return flag if mode is None or is_decisions_model_mode(mode) else None
+
+
+@dataclass(frozen=True, slots=True)
+class _ResolvedSupport:
+    box_questions: bool
+    audio: bool
+    videos: bool
+
+
+def _takes_audio(
+    provider_config: BaseDecisionsConfig, model: str, custom_llm_provider: str, deployment: _DeploymentCapabilities
+) -> bool:
+    return provider_config.supports_audio_input(
+        model,
+        custom_llm_provider,
+        deployment.decisions_media_flag(deployment.supports_audio_input, model, custom_llm_provider),
+    )
+
+
+def _takes_videos(
+    provider_config: BaseDecisionsConfig, model: str, custom_llm_provider: str, deployment: _DeploymentCapabilities
+) -> bool:
+    return provider_config.supports_video_input(
+        model,
+        custom_llm_provider,
+        deployment.decisions_media_flag(deployment.supports_video_input, model, custom_llm_provider),
+    )
+
+
+def deployment_refuses_media(
+    model: str,
+    custom_llm_provider: str,
+    model_info: Mapping[str, object],
+    call_kwargs: Mapping[str, object],
+    *,
+    audio: bool,
+    videos: bool,
+) -> bool:
+    if _drops_params(call_kwargs):
+        return False
+    provider_config: Final = get_decisions_provider_config(model, custom_llm_provider)
+    canonical_model: Final = provider_config.canonical_model(model)
+    deployment: Final = _DeploymentCapabilities.model_validate(model_info)
+    return (audio and not _takes_audio(provider_config, canonical_model, custom_llm_provider, deployment)) or (
+        videos and not _takes_videos(provider_config, canonical_model, custom_llm_provider, deployment)
+    )
+
+
+def _resolved_support(
     provider_config: BaseDecisionsConfig,
     model: str,
     custom_llm_provider: str,
+    headers: Mapping[str, str],
     kwargs: Mapping[str, object],
+    fields: RequestFields,
+) -> _ResolvedSupport:
+    deployment: Final = _DeploymentCapabilities.model_validate(kwargs.get("model_info") or {})
+    return _ResolvedSupport(
+        box_questions=provider_config.supports_box_questions(
+            model, custom_llm_provider, headers, deployment.supports_box_questions
+        ),
+        audio=bool(fields.audio) and _takes_audio(provider_config, model, custom_llm_provider, deployment),
+        videos=bool(fields.videos) and _takes_videos(provider_config, model, custom_llm_provider, deployment),
+    )
+
+
+def _fields_without_unsupported_media(
+    fields: RequestFields, support: _ResolvedSupport, kwargs: Mapping[str, object]
 ) -> RequestFields:
     if not _drops_params(kwargs):
         return fields
     return replace(
         fields,
-        audio=fields.audio if provider_config.supports_audio_input(model, custom_llm_provider) else None,
-        videos=fields.videos if provider_config.supports_video_input(model, custom_llm_provider) else None,
+        audio=fields.audio if support.audio else None,
+        videos=fields.videos if support.videos else None,
     )
 
 
@@ -292,10 +379,8 @@ class _MalformedImages:
 def _unsupported_params(
     ir_request: DecisionsIRRequest,
     provider_config: BaseDecisionsConfig,
-    model: str,
-    custom_llm_provider: str,
     headers: Mapping[str, str],
-    supports_box_questions: bool,
+    support: _ResolvedSupport,
 ) -> tuple[str, ...]:
     refuses_safety_identifier: Final = (
         ir_request.safety_identifier is not None and not provider_config.supports_safety_identifier
@@ -304,13 +389,9 @@ def _unsupported_params(
         None if provider_config.supports_images_in(ir_request.input, headers) else image_param(ir_request.input)
     )
     refuses_assets: Final = bool(ir_request.assets) and not provider_config.supports_assets(headers)
-    refuses_box_questions: Final = _has_box_question(ir_request.questions) and not supports_box_questions
-    refuses_audio: Final = bool(ir_request.audio) and not provider_config.supports_audio_input(
-        model, custom_llm_provider
-    )
-    refuses_videos: Final = bool(ir_request.videos) and not provider_config.supports_video_input(
-        model, custom_llm_provider
-    )
+    refuses_box_questions: Final = _has_box_question(ir_request.questions) and not support.box_questions
+    refuses_audio: Final = bool(ir_request.audio) and not support.audio
+    refuses_videos: Final = bool(ir_request.videos) and not support.videos
     return (
         *(("safety_identifier",) if refuses_safety_identifier else ()),
         *(() if refused_image_param is None else (refused_image_param,)),
@@ -333,7 +414,7 @@ def _without_unsupported_params(
     ir_request: DecisionsIRRequest,
     provider_config: BaseDecisionsConfig,
     headers: Mapping[str, str],
-    supports_box_questions: bool,
+    support: _ResolvedSupport,
 ) -> DecisionsIRRequest:
     return replace(
         ir_request,
@@ -345,9 +426,7 @@ def _without_unsupported_params(
         safety_identifier=ir_request.safety_identifier if provider_config.supports_safety_identifier else None,
         assets=ir_request.assets if provider_config.supports_assets(headers) else MappingProxyType({}),
         questions=(
-            ir_request.questions
-            if supports_box_questions
-            else tuple(filter(_is_not_box_question, ir_request.questions))
+            ir_request.questions if support.box_questions else tuple(filter(_is_not_box_question, ir_request.questions))
         ),
     )
 
@@ -368,39 +447,20 @@ def _without_malformed_images(
     return well_formed
 
 
-class _DeploymentBoxQuestions(BaseModel):
-    model_config = ConfigDict(frozen=True, from_attributes=True)
-
-    supports_box_questions: StrictBool | None = None
-
-
-def _deployment_supports_box_questions(kwargs: Mapping[str, object]) -> bool | None:
-    try:
-        return _DeploymentBoxQuestions.model_validate(kwargs.get("model_info") or {}).supports_box_questions
-    except ValidationError:
-        return None
-
-
 def _provider_ir_request(
     ir_request: DecisionsIRRequest,
     *,
     provider_config: BaseDecisionsConfig,
-    model: str,
-    custom_llm_provider: str,
     headers: Mapping[str, str],
+    support: _ResolvedSupport,
     kwargs: Mapping[str, object],
 ) -> DecisionsIRRequest | _UnsupportedParams | _NothingLeftAfterDrop:
-    supports_box_questions: Final = provider_config.supports_box_questions(
-        model, custom_llm_provider, headers, _deployment_supports_box_questions(kwargs)
-    )
-    unsupported: Final = _unsupported_params(
-        ir_request, provider_config, model, custom_llm_provider, headers, supports_box_questions
-    )
+    unsupported: Final = _unsupported_params(ir_request, provider_config, headers, support)
     if not unsupported:
         return ir_request
     if not _drops_params(kwargs):
         return _UnsupportedParams(names=unsupported)
-    supported: Final = _without_unsupported_params(ir_request, provider_config, headers, supports_box_questions)
+    supported: Final = _without_unsupported_params(ir_request, provider_config, headers, support)
     if _leaves_no_input(supported.input):
         return _NothingLeftAfterDrop(names=unsupported, missing="input to decide on")
     if not supported.questions:
@@ -479,10 +539,11 @@ def prepare_call(
         raise litellm.BadRequestError(message=str(error), model=model, llm_provider=provider) from error
     try:
         headers: Final = decisions_outbound_headers(extra_headers, provider, kwargs)
+        support: Final = _resolved_support(provider_config, canonical_model, provider, headers, kwargs, fields)
         request_safety_identifier: Final = _request_safety_identifier(safety_identifier, provider_config, kwargs)
-        request: Final = _fields_without_unsupported_media(
-            fields, provider_config, canonical_model, provider, kwargs
-        ).validate(safety_identifier=request_safety_identifier, drops_params=_drops_params(kwargs))
+        request: Final = _fields_without_unsupported_media(fields, support, kwargs).validate(
+            safety_identifier=request_safety_identifier, drops_params=_drops_params(kwargs)
+        )
     except ValidationError as error:
         raise litellm.BadRequestError(
             message=f"Invalid Decisions request: {error}",
@@ -523,12 +584,7 @@ def prepare_call(
             llm_provider=provider,
         )
     ir_request: Final = _provider_ir_request(
-        caller_ir_request,
-        provider_config=provider_config,
-        model=canonical_model,
-        custom_llm_provider=provider,
-        headers=headers,
-        kwargs=kwargs,
+        caller_ir_request, provider_config=provider_config, headers=headers, support=support, kwargs=kwargs
     )
     if isinstance(ir_request, _UnsupportedParams):
         raise litellm.UnsupportedParamsError(

litellm/endpoints/speech/speech_to_completion_bridge/handler.py

@@ -113,7 +113,7 @@ class SpeechToCompletionBridgeHandler:
             voice=voice,
         )
 
-        with internal_sub_call():
+        with internal_sub_call(logging_obj.litellm_call_id):
             result: Final = completion(
                 **request_data,
             )

litellm/guardrails.py

@@ -0,0 +1,66 @@
+"""Invoke configured gateway guardrails without generating a completion."""
+
+from collections.abc import Mapping, Sequence
+from enum import Enum
+from typing import Final
+
+import httpx
+from pydantic import TypeAdapter
+
+import litellm
+from litellm.llms.custom_httpx.http_handler import (
+    get_async_httpx_client,  # pyright: ignore[reportUnknownVariableType]  # shared factory has untyped optional parameters
+)
+from litellm.types.guardrails import NativeGuardrailResponse
+
+_NATIVE_RESPONSE: Final[TypeAdapter[NativeGuardrailResponse]] = TypeAdapter(NativeGuardrailResponse)
+
+
+class _InputType(Enum):
+    REQUEST = "request"
+    RESPONSE = "response"
+
+
+async def apply(
+    guardrail_name: str,
+    *,
+    input: str | None = None,
+    response: str | None = None,
+    messages: Sequence[Mapping[str, object]] | None = None,
+    metadata: Mapping[str, object] | None = None,
+    api_base: str | None = None,
+    api_key: str | None = None,
+    timeout: float | httpx.Timeout | None = None,
+) -> NativeGuardrailResponse:
+    """Return a configured guardrail's native structured findings.
+
+    Supply exactly one of ``input`` or ``response``. Grounding guardrails can
+    use ``messages`` as evidence. Connection settings default to
+    ``litellm.api_base`` and ``litellm.api_key``.
+    """
+    if (input is None) == (response is None):
+        raise ValueError("Supply exactly one of input or response.")
+    resolved_base: Final = api_base if api_base is not None else litellm.api_base
+    if not resolved_base:
+        raise ValueError("Set api_base or litellm.api_base to your LiteLLM gateway URL.")
+    resolved_key: Final = api_key if api_key is not None else litellm.api_key
+    input_type: Final = _InputType.RESPONSE if response is not None else _InputType.REQUEST
+    payload: Final = dict[str, object](
+        guardrail_name=guardrail_name,
+        text=response if response is not None else input,
+        input_type=input_type.value,
+        response_format="native",
+    )
+    if messages is not None:
+        payload["messages"] = [dict(message) for message in messages]
+    if metadata is not None:
+        payload["metadata"] = dict(metadata)
+    gateway_root: Final = resolved_base.rstrip("/").removesuffix("/v1")
+    client: Final = get_async_httpx_client(llm_provider="litellm_guardrails")
+    result: Final = await client.post(  # pyright: ignore[reportUnknownMemberType]  # shared handler has untyped optional parameters
+        url=f"{gateway_root}/guardrails/apply_guardrail",
+        json=payload,
+        headers={"Authorization": f"Bearer {resolved_key}"} if resolved_key else {},
+        timeout=timeout,
+    )
+    return _NATIVE_RESPONSE.validate_json(result.content)

litellm/integrations/anthropic_cache_control_hook.py

@@ -13,8 +13,8 @@ import copy
 import itertools
 import os
 import re
-from collections.abc import Callable, Container, Iterable, Mapping, Sequence
-from typing import TYPE_CHECKING, Any, Final, cast
+from collections.abc import Callable, Container, Iterable, Iterator, Mapping, Sequence
+from typing import TYPE_CHECKING, Any, Final, TypeAlias, TypeVar, cast
 from urllib.parse import urlparse
 
 from pydantic import TypeAdapter, ValidationError
@@ -32,6 +32,7 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     tool_call_ids_answered_by_client,
     tool_call_ids_answered_in,
 )
+from litellm.litellm_core_utils.prompt_templates.mid_conversation_system import string_field
 from litellm.llms.anthropic.common_utils import (
     is_claude_code_one_shot_subagent_request,
     supports_anthropic_cache_control,
@@ -102,6 +103,192 @@ def _validated_object_list(value: object) -> list[object] | None:
         return None
 
 
+_MessageT: Final = TypeVar("_MessageT")
+_CacheControlRewrite: TypeAlias = Callable[[object], ChatCompletionCachedContent | None]
+_MARK_CARRYING_MESSAGE_FIELDS: Final = ("content", "tool_calls")
+
+
+def _mark_carrying_blocks(fields: Mapping[str, object]) -> Sequence[object]:
+    lists: Final = tuple(fields.get(key) for key in _MARK_CARRYING_MESSAGE_FIELDS)
+    return tuple(
+        itertools.chain.from_iterable(
+            _OBJECT_LIST_ADAPTER.validate_python(value) for value in lists if isinstance(value, list)
+        )
+    )
+
+
+def _block_carries_cache_control(block: object) -> bool:
+    return isinstance(block, dict) and _CONTENT_PART_ADAPTER.validate_python(block).get("cache_control") is not None
+
+
+_ROLES_SENDING_MESSAGE_MARK_WITH_LIST_CONTENT: Final = frozenset({"tool", "function"})
+
+
+def _message_mark_reaches_provider(fields: Mapping[str, object]) -> bool:
+    if fields.get("cache_control") is None:
+        return False
+    return (
+        not isinstance(fields.get("content"), list)
+        or string_field(fields, "role") in _ROLES_SENDING_MESSAGE_MARK_WITH_LIST_CONTENT
+    )
+
+
+def cache_control_count(message: object) -> int:
+    if not isinstance(message, dict):
+        return 0
+    fields: Final = _CONTENT_PART_ADAPTER.validate_python(message)
+    block_marks: Final = sum(1 for block in _mark_carrying_blocks(fields) if _block_carries_cache_control(block))
+    return int(_message_mark_reaches_provider(fields)) + block_marks
+
+
+def _fields_with_cache_control(fields: Mapping[str, object], rewrite: _CacheControlRewrite) -> Mapping[str, object]:
+    others: Final = {key: value for key, value in fields.items() if key != "cache_control"}
+    if "cache_control" not in fields:
+        return others
+    cache_control: Final = rewrite(fields["cache_control"])
+    return others if cache_control is None else {**others, "cache_control": cache_control}
+
+
+def block_with_cache_control(block: object, rewrite: _CacheControlRewrite) -> object:
+    if not isinstance(block, dict):
+        return block
+    return _fields_with_cache_control(_CONTENT_PART_ADAPTER.validate_python(block), rewrite)
+
+
+def with_cache_control(message: _MessageT, rewrite: _CacheControlRewrite) -> _MessageT:
+    if not isinstance(message, dict):
+        return message
+    fields: Final = _CONTENT_PART_ADAPTER.validate_python(message)
+    blocks_rewritten: Final = {
+        key: (
+            [block_with_cache_control(block, rewrite) for block in _OBJECT_LIST_ADAPTER.validate_python(value)]
+            if key in _MARK_CARRYING_MESSAGE_FIELDS and isinstance(value, list)
+            else value
+        )
+        for key, value in fields.items()
+    }
+    return cast(  # cast-ok: the same message shape with each cache marker rewritten or dropped
+        _MessageT, _fields_with_cache_control(blocks_rewritten, rewrite)
+    )
+
+
+def dropped_cache_control(_: object) -> None:
+    return None
+
+
+def _first_marked_index(value: object) -> int | None:
+    if not isinstance(value, list):
+        return None
+    blocks: Final = _OBJECT_LIST_ADAPTER.validate_python(value)
+    return next((index for index, block in enumerate(blocks) if _block_carries_cache_control(block)), None)
+
+
+_MarkSlot: TypeAlias = tuple[str, int]
+_MESSAGE_MARK_SLOT: Final[_MarkSlot] = ("cache_control", -1)
+
+
+def _block_mark_slots(fields: Mapping[str, object], key: str) -> tuple[_MarkSlot, ...]:
+    value: Final = fields.get(key)
+    if not isinstance(value, list):
+        return ()
+    blocks: Final = _OBJECT_LIST_ADAPTER.validate_python(value)
+    return tuple((key, index) for index, block in enumerate(blocks) if _block_carries_cache_control(block))
+
+
+def _mark_slots_in_wire_order(fields: Mapping[str, object]) -> tuple[_MarkSlot, ...]:
+    content_slots: Final = _block_mark_slots(fields, "content")
+    tool_call_slots: Final = _block_mark_slots(fields, "tool_calls")
+    if not _message_mark_reaches_provider(fields):
+        return content_slots + tool_call_slots
+    if isinstance(fields.get("content"), list):
+        return content_slots + (_MESSAGE_MARK_SLOT,) + tool_call_slots
+    return (_MESSAGE_MARK_SLOT,) + content_slots + tool_call_slots
+
+
+def _blocks_without_marks(key: str, value: object, dropped_slots: frozenset[_MarkSlot]) -> object:
+    if key not in _MARK_CARRYING_MESSAGE_FIELDS or not isinstance(value, list):
+        return value
+    return [
+        block_with_cache_control(block, dropped_cache_control) if (key, index) in dropped_slots else block
+        for index, block in enumerate(_OBJECT_LIST_ADAPTER.validate_python(value))
+    ]
+
+
+def _without_oldest_marks(message: _MessageT, dropped: int) -> _MessageT:
+    if dropped == 0 or not isinstance(message, dict):
+        return message
+    fields: Final = _CONTENT_PART_ADAPTER.validate_python(message)
+    dropped_slots: Final = frozenset(_mark_slots_in_wire_order(fields)[:dropped])
+    blocks_unmarked: Final = {key: _blocks_without_marks(key, value, dropped_slots) for key, value in fields.items()}
+    return cast(  # cast-ok: the same message with its oldest wire-order marks dropped
+        _MessageT,
+        _fields_with_cache_control(blocks_unmarked, dropped_cache_control)
+        if _MESSAGE_MARK_SLOT in dropped_slots
+        else blocks_unmarked,
+    )
+
+
+def tool_message_with_single_cache_mark(message: _MessageT) -> _MessageT:
+    if not isinstance(message, dict):
+        return message
+    fields: Final = _CONTENT_PART_ADAPTER.validate_python(message)
+    content: Final = fields.get("content")
+    if fields.get("role") != "tool" or not isinstance(content, list):
+        return message
+    kept_index: Final = None if fields.get("cache_control") is not None else _first_marked_index(content)
+    return cast(  # cast-ok: the same tool message keeping only the mark its single cache point reads
+        _MessageT,
+        {
+            **fields,
+            "content": [
+                block if index == kept_index else block_with_cache_control(block, dropped_cache_control)
+                for index, block in enumerate(_OBJECT_LIST_ADAPTER.validate_python(content))
+            ],
+        },
+    )
+
+
+_BudgetSpend: TypeAlias = Callable[[tuple[int, int], int], tuple[int, int]]
+
+
+def _newest_marks_kept(state: tuple[int, int], marks: int) -> tuple[int, int]:
+    remaining, _ = state
+    kept: Final = min(marks, remaining)
+    return remaining - kept, marks - kept
+
+
+def _whole_message_kept(state: tuple[int, int], marks: int) -> tuple[int, int]:
+    remaining, _ = state
+    if marks > remaining:
+        return remaining, marks
+    return remaining - marks, 0
+
+
+def _messages_within_budget(
+    messages: Sequence[_MessageT], reserved_marks: int, spend: _BudgetSpend
+) -> Sequence[_MessageT]:
+    budget: Final = max(MAX_CACHE_CONTROL_BLOCKS - reserved_marks, 0)
+    newest_first: Final = tuple(reversed(messages))
+    budget_after_each: Final = itertools.accumulate(
+        (cache_control_count(message) for message in newest_first), spend, initial=(budget, 0)
+    )
+    dropped_counts: Final = tuple(dropped for _, dropped in itertools.islice(budget_after_each, 1, None))
+    kept_newest_first: Final = tuple(
+        _without_oldest_marks(message, dropped) for message, dropped in zip(newest_first, dropped_counts)
+    )
+    return tuple(reversed(kept_newest_first))
+
+
+def newest_marks_within_cache_control_limit(messages: Sequence[_MessageT], reserved_marks: int) -> Sequence[_MessageT]:
+    return _messages_within_budget(messages, reserved_marks, _newest_marks_kept)
+
+
+def whole_messages_within_cache_control_limit(
+    messages: Sequence[_MessageT], reserved_marks: int
+) -> Sequence[_MessageT]:
+    return _messages_within_budget(messages, reserved_marks, _whole_message_kept)
+
+
 def _part_with_cache_control_renamed(part: object) -> object:
     if not _accepts_prompt_cache_breakpoint(part):
         return part
@@ -259,22 +446,41 @@ def _as_object_list(value: object | None) -> list[object] | None:
     return _validated_object_list(value)
 
 
-def _tool_call_carries_cache_breakpoint(tool_call: object, message: object, answered_tool_ids: Container[str]) -> bool:
+def _server_tool_call_id(tool_call: object) -> str | None:
+    tool_call_id: Final = _attribute_or_key(tool_call, "id")
+    if isinstance(tool_call_id, str) and tool_call_id.startswith(ANTHROPIC_SERVER_TOOL_USE_ID_PREFIX):
+        return tool_call_id
+    return None
+
+
+def _tool_call_carries_cache_breakpoint(tool_call: object, client_tool_call_ids: Container[str]) -> bool:
     if _attribute_or_key(tool_call, "cache_control") is None:
         return False
+    server_tool_call_id: Final = _server_tool_call_id(tool_call)
+    return server_tool_call_id is None or server_tool_call_id in client_tool_call_ids
 
-    return not tool_call_is_rebuilt_as_server_tool_use(
-        _attribute_or_key(tool_call, "id"), _attribute_or_key(message, "provider_specific_fields"), answered_tool_ids
+
+def _server_tool_call_ids_without_a_kept_result(message: object) -> Iterator[str]:
+    provider_specific_fields: Final = _attribute_or_key(message, "provider_specific_fields")
+    tool_calls: Final = _as_object_list(_attribute_or_key(message, "tool_calls")) or ()
+    return (
+        tool_call_id
+        for tool_call in tool_calls
+        if (tool_call_id := _server_tool_call_id(tool_call)) is not None
+        and not tool_call_is_rebuilt_as_server_tool_use(tool_call_id, provider_specific_fields)
     )
 
 
-def _tool_call_ids_answered_by_client_when_a_server_tool_call_is_marked(
+def _client_tool_call_ids_when_a_server_tool_call_is_marked(
     messages: Sequence[object], model: str | None = None, custom_llm_provider: str | None = None
 ) -> frozenset[str]:
     if not _marks_a_server_tool_call(messages):
         return frozenset()
     if model is not None and not _builds_anthropic_messages(model, custom_llm_provider):
-        return tool_call_ids_answered_in(messages)
+        server_calls_sent_as_client_calls: Final = itertools.chain.from_iterable(
+            map(_server_tool_call_ids_without_a_kept_result, messages)
+        )
+        return tool_call_ids_answered_in(messages) | frozenset(server_calls_sent_as_client_calls)
     return tool_call_ids_answered_by_client(messages, _keeps_mid_conversation_system(model, custom_llm_provider))
 
 
@@ -331,12 +537,7 @@ def _model_info_flags_mid_conversation_system(model: str, custom_llm_provider: s
 
 
 def _is_marked_server_tool_call(tool_call: object) -> bool:
-    tool_call_id: Final = _attribute_or_key(tool_call, "id")
-    return (
-        isinstance(tool_call_id, str)
-        and tool_call_id.startswith(ANTHROPIC_SERVER_TOOL_USE_ID_PREFIX)
-        and _attribute_or_key(tool_call, "cache_control") is not None
-    )
+    return _server_tool_call_id(tool_call) is not None and _attribute_or_key(tool_call, "cache_control") is not None
 
 
 def _tool_carries_cache_breakpoint(tool: object) -> bool:
@@ -482,7 +683,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         reserved_blocks: Final = AnthropicCacheControlHook._blocks_reserved_outside_messages(
             remaining_points, external_breakpoints, openai_dialect
         )
-        answered_tool_ids: Final = _tool_call_ids_answered_by_client_when_a_server_tool_call_is_marked(
+        answered_tool_ids: Final = _client_tool_call_ids_when_a_server_tool_call_is_marked(
             processed_messages, model, custom_llm_provider
         )
         breakpoints_before: Final = AnthropicCacheControlHook.count_request_cache_breakpoints(
@@ -571,7 +772,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         )
         message_list: Final = tuple(messages)
         client_answers: Final = (
-            _tool_call_ids_answered_by_client_when_a_server_tool_call_is_marked(message_list)
+            _client_tool_call_ids_when_a_server_tool_call_is_marked(message_list)
             if answered_tool_ids is None
             else answered_tool_ids
         )
@@ -663,7 +864,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         so earlier points win when slots are scarce.
         """
         client_answers: Final = (
-            _tool_call_ids_answered_by_client_when_a_server_tool_call_is_marked(messages)
+            _client_tool_call_ids_when_a_server_tool_call_is_marked(messages)
             if answered_tool_ids is None
             else answered_tool_ids
         )
@@ -753,11 +954,7 @@ class AnthropicCacheControlHook(CustomPromptManagement):
         content_count: Final = sum(1 for block in content if _carries_cache_breakpoint(block)) if content else 0
         tool_calls: Final = _as_object_list(_attribute_or_key(message, "tool_calls"))
         tool_call_count: Final = (
-            sum(
-                1
-                for tool_call in tool_calls
-                if _tool_call_carries_cache_breakpoint(tool_call, message, answered_tool_ids)
-            )
+            sum(1 for tool_call in tool_calls if _tool_call_carries_cache_breakpoint(tool_call, answered_tool_ids))
             if tool_calls
             else 0
         )
@@ -1439,3 +1636,25 @@ class AnthropicCacheControlHook(CustomPromptManagement):
                 llm_router=None,
             )
         return None
+
+
+def messages_within_cache_control_cap(
+    messages: Sequence[_MessageT], tools: object, cache_control: object = None, request_kwargs: object = None
+) -> Sequence[_MessageT]:
+    """Under drop_params, drop the oldest message hints the provider's cache_control cap has no room for.
+
+    Tool hints and a top-level ``cache_control`` stay on the wire, so they are reserved first.
+    """
+    reserved_marks: Final = AnthropicCacheControlHook.count_external_cache_breakpoints(
+        _validated_object_list(tools), cache_control, request_kwargs
+    )
+    kept: Final = newest_marks_within_cache_control_limit(messages, reserved_marks)
+    dropped: Final = sum(map(cache_control_count, messages)) - sum(map(cache_control_count, kept))
+    if dropped > 0:
+        verbose_logger.warning(
+            "Dropped %d cache_control hint(s) from the oldest messages because drop_params is on and the "
+            "provider accepts at most %d cache_control blocks per request",
+            dropped,
+            MAX_CACHE_CONTROL_BLOCKS,
+        )
+    return kept

litellm/integrations/prometheus.py

@@ -66,7 +66,7 @@ from litellm.repositories.organization_repository import OrganizationRepository
 from litellm.repositories.table_repositories import EndUserRepository
 from litellm.repositories.team_repository import TeamRepository
 from litellm.repositories.user_repository import UserRepository
-from litellm.router_utils.router_callbacks.track_deployment_metrics import find_deployment_metadata
+from litellm.router_api import deployment_metadata
 from litellm.types.guardrails import GuardrailEventHooks
 from litellm.types.integrations.prometheus import *
 from litellm.types.integrations.prometheus import (
@@ -3022,8 +3022,8 @@ class PrometheusLogger(CustomLogger):
         ).get("custom_llm_provider")
         if isinstance(provider, str) and provider:
             return provider
-        deployment_metadata: Final = find_deployment_metadata(litellm_params)
-        return _deployment_model_provider(deployment_metadata.get("deployment") if deployment_metadata else None)
+        picked_deployment: Final = deployment_metadata(litellm_params)
+        return _deployment_model_provider(picked_deployment.get("deployment") if picked_deployment else None)
 
     @staticmethod
     def _extract_api_provider_from_exception(exception: Exception) -> str | None:
@@ -5209,7 +5209,7 @@ def _tag_matches_wildcard_configured_pattern(tags: Sequence[str], configured_tag
     """
     import re
 
-    from litellm.router_utils.pattern_match_deployments import PatternMatchRouter
+    from litellm.litellm_core_utils.pattern_match_deployments import PatternMatchRouter
 
     pattern_router: Final = PatternMatchRouter()
     regex_pattern: Final = pattern_router.pattern_to_regex(configured_tag)

litellm/litellm_core_utils/core_helpers.py

@@ -4,7 +4,7 @@ import copy
 import logging
 import math
 import re
-from collections.abc import Collection, Iterable, Iterator, Mapping
+from collections.abc import Collection, Iterable, Iterator, Mapping, Sequence
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, Literal, Protocol, TypeVar, cast, runtime_checkable
 
@@ -104,6 +104,10 @@ def as_str_mapping(value: object) -> Mapping[str, object] | None:
     return value if isinstance(value, Mapping) else None
 
 
+def as_object_list(value: object) -> Sequence[object] | None:
+    return value if isinstance(value, list) else None
+
+
 def decoded_json_object(value: object) -> Mapping[str, object]:
     mapping: Final = as_str_mapping(value)
     if mapping is not None:
@@ -311,9 +315,12 @@ _FINISH_REASON_MAP: Final[dict[str, OpenAIChatCompletionFinishReason]] = {
     "tool_use": "tool_calls",
     "refusal": "content_filter",
     "compaction": "length",
+    "pause_turn": "length",
     "model_context_window_exceeded": "length",
     # Cohere
     "COMPLETE": "stop",
+    "STOP_SEQUENCE": "stop",
+    "TOOL_CALL": "tool_calls",
     "ERROR_TOXIC": "content_filter",
     "ERROR": "stop",
     # HuggingFace / Together AI
@@ -1073,9 +1080,14 @@ def billed_usage_cost(response: object, custom_llm_provider: str | None) -> floa
 _MESSAGES_REPORTED_COST_PROVIDERS: Final = frozenset({"opencode_go"})
 
 
+def reports_native_messages_cost(custom_llm_provider: object) -> bool:
+    """Whether a Messages upstream is one whose own reported cost litellm bills from instead of the price map."""
+    return isinstance(custom_llm_provider, str) and custom_llm_provider in _MESSAGES_REPORTED_COST_PROVIDERS
+
+
 def native_messages_reported_cost(usage: object, custom_llm_provider: object) -> float | None:
     """Only a Messages upstream known to price its own responses is trusted, every other one keeps the price map."""
-    if not isinstance(custom_llm_provider, str) or custom_llm_provider not in _MESSAGES_REPORTED_COST_PROVIDERS:
+    if not isinstance(custom_llm_provider, str) or not reports_native_messages_cost(custom_llm_provider):
         return None
     return provider_reported_usage_cost(usage, custom_llm_provider)
 

litellm/litellm_core_utils/fallback_utils.py

@@ -3,13 +3,13 @@ from typing import Final
 import litellm
 from litellm._logging import verbose_logger
 from litellm._uuid import uuid
+from litellm.litellm_core_utils.add_retry_fallback_headers import (
+    add_fallback_headers_to_response,
+)
 from litellm.litellm_core_utils.core_helpers import (
     filter_internal_params,
     safe_deep_copy,
 )
-from litellm.router_utils.add_retry_fallback_headers import (
-    add_fallback_headers_to_response,
-)
 
 from .asyncify import run_async_function
 

litellm/litellm_core_utils/get_litellm_params.py

@@ -84,6 +84,7 @@ OPTIONAL_KWARGS_KEYS: Final = (
             "github_copilot_host",
             "github_copilot_user_session",
             "fireworks_forward_user_id",
+            "thought_signature_in_tool_call_id",
             "responses_api_version",
             "allowed_openai_params",
             PROVIDER_AFFINITY_HEADER_KWARG_KEY,
@@ -99,6 +100,8 @@ OPTIONAL_KWARGS_KEYS: Final = (
 # Backward-compatible alias for existing imports/tests.
 _OPTIONAL_KWARGS_KEYS: Final = OPTIONAL_KWARGS_KEYS
 
+SUPPLEMENTAL_KWARGS_KEYS: Final = OPTIONAL_KWARGS_KEYS | {"provider_specific_header", "extra_headers"}
+
 _CONTROL_OPTIONS: Final = TypeAdapter(ControlOptions)
 _CONTROL_OPTION_NAMES: Final = tuple(field.name for field in fields(ControlOptions))
 _MAX_SHOWN_INT_BITS: Final = 64

litellm/litellm_core_utils/get_llm_provider_logic.py

@@ -146,6 +146,10 @@ def handle_anthropic_text_model_custom_llm_provider(
     return model, custom_llm_provider
 
 
+def declared_provider(model: str | None, custom_llm_provider: str | None = None) -> str | None:
+    return custom_llm_provider or (model.split("/", 1)[0] if model and "/" in model else None)
+
+
 def declared_authenticating_provider(model: str | None, custom_llm_provider: str | None = None) -> str | None:
     """The authenticating provider this pair already names, or None.
 
@@ -154,7 +158,7 @@ def declared_authenticating_provider(model: str | None, custom_llm_provider: str
     and for a declared pair the resolver's answer is the declaration itself, so metadata callers
     adopt the declaration instead of resolving.
     """
-    declared: Final = custom_llm_provider or (model.split("/", 1)[0] if model and "/" in model else None)
+    declared: Final = declared_provider(model, custom_llm_provider)
     return declared if declared in PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO else None
 
 

litellm/litellm_core_utils/health_check_helpers.py

@@ -119,7 +119,7 @@ def _is_route_segment(custom_llm_provider: str, segment: str, model_after_segmen
 
 def wildcard_health_check_models(wildcard: str, custom_llm_provider: str) -> tuple[str, ...]:
     from litellm.litellm_core_utils.llm_request_utils import rank_chat_models_from_llm_provider
-    from litellm.router_utils.pattern_match_deployments import PatternMatchRouter
+    from litellm.litellm_core_utils.pattern_match_deployments import PatternMatchRouter
 
     provider_prefix: Final = f"{custom_llm_provider}/"
     segments: Final = wildcard.removeprefix(provider_prefix).split("/")

litellm/litellm_core_utils/litellm_logging.py

@@ -16,9 +16,10 @@ import traceback
 from collections.abc import Awaitable, Callable, Coroutine, Iterable, Iterator, Mapping, Sequence
 from dataclasses import dataclass
 from datetime import datetime as dt_object
+from enum import Enum
 from functools import lru_cache
 from types import MappingProxyType, TracebackType
-from typing import TYPE_CHECKING, Any, Final, Literal, NamedTuple, cast
+from typing import TYPE_CHECKING, Any, ClassVar, Final, Literal, NamedTuple, cast
 
 from httpx import Response, Timeout
 from pydantic import BaseModel, JsonValue, TypeAdapter
@@ -47,6 +48,7 @@ from litellm.constants import (
     KUBERNETES_POD_ROUTING_KEY,
     LITELLM_LOGGING_NO_UPSTREAM_LLM_CALL,
     PROVIDER_REQUEST_ID_HEADERS,
+    REALTIME_METERED_USAGE_KEY,
     REDACTED_BY_LITELLM,
 )
 from litellm.cost_calculator import (
@@ -84,6 +86,7 @@ from litellm.litellm_core_utils.core_helpers import (
     proxy_stamped_kubernetes_pod_routing,
     proxy_stamped_used_client_oauth_token,
     reconstruct_model_name,
+    reports_native_messages_cost,
     set_response_cost_in_hidden_params,
     single_log_line,
 )
@@ -163,6 +166,7 @@ from litellm.types.llms.openai import (
     ResponseIncompleteEvent,
     ResponsesAPIResponse,
 )
+from litellm.types.llms.vertex_ai import GenerateContentResponseBody, UsageMetadata
 from litellm.types.mcp import MCPPostCallResponseObject
 from litellm.types.prompts.init_prompts import PromptSpec
 from litellm.types.rerank import RerankResponse, get_rerank_usage
@@ -266,6 +270,9 @@ if TYPE_CHECKING:
     from litellm.litellm_core_utils.llm_cost_calc.utils import BilledTokenRates
     from litellm.llms.base_llm.passthrough.transformation import PassthroughStreamCollector
     from litellm.proxy.hooks.autorouter_baseline_cache import BaselineCacheContext, CapturedBaselineObservation
+    from litellm.proxy.hooks.search_free_tier import SearchAllowanceAllocation
+    from litellm.proxy.spend_tracking.usage_capture import CapturedUsage
+    from litellm.proxy.spend_tracking.usage_meter import UsageCaptureError
 try:
     from litellm_enterprise.enterprise_callbacks.callback_controls import (
         EnterpriseCallbackControls,
@@ -571,6 +578,7 @@ def _resolve_mantle_region_for_cost(
 _PER_CALL_LITELLM_PARAM_DICTS: Final = frozenset({"metadata", "litellm_metadata"})
 _LITELLM_PARAMS: Final = TypeAdapter(dict[str, object])
 _SERVED_ANTHROPIC_BODY: Final = TypeAdapter(dict[str, object])
+_GOOGLE_USAGE_FIELDS: Final = frozenset(UsageMetadata.__annotations__)
 _REDACTED_RESULT: Final = TypeAdapter(object)
 _GUARDRAIL_INFORMATION_KEY: Final = "standard_logging_guardrail_information"
 
@@ -630,7 +638,60 @@ def same_attempt_marker(first: object, second: object) -> bool:
     return first is second or first == second
 
 
+class UsageObservation(Enum):
+    KNOWN = "known"
+    UNKNOWN = "unknown"
+    ESTIMATED = "estimated"
+
+
+@dataclass(frozen=True, slots=True)
+class UsageCaptureObservation:
+    usage_json: str | None
+    provenance: UsageObservation
+    response_cost: float | None = None
+
+    @classmethod
+    def from_usage(
+        cls, usage: Usage | None, provenance: UsageObservation, response_cost: float | None = None
+    ) -> "UsageCaptureObservation | UsageCaptureError":
+        from litellm.proxy.spend_tracking.usage_meter import UsageCaptureError
+
+        try:
+            serialized: Final = (
+                None if usage is None or provenance is UsageObservation.UNKNOWN else usage.model_dump_json()
+            )
+        except Exception as error:  # noqa: BLE001  # observation failures must not interrupt legacy retry handling
+            return UsageCaptureError(str(error))
+        else:
+            return cls(serialized, provenance, response_cost)
+
+
 class Logging(LiteLLMLoggingBaseClass):
+    usage_capture_callback: ClassVar[
+        Callable[
+            ["Logging", object, datetime.datetime, datetime.datetime, bool], "CapturedUsage | UsageCaptureError | None"
+        ]
+        | None
+    ] = None
+
+    def capture_usage_before_logging(
+        self,
+        response: object,
+        start_time: datetime.datetime | None = None,
+        end_time: datetime.datetime | None = None,
+        *,
+        freeze_only: bool = False,
+    ) -> None:
+        capture: Final = type(self).usage_capture_callback
+        if capture is not None and (not self.stream or self._is_assembled_stream_success(response)):
+            capture(
+                self,
+                response,
+                start_time or self.start_time,
+                end_time or datetime.datetime.now(datetime.timezone.utc),
+                freeze_only,
+            )
+
     global \
         supabaseClient, \
         promptLayerLogger, \
@@ -828,6 +889,7 @@ class Logging(LiteLLMLoggingBaseClass):
         self.finalized_cost: FinalizedCost | None = None
         self.prepared_cost: PreparedCost | None = None
         self.cost_allowance_discount: float | None = None
+        self.cost_allowance_allocation: SearchAllowanceAllocation | 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
@@ -884,7 +946,7 @@ class Logging(LiteLLMLoggingBaseClass):
         new_call.start_time = start_time
         new_call.litellm_params = litellm_params
         new_call.model_call_details = {
-            **self.model_call_details,
+            **{key: value for key, value in self.model_call_details.items() if not key.startswith("_accounting_")},
             "litellm_call_id": litellm_call_id,
             "start_time": start_time,
             "first_api_call_start_time": None,
@@ -894,6 +956,7 @@ class Logging(LiteLLMLoggingBaseClass):
         new_call.finalized_cost = None
         new_call.prepared_cost = None
         new_call.cost_allowance_discount = None
+        new_call.cost_allowance_allocation = None
         new_call.cost_finalization_lock = asyncio.Lock()
         new_call.deferred_success_result = None
         new_call.streaming_chunks = []
@@ -2424,7 +2487,7 @@ class Logging(LiteLLMLoggingBaseClass):
 
     _is_sync_litellm_request = is_sync_litellm_request
 
-    def _is_assembled_stream_success(self, result=None) -> bool:
+    def _is_assembled_stream_success(self, result: object = None) -> bool:
         """Final assembled stream export (not a per-chunk success call).
 
         Per-chunk callers pass a ``ModelResponseStream`` (or ``None``); the
@@ -2459,7 +2522,7 @@ class Logging(LiteLLMLoggingBaseClass):
         """
         from litellm.litellm_core_utils.thread_pool_executor import executor
 
-        if self._is_assembled_stream_success(result):
+        if self._is_assembled_stream_success(TypeAdapter(object).validate_python(result)):
             if self.model_call_details.get("has_dispatched_final_stream_success"):
                 return
             self.model_call_details["has_dispatched_final_stream_success"] = True
@@ -2477,6 +2540,11 @@ class Logging(LiteLLMLoggingBaseClass):
             )
             return
 
+        self.capture_usage_before_logging(
+            TypeAdapter(object).validate_python(result),
+            TypeAdapter[datetime.datetime | None](datetime.datetime | None).validate_python(start_time),
+            TypeAdapter[datetime.datetime | None](datetime.datetime | None).validate_python(end_time),
+        )
         await self.async_success_handler(
             result,
             start_time=start_time,
@@ -2503,6 +2571,17 @@ class Logging(LiteLLMLoggingBaseClass):
             return self.prepared_cost
         recorded_cache_hit: Final[object] = self.model_call_details.get("cache_hit")
         deferred: Final = self.deferred_success_result
+        observed_response: Final = _LITELLM_PARAMS.validate_python(
+            _extract_response_obj_and_hidden_params(deferred[0] if deferred else response, None)[0]
+        )
+        self.model_call_details["_accounting_usage_observed"] = (
+            UsageObservation.KNOWN
+            if (
+                self.model_call_details.get("combined_usage_object")
+                or StandardLoggingPayloadSetup.get_usage_as_dict(observed_response)
+            )
+            else UsageObservation.UNKNOWN
+        )
         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,
@@ -2540,17 +2619,29 @@ class Logging(LiteLLMLoggingBaseClass):
             },
             usage,
         )
-        self.record_partial_usage_for_failure(usage, raw_cost)
+        self.record_partial_usage_for_failure(
+            usage, raw_cost, observation=self.model_call_details["_accounting_usage_observed"]
+        )
         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:
+    def record_usage_capture(self, captured: "CapturedUsage") -> None:
+        self.model_call_details["_accounting_capture"] = captured
+
+    def record_usage_pricing(self, pricing: object) -> None:
+        self.model_call_details["_accounting_pricing_snapshot"] = pricing
+
+    def record_partial_usage_for_failure(
+        self, usage: Usage, response_cost: float, *, observation: UsageObservation = UsageObservation.KNOWN
+    ) -> 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
         self.model_call_details["response_cost"] = response_cost
+        if type(self).usage_capture_callback is not None:
+            self.model_call_details["_accounting_usage_observed"] = observation
 
     def record_failure_after_earlier_rounds(
         self, earlier: BilledRoundsSpend, failed_usage: Usage, failed_cost: float
@@ -2630,6 +2721,11 @@ class Logging(LiteLLMLoggingBaseClass):
         own usage while the request's failure log still bills every attempt."""
         usage: Final = self._model_call_details_view.get("combined_usage_object")
         response_cost: Final = self._model_call_details_view.get("response_cost")
+        if isinstance(usage, Usage) and type(self).usage_capture_callback is not None:
+            self.capture_failed_attempt_usage(usage)
+        self.model_call_details.pop("_accounting_capture", None)
+        self.model_call_details.pop("_accounting_pricing_snapshot", None)
+        self.model_call_details.pop("_accounting_usage_observed", None)
         self.model_call_details.pop("combined_usage_object", None)
         self.model_call_details.pop("response_cost", None)
         if isinstance(usage, Usage):
@@ -2639,6 +2735,53 @@ class Logging(LiteLLMLoggingBaseClass):
                 (usage, float(response_cost) if isinstance(response_cost, (int, float)) else 0.0),
             )
 
+    def capture_failed_attempt_usage(self, usage: Usage) -> None:
+        from litellm.constants import BILLED_RETRY_ATTEMPT_METADATA_KEY
+        from litellm.proxy.spend_tracking.usage_capture import CapturedUsage
+
+        capture: Final = type(self).usage_capture_callback
+        if capture is None:
+            return
+        attempt: Final = self.attempt_scoped_copy()
+        attempt.prepared_cost = None
+        attempt.finalized_cost = None
+        identity: Final = (
+            f"{self.litellm_call_id}-attempt-{len(self.model_call_details.get('failed_attempts_usage', ()))}"
+        )
+        params: Final = _LITELLM_PARAMS.validate_python(self.model_call_details.get("litellm_params") or {})
+        metadata: Final = StandardLoggingPayloadSetup.merge_litellm_metadata(params)
+        observed: Final[object] = self.model_call_details.get("_accounting_usage_observed")
+        cost: Final[object] = self.model_call_details.get("response_cost")
+        attempt.model_call_details = {
+            **attempt.model_call_details,
+            "litellm_call_id": identity,
+            "parent_request_id": self.litellm_call_id,
+            "_accounting_capture": None,
+            "_accounting_prior_captures": (),
+            "_accounting_usage_snapshot": UsageCaptureObservation.from_usage(
+                usage,
+                observed if isinstance(observed, UsageObservation) else UsageObservation.KNOWN,
+                float(cost) if isinstance(cost, (int, float)) else None,
+            ),
+            "litellm_params": {
+                **params,
+                "metadata": {**metadata, BILLED_RETRY_ATTEMPT_METADATA_KEY: True, "status": "failure"},
+                "litellm_metadata": {**metadata, BILLED_RETRY_ATTEMPT_METADATA_KEY: True, "status": "failure"},
+            },
+        }
+        captured: Final = capture(
+            attempt,
+            {"id": identity, "usage": usage.model_dump(), "status": "failed"},
+            self.start_time,
+            datetime.datetime.now(datetime.timezone.utc),
+            False,
+        )
+        if isinstance(captured, CapturedUsage):
+            self.model_call_details["_accounting_prior_captures"] = (
+                *self.model_call_details.get("_accounting_prior_captures", ()),
+                captured,
+            )
+
     def record_continued_partial(self, usage: Usage, response_cost: float, response_id: str) -> None:
         """Bill what a cut stream already consumed on this continuation's success log, under the id the caller saw."""
         self.model_call_details["continued_partial_usage"] = usage
@@ -2796,6 +2939,55 @@ class Logging(LiteLLMLoggingBaseClass):
 
     _update_completion_start_time = update_completion_start_time
 
+    def normalize_endpoint_logging_result(self, result: object) -> object:
+        call_type: Final[object] = self.call_type
+        if call_type == CallTypes.anthropic_messages.value:
+            return self._anthropic_messages_logged_response(result=result)
+        if call_type in (CallTypes.generate_content.value, CallTypes.agenerate_content.value):
+            return self._handle_non_streaming_google_genai_generate_content_response_logging(result=result)
+        if call_type in (CallTypes.asend_message.value, CallTypes.send_message.value):
+            return self._handle_a2a_response_logging(result=result)
+        return result
+
+    def usage_capture_response(self, result: object) -> object:
+        if self.stream and not self._is_assembled_stream_success(result):
+            return None
+        copied: Final = result.model_copy(deep=True) if isinstance(result, BaseModel) else result
+        normalized: Final = self.normalize_logging_result(self.normalize_endpoint_logging_result(copied))
+        if self.model_call_details.get("call_type") == CallTypes.arealtime.value and isinstance(result, list):
+            frames: Final = TypeAdapter(tuple[dict[str, object], ...]).validate_python(result)
+            measured: Final = any(
+                isinstance(_LITELLM_PARAMS.validate_python(frame.get("response") or {}).get("usage"), Mapping)
+                for frame in frames
+                if frame.get("type") == "response.done"
+            )
+            if not measured:
+                metered: Final = self.model_call_details.get(REALTIME_METERED_USAGE_KEY)
+                return {
+                    "id": self.litellm_call_id,
+                    "usage": {"metered_usage": TypeAdapter(object).dump_python(metered, mode="json")}
+                    if metered is not None
+                    else None,
+                }
+        assembled: Final = self._get_assembled_streaming_response(
+            normalized, self.start_time, self.start_time, True, []
+        )
+        return normalized if assembled is None else assembled
+
+    def usage_capture_pricing(self, result: object) -> Mapping[str, JsonValue]:
+        from litellm.utils import count_characters
+
+        return TypeAdapter(dict[str, JsonValue]).validate_python(
+            {
+                "custom_pricing": self.custom_pricing_for(result),
+                "custom_cost_per_token": self.custom_cost_per_token,
+                "custom_cost_per_second": self.custom_cost_per_second,
+                "prompt_characters": count_characters(self._prompt_for_cost_calculation())
+                if self.model_call_details.get("call_type") in (CallTypes.speech.value, CallTypes.aspeech.value)
+                else None,
+            }
+        )
+
     def normalize_logging_result(self, result: object) -> object:
         """
         Some endpoints return a different type of result than what is expected by the logging system.
@@ -2806,8 +2998,20 @@ class Logging(LiteLLMLoggingBaseClass):
             combined_usage_object: Final = (
                 RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(results=result)
             )
+            metered: Final[object] = self.model_call_details.get(REALTIME_METERED_USAGE_KEY)
+            captured_usage: Final = (
+                combined_usage_object.model_copy(
+                    update={
+                        "metered_usage": TypeAdapter(dict[str, JsonValue]).validate_python(
+                            TypeAdapter(object).dump_python(metered, mode="json")
+                        )
+                    }
+                )
+                if metered is not None
+                else combined_usage_object
+            )
             logging_result = RealtimeAPITokenUsageProcessor.create_logging_realtime_object(
-                usage=combined_usage_object,
+                usage=captured_usage,
                 results=result,
             )
 
@@ -3068,7 +3272,14 @@ class Logging(LiteLLMLoggingBaseClass):
                 start_time = self.start_time
             if end_time is None:
                 end_time = datetime.datetime.now()
-            if self.completion_start_time is None:
+            # A request re-dispatched to a fallback reuses this logging object but runs in
+            # its own window, so a first-token time from the previous attempt cannot belong
+            # to it. Keeping it makes the attempt report a negative TTFT.
+            first_token_seconds: Final = _timestamp_seconds(self.completion_start_time)
+            attempt_start_seconds: Final = _timestamp_seconds(start_time)
+            if first_token_seconds is None or (
+                attempt_start_seconds is not None and first_token_seconds < attempt_start_seconds
+            ):
                 self.completion_start_time = end_time
                 self.model_call_details["completion_start_time"] = self.completion_start_time
 
@@ -3080,15 +3291,7 @@ class Logging(LiteLLMLoggingBaseClass):
                 else cache_hit
             )
 
-            if self.call_type == CallTypes.anthropic_messages.value:
-                result = self._anthropic_messages_logged_response(result=result)
-            elif (
-                self.call_type == CallTypes.generate_content.value
-                or self.call_type == CallTypes.agenerate_content.value
-            ):
-                result = self._handle_non_streaming_google_genai_generate_content_response_logging(result=result)
-            elif self.call_type == CallTypes.asend_message.value or self.call_type == CallTypes.send_message.value:
-                result = self._handle_a2a_response_logging(result=result)
+            result = self.normalize_endpoint_logging_result(result)
 
             logging_result: Final = self.normalize_logging_result(result=result)
             if cache_hit is None and _served_from_cache_at_no_charge(logging_result):
@@ -3441,6 +3644,7 @@ class Logging(LiteLLMLoggingBaseClass):
                     self._with_client_facing_stream_model(complete_streaming_response)
                 )
                 self.model_call_details["complete_streaming_response"] = logged_streaming_response
+                self.capture_usage_before_logging(logged_streaming_response, start_time, end_time)
                 ## STANDARDIZED LOGGING PAYLOAD
                 if not for_async_request:
                     self.model_call_details["standard_logging_object"] = self._build_standard_logging_payload(
@@ -3964,6 +4168,8 @@ class Logging(LiteLLMLoggingBaseClass):
                     cost_for_built_in_tools_cost_usd_dollar=0.0,
                 )
 
+        self.capture_usage_before_logging(result, start_time, end_time)
+
         self.truncated_messages_for_logging = await truncate_base64_in_messages_async(
             StandardLoggingPayloadSetup.append_system_prompt_messages(
                 kwargs=self.model_call_details, messages=self.model_call_details.get("messages")
@@ -4990,16 +5196,34 @@ class Logging(LiteLLMLoggingBaseClass):
 
     def billable_result(self, result: object) -> object:
         """The response the cost calculator prices: a /v1/messages body served through the chat
-        bridge is replaced by the ModelResponse its spend row is built from, anything else is
+        bridge is replaced by the ModelResponse its spend row is built from, a native one is only
+        converted when its provider reports the cost the bill is read from, anything else is
         priced as is."""
         served: Final[object] = result
         call_type: Final[object] = getattr(self, "call_type", None)
         if not isinstance(result, dict):
             return served
-        if self.bridged_model_response is None and call_type != CallTypes.anthropic_messages.value:
+        if self.bridged_model_response is not None:
+            return self._anthropic_messages_logged_response(result=served)
+        if call_type != CallTypes.anthropic_messages.value:
             return served
+        if not reports_native_messages_cost(self.model_call_details.get("custom_llm_provider")):
+            return self._served_with_requested_speed(result=served)
         return self._anthropic_messages_logged_response(result=served)
 
+    def _served_with_requested_speed(self, result: object) -> object:
+        """A native /v1/messages body priced as served bills the speed the client asked for when its
+        usage block reports none, the same speed its spend row is priced at."""
+        body: Final = _SERVED_ANTHROPIC_BODY.validate_python(result)
+        usage: Final = body.get("usage")
+        requested: Final = _LITELLM_PARAMS.validate_python(getattr(self, "optional_params", None) or {}).get("speed")
+        if not isinstance(requested, str) or not isinstance(usage, dict):
+            return result
+        reported: Final = _SERVED_ANTHROPIC_BODY.validate_python(usage)
+        if isinstance(reported.get("speed"), str):
+            return result
+        return {**body, "usage": {**reported, "speed": requested}}
+
     def _anthropic_messages_logged_response(self, result: object) -> ModelResponse:
         """
         The ModelResponse a /v1/messages spend_logs row is built from.
@@ -6808,45 +7032,19 @@ class StandardLoggingPayloadSetup:
 
     @staticmethod
     def get_usage_from_response_obj(response_obj: dict | None, combined_usage_object: Usage | None = None) -> Usage:
-        ## BASE CASE ##
         if combined_usage_object is not None:
             return combined_usage_object
-        if response_obj is None:
-            return Usage(
-                prompt_tokens=0,
-                completion_tokens=0,
-                total_tokens=0,
-            )
-
-        usage: Final = response_obj.get("usage", None)
-        if usage is None:
-            rerank_usage: Final = get_rerank_usage(_RESPONSE_MAPPING.validate_python(response_obj))
-            if rerank_usage is not None:
-                return Usage(**rerank_usage)
-        if usage is None or (not isinstance(usage, dict) and not isinstance(usage, Usage)):
-            return Usage(
-                prompt_tokens=0,
-                completion_tokens=0,
-                total_tokens=0,
-            )
-        elif isinstance(usage, Usage):
-            return usage
-        elif isinstance(usage, ResponseAPIUsage):
-            return ResponseAPILoggingUtils.transform_response_api_usage_to_chat_usage(usage)
-        elif isinstance(usage, dict):
-            if ResponseAPILoggingUtils.is_response_api_usage(usage):
-                return ResponseAPILoggingUtils.transform_response_api_usage_to_chat_usage(usage)
-            if InteractionsUsageObjectTransformation.is_interactions_usage_object(usage):
-                return InteractionsUsageObjectTransformation.transform_interactions_usage_object(usage)
-            return Usage(**usage)
-
-        raise ValueError(f"usage is required, got={usage} of type {type(usage)}")
+        reported: Final = response_obj.get("usage") if response_obj else None
+        if isinstance(reported, Usage):
+            return reported
+        normalized: Final = StandardLoggingPayloadSetup.get_usage_as_dict(response_obj)
+        return Usage(**normalized) if normalized else Usage(prompt_tokens=0, completion_tokens=0, total_tokens=0)
 
     @staticmethod
     def get_usage_as_dict(
-        response_obj: dict | None,
+        response_obj: dict[str, Any] | None,
         combined_usage_object: Usage | None = None,
-    ) -> dict:
+    ) -> dict[str, Any]:
         """
         Like get_usage_from_response_obj but returns a plain dict, skipping
         the Pydantic Usage construction on the hot path.
@@ -6858,7 +7056,21 @@ class StandardLoggingPayloadSetup:
             return _empty
         _raw: Final = response_obj.get("usage", None)
         if _raw is None:
-            return dict(get_rerank_usage(_RESPONSE_MAPPING.validate_python(response_obj)) or _empty)
+            google_usage: Final[object] = response_obj.get("usageMetadata")
+            if isinstance(google_usage, dict) and _GOOGLE_USAGE_FIELDS.intersection(google_usage):
+                return litellm.VertexGeminiConfig.calculate_usage(
+                    cast(  # cast-ok: retain converter-supported modalities and token_count aliases
+                        GenerateContentResponseBody, response_obj
+                    )
+                ).model_dump()
+            reported: Final = response_obj.get("usage_info")
+            if isinstance(reported, BaseModel):
+                return reported.model_dump(exclude_none=True)
+            return (
+                {key: value for key, value in reported.items() if value is not None}
+                if isinstance(reported, dict)
+                else dict(get_rerank_usage(_RESPONSE_MAPPING.validate_python(response_obj)) or _empty)
+            )
         if isinstance(_raw, ResponseAPIUsage):
             return ResponseAPILoggingUtils.transform_response_api_usage_to_chat_usage(_raw).model_dump()
         if isinstance(_raw, dict):

litellm/litellm_core_utils/llm_cost_calc/tool_call_cost_tracking.py

@@ -208,11 +208,14 @@ class StandardBuiltInToolCostTracking:
             usage=usage, response_object=response_object
         )
 
+        response_model: Final = _output_item_field(response_object, "model")
         if model_info is not None and resolved_usage is not None and resolved_provider is not None:
             result: Final = get_cost_for_web_search_request(
                 custom_llm_provider=resolved_provider,
                 usage=resolved_usage,
                 model_info=model_info,
+                response_model=response_model if isinstance(response_model, str) else None,
+                lookup_model_info=StandardBuiltInToolCostTracking._safe_get_model_info,
             )
             if result is not None:
                 return result

litellm/litellm_core_utils/logging_utils.py

@@ -3,13 +3,15 @@ import functools
 import inspect
 import re
 import time
-from collections.abc import Iterator, Mapping, Sequence
+from collections.abc import Callable, Iterator, Mapping, Sequence
 from datetime import datetime
 from typing import TYPE_CHECKING, Any, Final, TypeAlias, cast
 
 from litellm._logging import format_base64_size, verbose_logger
 from litellm.constants import (
+    BASE64_TRUNCATION_INLINE_COPY_MAX_CHARS,
     BASE64_TRUNCATION_OFFLOAD_THRESHOLD_CHARS,
+    BASE64_TRUNCATION_OFFLOAD_THRESHOLD_STRINGS,
     MAX_BASE64_LENGTH_FOR_LOGGING,
 )
 from litellm.litellm_core_utils.core_helpers import get_litellm_metadata_from_kwargs
@@ -44,6 +46,8 @@ Helper utils used for logging callbacks
 # Regex matching data-URI base64 content: "data:<mime>;base64,<payload>"
 # Captures: group(1)=mime_type, group(2)=base64_payload
 _DATA_URI_RE: Final = re.compile(r"data:([^;,\s]{1,255});base64,([A-Za-z0-9+/=]+)")
+_DATA_URI_PREFIX: Final = "data:"
+_DATA_URI_BASE64_MARKER: Final = ";base64,"
 
 # Maximum nesting depth for _truncate_base64_in_value to guard against
 # pathological payloads. OpenAI message format is typically 3-4 levels deep.
@@ -60,15 +64,20 @@ def _base64_data_uri_replacer(match: re.Match[str]) -> str:
     return f"data:{mime_type};base64,[base64_data truncated: {size_str}]"
 
 
+def _may_contain_base64_data_uri(value: str) -> bool:
+    return ";" in value and _DATA_URI_PREFIX in value and _DATA_URI_BASE64_MARKER in value
+
+
 def _truncate_base64_in_string(value: str) -> str:
     """Replace long base64 data-URI payloads in a string with a size placeholder."""
-    if MAX_BASE64_LENGTH_FOR_LOGGING <= 0:
+    if MAX_BASE64_LENGTH_FOR_LOGGING <= 0 or not _may_contain_base64_data_uri(value):
         return value
     return _DATA_URI_RE.sub(_base64_data_uri_replacer, value)
 
 
 def _truncate_base64_in_value(
     value: str | dict[str, object] | list[object] | None,
+    truncate_string: Callable[[str], str],
 ) -> str | dict[str, object] | list[object] | None:
     """Iteratively truncate base64 data URIs in a JSON-like value (str/list/dict).
 
@@ -78,13 +87,13 @@ def _truncate_base64_in_value(
     # Stack entries: (source_value, depth, parent_container, key_or_index)
     # We mutate *copies* of dicts/lists in-place via parent references.
     if isinstance(value, str):
-        return _truncate_base64_in_string(value)
+        return truncate_string(value)
     if not isinstance(value, (dict, list)):
         return value
 
     # Shallow-copy the root so we don't mutate the caller's data.
     root: Final = {k: v for k, v in value.items()} if isinstance(value, dict) else list(value)
-    stack: Final[list] = [(root, 0)]
+    stack: Final[list[tuple[dict[str, object] | list[object], int]]] = [(root, 0)]  # mutable-ok: explicit stack
 
     while stack:
         container, depth = stack.pop()
@@ -93,19 +102,19 @@ def _truncate_base64_in_value(
         if isinstance(container, dict):
             for k, v in container.items():
                 if isinstance(v, str):
-                    container[k] = _truncate_base64_in_string(v)
+                    container[k] = truncate_string(v)
                 elif isinstance(v, dict):
-                    copy: dict | list = {ck: cv for ck, cv in v.items()}
+                    copy: dict[str, object] | list[object] = {ck: cv for ck, cv in v.items()}
                     container[k] = copy
                     stack.append((copy, depth + 1))
                 elif isinstance(v, list):
                     copy = list(v)
                     container[k] = copy
                     stack.append((copy, depth + 1))
-        elif isinstance(container, list):
+        else:
             for i, v in enumerate(container):
                 if isinstance(v, str):
-                    container[i] = _truncate_base64_in_string(v)
+                    container[i] = truncate_string(v)
                 elif isinstance(v, dict):
                     copy = {ck: cv for ck, cv in v.items()}
                     container[i] = copy
@@ -127,8 +136,15 @@ def truncate_base64_in_messages(
     """
     if messages is None or MAX_BASE64_LENGTH_FOR_LOGGING <= 0:
         return messages
+    return _copy_messages(messages, _truncate_base64_in_string)
+
+
+def _copy_messages(
+    messages: str | dict[str, object] | list[object],  # mutable-ok: same contract as truncate_base64_in_messages
+    truncate_string: Callable[[str], str],
+) -> str | dict[str, object] | list[object] | None:  # mutable-ok: same contract as truncate_base64_in_messages
     try:
-        return _truncate_base64_in_value(messages)
+        return _truncate_base64_in_value(messages, truncate_string)
     except Exception as e:
         verbose_logger.debug("Failed to truncate base64 in messages: %s", e)
         return messages
@@ -151,19 +167,31 @@ def _iter_string_leaves(value: _StringTree) -> Iterator[str]:
                 pass
 
 
+def _is_few_plain_strings(leaves: tuple[str, ...]) -> bool:
+    return len(leaves) < BASE64_TRUNCATION_OFFLOAD_THRESHOLD_STRINGS and not any(
+        _may_contain_base64_data_uri(leaf) for leaf in leaves
+    )
+
+
 async def truncate_base64_in_messages_async(
     messages: str | list | dict | None,  # mutable-ok: same contract as truncate_base64_in_messages
 ) -> str | list | dict | None:  # mutable-ok: same contract as truncate_base64_in_messages
     """
     Same result as truncate_base64_in_messages, but payloads whose string content
-    reaches BASE64_TRUNCATION_OFFLOAD_THRESHOLD_CHARS are scanned in a worker
-    thread so the regex pass over multi-MB base64 images does not block the event loop.
+    reaches BASE64_TRUNCATION_OFFLOAD_THRESHOLD_CHARS are copied in a worker thread,
+    so neither the regex pass over multi-MB base64 images nor the copy of a long
+    history blocks the event loop. A few strings with no data URI, under
+    BASE64_TRUNCATION_INLINE_COPY_MAX_CHARS in total, are copied inline, since
+    checking them for data URIs and copying them costs less than the thread hop.
     """
     if messages is None or MAX_BASE64_LENGTH_FOR_LOGGING <= 0:
         return messages
-    total_chars: Final = sum(len(leaf) for leaf in _iter_string_leaves(messages))
+    leaves: Final = tuple(_iter_string_leaves(messages))
+    total_chars: Final = sum(len(leaf) for leaf in leaves)
     if total_chars < BASE64_TRUNCATION_OFFLOAD_THRESHOLD_CHARS:
         return truncate_base64_in_messages(messages)
+    if total_chars < BASE64_TRUNCATION_INLINE_COPY_MAX_CHARS and _is_few_plain_strings(leaves):
+        return _copy_messages(messages, lambda text: text)
     return await asyncio.to_thread(truncate_base64_in_messages, messages)
 
 

litellm/litellm_core_utils/model_param_helper.py

@@ -17,6 +17,7 @@ from openai.types.responses.response_create_params import (
     ResponseCreateParamsNonStreaming,
     ResponseCreateParamsStreaming,
 )
+from pydantic import TypeAdapter, ValidationError
 
 from litellm._logging import verbose_logger
 from litellm.types.llms.anthropic import AnthropicMessagesRequest
@@ -41,6 +42,16 @@ def peek_reasoning_summary_aliases(optional_params: Mapping[str, object]) -> obj
     return None
 
 
+_OPTIONAL_BOOL: Final = TypeAdapter[bool | None](bool | None)
+
+
+def embeds_thought_signature_in_tool_call_id(litellm_params: Mapping[str, object]) -> bool:
+    try:
+        return _OPTIONAL_BOOL.validate_python(litellm_params.get("thought_signature_in_tool_call_id")) is not False
+    except ValidationError:
+        return True
+
+
 class ModelParamHelper:
     # Cached at class level — deterministic set built from static OpenAI type annotations
     relevant_logging_args: ClassVar[frozenset[str]] = frozenset()

litellm/litellm_core_utils/prompt_templates/common_utils.py

@@ -8,7 +8,7 @@ import io
 import json
 import mimetypes
 import re
-from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
+from collections.abc import Callable, Hashable, Iterable, Iterator, Mapping, Sequence
 from dataclasses import dataclass
 from graphlib import CycleError, TopologicalSorter
 from itertools import chain, groupby, islice
@@ -88,17 +88,22 @@ def handle_any_messages_to_chat_completion_str_messages_conversion(
         return [{"input": str(messages)}]
 
 
+def _message_with_str_content(message: AllMessageValues) -> AllMessageValues:
+    texts: Final = convert_content_list_to_str(message=message)
+    if not texts:
+        return message
+    return cast(  # cast-ok: the same message with its content flattened to one string
+        AllMessageValues, {**message, "content": texts}
+    )
+
+
 def handle_messages_with_content_list_to_str_conversion(
     messages: list[AllMessageValues],
 ) -> list[AllMessageValues]:
     """
-    Handles messages with content list conversion
+    Returns new messages with each content list flattened to a string, leaving the caller's messages untouched
     """
-    for message in messages:
-        texts = convert_content_list_to_str(message=message)
-        if texts:
-            message["content"] = texts
-    return messages
+    return [_message_with_str_content(message) for message in messages]
 
 
 def strip_name_from_message(message: AllMessageValues, allowed_name_roles: list[str] = ["user"]) -> AllMessageValues:
@@ -233,7 +238,7 @@ def is_non_content_values_set(message: AllMessageValues) -> bool:
     return any(message.get(key, None) is not None for key in message if key not in ignore_keys)
 
 
-_IMAGE_CONTENT_PART_TYPES: Final = frozenset({"image_url", "input_image", "image"})
+_IMAGE_CONTENT_PART_TYPES: Final = ("image_url", "input_image", "image", "computer_screenshot")
 _IMAGE_SCAN_MAX_DEPTH: Final = 4
 
 
@@ -552,15 +557,24 @@ def as_openai_image_part(part: Mapping[str, object]) -> ChatCompletionImageObjec
     return {"type": "image_url", "image_url": {"url": url}} if url else None
 
 
-def request_contains_image_content(messages: Sequence[Mapping[str, object]]) -> bool:
-    """Whether any message carries an image content part, across the dialects that reach
-    pre-routing hooks untranslated: chat-completions ``image_url``, Responses ``input_image``,
-    and Anthropic Messages ``image``, including images nested inside ``tool_result`` blocks."""
-    return any(
-        isinstance(content, list) and _content_parts_contain_image(content)
-        for message in messages
-        for content in (message.get("content"),)
-    )
+def _message_contains_image_content(message: object) -> bool:
+    match message:
+        case {"content": list()}:
+            return _content_parts_contain_image(content_parts(_CONTENT_PART.validate_python(message)))
+        case {"type": "function_call_output" | "custom_tool_call_output" | "tool_result", "output": list()}:
+            output: Final = _CONTENT_PART.validate_python(message)["output"]
+            return _content_parts_contain_image(_CONTENT_PARTS.validate_python(output))
+        case {"type": "computer_call_output", "output": dict()}:
+            return _content_parts_contain_image((_CONTENT_PART.validate_python(message)["output"],))
+        case _:
+            return False
+
+
+def request_contains_image_content(messages: object) -> bool:
+    """Inspect native message content without translating or validating the request."""
+    if not isinstance(messages, (list, tuple)):
+        return False
+    return any(_message_contains_image_content(message) for message in _CONTENT_PARTS.validate_python(messages))
 
 
 def audio_or_image_in_message_content(message: AllMessageValues) -> bool:
@@ -3221,3 +3235,15 @@ def text_completion_prompt_to_messages(prompt: object) -> tuple[AllMessageValues
     if string_entries and len(string_entries) == len(entries):
         return tuple(ChatCompletionUserMessage(role="user", content=entry) for entry in string_entries)
     raise ValueError(f"`prompt` must be a non-empty string or a non-empty list of strings. Got: {prompt_type_name}.")
+
+
+def is_present_non_string(value: object) -> bool:
+    return value is not None and not isinstance(value, str)
+
+
+def invalid_type_message(field_path: str, value: object) -> str:
+    return f"Invalid type for '{field_path}': expected a string, got {type(value).__name__}"
+
+
+def is_unhashable_value_behind(error: TypeError, value: object) -> bool:
+    return not isinstance(value, Hashable) and f"unhashable type: '{type(value).__name__}'" in str(error)

litellm/litellm_core_utils/prompt_templates/factory.py

@@ -26,11 +26,13 @@ from litellm.litellm_core_utils.core_helpers import as_str_mapping, attribute_or
 from litellm.litellm_core_utils.prompt_templates.mid_conversation_system import (
     anthropic_system_messages,
     find_anthropic_server_tool_result,
+    is_anthropic_server_tool_use,
     is_anthropic_tool_search_use,
     message_field,
     parts_of,
     place_mid_conversation_system,
     split_leading_system_run,
+    string_field,
 )
 from litellm.litellm_core_utils.url_utils import async_safe_get, safe_get
 from litellm.llms.custom_httpx.http_handler import HTTPHandler, get_async_httpx_client, header_value
@@ -1252,6 +1254,22 @@ def encode_tool_call_id_with_signature(tool_call_id: str, thought_signature: str
 _encode_tool_call_id_with_signature = encode_tool_call_id_with_signature
 
 
+def _tool_call_id_prefix(tool_call_id: object) -> str | None:
+    return tool_call_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] if isinstance(tool_call_id, str) else None
+
+
+def _called_tool_name(tool_call_id: str, tool_calls: Sequence[Mapping[str, object]]) -> str | None:
+    exact_matches: Final = tuple(call for call in tool_calls if call.get("id") == tool_call_id)
+    prefix: Final = _tool_call_id_prefix(tool_call_id)
+    prefix_matches: Final = tuple(
+        call for call in tool_calls if prefix and _tool_call_id_prefix(call.get("id")) == prefix
+    )
+    matches: Final = exact_matches or (prefix_matches if len(prefix_matches) == 1 else ())
+    function: Final = matches[0].get("function") if matches else None
+    name: Final = function.get("name") if isinstance(function, Mapping) else None
+    return name if isinstance(name, str) else None
+
+
 def get_thought_signature_from_tool(tool: Mapping[str, object]) -> str | None:
     """Extract thought signature from tool call's provider_specific_fields.
 
@@ -1691,16 +1709,14 @@ def convert_to_gemini_tool_call_result(
                     )
                 ) is not None:
                     media_parts.append(media_part)
-    name: str | None = message.get("name", "")
-
     # Recover name from last message with tool calls
-    if last_message_with_tool_calls:
-        tools: Final = last_message_with_tool_calls.get("tool_calls", [])
-        msg_tool_call_id: Final = message.get("tool_call_id", None)
-        for tool in tools:
-            prev_tool_call_id = tool.get("id", None)
-            if msg_tool_call_id and prev_tool_call_id and msg_tool_call_id == prev_tool_call_id:
-                name = tool.get("function", {}).get("name", "")
+    msg_tool_call_id: Final = message.get("tool_call_id", None)
+    called_tool_name: Final = (
+        _called_tool_name(msg_tool_call_id, last_message_with_tool_calls.get("tool_calls") or ())
+        if last_message_with_tool_calls and isinstance(msg_tool_call_id, str) and msg_tool_call_id
+        else None
+    )
+    name: Final[str | None] = called_tool_name if called_tool_name is not None else message.get("name", "")
 
     # Echo the OpenAI tool_call_id on functionResponse (strip thought-signature suffix).
     gemini_call_id: str | None = None
@@ -2008,7 +2024,7 @@ def _server_tool_answers_replaced_after(messages: Sequence[object], index: int)
     following: Final = _messages_until_the_next_assistant(messages, index)
     a_user_turn_remains: Final = any(
         message_field(message, "role") in ("user", "tool", "function")
-        and message_field(message, "tool_call_id") not in kept_server_result_ids
+        and string_field(message, "tool_call_id") not in kept_server_result_ids
         for message in following
     )
     return _tool_call_ids_in_tool_messages(following) & kept_server_result_ids if a_user_turn_remains else frozenset()
@@ -2059,6 +2075,54 @@ def _placed_and_paired(
 _AnthropicToolInvokeItem: TypeAlias = AnthropicMessagesToolUseParam | dict[str, Any]
 
 
+def _is_server_tool_result(block: Mapping[str, object]) -> bool:
+    block_type: Final = block.get("type")
+    return isinstance(block_type, str) and block_type.endswith("_tool_result")
+
+
+def _split_unrun_server_tool_uses(
+    groups: Sequence[Sequence[_AnthropicToolInvokeItem]],
+) -> tuple[tuple[Sequence[_AnthropicToolInvokeItem], ...], tuple[_AnthropicToolInvokeItem, ...]]:
+    completed: Final = tuple(group for group in groups if any(_is_server_tool_result(block) for block in group))
+    unrun: Final = tuple(group[0] for group in groups if not any(_is_server_tool_result(block) for block in group))
+    return completed, unrun
+
+
+def _block_id(block: Mapping[str, object]) -> object:
+    return block.get("id")
+
+
+def _unrun_server_tool_use_ids(blocks: Sequence[Mapping[str, object]]) -> frozenset[object]:
+    result_ids: Final = frozenset(block.get("tool_use_id") for block in blocks if _is_server_tool_result(block))
+    return frozenset(
+        _block_id(block)
+        for block in blocks
+        if block.get("type") == "server_tool_use" and _block_id(block) not in result_ids
+    )
+
+
+def _last_block_id_among(blocks: Sequence[Mapping[str, object]], ids: frozenset[object]) -> object:
+    return next(block_id for block in reversed(blocks) if (block_id := _block_id(block)) in ids)
+
+
+def _paused_turn_replay(
+    tool_invokes: Sequence[_AnthropicToolInvokeItem],
+    thinking_blocks: Sequence[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock],
+    *,
+    is_final_message: bool,
+) -> tuple[
+    tuple[_AnthropicToolInvokeItem, ...],
+    tuple[ChatCompletionThinkingBlock | ChatCompletionRedactedThinkingBlock, ...],
+]:
+    unrun_ids: Final = _unrun_server_tool_use_ids(tool_invokes)
+    if not unrun_ids:
+        return tuple(tool_invokes), tuple(thinking_blocks)
+    dropped_ids: Final = (
+        unrun_ids - frozenset((_last_block_id_among(tool_invokes, unrun_ids),)) if is_final_message else unrun_ids
+    )
+    return tuple(block for block in tool_invokes if _block_id(block) not in dropped_ids), ()
+
+
 def convert_to_anthropic_tool_invoke(
     tool_calls: list[ChatCompletionAssistantToolCall],
     web_search_results: Sequence[object] | None = None,
@@ -2141,6 +2205,15 @@ def convert_to_anthropic_tool_invoke(
             anthropic_tool_invoke.append(server_tool_result)
         elif is_anthropic_tool_search_use(tool_id, tool_name) and tool_id not in answered_tool_ids:
             continue
+        elif is_anthropic_server_tool_use(tool_id) and tool_id not in answered_tool_ids:
+            anthropic_tool_invoke.append(
+                {
+                    "type": "server_tool_use",
+                    "id": tool_id,
+                    "name": tool_name,
+                    "input": tool_input,
+                }
+            )
         else:
             sanitized_tool_id = sanitize_anthropic_tool_use_id(tool_id)
             _anthropic_tool_use_param = AnthropicMessagesToolUseParam(
@@ -3109,6 +3182,30 @@ def anthropic_messages_pt(
                         _has_server_tool_calls = True
                         break
 
+            # Get web_search_results and tool_results from provider_specific_fields
+            # for server_tool_use reconstruction.
+            # Fixes: https://github.com/BerriAI/litellm/issues/17737
+            _provider_specific_fields_raw = assistant_content_block.get("provider_specific_fields")
+            _provider_specific_fields: dict[str, Any] = (
+                cast(dict[str, Any], _provider_specific_fields_raw)
+                if isinstance(_provider_specific_fields_raw, dict)
+                else {}
+            )
+            tool_invoke_results = (
+                convert_to_anthropic_tool_invoke(
+                    assistant_tool_calls,
+                    web_search_results=_provider_specific_fields.get("web_search_results"),
+                    tool_results=_provider_specific_fields.get("tool_results"),
+                    answered_tool_ids=client_answered_tool_ids,
+                )
+                if assistant_tool_calls is not None
+                else ()
+            )
+            turn_paused = bool(_unrun_server_tool_use_ids(tool_invoke_results))
+            replayed_tool_invokes, replayed_thinking_blocks = _paused_turn_replay(
+                tool_invoke_results, thinking_blocks or (), is_final_message=msg_i == len(messages) - 1
+            )
+
             if (
                 thinking_blocks is not None
                 and _has_server_tool_calls
@@ -3121,26 +3218,12 @@ def anthropic_messages_pt(
                 # We must preserve this interleaved order because Anthropic
                 # verifies thinking block signatures based on position.
 
-                # Build the tool call groups (server_tool_use + its result)
-                _provider_specific_fields_raw_tc = assistant_content_block.get("provider_specific_fields")
-                _provider_specific_fields_tc: dict[str, Any] = {}
-                if isinstance(_provider_specific_fields_raw_tc, dict):
-                    _provider_specific_fields_tc = cast(dict[str, Any], _provider_specific_fields_raw_tc)
-                _web_search_results_tc = _provider_specific_fields_tc.get("web_search_results")
-                _tool_results_tc = _provider_specific_fields_tc.get("tool_results")
-                tool_invoke_results = convert_to_anthropic_tool_invoke(
-                    assistant_tool_calls,
-                    web_search_results=_web_search_results_tc,
-                    tool_results=_tool_results_tc,
-                    answered_tool_ids=client_answered_tool_ids,
-                )
-
                 # Group tool invoke results into (server_tool_use, result) pairs
                 # and separate regular tool_use blocks
                 server_tool_groups: list[list[_AnthropicToolInvokeItem]] = []
                 regular_tool_uses: list[_AnthropicToolInvokeItem] = []
                 _current_group: list[_AnthropicToolInvokeItem] = []
-                for item in tool_invoke_results:
+                for item in replayed_tool_invokes:
                     item_type = item.get("type", "") if isinstance(item, dict) else getattr(item, "type", "")
                     if item_type == "server_tool_use":
                         if _current_group:
@@ -3154,6 +3237,9 @@ def anthropic_messages_pt(
                         _current_group.append(item)
                 if _current_group:
                     server_tool_groups.append(_current_group)
+                completed_server_tool_groups, pending_server_tool_uses = _split_unrun_server_tool_uses(
+                    server_tool_groups
+                )
 
                 # Build the text block if content is a non-empty string
                 text_element = None
@@ -3177,15 +3263,15 @@ def anthropic_messages_pt(
                 # Any remaining groups (after all thinking blocks) go after.
                 tb_idx = 0
                 grp_idx = 0
-                num_tb = len(thinking_blocks) if thinking_blocks else 0
-                num_grp = len(server_tool_groups)
+                num_tb = len(replayed_thinking_blocks)
+                num_grp = len(completed_server_tool_groups)
 
                 while tb_idx < num_tb or grp_idx < num_grp:
                     if tb_idx < num_tb and grp_idx < num_grp:
                         # Emit thinking block then its tool group
-                        assistant_content.append(thinking_blocks[tb_idx])
+                        assistant_content.append(replayed_thinking_blocks[tb_idx])
                         tb_idx += 1
-                        for block in server_tool_groups[grp_idx]:
+                        for block in completed_server_tool_groups[grp_idx]:
                             item_id = block.get("id") if isinstance(block, dict) else getattr(block, "id", None)
                             if item_id and item_id in unique_tool_ids:
                                 continue
@@ -3195,11 +3281,11 @@ def anthropic_messages_pt(
                         grp_idx += 1
                     elif tb_idx < num_tb:
                         # More thinking blocks than tool groups - emit before text
-                        assistant_content.append(thinking_blocks[tb_idx])
+                        assistant_content.append(replayed_thinking_blocks[tb_idx])
                         tb_idx += 1
                     else:
                         # More tool groups than thinking blocks - emit remaining
-                        for block in server_tool_groups[grp_idx]:
+                        for block in completed_server_tool_groups[grp_idx]:
                             item_id = block.get("id") if isinstance(block, dict) else getattr(block, "id", None)
                             if item_id and item_id in unique_tool_ids:
                                 continue
@@ -3212,8 +3298,8 @@ def anthropic_messages_pt(
                 if text_element is not None:
                     assistant_content.append(text_element)
 
-                # Add regular (non-server) tool calls at the end
-                for item in regular_tool_uses:
+                # Add regular tool calls, then a paused turn's unrun server tool call, at the end
+                for item in (*regular_tool_uses, *pending_server_tool_uses):
                     item_id = item.get("id") if isinstance(item, dict) else getattr(item, "id", None)
                     if item_id and item_id in unique_tool_ids:
                         continue
@@ -3233,18 +3319,18 @@ def anthropic_messages_pt(
                 )
                 _content_list = assistant_content_block.get("content") if _content_is_list else None
 
-                if (
-                    thinking_blocks is not None and not _content_is_list
-                ):  # IMPORTANT: ADD THIS FIRST, ELSE ANTHROPIC WILL RAISE AN ERROR
-                    assistant_content.extend(thinking_blocks)
+                if not _content_is_list:  # IMPORTANT: ADD THIS FIRST, ELSE ANTHROPIC WILL RAISE AN ERROR
+                    assistant_content.extend(replayed_thinking_blocks)
                 if _content_is_list and _content_list is not None:
-                    for m in _with_missing_thinking_blocks(_content_list, thinking_blocks or ()):
+                    for m in _with_missing_thinking_blocks(_content_list, replayed_thinking_blocks):
                         if not isinstance(m, dict):
                             continue
                         # handle thinking blocks
                         if (thinking_block := _as_thinking_block(m)) is not None:
                             assistant_content.extend(
-                                _replayable_thinking_blocks([thinking_block], drop_params=drop_params)
+                                ()
+                                if turn_paused
+                                else _replayable_thinking_blocks([thinking_block], drop_params=drop_params)
                             )
                             continue
                         text_block = cast(str, m.get("text", ""))
@@ -3284,25 +3370,9 @@ def anthropic_messages_pt(
                     assistant_content.append(_anthropic_text_content_element)
 
             if assistant_tool_calls is not None:  # support assistant tool invoke conversion
-                # Get web_search_results and tool_results from provider_specific_fields
-                # for server_tool_use reconstruction.
-                # Fixes: https://github.com/BerriAI/litellm/issues/17737
-                _provider_specific_fields_raw = assistant_content_block.get("provider_specific_fields")
-                _provider_specific_fields: dict[str, Any] = {}
-                if isinstance(_provider_specific_fields_raw, dict):
-                    _provider_specific_fields = cast(dict[str, Any], _provider_specific_fields_raw)
-                _web_search_results = _provider_specific_fields.get("web_search_results")
-                _tool_results = _provider_specific_fields.get("tool_results")
-                tool_invoke_results = convert_to_anthropic_tool_invoke(
-                    assistant_tool_calls,
-                    web_search_results=_web_search_results,
-                    tool_results=_tool_results,
-                    answered_tool_ids=client_answered_tool_ids,
-                )
-
                 # Prevent "tool_use ids must be unique" errors by filtering duplicates
                 # This can happen when merging history that already contains the tool calls
-                for item in tool_invoke_results:
+                for item in replayed_tool_invokes:
                     # tool_use items are typically dicts, but handle objects just in case
                     item_id = item.get("id") if isinstance(item, dict) else getattr(item, "id", None)
 

litellm/litellm_core_utils/prompt_templates/mid_conversation_system.py

@@ -85,6 +85,11 @@ def message_field(message: object, key: str) -> object:
     return mapping.get(key) if mapping is not None else getattr(message, key, None)
 
 
+def string_field(message: object, key: str) -> str | None:
+    value: Final = message_field(message, key)
+    return value if isinstance(value, str) else None
+
+
 def is_system_message(message: object) -> bool:
     return message_field(message, "role") == "system"
 
@@ -286,6 +291,10 @@ def _separate_thinking_blocks_render(message: object) -> bool:
     return any(_thinking_block_renders(block) for block in parts_of(message_field(message, "thinking_blocks")))
 
 
+def is_anthropic_server_tool_use(tool_id: str) -> bool:
+    return tool_id.startswith(ANTHROPIC_SERVER_TOOL_USE_ID_PREFIX)
+
+
 def is_anthropic_tool_search_use(tool_id: str, tool_name: str) -> bool:
     return tool_id.startswith(ANTHROPIC_SERVER_TOOL_USE_ID_PREFIX) and tool_name in ANTHROPIC_TOOL_SEARCH_TOOL_NAMES
 

litellm/litellm_core_utils/ptu_shares.py

@@ -12,13 +12,13 @@ from datetime import date
 from typing import Final, Generic, TypeAlias, TypeVar
 
 from litellm.litellm_core_utils.ptu_pricing import parsed_ptu_shares, ptu_terms
+from litellm.litellm_core_utils.routing_common_utils import get_request_team_id, team_may_use_deployment
 from litellm.llms.azure.ptu_capacity import (
     PTUCapacity,
     deployment_ptu_capacity,
     is_azure_deployment,
     is_str_keyed_mapping,
 )
-from litellm.router_utils.common_utils import get_request_team_id, team_may_use_deployment
 
 _DeploymentT: Final = TypeVar("_DeploymentT", bound=Mapping[str, object])
 WildcardRoute: TypeAlias = Callable[[str], Sequence[Mapping[str, object]]]

litellm/litellm_core_utils/realtime_streaming.py

@@ -631,6 +631,8 @@ class RealTimeStreaming:
             if self.session_tools or self.tool_calls:
                 self.logging_obj.model_call_details["realtime_tools"] = self.session_tools
                 self.logging_obj.model_call_details["realtime_tool_calls"] = self.tool_calls
+            if LiteLLMLogging.usage_capture_callback is not None:
+                self.logging_obj.capture_usage_before_logging(self.messages)
             # Route through the bounded logging worker (per-coroutine timeout +
             # concurrency cap) instead of a bare create_task, so a slow callback
             # can't leave suspended tasks pinning each call's response in memory.

litellm/litellm_core_utils/streaming_chunk_builder_utils.py

@@ -1509,11 +1509,20 @@ def _delta_reasoning_items(delta: object) -> Sequence[object]:
     return cast(Sequence[object], items) if isinstance(items, list) else ()  # cast-ok: items forwarded verbatim
 
 
-def _chunk_reasoning_items(chunk: object) -> Sequence[object]:
+def _chunk_first_delta(chunk: object) -> object:
     choices: Final = _field_of(chunk, "choices")
     if not isinstance(choices, list) or not choices:
-        return ()
-    return _delta_reasoning_items(_field_of(cast(Sequence[object], choices)[0], "delta"))  # cast-ok: untyped chunk
+        return None
+    return _field_of(cast(Sequence[object], choices)[0], "delta")  # cast-ok: untyped chunk
+
+
+def _chunk_reasoning_items(chunk: object) -> Sequence[object]:
+    return _delta_reasoning_items(_chunk_first_delta(chunk))
+
+
+def _chunk_refusal(chunk: object) -> str:
+    refusal: Final = _field_of(_chunk_first_delta(chunk), "refusal")
+    return refusal if isinstance(refusal, str) else ""
 
 
 _NON_TEXT_DELTA_FIELDS: Final = (
@@ -1526,6 +1535,7 @@ _NON_TEXT_DELTA_FIELDS: Final = (
     "audio",
     "images",
     "provider_specific_fields",
+    "refusal",
 )
 
 
@@ -1674,6 +1684,9 @@ def _assembled_message_fields(processor: ChunkProcessor, chunks: Sequence) -> It
         yield "reasoning_content", processor.get_combined_reasoning_content(reasoning_chunks)
 
     typed_chunks: Final = cast(Sequence[object], chunks)  # cast-ok: chunks are untyped upstream
+    combined_refusal: Final = "".join(_chunk_refusal(chunk) for chunk in typed_chunks)
+    if combined_refusal:
+        yield "refusal", combined_refusal
     reasoning_items: Final = tuple(chain.from_iterable(_chunk_reasoning_items(chunk) for chunk in typed_chunks))
     if reasoning_items:
         yield "reasoning_items", list(reasoning_items)

litellm/litellm_core_utils/streaming_handler.py

@@ -349,11 +349,14 @@ class CustomStreamWrapper:
         make_call: Callable | None = None,
         _response_headers: dict | httpx.Headers | None = None,
         count_prompt_tokens: Callable[[], int] | None = None,
+        is_mock: bool = False,
     ):
         self.model = model
         self.make_call = make_call
         self.count_prompt_tokens = count_prompt_tokens
         self.custom_llm_provider = custom_llm_provider
+        self.is_mock = is_mock
+        self.uses_openai_chat_stream = False
         self.logging_obj: LiteLLMLoggingObject = logging_obj
         self.completion_stream = completion_stream
         self.sent_first_chunk = False
@@ -376,6 +379,7 @@ class CustomStreamWrapper:
         self._upstream_provider: str | None = None
         self.received_finish_reason: str | None = None
         self.intermittent_finish_reason: str | None = None  # finish reasons that show up mid-stream
+        self.received_any_finish_reason = False  # a later chunk can reset intermittent_finish_reason
         self.special_tokens = [
             "<|assistant|>",
             "<|system|>",
@@ -901,13 +905,21 @@ class CustomStreamWrapper:
         except Exception as e:
             raise e
 
+    def _served_openrouter_model(self) -> str | None:
+        if self.custom_llm_provider == LlmProviders.OPENROUTER.value or (
+            self.custom_llm_provider == "cached_response"
+            and self.logging_obj.model_call_details.get("custom_llm_provider") == LlmProviders.OPENROUTER.value
+        ):
+            return self._provider_response_model
+        return None
+
     def model_response_creator(
         self,
         chunk: dict | None = None,
         hidden_params: Mapping[str, object] | None = None,
         delta: Delta | None = None,
     ) -> ModelResponseStream:
-        _model: Final = self._cached_model_name
+        _model: Final = self._served_openrouter_model() or self._cached_model_name
         _logging_obj_llm_provider: Final = self._cached_logging_llm_provider
 
         if chunk is None:
@@ -1302,8 +1314,8 @@ class CustomStreamWrapper:
             if _is_delta_empty:
                 model_response.choices[0].delta = Delta(content=self._close_open_think_block())
                 # get any function call arguments
-                model_response.choices[0].finish_reason = map_finish_reason(
-                    finish_reason=self.received_finish_reason
+                model_response.choices[0].finish_reason = self._normalize_tool_call_finish_reason(
+                    map_finish_reason(finish_reason=self.received_finish_reason)
                 )  # ensure consistent output to openai
 
                 self.sent_last_chunk = True
@@ -1597,6 +1609,7 @@ class CustomStreamWrapper:
             if response_obj["is_finished"]:
                 self.received_finish_reason = response_obj["finish_reason"]
         else:  # openai / azure chat model
+            self.uses_openai_chat_stream = True
             if isinstance(chunk, ChatCompletionChunk):
                 self._openai_handler_receives_chat_chunks = True
                 if any(str(choice.finish_reason) == "abort" for choice in chunk.choices or ()):
@@ -1617,6 +1630,7 @@ class CustomStreamWrapper:
                 return _ProviderChunkEarlyReturn(None)
             completion_obj["content"] = response_obj["text"]
             self.intermittent_finish_reason = response_obj.get("finish_reason", None)
+            self.received_any_finish_reason |= response_obj["finish_reason"] is not None
             if response_obj["is_finished"]:
                 if response_obj["finish_reason"] == "error":
                     raise Exception(
@@ -1693,7 +1707,7 @@ class CustomStreamWrapper:
                 return dispatch_result.value
             response_obj = dispatch_result.response_obj
 
-            model_response.model = self.model
+            model_response.model = self._served_openrouter_model() or self.model
             ## FUNCTION CALL PARSING
             original_chunk: Final[_UpstreamChunkLike | None] = (
                 response_obj.get("original_chunk") if response_obj is not None else None
@@ -1737,6 +1751,8 @@ class CustomStreamWrapper:
                                         # if function returned but type set to None - mistral's api returns type: None
                                         tool["type"] = "function"
                             model_response.choices[0].delta = Delta(**_json_delta)
+                            if model_response.choices[0].delta.tool_calls:
+                                self.tool_call = True
                         except Exception as e:
                             verbose_logger.exception(
                                 "litellm.CustomStreamWrapper.chunk_creator(): Exception occured - %s", e
@@ -1966,6 +1982,11 @@ class CustomStreamWrapper:
         if self.logging_obj.is_sync_litellm_request(litellm_params):
             self.logging_obj.success_handler(processed_chunk, None, None, cache_hit)
 
+    def _normalize_tool_call_finish_reason(self, finish_reason: str) -> str:
+        if finish_reason == "stop" and self.tool_call:
+            return "tool_calls"
+        return finish_reason
+
     def finish_reason_handler(self):
         model_response: Final = self.model_response_creator(
             hidden_params=None if self.received_model_id is None else {"received_model_id": self.received_model_id}
@@ -1981,21 +2002,30 @@ class CustomStreamWrapper:
         if served_tier is not None:
             setattr(model_response, "service_tier", served_tier)  # noqa: B010  # pydantic extra, not a declared field
         _finish_reason: Final = self.received_finish_reason or self.intermittent_finish_reason
-        if _finish_reason is not None:
-            model_response.choices[0].finish_reason = _finish_reason
-        else:
-            model_response.choices[0].finish_reason = "stop"
-
-        ## if tool use
-        if (
-            model_response.choices[0].finish_reason == "stop" and self.tool_call
-        ):  # don't overwrite for other - potential error finish reasons
-            model_response.choices[0].finish_reason = "tool_calls"
+        model_response.choices[0].finish_reason = self._normalize_tool_call_finish_reason(
+            _finish_reason if _finish_reason is not None else "stop"
+        )
         close_tag: Final = self._close_open_think_block()
         if close_tag is not None:
             model_response.choices[0].delta.content = close_tag
         return model_response
 
+    def _should_fail_on_clean_eof(self) -> bool:
+        return (
+            not self.is_mock
+            and self.uses_openai_chat_stream
+            and self.received_finish_reason is None
+            and self.intermittent_finish_reason is None
+            and not self.received_any_finish_reason
+        )
+
+    @staticmethod
+    def _stream_eof_error() -> httpx.RemoteProtocolError:
+        return httpx.RemoteProtocolError(
+            "Stream ended before a finish_reason was received",
+            request=httpx.Request(method="POST", url="https://docs.litellm.ai/docs"),
+        )
+
     def _final_chunk_for_logging(self, processed_chunk: "ModelResponseStream") -> "ModelResponseStream | None":
         if self.received_finish_reason is not None or self.intermittent_finish_reason is not None:
             return processed_chunk
@@ -2181,6 +2211,13 @@ class CustomStreamWrapper:
                     return response
 
         except StopIteration:
+            if self._should_fail_on_clean_eof():
+                error: Final = self._stream_eof_error()
+                eof_traceback: Final = traceback.format_exc()
+                self._record_partial_usage_for_failure()
+                threading.Thread(target=self.logging_obj.failure_handler, args=(error, eof_traceback)).start()
+                self.close()
+                self._handle_stream_fallback_error(error)
             if self.sent_last_chunk is True:
                 complete_streaming_response: Final = self._build_complete_streaming_response()
                 response = self._new_usage_chunk()
@@ -2384,6 +2421,9 @@ class CustomStreamWrapper:
                             continue
                         return usage_free_chunk
         except (StopAsyncIteration, StopIteration):
+            if self._should_fail_on_clean_eof():
+                await self.aclose()
+                self._log_stream_failure_and_raise(self._stream_eof_error())
             return await self._finalize_completed_stream(cache_hit=cache_hit)
         except httpx.TimeoutException as e:  # if httpx read timeout error occues
             traceback_exception = traceback.format_exc()
@@ -2529,7 +2569,7 @@ class CustomStreamWrapper:
         if partial is None or not isinstance(usage, Usage):
             return None
         if self.model:
-            partial.model = self.model
+            partial.model = self._served_openrouter_model() or self.model
         backfill_missing_cache_usage_fields(usage)
         return partial
 

litellm/litellm_core_utils/token_counter.py

@@ -56,9 +56,9 @@ from litellm.litellm_core_utils.tokenizer import HuggingFaceTokenizer, OpenAIEnc
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.tokenizer import Encoding, HuggingFace
+from litellm.litellm_core_utils.deployment_pricing import deployment_pricing_models
 from litellm.litellm_core_utils.url_utils import safe_get
 from litellm.llms.custom_httpx.http_handler import get_httpx_client, header_value
-from litellm.router_utils.deployment_pricing import deployment_pricing_models
 from litellm.rust_bridge.tokenizer import get_encoding
 from litellm.types.llms.anthropic import (
     AnthropicContentParamSource,

litellm/llms/__init__.py

@@ -9,6 +9,8 @@ from litellm.types.utils import CallTypes
 from . import *
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     from litellm.llms.base_llm.guardrail_translation.base_translation import (
         BaseTranslation,
     )
@@ -30,7 +32,29 @@ def get_cost_for_google_maps_grounding_request(
     return cost_per_google_maps_grounding_request(usage=usage, model_info=model_info)
 
 
-def get_cost_for_web_search_request(custom_llm_provider: str, usage: "Usage", model_info: "ModelInfo") -> float | None:
+def _with_served_model_search_pricing(
+    model_info: "ModelInfo",
+    response_model: str | None,
+    served_model_provider: str,
+    lookup_model_info: "Callable[[str, str | None], ModelInfo | None] | None",
+) -> "ModelInfo":
+    if model_info.get("search_context_cost_per_query") is not None or response_model is None:
+        return model_info
+    served_model_info: Final = (
+        lookup_model_info(response_model, served_model_provider) if lookup_model_info is not None else None
+    )
+    if served_model_info is None:
+        return model_info
+    return {**model_info, "search_context_cost_per_query": served_model_info.get("search_context_cost_per_query")}
+
+
+def get_cost_for_web_search_request(
+    custom_llm_provider: str,
+    usage: "Usage",
+    model_info: "ModelInfo",
+    response_model: str | None = None,
+    lookup_model_info: "Callable[[str, str | None], ModelInfo | None] | None" = None,
+) -> float | None:
     """
     Get the cost for a web search request for a given model.
 
@@ -38,6 +62,8 @@ def get_cost_for_web_search_request(custom_llm_provider: str, usage: "Usage", mo
         custom_llm_provider: The custom LLM provider.
         usage: The usage object.
         model_info: The model info.
+        response_model: The model the provider response names.
+        lookup_model_info: Looks up a model's cost map entry, returning None when it is not mapped.
     """
     if custom_llm_provider == "gemini":
         from .gemini.cost_calculator import cost_per_web_search_request
@@ -46,17 +72,25 @@ def get_cost_for_web_search_request(custom_llm_provider: str, usage: "Usage", mo
     elif custom_llm_provider == "anthropic":
         from .anthropic.cost_calculation import get_cost_for_anthropic_web_search
 
-        return get_cost_for_anthropic_web_search(model_info=model_info, usage=usage)
+        return get_cost_for_anthropic_web_search(
+            model_info=_with_served_model_search_pricing(model_info, response_model, "anthropic", lookup_model_info),
+            usage=usage,
+        )
     elif custom_llm_provider.startswith("vertex_ai"):
         # Anthropic Claude models on Vertex AI populate server_tool_use.web_search_requests
         # (same as the direct Anthropic API), not prompt_tokens_details.web_search_requests
         # (which is the Gemini field). Route claude-* models to the Anthropic calculator.
         model_key: Final[str] = model_info.get("key", "") if model_info else ""
-        if "claude" in model_key.lower():
+        if "claude" in (response_model or model_key).lower():
             from .anthropic.cost_calculation import get_cost_for_anthropic_web_search
 
             verbose_logger.debug("vertex_ai/claude model detected — routing web search cost to Anthropic calculator")
-            return get_cost_for_anthropic_web_search(model_info=model_info, usage=usage)
+            return get_cost_for_anthropic_web_search(
+                model_info=_with_served_model_search_pricing(
+                    model_info, response_model, "vertex_ai", lookup_model_info
+                ),
+                usage=usage,
+            )
 
         from .vertex_ai.gemini.cost_calculator import (
             cost_per_web_search_request as cost_per_web_search_request_vertex_ai,
@@ -71,7 +105,10 @@ def get_cost_for_web_search_request(custom_llm_provider: str, usage: "Usage", mo
             return None
         from .anthropic.cost_calculation import get_cost_for_anthropic_web_search
 
-        return get_cost_for_anthropic_web_search(model_info=model_info, usage=usage)
+        return get_cost_for_anthropic_web_search(
+            model_info=_with_served_model_search_pricing(model_info, response_model, "azure_ai", lookup_model_info),
+            usage=usage,
+        )
     elif custom_llm_provider == "perplexity":
         # Perplexity handles search costs internally in its own cost calculator
         # Return 0.0 to indicate costs are already accounted for

litellm/llms/a2a/common_utils.py

@@ -160,8 +160,11 @@ async def resolve_a2a_hop_auth_header(
     litellm_params: Mapping[str, object],
     custom_llm_provider: object,
     resolve_entra_header: AgentAuthHeaderResolver = resolve_azure_ai_agent_auth_header,
+    agent_extra_headers: Mapping[str, str] | None = None,
 ) -> Mapping[str, str] | None:
-    """Entra credentials authenticate the A2A hop only; a completion-bridge agent hands them to the model provider it bridges to."""
-    if not a2a_hop_uses_entra(litellm_params, custom_llm_provider):
+    if a2a_hop_uses_entra(litellm_params, custom_llm_provider):
+        return await resolve_entra_header(litellm_params)
+    if custom_llm_provider or any(name.lower() == "authorization" for name in (agent_extra_headers or {})):
         return None
-    return await resolve_entra_header(litellm_params)
+    api_key: Final = litellm_params.get("api_key")
+    return {"Authorization": f"Bearer {api_key}"} if isinstance(api_key, str) and api_key else None

litellm/llms/anthropic/chat/handler.py

@@ -70,7 +70,12 @@ from litellm.types.utils import (
 )
 
 from ...base import BaseLLM
-from ..common_utils import AnthropicError, process_anthropic_headers, refusal_provider_specific_fields
+from ..common_utils import (
+    AnthropicError,
+    process_anthropic_headers,
+    refusal_params,
+    stop_reason_provider_specific_fields,
+)
 from .transformation import ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY, AnthropicConfig, JsonModeRequest
 
 if TYPE_CHECKING:
@@ -926,6 +931,17 @@ class ModelResponseIterator:
     def _web_search_call_snapshot(self) -> dict[str, object]:
         return dict(self._web_search_calls)
 
+    def _refresh_pending_web_search_call(self, tool_use_id: str) -> bool:
+        if tool_use_id not in self._web_search_calls:
+            return False
+        self._web_search_calls[tool_use_id] = build_web_search_call(
+            tool_use_id,
+            self._server_tool_inputs.get(tool_use_id, {}),
+            {"content": []},
+            status="in_progress",
+        )
+        return True
+
     def _complete_web_search_call(self, result: dict[str, object]) -> None:
         tool_use_id: Final = result.get("tool_use_id")
         if not isinstance(tool_use_id, str) or tool_use_id not in self._web_search_calls:
@@ -1128,6 +1144,8 @@ class ModelResponseIterator:
                                 self._server_tool_inputs[self._current_server_tool_id] = json.loads(args)
                             except (json.JSONDecodeError, TypeError):
                                 pass
+                        if self._refresh_pending_web_search_call(self._current_server_tool_id):
+                            provider_specific_fields["web_search_calls"] = self._web_search_call_snapshot()
                         self._current_server_tool_id = None
                 if self.is_response_format_tool:
                     text = self._flush_response_format_buffer()
@@ -1140,8 +1158,8 @@ class ModelResponseIterator:
                 # These are automatically handled by Anthropic API, we just pass them through
                 pass
             elif type_chunk == "message_delta":
-                finish_reason, usage, container, refusal_fields = self._handle_message_delta(chunk)
-                provider_specific_fields.update(refusal_fields)
+                finish_reason, usage, container, stop_reason_fields = self._handle_message_delta(chunk)
+                provider_specific_fields.update(stop_reason_fields)
                 text = self._release_answer_close() + self._drain_split_answer_blocks()
                 if container:
                     provider_specific_fields["container"] = container
@@ -1190,6 +1208,7 @@ class ModelResponseIterator:
                 )
 
             text, tool_use = self._handle_json_mode_chunk(text=text, tool_use=tool_use)
+            refusal_fields: Final = refusal_params(provider_specific_fields)
 
             returned_chunk: Final = ModelResponseStream(
                 choices=[
@@ -1201,6 +1220,7 @@ class ModelResponseIterator:
                             provider_specific_fields=(provider_specific_fields if provider_specific_fields else None),
                             thinking_blocks=(thinking_blocks if thinking_blocks else None),
                             reasoning_content=reasoning_content,
+                            **refusal_fields,
                         ),
                         finish_reason=finish_reason,
                     )
@@ -1371,7 +1391,7 @@ class ModelResponseIterator:
             self._handle_usage(anthropic_usage_chunk=message_delta["usage"]) if "usage" in message_delta else None
         )
         container: Final = message_delta["delta"].get("container")
-        return finish_reason, usage, container, refusal_provider_specific_fields(message_delta["delta"])
+        return finish_reason, usage, container, stop_reason_provider_specific_fields(message_delta["delta"])
 
     def _handle_accumulated_json_chunk(self, data_str: str, is_final: bool = False) -> ModelResponseStream | None:
         """

litellm/llms/anthropic/chat/transformation.py

@@ -2,7 +2,9 @@ import copy
 import json
 import re
 import time
-from collections.abc import Callable, Mapping, Sequence
+from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
+from functools import partial
+from itertools import chain
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, ClassVar, Final, NoReturn, cast
 
@@ -27,7 +29,10 @@ from litellm.constants import (
 from litellm.litellm_core_utils.core_helpers import drops_unsupported_params, map_finish_reason
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     DEFS_MAX_INLINED_BYTES,
+    invalid_type_message,
     is_object_schema,
+    is_present_non_string,
+    is_unhashable_value_behind,
     sanitize_input_schema_for_anthropic,
     schema_fragment_ref,
     schema_grammar_branch_count,
@@ -45,6 +50,8 @@ from litellm.litellm_core_utils.prompt_templates.image_handling import (
     inline_remote_image_urls,
 )
 from litellm.litellm_core_utils.prompt_templates.mid_conversation_system import (
+    message_field,
+    parts_of,
     place_mid_conversation_system,
     split_leading_system_run,
 )
@@ -120,13 +127,14 @@ from litellm.utils import (
 )
 
 from ..common_utils import (
-    REFUSAL_STOP_FIELDS,
+    STOP_REASON_FIELDS,
     AnthropicError,
     AnthropicModelInfo,
     eager_input_streaming_flag,
     process_anthropic_headers,
-    refusal_provider_specific_fields,
+    refusal_params,
     requires_native_compaction_beta,
+    stop_reason_provider_specific_fields,
     strip_advisor_blocks_from_messages,
     tools_marked_for_a_tool_config_point,
 )
@@ -167,6 +175,9 @@ _DEFER_LOADING_ADAPTER: Final = TypeAdapter(bool)
 _TOOL_FIELDS_ADAPTER: Final = TypeAdapter(Mapping[str, object])
 _TOOLS_ADAPTER: Final = TypeAdapter(list[object])
 _LITELLM_PARAMS_ADAPTER: Final = TypeAdapter(Mapping[str, object])
+_RESPONSE_BLOCKS: Final = TypeAdapter(tuple[Mapping[str, object], ...])
+_PENDING_WEB_SEARCH_RESULT: Final = MappingProxyType({"content": ()})
+_REQUEST_PARAMS_ADAPTER: Final = TypeAdapter(Mapping[str, object])
 _NATIVE_OUTPUT_STRING_FORMATS: Final = frozenset(
     ("date-time", "time", "date", "duration", "email", "hostname", "uri", "ipv4", "ipv6", "uuid")
 )
@@ -175,6 +186,76 @@ NATIVE_OUTPUT_MAX_OPTIONAL_PARAMETERS: Final = 24
 NATIVE_OUTPUT_MAX_GRAMMAR_BRANCHES: Final = 2048
 
 
+def _tool_call_ids_failing(
+    index: int, tool_calls: object, fails: Callable[[object], bool]
+) -> Iterator[tuple[str, object]]:
+    ids: Final = ((position, message_field(tool_call, "id")) for position, tool_call in enumerate(parts_of(tool_calls)))
+    return (
+        (f"messages[{index}].tool_calls[{position}].id", tool_call_id)
+        for position, tool_call_id in ids
+        if fails(tool_call_id)
+    )
+
+
+def _roles_failing(messages: Sequence[object], fails: Callable[[object], bool]) -> Iterator[tuple[str, object]]:
+    roles: Final = ((index, message_field(message, "role")) for index, message in enumerate(messages))
+    return ((f"messages[{index}].role", role) for index, role in roles if fails(role))
+
+
+def _is_non_empty_non_string(value: object) -> bool:
+    return bool(value) and is_present_non_string(value)
+
+
+def _assistant_tool_call_ids_failing(
+    messages: Sequence[object], fails: Callable[[object], bool]
+) -> Iterator[tuple[str, object]]:
+    assistant_turns: Final = (
+        (index, message) for index, message in enumerate(messages) if message_field(message, "role") == "assistant"
+    )
+    return chain.from_iterable(
+        _tool_call_ids_failing(index, message_field(message, "tool_calls"), fails) for index, message in assistant_turns
+    )
+
+
+def _tool_answer_ids_failing(
+    messages: Sequence[object], fails: Callable[[object], bool]
+) -> Iterator[tuple[str, object]]:
+    answers: Final = (
+        (index, message_field(message, "tool_call_id"))
+        for index, message in enumerate(messages)
+        if message_field(message, "role") in ("tool", "function")
+    )
+    return ((f"messages[{index}].tool_call_id", tool_call_id) for index, tool_call_id in answers if fails(tool_call_id))
+
+
+def _is_non_string_value_behind(error: AttributeError, value: object) -> bool:
+    return is_present_non_string(value) and value is error.obj
+
+
+def _fields_behind(messages: Sequence[object], error: Exception) -> Iterable[tuple[str, object]]:
+    match error:
+        case TypeError():
+            caused_error: Final = partial(is_unhashable_value_behind, error)
+            return chain(
+                _assistant_tool_call_ids_failing(messages, caused_error),
+                _tool_answer_ids_failing(messages, caused_error),
+                _roles_failing(messages, caused_error),
+            )
+        case AttributeError():
+            return _assistant_tool_call_ids_failing(messages, partial(_is_non_string_value_behind, error))
+        case _:
+            return ()
+
+
+def non_string_message_field_error(messages: Sequence[object], error: Exception) -> AnthropicError | None:
+    malformed_field: Final = next(
+        chain(_roles_failing(messages, _is_non_empty_non_string), _fields_behind(messages, error)), None
+    )
+    if malformed_field is None:
+        return None
+    return AnthropicError(status_code=400, message=invalid_type_message(*malformed_field))
+
+
 def _tool_type(tool: object) -> str | None:
     match tool:
         case {"type": str() as tool_type}:
@@ -2254,6 +2335,16 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             headers=headers,
         )
 
+    @staticmethod
+    def _messages_within_cache_control_cap(
+        messages: Sequence[AllMessageValues], optional_params: Mapping[str, object]
+    ) -> Sequence[AllMessageValues]:
+        from litellm.integrations.anthropic_cache_control_hook import messages_within_cache_control_cap
+
+        return messages_within_cache_control_cap(
+            messages, optional_params.get("tools"), optional_params.get("cache_control"), optional_params
+        )
+
     def transform_request(
         self,
         model: str,
@@ -2271,9 +2362,18 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             anthropic_messages_pt,
         )
 
-        drop_params: Final = litellm.drop_params is True or litellm_params.get("drop_params") is True
+        drop_params: Final = drops_unsupported_params(litellm_params.get("drop_params"))
+        capped_messages: Final = (
+            list(
+                self._messages_within_cache_control_cap(
+                    messages, _REQUEST_PARAMS_ADAPTER.validate_python(optional_params)
+                )
+            )
+            if drop_params
+            else messages
+        )
 
-        if "tools" not in optional_params and messages is not None and has_tool_call_blocks(messages):
+        if "tools" not in optional_params and has_tool_call_blocks(capped_messages):
             optional_params["tools"], _ = self._map_tools(add_dummy_tool(custom_llm_provider="anthropic"))
 
         # Drop thinking param if thinking is enabled but thinking_blocks are missing
@@ -2285,9 +2385,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         # Related issue: https://github.com/BerriAI/litellm/issues/18926
         if (
             optional_params.get("thinking") is not None
-            and messages is not None
-            and last_assistant_with_tool_calls_has_no_thinking_blocks(messages)
-            and not any_assistant_message_has_thinking_blocks(messages)
+            and last_assistant_with_tool_calls_has_no_thinking_blocks(capped_messages)
+            and not any_assistant_message_has_thinking_blocks(capped_messages)
         ):
             if litellm.modify_params:
                 optional_params.pop("thinking", None)
@@ -2332,25 +2431,34 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         _name_forward_map, _name_reverse_map = self._sanitize_tool_names_in_request(
             optional_params=optional_params,
         )
-        if _name_forward_map:
-            messages = self._rewrite_tool_names_in_messages(messages, _name_forward_map)
+        request_messages: Final = (
+            self._rewrite_tool_names_in_messages(capped_messages, _name_forward_map)
+            if _name_forward_map
+            else capped_messages
+        )
         if _name_reverse_map and isinstance(litellm_params, dict):
             litellm_params[ANTHROPIC_TOOL_NAME_REVERSE_MAP_KEY] = _name_reverse_map
 
         # Only the leading system run becomes the top-level system prompt. A later
         # system message stays in the conversation: hoisting it rewrites the cached
         # prefix and re-bills the whole history at cache-write pricing (#36559).
-        leading_system_run, later_messages = split_leading_system_run(messages)
+        leading_system_run, later_messages = split_leading_system_run(request_messages)
         anthropic_system_message_list: Final = self.translate_system_message(messages=list(leading_system_run))
         # Handling anthropic API Prompt Caching
         if len(anthropic_system_message_list) > 0:
             optional_params["system"] = anthropic_system_message_list
-        conversation: Final = place_mid_conversation_system(
-            later_messages,
-            supports_mid_conversation_system=supports_mid_conversation_system(
-                model=model, custom_llm_provider=self.custom_llm_provider
-            ),
-        )
+        try:
+            conversation: Final = place_mid_conversation_system(
+                later_messages,
+                supports_mid_conversation_system=supports_mid_conversation_system(
+                    model=model, custom_llm_provider=self.custom_llm_provider
+                ),
+            )
+        except TypeError as error:
+            field_error: Final = non_string_message_field_error(messages, error)
+            if field_error is None:
+                raise
+            raise field_error from error
         # Format rest of message according to anthropic guidelines
         try:
             anthropic_messages = anthropic_messages_pt(
@@ -2360,9 +2468,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
                 drop_params=drop_params,
             )
         except Exception as e:
-            raise AnthropicError(
+            conversion_field_error: Final = non_string_message_field_error(messages, e)
+            raise conversion_field_error or AnthropicError(
                 status_code=400,
-                message=f"{e}\nReceived Messages={messages}",
+                message=f"{e}\nReceived Messages={request_messages}",
             )  # don't use verbose_logger.exception, if exception is raised
 
         self.update_headers_with_optional_anthropic_beta(
@@ -2897,28 +3006,24 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         web_search_results: Sequence[object],
         completion_response: Mapping[str, object],
     ) -> list[object]:
-        content: Final = completion_response.get("content")
-        blocks: Final = content if isinstance(content, Sequence) else ()
-        inputs: Final = {
-            call_id: tool_input
-            for block in blocks
-            if isinstance(block, Mapping)
-            and block.get("type") == "server_tool_use"
-            and block.get("name") == "web_search"
-            and isinstance((call_id := block.get("id")), str)
-            and isinstance((tool_input := block.get("input")), Mapping)
+        blocks: Final = _RESPONSE_BLOCKS.validate_python(completion_response.get("content") or ())
+        results_by_id: Final = {
+            tool_use_id: result
+            for result in _RESPONSE_BLOCKS.validate_python(web_search_results)
+            if result.get("type") == "web_search_tool_result"
+            and isinstance((tool_use_id := result.get("tool_use_id")), str)
         }
         return [
             build_web_search_call(
-                tool_id=tool_use_id,
-                tool_input=inputs.get(tool_use_id, {}),
-                result=result,
+                tool_id=call_id,
+                tool_input=block.get("input"),
+                result=results_by_id.get(call_id, _PENDING_WEB_SEARCH_RESULT),
+                status=None if call_id in results_by_id else "in_progress",
             )
-            for result in web_search_results
-            if isinstance(result, dict)
-            and result.get("type") == "web_search_tool_result"
-            and isinstance((tool_use_id := result.get("tool_use_id")), str)
-            and tool_use_id in inputs
+            for block in blocks
+            if block.get("type") == "server_tool_use"
+            and block.get("name") == "web_search"
+            and isinstance((call_id := block.get("id")), str)
         ]
 
     def _build_provider_specific_fields(
@@ -2942,10 +3047,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
 
         if web_search_results is not None:
             provider_specific_fields["web_search_results"] = web_search_results
-            provider_specific_fields["web_search_calls"] = self._build_web_search_calls(
-                web_search_results,
-                completion_response,
-            )
+
+        web_search_calls: Final = self._build_web_search_calls(web_search_results or (), completion_response)
+        if web_search_results is not None or web_search_calls:
+            provider_specific_fields["web_search_calls"] = web_search_calls
 
         if tool_results is not None:
             provider_specific_fields["tool_results"] = tool_results
@@ -2967,10 +3072,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         if compaction_blocks is not None:
             provider_specific_fields["compaction_blocks"] = compaction_blocks
 
-        refusal_fields: Final = refusal_provider_specific_fields(
-            REFUSAL_STOP_FIELDS.validate_python(completion_response)
+        stop_reason_fields: Final = stop_reason_provider_specific_fields(
+            STOP_REASON_FIELDS.validate_python(completion_response)
         )
-        return {**provider_specific_fields, **refusal_fields}
+        return {**provider_specific_fields, **stop_reason_fields}
 
     def transform_parsed_response(
         self,
@@ -3045,22 +3150,26 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
         if json_extra_content:
             merged_text = merged_text + json_extra_content if merged_text else json_extra_content
 
+        refusal_fields: Final = refusal_params(STOP_REASON_FIELDS.validate_python(completion_response))
         _message = litellm.Message(
             tool_calls=tool_calls_for_message,
             content=merged_text or None,
             provider_specific_fields=provider_specific_fields,
             thinking_blocks=thinking_blocks,
             reasoning_content=reasoning_content,
+            **refusal_fields,
         )
         _message.provider_specific_fields = provider_specific_fields
 
         if json_mode_message is not None:
-            refusal_fields: Final = refusal_provider_specific_fields(
-                REFUSAL_STOP_FIELDS.validate_python(completion_response)
+            stop_reason_fields: Final = stop_reason_provider_specific_fields(
+                STOP_REASON_FIELDS.validate_python(completion_response)
             )
             completion_response["stop_reason"] = "stop"
-            if refusal_fields:
-                json_mode_message.provider_specific_fields = dict(refusal_fields)
+            if stop_reason_fields:
+                json_mode_message.provider_specific_fields = dict(stop_reason_fields)
+            if "refusal" in refusal_fields:
+                json_mode_message.refusal = refusal_fields["refusal"]
             if thinking_blocks:
                 json_mode_message.thinking_blocks = thinking_blocks
                 json_mode_message.reasoning_content = reasoning_content

litellm/llms/anthropic/common_utils.py

@@ -4,6 +4,7 @@ This file contains common utils for anthropic calls.
 
 import copy
 import json
+import os
 import re
 from collections.abc import Container, Mapping, MutableMapping, Sequence
 from dataclasses import dataclass
@@ -19,6 +20,8 @@ from typing_extensions import ReadOnly, TypedDict
 
 import litellm
 from litellm.constants import (
+    ANTHROPIC_COMPATIBLE_MAX_USER_ID_LENGTH,
+    ANTHROPIC_MAX_USER_ID_LENGTH,
     DEFAULT_MODEL_CREATED_AT_TIME,
     DEFAULT_REASONING_EFFORT_HIGH_THINKING_BUDGET,
     DEFAULT_REASONING_EFFORT_MEDIUM_THINKING_BUDGET,
@@ -178,6 +181,40 @@ def tools_without_custom_field(tools: Sequence[object], hoist_defer_loading: boo
     return [_tool_without_custom_field(tool, hoist_defer_loading, drop_custom) for tool in tools]
 
 
+def anthropic_pass_through_api_base() -> str:
+    return os.getenv("ANTHROPIC_API_BASE") or os.getenv("ANTHROPIC_BASE_URL") or "https://api.anthropic.com"
+
+
+def _same_host_and_port(url: httpx.URL, other: str) -> bool:
+    try:
+        other_url: Final = httpx.URL(other)
+    except httpx.InvalidURL:
+        return False
+    return (url.host, url.port) == (other_url.host, other_url.port)
+
+
+def anthropic_upstream_user_id_limit(url: str) -> int | None:
+    upstream: Final = httpx.URL(url)
+    if not upstream.path.removesuffix("/").endswith("/v1/messages"):
+        return None
+    if upstream.host == "api.anthropic.com":
+        return ANTHROPIC_MAX_USER_ID_LENGTH
+    return (
+        ANTHROPIC_COMPATIBLE_MAX_USER_ID_LENGTH
+        if _same_host_and_port(upstream, anthropic_pass_through_api_base())
+        else None
+    )
+
+
+def _has_email_shape(token: str) -> bool:
+    at_sign: Final = token.find("@", 1)
+    return at_sign != -1 and token.find(".", at_sign + 2, -1) != -1
+
+
+def anthropic_accepts_user_id(user_id: str, max_length: int) -> bool:
+    return len(user_id) <= max_length and user_id.isprintable() and not any(map(_has_email_shape, user_id.split()))
+
+
 def is_anthropic_messages_url(url: str) -> bool:
     """Check whether a URL addresses Anthropic's Messages API."""
     parsed_url: Final = urlparse(url)
@@ -1260,7 +1297,7 @@ class AnthropicModelInfo(BaseLLMModelInfo):
             return None
         return anthropic_beta_header.split(",")
 
-    def get_computer_tool_beta_header(self, computer_tool_version: str) -> str:
+    def get_computer_tool_beta_header(self, computer_tool_version: str) -> str | None:
         """
         Get the appropriate beta header for a given computer tool version.
 
@@ -1268,16 +1305,14 @@ class AnthropicModelInfo(BaseLLMModelInfo):
             computer_tool_version: The computer tool version (e.g., 'computer_20250124', 'computer_20241022')
 
         Returns:
-            The corresponding beta header string
+            The corresponding beta header string, or None for a version with no known beta
         """
         computer_tool_beta_mapping: Final = {
+            "computer_20251124": "computer-use-2025-11-24",
             "computer_20250124": "computer-use-2025-01-24",
             "computer_20241022": "computer-use-2024-10-22",
         }
-        return computer_tool_beta_mapping.get(
-            computer_tool_version,
-            "computer-use-2024-10-22",  # Default fallback
-        )
+        return computer_tool_beta_mapping.get(computer_tool_version)
 
     def get_anthropic_beta_list(
         self,
@@ -1309,9 +1344,8 @@ class AnthropicModelInfo(BaseLLMModelInfo):
         if effort_used:
             betas.append(ANTHROPIC_EFFORT_BETA_HEADER)  # effort-2025-11-24
 
-        if computer_tool_used:
-            beta_header: Final = self.get_computer_tool_beta_header(computer_tool_used)
-            betas.append(beta_header)
+        if computer_tool_used and (computer_tool_beta := self.get_computer_tool_beta_header(computer_tool_used)):
+            betas.append(computer_tool_beta)
 
         # Anthropic no longer requires the prompt-caching beta header
         # Prompt caching now works automatically when cache_control is used in messages
@@ -1400,9 +1434,8 @@ class AnthropicModelInfo(BaseLLMModelInfo):
         # Anthropic no longer requires the prompt-caching beta header
         # Prompt caching now works automatically when cache_control is used in messages
         # Reference: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching
-        if computer_tool_used:
-            beta_header: Final = self.get_computer_tool_beta_header(computer_tool_used)
-            betas.add(beta_header)
+        if computer_tool_used and (computer_tool_beta := self.get_computer_tool_beta_header(computer_tool_used)):
+            betas.add(computer_tool_beta)
         # if pdf_used:
         #     betas.add("pdfs-2024-09-25")
         if file_id_used:
@@ -2481,7 +2514,7 @@ def create_anthropic_model_list_response(
     }
 
 
-class RefusalStopFields(TypedDict, total=False):
+class StopReasonFields(TypedDict, total=False):
     stop_reason: ReadOnly[object]
     stop_details: ReadOnly[object]
 
@@ -2498,16 +2531,38 @@ def _dict_or_empty(value: object) -> object:
     return value if _is_dict(value) else _no_stop_fields()
 
 
-REFUSAL_STOP_FIELDS: Final[TypeAdapter[RefusalStopFields]] = TypeAdapter(
-    Annotated[RefusalStopFields, BeforeValidator(_dict_or_empty)]
+STOP_REASON_FIELDS: Final[TypeAdapter[StopReasonFields]] = TypeAdapter(
+    Annotated[StopReasonFields, BeforeValidator(_dict_or_empty)]
 )
 
+_STOP_REASONS_KEPT_RAW: Final = frozenset({"refusal", "pause_turn"})
 
-def refusal_provider_specific_fields(message: Mapping[str, object]) -> Mapping[str, object]:
+
+def stop_reason_provider_specific_fields(message: Mapping[str, object]) -> Mapping[str, object]:
     stop_reason: Final = message.get("stop_reason")
     stop_details: Final = message.get("stop_details")
     if stop_details is not None:
         return {"stop_reason": stop_reason, "stop_details": stop_details}
-    if stop_reason == "refusal":
+    if stop_reason in _STOP_REASONS_KEPT_RAW:
         return {"stop_reason": stop_reason}
     return {}
+
+
+class _RefusalStopDetails(TypedDict, total=False):
+    explanation: ReadOnly[object]
+
+
+_REFUSAL_STOP_DETAILS: Final[TypeAdapter[_RefusalStopDetails]] = TypeAdapter(
+    Annotated[_RefusalStopDetails, BeforeValidator(_dict_or_empty)]
+)
+
+
+class RefusalParams(TypedDict, total=False):
+    refusal: ReadOnly[str]
+
+
+def refusal_params(message: Mapping[str, object]) -> RefusalParams:
+    if message.get("stop_reason") != "refusal":
+        return {}
+    explanation: Final = _REFUSAL_STOP_DETAILS.validate_python(message.get("stop_details")).get("explanation")
+    return {"refusal": explanation} if isinstance(explanation, str) and explanation else {}

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

@@ -118,9 +118,10 @@ def _extract_proxy_litellm_metadata(
 
 def _requested_router(kwargs: Mapping[str, object]) -> "Router | None":
     from litellm.router import Router
+    from litellm.router_python_core import PythonCore
 
     requested: Final = kwargs.get("litellm_router")
-    return requested if isinstance(requested, Router) else None
+    return requested if isinstance(requested, (Router, PythonCore)) else None  # pyright: ignore[reportReturnType]  # the router passes its core, which carries Router's members
 
 
 def request_safeguards_evaluator(

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

@@ -33,6 +33,7 @@ from litellm.llms.anthropic.messages.safeguards import StreamedSafeguardResults
 from litellm.llms.anthropic.messages.utils import anthropic_sse_frame
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.types.llms.anthropic import (
+    AnthropicStopDetails,
     AppliedEdit,
     CompactionBlock,
     ContentBlockDelta,
@@ -284,9 +285,7 @@ class _CombinedChunkSplitter:
     @staticmethod
     def _is_combined(chunk: "ModelResponseStream") -> bool:
         """True if ``chunk`` carries response content AND a finish_reason."""
-        from litellm.llms.anthropic.messages.native.utils import (
-            openai_chat_refusal_text,
-        )
+        from litellm.llms.anthropic.messages.native.utils import refusal_reply_text
 
         choices: Final = _optional_attr_sequence(chunk, "choices")
         if not choices:
@@ -302,7 +301,7 @@ class _CombinedChunkSplitter:
             or _optional_attr(delta, "tool_calls")
             or _optional_attr(delta, "reasoning_content")
             or _optional_attr(delta, "thinking_blocks")
-            or openai_chat_refusal_text(delta)
+            or refusal_reply_text(delta)
         )
 
     _PAYLOAD_FIELD_GROUPS: "tuple[tuple[str, ...], ...]" = (
@@ -659,6 +658,8 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
         self.compaction_block = compaction_block
         self.iterations_usage = iterations_usage
         self._refusal_text: str = ""
+        self._paused_turn: bool = False
+        self._claude_refusal_stop_details: AnthropicStopDetails | None = None
         self.sent_compaction_block: bool = False
         # Per-phase flags so the compaction block's start/delta/stop events
         # are emitted (and the public state machine is advanced) in
@@ -953,7 +954,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
                     current_content_block_index=self.current_content_block_index,
                     applied_edits=(self.applied_edits if is_final_chunk and not will_merge_into_held else None),
                 )
-                processed_chunk = self._with_refusal_stop_details(processed_chunk)
+                processed_chunk = self._with_stop_reason_fields(processed_chunk)
 
                 # Check if this is a usage chunk and we have a held stop_reason chunk
                 if will_merge_into_held:
@@ -1188,7 +1189,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
                     current_content_block_index=self.current_content_block_index,
                     applied_edits=(self.applied_edits if is_final_chunk and not will_merge_into_held else None),
                 )
-                processed_chunk = self._with_refusal_stop_details(processed_chunk)
+                processed_chunk = self._with_stop_reason_fields(processed_chunk)
 
                 # Check if this is a usage chunk and we have a held stop_reason chunk
                 if will_merge_into_held:
@@ -1406,14 +1407,22 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
     def _increment_content_block_index(self):
         self.current_content_block_index += 1
 
-    def _with_refusal_stop_details(
+    def _with_stop_reason_fields(
         self,
         processed_chunk: ContentBlockDelta | MessageBlockDelta,
     ) -> ContentBlockDelta | MessageBlockDelta:
-        if processed_chunk["type"] != "message_delta" or not self._refusal_text:
+        if processed_chunk["type"] != "message_delta":
             return processed_chunk
         delta: Final = processed_chunk["delta"]
-        if delta.get("stop_reason") == "max_tokens":
+        if self._paused_turn:
+            paused_delta: Final[MessageDelta] = {**delta, "stop_reason": "pause_turn"}
+            paused_chunk: Final[MessageBlockDelta] = {**processed_chunk, "delta": paused_delta}
+            return paused_chunk
+        if delta.get("stop_reason") == "refusal" and self._claude_refusal_stop_details is not None:
+            claude_refusal_delta: Final[MessageDelta] = {**delta, "stop_details": self._claude_refusal_stop_details}
+            claude_refusal_chunk: Final[MessageBlockDelta] = {**processed_chunk, "delta": claude_refusal_delta}
+            return claude_refusal_chunk
+        if not self._refusal_text or delta.get("stop_reason") == "max_tokens":
             return processed_chunk
         from litellm.llms.anthropic.messages.native.utils import (
             refusal_stop_details,
@@ -1470,9 +1479,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
     @staticmethod
     def _is_blank_delta(chunk: "ModelResponseStream") -> bool:
         from litellm.llms.anthropic.common_utils import is_empty_unsigned_thinking_block
-        from litellm.llms.anthropic.messages.native.utils import (
-            openai_chat_refusal_text,
-        )
+        from litellm.llms.anthropic.messages.native.utils import refusal_reply_text
 
         choice: Final = chunk.choices[0]
         if choice.finish_reason is not None:
@@ -1482,7 +1489,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
             return False
         if getattr(delta, "content", None):
             return False
-        if openai_chat_refusal_text(delta):
+        if refusal_reply_text(delta):
             return False
         if getattr(delta, "reasoning_content", None):
             return False
@@ -1497,6 +1504,18 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
             return False
         return True
 
+    def _record_anthropic_stop_fields(self, delta: object) -> None:
+        from litellm.llms.anthropic.messages.native.utils import (
+            anthropic_refusal_stop_details,
+            anthropic_stop_reason,
+        )
+
+        if anthropic_stop_reason(delta) == "pause_turn":
+            self._paused_turn = True
+        claude_refusal_stop_details: Final = anthropic_refusal_stop_details(delta)
+        if claude_refusal_stop_details is not None:
+            self._claude_refusal_stop_details = claude_refusal_stop_details
+
     def _should_start_new_content_block(self, chunk: "ModelResponseStream") -> bool:
         """
         Determine if we should start a new content block based on the processed chunk.
@@ -1507,16 +1526,15 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper):
         - Different content types in the response
         - Specific markers in the content
         """
-        from litellm.llms.anthropic.messages.native.utils import (
-            openai_chat_refusal_text,
-        )
+        from litellm.llms.anthropic.messages.native.utils import refusal_reply_text
 
         from .transformation import LiteLLMAnthropicMessagesAdapter
 
+        self._record_anthropic_stop_fields(chunk.choices[0].delta)
         if chunk.choices[0].finish_reason is not None:
             return False
 
-        refusal_text: Final = openai_chat_refusal_text(chunk.choices[0].delta)
+        refusal_text: Final = refusal_reply_text(chunk.choices[0].delta)
         if refusal_text is not None:
             self._refusal_text = self._refusal_text + refusal_text
 

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

@@ -150,7 +150,9 @@ from litellm.llms.anthropic.messages.native.mid_conversation_system import (
     is_system_role_message,
 )
 from litellm.llms.anthropic.messages.native.utils import (
-    openai_chat_refusal_text,
+    anthropic_refusal_stop_details,
+    anthropic_stop_reason,
+    refusal_reply_text,
     refusal_stop_details,
 )
 from litellm.types.llms.anthropic import (
@@ -398,6 +400,10 @@ class LiteLLMAnthropicMessagesAdapter:
 
         return None
 
+    def _streamed_tool_call_signature(self, tool_call_id: str, tool_call: object) -> str | None:
+        id_parts: Final = tool_call_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)
+        return id_parts[1] if len(id_parts) > 1 else self._extract_signature_from_tool_call(tool_call)
+
     def _extract_signature_from_tool_use_content(self, content: Mapping[str, object]) -> str | None:
         """
         Extract signature from a tool_use content block's provider_specific_fields.
@@ -1467,7 +1473,7 @@ class LiteLLMAnthropicMessagesAdapter:
                 new_content.append(
                     AnthropicResponseContentBlockText(type="text", text=choice.message.content).model_dump()
                 )
-            if (refusal_text := openai_chat_refusal_text(choice.message)) is not None:
+            if (refusal_text := refusal_reply_text(choice.message)) is not None:
                 new_content.append(AnthropicResponseContentBlockText(type="text", text=refusal_text).model_dump())
             # Handle tool calls (in parallel to text content)
             if choice.message.tool_calls is not None and len(choice.message.tool_calls) > 0:
@@ -1634,7 +1640,15 @@ class LiteLLMAnthropicMessagesAdapter:
             tool_name_mapping=tool_name_mapping,
         )
         refusal_text: Final = next(
-            (text for choice in response.choices if (text := openai_chat_refusal_text(choice.message)) is not None),
+            (text for choice in response.choices if (text := refusal_reply_text(choice.message)) is not None),
+            None,
+        )
+        claude_refusal_stop_details: Final = next(
+            (
+                details
+                for choice in response.choices
+                if (details := anthropic_refusal_stop_details(choice.message)) is not None
+            ),
             None,
         )
 
@@ -1643,8 +1657,10 @@ class LiteLLMAnthropicMessagesAdapter:
 
         ## extract finish reason
         openai_finish_reason: Final = response.choices[0].finish_reason if response.choices else "stop"
-        translated_finish_reason: Final = self._translate_openai_finish_reason_to_anthropic(
-            openai_finish_reason=openai_finish_reason
+        translated_finish_reason: Final = (
+            "pause_turn"
+            if response.choices and anthropic_stop_reason(response.choices[0].message) == "pause_turn"
+            else self._translate_openai_finish_reason_to_anthropic(openai_finish_reason=openai_finish_reason)
         )
         anthropic_finish_reason: Final = (
             "compaction"
@@ -1686,7 +1702,11 @@ class LiteLLMAnthropicMessagesAdapter:
             usage=anthropic_usage,
             content=anthropic_content,
             stop_reason=anthropic_finish_reason,
-            stop_details=(refusal_stop_details(refusal_text) if anthropic_finish_reason == "refusal" else None),
+            stop_details=(
+                (claude_refusal_stop_details or refusal_stop_details(refusal_text))
+                if anthropic_finish_reason == "refusal"
+                else None
+            ),
         )
 
         applied_edits: Final = polyfill_result.applied_edits_for_response() if polyfill_result else None
@@ -1712,10 +1732,7 @@ class LiteLLMAnthropicMessagesAdapter:
             ):
                 raw_id = choice.delta.tool_calls[0].id or str(uuid.uuid4())
                 tool_name = choice.delta.tool_calls[0].function.name or ""
-                thought_sig: str | None = None
-                if THOUGHT_SIGNATURE_SEPARATOR in raw_id:
-                    parts = raw_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)
-                    thought_sig = parts[1] if len(parts) > 1 else None
+                thought_sig = self._streamed_tool_call_signature(raw_id, choice.delta.tool_calls[0])
                 tool_block: dict[str, object] = {
                     "type": "tool_use",
                     "id": normalize_anthropic_tool_use_id(raw_id),
@@ -1727,7 +1744,7 @@ class LiteLLMAnthropicMessagesAdapter:
                         "signature": thought_sig,
                     }
                 return "tool_use", cast("ContentBlockContentBlockDict", tool_block)
-            elif (choice.delta.content is not None and len(choice.delta.content) > 0) or openai_chat_refusal_text(
+            elif (choice.delta.content is not None and len(choice.delta.content) > 0) or refusal_reply_text(
                 choice.delta
             ) is not None:
                 return "text", TextBlock(type="text", text="")
@@ -1810,7 +1827,7 @@ class LiteLLMAnthropicMessagesAdapter:
             return "thinking_delta", ContentThinkingBlockDelta(type="thinking_delta", thinking=reasoning_content)
         else:
             refusal_text: Final = "".join(
-                refusal for choice in choices if (refusal := openai_chat_refusal_text(choice.delta)) is not None
+                refusal for choice in choices if (refusal := refusal_reply_text(choice.delta)) is not None
             )
             return "text_delta", ContentTextBlockDelta(type="text_delta", text=text + refusal_text)
 

litellm/llms/anthropic/messages/context_management/editors/clear_tool_uses.py

@@ -74,6 +74,7 @@ class _ClearToolUsesFields(BaseModel):
     clear_tool_inputs: bool | tuple[str, ...] | None = None
 
 
+_BLOCK: Final = TypeAdapter(Mapping[str, object])
 _BLOCKS: Final = TypeAdapter(tuple[Mapping[str, object], ...])
 # api.anthropic.com, measured 2026-10-19 and 2026-10-28: its wording for the shapes pydantic words differently
 _API_MESSAGES: Final = MappingProxyType(
@@ -317,12 +318,10 @@ def _clear_tool_results(
         new_blocks: list[object] = []
         mutated = False
         for block in content:
-            if (
-                isinstance(block, dict)
-                and block.get("type") == "tool_result"
-                and block.get("tool_use_id") in ids_to_clear
-            ):
-                new_blocks.append(_cleared_result(block))
+            if (parsed := _parse(_BLOCK, block)) is not None and _block_id(
+                parsed, "tool_result", "tool_use_id"
+            ) in ids_to_clear:
+                new_blocks.append(_cleared_result(parsed))
                 mutated = True
                 cleared += 1
             else:

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

@@ -8,6 +8,7 @@
 import asyncio
 import contextvars
 from collections.abc import AsyncIterator, Awaitable, Callable, Coroutine, Iterator, Mapping, Sequence
+from dataclasses import dataclass
 from functools import partial
 from typing import Final, cast
 
@@ -15,10 +16,18 @@ from pydantic import TypeAdapter
 
 import litellm
 from litellm.anthropic_beta_headers_manager import aload_beta_headers_config
+from litellm.constants import PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO
 from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook
 from litellm.litellm_core_utils.core_helpers import as_str_mapping
 from litellm.litellm_core_utils.exception_mapping_utils import exception_type
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.reasoning_effort_capability import (
+    RequestedReasoningEffort,
+    deployment_reasoning_effort_levels,
+    keeps_effort,
+    model_group_from_metadata,
+    resolve_deployment_reasoning_effort,
+)
 from litellm.llms.anthropic.common_utils import (
     AnthropicModelInfo,
     strip_provider_specific_fields_from_anthropic_messages,
@@ -31,20 +40,13 @@ from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler
 from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
 from litellm.responses.mcp.tool_precheck import may_name_litellm_mcp_gateway
-from litellm.router_utils.reasoning_effort_capability import (
-    RequestedReasoningEffort,
-    deployment_reasoning_effort_levels,
-    keeps_effort,
-    model_group_from_metadata,
-    resolve_deployment_reasoning_effort,
-)
 from litellm.types.llms.anthropic import AllAnthropicToolsValues
 from litellm.types.llms.anthropic_messages.anthropic_request import AnthropicMetadata
 from litellm.types.llms.anthropic_messages.anthropic_response import (
     AnthropicMessagesResponse,
 )
 from litellm.types.router import GenericLiteLLMParams
-from litellm.types.utils import CallTypes, LlmProviders
+from litellm.types.utils import CallTypes, LlmProviders, LlmProvidersSet
 from litellm.utils import ProviderConfigManager, client
 
 from ..adapters.handler import LiteLLMMessagesToCompletionTransformationHandler, request_safeguards_evaluator
@@ -94,6 +96,68 @@ def _responses_mode_is_lost_by_prefix_strip(
     ) and not _bridges_to_responses_api(resolved_model, custom_llm_provider, deployment_model_info)
 
 
+@dataclass(frozen=True, slots=True)
+class _BridgeTarget:
+    model: str
+    api_key: str | None
+    api_base: str | None
+
+
+def _resolve_requested_provider(
+    model: str, custom_llm_provider: str | None, litellm_params: GenericLiteLLMParams
+) -> tuple[str, str, str | None, str | None]:
+    resolved: Final = litellm.get_llm_provider(
+        model=model,
+        custom_llm_provider=custom_llm_provider,
+        api_base=litellm_params.api_base,
+        api_key=litellm_params.api_key,
+        litellm_params=litellm_params,
+    )
+    requested_provider: Final = model.partition("/")[0]
+    if (
+        custom_llm_provider in (None, requested_provider)
+        or requested_provider not in LlmProvidersSet
+        or requested_provider in PROVIDERS_THAT_AUTHENTICATE_ON_PROVIDER_INFO
+    ):
+        return resolved
+    own: Final = _resolve_by_model_prefix(model, litellm_params)
+    return own if own is not None and own[1] == custom_llm_provider else resolved
+
+
+def _resolve_by_model_prefix(
+    model: str, litellm_params: GenericLiteLLMParams
+) -> tuple[str, str, str | None, str | None] | None:
+    try:
+        return litellm.get_llm_provider(
+            model=model,
+            api_base=litellm_params.api_base,
+            api_key=litellm_params.api_key,
+            litellm_params=litellm_params.model_copy(update={"custom_llm_provider": None}),
+        )
+    except litellm.BadRequestError:
+        return None
+
+
+def _sets_own_entra_credential(litellm_params: GenericLiteLLMParams) -> bool:
+    return bool(
+        litellm_params.azure_ad_token
+        or (litellm_params.model_extra or {}).get("azure_ad_token_provider") is not None
+        or (litellm_params.tenant_id and litellm_params.client_id and litellm_params.client_secret)
+        or (litellm_params.azure_username and litellm_params.azure_password and litellm_params.client_id)
+    )
+
+
+def _bridge_target(requested: _BridgeTarget, resolved: _BridgeTarget, custom_llm_provider: str) -> _BridgeTarget:
+    requested_provider: Final = requested.model.partition("/")[0]
+    if requested_provider == custom_llm_provider or requested_provider not in LlmProvidersSet:
+        return requested
+    return _BridgeTarget(
+        model=f"{custom_llm_provider}/{resolved.model}",
+        api_key=requested.api_key if requested.api_base else requested.api_key or resolved.api_key,
+        api_base=resolved.api_base,
+    )
+
+
 def _should_route_to_responses_api(
     custom_llm_provider: str | None,
     requested_model: str | None = None,
@@ -624,13 +688,7 @@ def anthropic_messages_handler(
         custom_llm_provider,
         dynamic_api_key,
         dynamic_api_base,
-    ) = litellm.get_llm_provider(
-        model=model,
-        custom_llm_provider=custom_llm_provider,
-        api_base=litellm_params.api_base,
-        api_key=litellm_params.api_key,
-        litellm_params=litellm_params,
-    )
+    ) = _resolve_requested_provider(model, custom_llm_provider, litellm_params)
 
     # Store agentic loop params in logging object for agentic hooks
     # This provides original request context needed for follow-up calls
@@ -656,6 +714,16 @@ def anthropic_messages_handler(
             mock_response=litellm_params.mock_response,
         )
 
+    bridge: Final = _bridge_target(
+        _BridgeTarget(model=original_model, api_key=api_key, api_base=api_base),
+        _BridgeTarget(
+            model=model,
+            api_key=None if _sets_own_entra_credential(litellm_params) else dynamic_api_key,
+            api_base=dynamic_api_base,
+        ),
+        custom_llm_provider,
+    )
+
     # Expand litellm_proxy MCP references through the MCP gateway before dispatch, so every
     # downstream path (native passthrough and both bridges) gets real tools rather than a
     # reference the provider cannot resolve. Popped from kwargs so it never reaches the provider.
@@ -682,8 +750,8 @@ def anthropic_messages_handler(
             top_k=top_k,
             top_p=top_p,
             container=container,
-            api_key=api_key,
-            api_base=api_base,
+            api_key=bridge.api_key,
+            api_base=bridge.api_base,
             client=client,
             custom_llm_provider=custom_llm_provider,
             **kwargs,
@@ -700,7 +768,7 @@ def anthropic_messages_handler(
             return LiteLLMMessagesToResponsesAPIHandler.anthropic_messages_handler(
                 max_tokens=max_tokens,
                 messages=messages,
-                model=original_model,
+                model=bridge.model,
                 metadata=metadata,
                 stop_sequences=stop_sequences,
                 stream=stream,
@@ -716,8 +784,8 @@ def anthropic_messages_handler(
                 top_k=top_k,
                 top_p=top_p,
                 _is_async=is_async,
-                api_key=api_key,
-                api_base=api_base,
+                api_key=bridge.api_key,
+                api_base=bridge.api_base,
                 client=client,
                 custom_llm_provider=custom_llm_provider,
                 **kwargs,
@@ -730,7 +798,7 @@ def anthropic_messages_handler(
         return LiteLLMMessagesToCompletionTransformationHandler.anthropic_messages_handler(
             max_tokens=max_tokens,
             messages=messages,
-            model=original_model,
+            model=bridge.model,
             metadata=metadata,
             stop_sequences=stop_sequences,
             stream=stream,
@@ -744,8 +812,8 @@ def anthropic_messages_handler(
             top_k=top_k,
             top_p=top_p,
             _is_async=is_async,
-            api_key=api_key,
-            api_base=api_base,
+            api_key=bridge.api_key,
+            api_base=bridge.api_base,
             client=client,
             custom_llm_provider=custom_llm_provider,
             **kwargs,

litellm/llms/anthropic/messages/native/interceptors/advisor.py

@@ -22,7 +22,7 @@ import litellm
 import litellm.constants as _c
 from litellm.litellm_core_utils.url_utils import validate_url
 from litellm.llms.anthropic.common_utils import strip_advisor_blocks_from_messages
-from litellm.router_utils.cooldown_handlers import mark_advisor_orchestration_failure
+from litellm.router_api import mark_advisor_orchestration_failure
 from litellm.types.llms.anthropic import ANTHROPIC_ADVISOR_TOOL_TYPE
 from litellm.types.llms.anthropic_messages.anthropic_response import (
     AnthropicMessagesResponse,

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

@@ -2,7 +2,7 @@ from collections.abc import Iterable, Mapping, Sequence
 from functools import lru_cache
 from typing import TYPE_CHECKING, Final, cast, get_type_hints
 
-from pydantic import JsonValue, TypeAdapter, ValidationError
+from pydantic import ConfigDict, JsonValue, TypeAdapter, ValidationError, with_config
 
 from litellm.integrations.anthropic_cache_control_hook import AnthropicCacheControlHook
 from litellm.litellm_core_utils.core_helpers import as_str_mapping
@@ -162,6 +162,34 @@ def openai_chat_refusal_text(message_or_delta: object) -> str | None:
     return _mapping_str_field(getattr(message_or_delta, "provider_specific_fields", None), "refusal")
 
 
+def anthropic_stop_reason(message_or_delta: object) -> str | None:
+    return _mapping_str_field(getattr(message_or_delta, "provider_specific_fields", None), "stop_reason")
+
+
+def refusal_reply_text(message_or_delta: object) -> str | None:
+    if anthropic_stop_reason(message_or_delta) == "refusal":
+        return None
+    return openai_chat_refusal_text(message_or_delta)
+
+
+@with_config(ConfigDict(extra="allow"))
+class _AnthropicRefusalStopDetails(AnthropicStopDetails, total=False):
+    pass
+
+
+_ANTHROPIC_REFUSAL_STOP_DETAILS: Final = TypeAdapter(_AnthropicRefusalStopDetails)
+
+
+def anthropic_refusal_stop_details(message_or_delta: object) -> AnthropicStopDetails | None:
+    provider_specific_fields: Final = getattr(message_or_delta, "provider_specific_fields", None)
+    if _mapping_str_field(provider_specific_fields, "stop_reason") != "refusal":
+        return None
+    try:
+        return _ANTHROPIC_REFUSAL_STOP_DETAILS.validate_python(_mapping_field(provider_specific_fields, "stop_details"))
+    except ValidationError:
+        return refusal_stop_details(openai_chat_refusal_text(message_or_delta))
+
+
 def _responses_message_refusal_text(item: object) -> str | None:
     from openai.types.responses import ResponseOutputMessage, ResponseOutputRefusal
 

litellm/llms/anthropic/messages/stream_assembly.py

@@ -155,6 +155,8 @@ def collapse_pure_text_chunks(
             continue
 
         json_str = ev[idx + len("data:") :].strip()
+        if not json_str.startswith("{"):
+            continue
         try:
             data = json.loads(json_str)
         except (json.JSONDecodeError, ValueError):
@@ -206,6 +208,11 @@ def collapse_pure_text_chunks(
     return out
 
 
+def _has_non_object_data(event_str: str) -> bool:
+    data_start: Final = event_str.find("data:")
+    return data_start != -1 and not event_str[data_start + len("data:") :].lstrip().startswith("{")
+
+
 def build_complete_streaming_response_legacy(
     all_chunks: Sequence[str | bytes],
     litellm_logging_obj: LiteLLMLoggingObj,
@@ -243,6 +250,8 @@ def build_complete_streaming_response_legacy(
                 # text payload happens to contain "[DONE]" is not dropped.
                 if any(line.strip() == "data: [DONE]" for line in event_str.split("\n")):
                     continue
+                if _has_non_object_data(event_str):
+                    continue
                 transformed_openai_chunk = anthropic_model_response_iterator.convert_str_chunk_to_generic_chunk(
                     chunk=event_str
                 )
@@ -268,21 +277,25 @@ def build_complete_streaming_response_legacy(
     return complete_streaming_response
 
 
-def extract_sse_data(event_str: str) -> dict | None:
-    """Parse the JSON object from the ``data:`` line of an Anthropic SSE event."""
+def _sse_data_payload(event_str: str) -> str | None:
     for line in split_sse_lines(event_str):
         stripped = line.strip()
         if stripped.startswith("data:"):
-            payload = stripped[len("data:") :].strip()
-            if not payload or payload == "[DONE]":
-                return None
-            try:
-                return json.loads(payload)
-            except (ValueError, TypeError):
-                return None
+            return stripped[len("data:") :].strip()
     return None
 
 
+def extract_sse_data(event_str: str) -> dict | None:
+    """Parse the JSON object from the ``data:`` line of an Anthropic SSE event."""
+    payload: Final = _sse_data_payload(event_str)
+    if payload is None or not payload.startswith("{"):
+        return None
+    try:
+        return json.loads(payload)
+    except (ValueError, TypeError):
+        return None
+
+
 def build_usage_only_response_from_chunks(
     all_chunks: Sequence[str | bytes],
     model: str,
@@ -415,27 +428,81 @@ _ANTHROPIC_EVENT_TYPES: Final = frozenset(
 )
 
 
+_ERROR_PAYLOAD_KEYS: Final = frozenset({"type", "error", "request_id"})
+
+
+def _is_anthropic_event_type(event_type: object) -> bool:
+    return isinstance(event_type, str) and event_type in _ANTHROPIC_EVENT_TYPES
+
+
 def is_raw_sse_stream(all_chunks: Sequence[object]) -> bool:
     return any(isinstance(chunk, (str, bytes)) for chunk in all_chunks)
 
 
-def _joined_sse_stream(all_chunks: Sequence[object]) -> str | None:
-    raw: Final = b"".join(
+def _joined_sse_bytes(all_chunks: Sequence[object]) -> bytes:
+    return b"".join(
         chunk if isinstance(chunk, bytes) else chunk.encode("utf-8")
         for chunk in all_chunks
         if isinstance(chunk, (str, bytes))
     )
+
+
+def _joined_sse_stream(all_chunks: Sequence[object]) -> str | None:
     try:
-        return codecs.getincrementaldecoder("utf-8")().decode(raw, final=False)
+        return codecs.getincrementaldecoder("utf-8")().decode(_joined_sse_bytes(all_chunks), final=False)
     except UnicodeDecodeError:
         return None
 
 
+def _sse_event_data(event: str) -> Mapping[str, object] | None:
+    return as_str_mapping(extract_sse_data(event))
+
+
+_JSON_ESCAPED_LINE_BREAKS: Final = MappingProxyType(
+    str.maketrans({"\x85": "\\u0085", "\u2028": "\\u2028", "\u2029": "\\u2029"})
+)
+
+
+def _sse_frames(sse_stream: str) -> Sequence[str]:
+    return "\n".join(split_sse_lines(sse_stream)).split("\n\n")
+
+
+def _frames_of_the_widest_reader(sse_stream: str) -> Sequence[str]:
+    """The stream's frames as httpx's ``str.splitlines`` reads them, past a form feed or U+2028 in any line."""
+    return "\n".join(sse_stream.splitlines()).split("\n\n")
+
+
+def _sse_field(line: str) -> tuple[str, str]:
+    name, _, value = line.partition(":")
+    return name, value.removeprefix(" ")
+
+
+def _sse_fields(frame: str) -> tuple[tuple[str, str], ...]:
+    """The frame's fields as a client reads them: a name matches exactly and a comment is no field."""
+    return tuple(_sse_field(line) for line in frame.split("\n") if line and not line.startswith(":"))
+
+
+def _carries_no_data(fields: Sequence[tuple[str, str]]) -> bool:
+    match fields:
+        case () | (("event", _),) | (("data", "[DONE]"),):
+            return True
+        case _:
+            return False
+
+
+def _json_mapping(payload: str) -> Mapping[str, object] | None:
+    try:
+        value: Final[object] = json.loads(payload)
+    except ValueError:
+        return None
+    return as_str_mapping(value)
+
+
 def _parsed_sse_events(sse_stream: str) -> tuple[Mapping[str, object], ...]:
     return tuple(
         event_data
         for event in split_sse_chunk_into_events(sse_stream)
-        if (event_data := extract_sse_data(event)) is not None
+        if (event_data := _sse_event_data(event)) is not None
     )
 
 
@@ -444,7 +511,8 @@ def _anthropic_message_start(sse_stream: str) -> Mapping[str, object] | None:
         (
             message
             for event_data in _parsed_sse_events(sse_stream)
-            if event_data.get("type") == "message_start" and isinstance(message := event_data.get("message"), dict)
+            if event_data.get("type") == "message_start"
+            and (message := as_str_mapping(event_data.get("message"))) is not None
         ),
         None,
     )
@@ -482,11 +550,151 @@ def is_anthropic_sse_stream(all_chunks: Sequence[object]) -> bool:
     sse_stream: Final = _joined_sse_stream(all_chunks)
     if sse_stream is None:
         return False
-    return any(event.get("type") in _ANTHROPIC_EVENT_TYPES for event in _parsed_sse_events(sse_stream))
+    return any(_is_anthropic_event_type(event.get("type")) for event in _parsed_sse_events(sse_stream))
+
+
+def _is_json_no_reader_can_take(payload: str) -> bool:
+    try:
+        value: Final[object] = json.loads(payload)
+    except ValueError:
+        return False
+    if value is None:
+        return False
+    event: Final = as_str_mapping(value)
+    return event is None or isinstance(event.get("type"), (list, dict))
+
+
+def has_unreadable_json_event(all_chunks: Sequence[object]) -> bool:
+    """Whether a frame's data is JSON that no reader of these streams can take as an event."""
+    sse_stream: Final = codecs.getincrementaldecoder("utf-8")(errors="replace").decode(
+        _joined_sse_bytes(all_chunks), final=False
+    )
+    return any(_is_json_no_reader_can_take(_sse_data_payload(event) or "") for event in _sse_frames(sse_stream))
+
+
+def anthropic_sse_event_type(event: str) -> object:
+    return next((event_data.get("type") for event_data in _parsed_sse_events(event)), None)
+
+
+def anthropic_sse_event_types(all_chunks: Sequence[object]) -> frozenset[str]:
+    """The string ``type`` of every event the stream sent."""
+    event_types: Final = (event.get("type") for event in _parsed_sse_events(_joined_sse_stream(all_chunks) or ""))
+    return frozenset(event_type for event_type in event_types if isinstance(event_type, str))
+
+
+def _is_named_error_event(event: str) -> bool:
+    names: Final = tuple(value for name, value in _sse_fields(event) if name == "event")
+    return names[-1:] == ("error",)
+
+
+def _is_error_event(event: str) -> bool:
+    event_type: Final = anthropic_sse_event_type(event)
+    return event_type == "error" or (_is_named_error_event(event) and not _is_anthropic_event_type(event_type))
+
+
+def _frames_a_client_dispatches(sse_stream: str) -> Sequence[str]:
+    return "\n".join((*split_sse_lines(sse_stream), "")).split("\n\n")[:-1]
+
+
+def _error_events(sse_stream: str) -> tuple[str, ...]:
+    return tuple(event for event in _frames_a_client_dispatches(sse_stream) if _is_error_event(event))
+
+
+def _plain_error_event(event: str) -> str:
+    payload: Final = _sse_data_payload(event) or ""
+    one_line: Final = payload.translate(_JSON_ESCAPED_LINE_BREAKS) if _json_mapping(payload) is not None else payload
+    return "\n".join(("event: error", *(f"data: {line}" for line in one_line.splitlines()[:1])))
+
+
+def anthropic_sse_error_events(all_chunks: Sequence[object]) -> tuple[str, ...]:
+    """The ``error`` events of the stream, each as an ``event:`` line and the ``data:`` line assembly read.
+
+    A stream whose upstream fails after it started ends with an ``error`` event in place of
+    ``message_stop``. That frame is the outcome the client is owed, not content, so assembly through
+    the upstream error leaves it out and a caller re-emitting a rewritten response puts it back,
+    without any other line of the frame, since that is content no guardrail read.
+    A frame counts by its data ``type``, or by its SSE ``event:`` name unless its data is another
+    Anthropic event, since a client SDK raises on the name alone and an upstream that is not
+    Anthropic may send anything as the data.
+    """
+    return tuple(_plain_error_event(event) for event in _error_events(_joined_sse_stream(all_chunks) or ""))
+
+
+def anthropic_sse_stopped_block_index(event: str) -> object:
+    return next(
+        (
+            event_data.get("index")
+            for event_data in _parsed_sse_events(event)
+            if event_data.get("type") == "content_block_stop"
+        ),
+        None,
+    )
+
+
+def _block_index(event_data: Mapping[str, object]) -> int | None:
+    index: Final = event_data.get("index")
+    return index if isinstance(index, int) else None
+
+
+def _started_blocks(events: Sequence[Mapping[str, object]]) -> Mapping[int, Mapping[str, object]]:
+    starts: Final = (event for event in events if event.get("type") == "content_block_start")
+    return MappingProxyType(
+        {
+            index: block
+            for event in starts
+            if (index := _block_index(event)) is not None
+            and (block := as_str_mapping(event.get("content_block"))) is not None
+        }
+    )
+
+
+def anthropic_sse_started_blocks(all_chunks: Sequence[object]) -> Mapping[int, Mapping[str, object]]:
+    """The ``content_block`` each ``content_block_start`` of the stream opened, by block index."""
+    return _started_blocks(_parsed_sse_events(_joined_sse_stream(all_chunks) or ""))
+
+
+def _unfinished_blocks(events: Sequence[Mapping[str, object]]) -> Mapping[int, Mapping[str, object]]:
+    stopped: Final = frozenset(_block_index(event) for event in events if event.get("type") == "content_block_stop")
+    return MappingProxyType({index: block for index, block in _started_blocks(events).items() if index not in stopped})
+
+
+def anthropic_sse_unfinished_blocks(all_chunks: Sequence[object]) -> Mapping[int, Mapping[str, object]]:
+    """The blocks the stream opened and never stopped, which only a failed upstream leaves."""
+    return _unfinished_blocks(_parsed_sse_events(_joined_sse_stream(all_chunks) or ""))
+
+
+def _sse_events_with_data(sse_stream: str) -> tuple[tuple[str, Mapping[str, object]], ...]:
+    return tuple(
+        (event, event_data)
+        for event in split_sse_chunk_into_events(sse_stream)
+        if (event_data := _sse_event_data(event)) is not None
+    )
+
+
+def _content_events_before_upstream_error(sse_stream: str) -> tuple[str, ...]:
+    """The stream's events minus its upstream ``error`` and every cut-off block assembly cannot read.
+
+    A cut-off text block is still text the client was sent, so it stays. A cut-off tool call's
+    input is a JSON prefix, and a cut-off thinking block has no signature, so neither is a block the
+    client can act on, and both are left out.
+    """
+    if not _error_events(sse_stream):
+        return (sse_stream,)
+    events: Final = _sse_events_with_data(sse_stream)
+    cut_off: Final = frozenset(
+        index
+        for index, block in _unfinished_blocks(tuple(event_data for _, event_data in events)).items()
+        if block.get("type") != "text"
+    )
+    return tuple(
+        event
+        for event, event_data in events
+        if event_data.get("type") != "error" and _block_index(event_data) not in cut_off
+    )
 
 
 def assemble_anthropic_sse_stream(
-    all_chunks: Sequence[object], *, restore_identity: bool = False
+    all_chunks: Sequence[object], *, restore_identity: bool = False, through_upstream_error: bool = False
 ) -> ModelResponse | None:
     """Assemble raw Anthropic SSE frames into a ModelResponse.
 
@@ -494,6 +702,11 @@ def assemble_anthropic_sse_stream(
     assembler does not carry through. It is off by default so callers that re-emit the assembled
     response keep the wire shape they had before this helper was shared. The writes land on a
     freshly built object that is unreachable from caller state until returned.
+
+    A stream whose upstream failed after it started ends with an ``error`` event in place of
+    ``message_stop`` and assembles to None, unless ``through_upstream_error`` asks for the content
+    the client was sent before the failure. That is for a guardrail that scans the stream and then
+    passes the upstream error on; a caller recording what was served keeps None.
     """
     sse_stream: Final = _joined_sse_stream(all_chunks)
     if sse_stream is None:
@@ -502,9 +715,12 @@ def assemble_anthropic_sse_stream(
     if message_start is None:
         return None
     model: Final = message_start.get("model") if restore_identity else None
+    content_events: Final = (
+        _content_events_before_upstream_error(sse_stream) if through_upstream_error else (sse_stream,)
+    )
     try:
         assembled: Final = build_complete_streaming_response(
-            all_chunks=(sse_stream,),
+            all_chunks=content_events,
             litellm_logging_obj=None,  # pyright: ignore[reportArgumentType]  # only forwarded to stream_chunk_builder, which accepts None
             model=model if isinstance(model, str) else "",
         )
@@ -522,6 +738,21 @@ def assemble_anthropic_sse_stream(
     return assembled
 
 
+def _is_error_payload(payload: str) -> bool:
+    event: Final = _json_mapping(payload)
+    if event is None or not _ERROR_PAYLOAD_KEYS.issuperset(event):
+        return False
+    return event.get("type") == "error" or ("type" not in event and isinstance(event.get("error"), Mapping))
+
+
+def _is_error_frame(fields: Sequence[tuple[str, str]]) -> bool:
+    match fields:
+        case (("data", payload),) | (("event", "error"), ("data", payload)):
+            return _is_error_payload(payload)
+        case _:
+            return False
+
+
 def is_sse_error_stream(all_chunks: Sequence[object]) -> bool:
     """Whether the buffered stream carries nothing but error frames.
 
@@ -537,7 +768,6 @@ def is_sse_error_stream(all_chunks: Sequence[object]) -> bool:
     sse_stream: Final = _joined_sse_stream(all_chunks)
     if sse_stream is None:
         return False
-    events: Final = _parsed_sse_events(sse_stream)
-    return len(events) > 0 and all(
-        event.get("type") == "error" or isinstance(event.get("error"), Mapping) for event in events
-    )
+    frames: Final = tuple(_sse_fields(frame) for frame in _frames_of_the_widest_reader(sse_stream))
+    with_data: Final = tuple(fields for fields in frames if not _carries_no_data(fields))
+    return len(with_data) > 0 and all(_is_error_frame(fields) for fields in with_data)

litellm/llms/anthropic/messages/utils.py

@@ -214,7 +214,7 @@ def normalize_reasoning_effort_value(
     if chain is None:
         return effort
 
-    from litellm.router_utils.reasoning_effort_capability import (
+    from litellm.litellm_core_utils.reasoning_effort_capability import (
         deployment_rounds_reasoning_effort,
         resolve_supported_reasoning_efforts,
     )

litellm/llms/base_llm/decisions/transformation.py

@@ -12,6 +12,7 @@ from pydantic_core import ErrorDetails
 from typing_extensions import assert_never
 
 import litellm
+from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.secret_managers.main import get_secret_str
@@ -297,13 +298,7 @@ def _systemone_question(question: DecisionsIRQuestion) -> Mapping[str, object]:
             assert_never(question)
 
 
-def ir_to_systemone_request(
-    model: str, request: DecisionsIRRequest, *, supports_audio: bool = False, supports_video: bool = False
-) -> Mapping[str, object] | UnsupportedDecisionsRequest:
-    if request.audio and not supports_audio:
-        return UnsupportedDecisionsRequest(reason="audio input is not supported by this Decisions model")
-    if request.videos and not supports_video:
-        return UnsupportedDecisionsRequest(reason="video input is not supported by this Decisions model")
+def ir_to_systemone_request(model: str, request: DecisionsIRRequest) -> Mapping[str, object]:
     state: Final = _systemone_state(request.input)
     keyed_questions: Final = zip(systemone_keys(request.questions), request.questions, strict=True)
     images: Final = input_images(request.input)
@@ -503,6 +498,16 @@ def ir_to_systemone_response(response: DecisionsIRResponse, request: DecisionsIR
     )
 
 
+def catalog_mode(model: str, custom_llm_provider: str) -> str | None:
+    try:
+        key: Final = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider)["key"]
+    except Exception:  # noqa: BLE001  # get_model_info raises bare Exception for an unmapped model, which the catalog does not list
+        return None
+    entry: Final = GetModelCostMap.loaded_model_cost_map().get(key)
+    mode: Final = entry.get("mode") if entry is not None else None
+    return mode if isinstance(mode, str) else None
+
+
 def _is_decisions_model(model: str, custom_llm_provider: str) -> bool:
     try:
         model_mode: Final = litellm.get_model_info(model=model, custom_llm_provider=custom_llm_provider).get("mode")
@@ -640,12 +645,16 @@ class BaseDecisionsConfig(ABC):
             model=model, custom_llm_provider=custom_llm_provider
         )
 
-    def supports_audio_input(self, model: str, custom_llm_provider: str) -> bool:
+    def supports_audio_input(self, model: str, custom_llm_provider: str, deployment_flag: bool | None) -> bool:
+        if deployment_flag is not None:
+            return deployment_flag
         return _is_decisions_model(model, custom_llm_provider) and litellm.supports_audio_input(
             model=model, custom_llm_provider=custom_llm_provider
         )
 
-    def supports_video_input(self, model: str, custom_llm_provider: str) -> bool:
+    def supports_video_input(self, model: str, custom_llm_provider: str, deployment_flag: bool | None) -> bool:
+        if deployment_flag is not None:
+            return deployment_flag
         return _is_decisions_model(model, custom_llm_provider) and litellm.supports_video_input(
             model=model, custom_llm_provider=custom_llm_provider
         )
@@ -656,12 +665,7 @@ class BaseDecisionsConfig(ABC):
         request: DecisionsIRRequest,
         custom_llm_provider: str,
     ) -> Mapping[str, object] | UnsupportedDecisionsRequest:
-        return ir_to_systemone_request(
-            self.request_model(model),
-            request,
-            supports_audio=self.supports_audio_input(model, custom_llm_provider),
-            supports_video=self.supports_video_input(model, custom_llm_provider),
-        )
+        return ir_to_systemone_request(self.request_model(model), request)
 
     def unwrap_response(self, payload: object) -> object:
         return payload

litellm/llms/base_llm/files/batch_records.py

@@ -19,6 +19,8 @@ def responses_batch_body_to_chat_body(
     openai_request_body: Mapping[str, object],
     custom_llm_provider: str | None = None,
     drop_params: bool | None = None,
+    *,
+    caller_input: str | ResponseInputParam | None = None,
 ) -> dict[str, object]:  # mutable-ok: provider transforms take the bridged chat body as a plain dict
     """
     Rewrite the body of an OpenAI `/v1/responses` batch record as a Chat Completions body.
@@ -52,5 +54,6 @@ def responses_batch_body_to_chat_body(
         responses_api_request=responses_request,
         custom_llm_provider=custom_llm_provider,
         drop_params=drop_params,
+        caller_input=caller_input,
         metadata=openai_request_body.get("metadata"),
     )

litellm/llms/bedrock/chat/agentcore/transformation.py

@@ -544,6 +544,17 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM):
 
         return AgentCoreParsedResponse(content=content, usage=usage_data, final_message=final_message)
 
+    @staticmethod
+    def _message_stop_chunk(model: str) -> ModelResponseStream:
+        chunk: Final = ModelResponseStream(
+            id=f"chatcmpl-{uuid.uuid4()}",
+            created=0,
+            model=model,
+            object="chat.completion.chunk",
+        )
+        chunk.choices = [StreamingChoices(finish_reason="stop", index=0, delta=Delta())]
+        return chunk
+
     def _stream_agentcore_response_sync(
         self,
         response: httpx.Response,
@@ -553,6 +564,8 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM):
         Internal sync generator that parses SSE and yields ModelResponse chunks.
         """
         buffer = ""
+        sent_finish = False  # rebind-ok: flips when a finish chunk goes out
+        message_stopped = False  # rebind-ok: flips on messageStop, the only end some runtimes send
         for text_chunk in response.iter_text():
             buffer += text_chunk
 
@@ -624,7 +637,9 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM):
                                     total_tokens=usage_data.get("totalTokens", 0),
                                 ),
                             )
+                            sent_finish = True
                             yield chunk
+                        message_stopped |= "messageStop" in event_payload
 
                     # Process final message
                     if "message" in data_obj and isinstance(data_obj["message"], dict):
@@ -641,12 +656,16 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM):
                                 delta=Delta(),
                             )
                         ]
+                        sent_finish = True
                         yield chunk
 
                 except json.JSONDecodeError:
                     verbose_logger.debug("Skipping non-JSON SSE line: %s", line[:100])
                     continue
 
+        if message_stopped and not sent_finish:
+            yield self._message_stop_chunk(model)
+
     def get_sync_custom_stream_wrapper(
         self,
         model: str,
@@ -775,6 +794,8 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM):
         Internal async generator that parses SSE and yields ModelResponse chunks.
         """
         buffer = ""
+        sent_finish = False  # rebind-ok: flips when a finish chunk goes out
+        message_stopped = False  # rebind-ok: flips on messageStop, the only end some runtimes send
         async for text_chunk in response.aiter_text():
             buffer += text_chunk
 
@@ -846,7 +867,9 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM):
                                     total_tokens=usage_data.get("totalTokens", 0),
                                 ),
                             )
+                            sent_finish = True
                             yield chunk
+                        message_stopped |= "messageStop" in event_payload
 
                     # Process final message
                     if "message" in data_obj and isinstance(data_obj["message"], dict):
@@ -863,12 +886,16 @@ class AmazonAgentCoreConfig(BaseConfig, BaseAWSLLM):
                                 delta=Delta(),
                             )
                         ]
+                        sent_finish = True
                         yield chunk
 
                 except json.JSONDecodeError:
                     verbose_logger.debug("Skipping non-JSON SSE line: %s", line[:100])
                     continue
 
+        if message_stopped and not sent_finish:
+            yield self._message_stop_chunk(model)
+
     async def get_async_custom_stream_wrapper(
         self,
         model: str,

litellm/llms/bedrock/chat/converse_transformation.py

@@ -25,6 +25,7 @@ from litellm.constants import (
 )
 from litellm.litellm_core_utils.core_helpers import (
     as_str_mapping,
+    drops_unsupported_params,
     filter_exceptions_from_params,
     filter_internal_params,
     map_finish_reason,
@@ -2129,6 +2130,24 @@ class AmazonConverseConfig(BaseConfig):
 
         return data
 
+    @staticmethod
+    def _claude_messages_within_cache_control_cap(
+        messages: Sequence[AllMessageValues],
+        capability_model: str,
+        optional_params: Mapping[str, object],
+        litellm_params: Mapping[str, object],
+    ) -> Sequence[AllMessageValues]:
+        if not drops_unsupported_params(litellm_params.get("drop_params")) or "claude" not in capability_model.lower():
+            return messages
+        from litellm.integrations.anthropic_cache_control_hook import (
+            messages_within_cache_control_cap,
+            tool_message_with_single_cache_mark,
+        )
+
+        return messages_within_cache_control_cap(
+            tuple(map(tool_message_with_single_cache_mark, messages)), optional_params.get("tools")
+        )
+
     async def _async_transform_request(
         self,
         model: str,
@@ -2140,7 +2159,13 @@ class AmazonConverseConfig(BaseConfig):
         base_model: Final = litellm_params.get("base_model")
         capability_model: Final = bedrock_capability_model(model, base_model)
         messages, system_content_blocks = self._transform_system_message(
-            messages, model=capability_model, deployment_model=model
+            list(
+                self._claude_messages_within_cache_control_cap(
+                    messages, capability_model, _JSON_OBJECT.validate_python(optional_params), litellm_params
+                )
+            ),
+            model=capability_model,
+            deployment_model=model,
         )
 
         # Convert last user message to guarded_text if guardrailConfig is present
@@ -2221,7 +2246,13 @@ class AmazonConverseConfig(BaseConfig):
         base_model: Final = litellm_params.get("base_model")
         capability_model: Final = bedrock_capability_model(model, base_model)
         messages, system_content_blocks = self._transform_system_message(
-            messages, model=capability_model, deployment_model=model
+            list(
+                self._claude_messages_within_cache_control_cap(
+                    messages, capability_model, _JSON_OBJECT.validate_python(optional_params), litellm_params
+                )
+            ),
+            model=capability_model,
+            deployment_model=model,
         )
 
         # Convert last user message to guarded_text if guardrailConfig is present

litellm/llms/bedrock/files/transformation.py

@@ -42,6 +42,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
     extract_file_data,
     text_completion_prompt_to_messages,
 )
+from litellm.llms.anthropic.common_utils import AnthropicError
 from litellm.llms.base_llm.base_utils import map_developer_role_to_system_role
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.base_llm.files.batch_records import responses_batch_body_to_chat_body
@@ -347,7 +348,14 @@ class _BatchInputLine:
     record: _OpenAIBatchRecord
 
 
-_BATCH_RECORD_CONVERSION_ERRORS: Final = (AttributeError, LookupError, TypeError, ValueError, BadRequestError)
+_BATCH_RECORD_CONVERSION_ERRORS: Final = (
+    AttributeError,
+    LookupError,
+    TypeError,
+    ValueError,
+    BadRequestError,
+    AnthropicError,
+)
 
 
 def _batch_line_error(line_number: int, problem: str) -> BedrockError:

litellm/llms/clf_ai_gateway/chat/transformation.py

@@ -4,8 +4,8 @@ import litellm
 from litellm._logging import verbose_logger
 from litellm.exceptions import UnsupportedParamsError
 from litellm.litellm_core_utils.core_helpers import as_str_mapping
+from litellm.litellm_core_utils.reasoning_effort_capability import declared_reasoning_efforts_for_model
 from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig
-from litellm.router_utils.reasoning_effort_capability import declared_reasoning_efforts_for_model
 from litellm.secret_managers.main import get_secret_str
 
 SUPPORTED_OPENAI_PARAMS: Final = (

litellm/llms/cohere/common_utils.py

@@ -3,6 +3,7 @@ from typing import Final, Literal
 
 from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.llms.cohere import CohereV2StreamedToolCall
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import (
     ChatCompletionToolCallChunk,
@@ -222,6 +223,7 @@ class CohereV2ModelResponseIterator:
         self.content_blocks: list = []
         self.tool_index = -1
         self.json_mode = json_mode
+        self.tool_call_indexes_with_arguments: frozenset[int] = frozenset()
 
     def _parse_content_delta(self, chunk: dict) -> str:
         """Parse content-delta chunks to extract text."""
@@ -234,20 +236,23 @@ class CohereV2ModelResponseIterator:
             return content
         return ""
 
-    def _parse_tool_call_delta(self, chunk: dict) -> ChatCompletionToolCallChunk | None:
-        """Parse tool-call-delta chunks to extract tool calls."""
-        delta: Final = chunk.get("delta", {})
-        tool_calls: Final = delta.get("tool_calls", [])
-        if tool_calls:
-            return {
-                "id": tool_calls[0].get("id", ""),
-                "type": "function",
-                "function": {
-                    "name": tool_calls[0].get("name", ""),
-                    "arguments": tool_calls[0].get("arguments", ""),
-                },
-            }
-        return None
+    def _parse_tool_call_delta(self, chunk: dict, index: int) -> ChatCompletionToolCallChunk:
+        """Parse tool-call-start and tool-call-delta chunks to extract tool calls."""
+        tool_call: Final = CohereV2StreamedToolCall.model_validate(chunk)
+        if tool_call.arguments:
+            self.tool_call_indexes_with_arguments = self.tool_call_indexes_with_arguments | {index}
+        return {
+            "id": tool_call.id,
+            "type": "function",
+            "function": {"name": tool_call.name, "arguments": tool_call.arguments},
+            "index": index,
+        }
+
+    def _parse_tool_call_end(self, index: int) -> ChatCompletionToolCallChunk | None:
+        """Close a tool call that streamed no arguments with an empty JSON object, as OpenAI streams one."""
+        if index in self.tool_call_indexes_with_arguments:
+            return None
+        return {"id": None, "type": "function", "function": {"name": None, "arguments": "{}"}, "index": index}
 
     def _parse_tool_plan_delta(self, chunk: dict) -> dict | None:
         """Parse tool-plan-delta events to extract tool plan."""
@@ -301,7 +306,8 @@ class CohereV2ModelResponseIterator:
 
         v2 format:
         - Content: chunk.type == "content-delta" -> chunk.delta.message.content.text
-        - Tool calls: chunk.type == "tool-call-delta" -> chunk.delta.tool_calls
+        - Tool calls: chunk.type in ("tool-call-start", "tool-call-delta") -> chunk.delta.message.tool_calls
+        - Tool call end: chunk.type == "tool-call-end" -> "{}" arguments when the call streamed none
         - Tool plan: chunk.event == "tool-plan-delta" -> chunk.data.delta.message.tool_plan
         - Citations: chunk.event == "citation-start" -> chunk.data.delta.message.citations
         - Finish: chunk.event == "message-end" -> chunk.data.delta.finish_reason
@@ -321,8 +327,12 @@ class CohereV2ModelResponseIterator:
             # Handle different chunk types
             if chunk_type == "content-delta":
                 text = self._parse_content_delta(chunk)
-            elif chunk_type == "tool-call-delta":
-                tool_use = self._parse_tool_call_delta(chunk)
+            elif chunk_type in ("tool-call-start", "tool-call-delta", "tool-call-end"):
+                tool_use = (
+                    self._parse_tool_call_end(index)
+                    if chunk_type == "tool-call-end"
+                    else self._parse_tool_call_delta(chunk, index)
+                )
             elif event_type == "tool-plan-delta":
                 provider_specific_fields = self._parse_tool_plan_delta(chunk)
             elif event_type == "citation-start":

litellm/llms/compaction.py

@@ -9,7 +9,7 @@ from pydantic import TypeAdapter
 from litellm.types.llms.openai import AllMessageValues
 
 if TYPE_CHECKING:
-    from litellm.router import Router
+    from litellm.router_api import RoutingContext
 
 CompactionProtocol: TypeAlias = Literal["chat", "messages"]
 _MAPPING: Final = TypeAdapter(Mapping[str, object])
@@ -32,7 +32,9 @@ def get_native_compaction_provider(params: Mapping[str, object]) -> NativeCompac
     return compaction if compaction.supports_native_compaction(params) else None
 
 
-async def dispatch(router: Router, protocol: CompactionProtocol, payload: Mapping[str, object]) -> Mapping[str, object]:
+async def dispatch(
+    router: RoutingContext, protocol: CompactionProtocol, payload: Mapping[str, object]
+) -> Mapping[str, object]:
     if protocol == "messages":
         return _MAPPING.validate_python(
             await router.aanthropic_messages(custom_llm_provider=None, client=None, **payload)
@@ -45,4 +47,4 @@ async def dispatch(router: Router, protocol: CompactionProtocol, payload: Mappin
             {key: value for key, value in payload.items() if key not in ("model", "messages", "stream")}
         ),
     )
-    return _MAPPING.validate_python(response.model_dump())
+    return _MAPPING.validate_python(response.model_dump())  # pyright: ignore[reportAttributeAccessIssue]  # stream=False: acompletion returns a ModelResponse

litellm/llms/custom_httpx/aiohttp_handler.py

@@ -14,6 +14,7 @@ from litellm.llms.base_llm.chat.transformation import BaseConfig
 from litellm.llms.base_llm.image_variations.transformation import (
     BaseImageVariationConfig,
 )
+from litellm.llms.custom_httpx.aiohttp_pool_diagnostics import build_pool_wait_trace_config
 from litellm.llms.custom_httpx.aiohttp_transport import LiteLLMAiohttpTransport
 from litellm.llms.custom_httpx.http_handler import (
     AsyncHTTPHandler,
@@ -90,7 +91,7 @@ class BaseLLMAIOHTTPHandler:
         if self.transport is None:
             connector: Final = self._get_connector()
             if connector:
-                return aiohttp.ClientSession(connector=connector)
+                return aiohttp.ClientSession(connector=connector, trace_configs=[build_pool_wait_trace_config()])
 
         transport: Final = self.transport or self._get_or_create_transport()
         if transport is not None and hasattr(transport, "_get_valid_client_session"):
@@ -99,7 +100,7 @@ class BaseLLMAIOHTTPHandler:
             except RuntimeError:
                 pass
 
-        return aiohttp.ClientSession()
+        return aiohttp.ClientSession(trace_configs=[build_pool_wait_trace_config()])
 
     def _get_async_client_session(self, dynamic_client_session: ClientSession | None = None) -> ClientSession:
         if dynamic_client_session:

litellm/llms/custom_httpx/aiohttp_pool_diagnostics.py

@@ -0,0 +1,130 @@
+import asyncio
+from types import SimpleNamespace
+from typing import Final
+
+from aiohttp import (
+    ClientSession,
+    TraceConfig,
+    TraceConnectionQueuedEndParams,
+    TraceConnectionQueuedStartParams,
+    TraceRequestExceptionParams,
+    TraceRequestRedirectParams,
+    TraceRequestStartParams,
+)
+from yarl import URL
+
+from litellm._logging import verbose_logger
+
+_POOL_WAIT_WARNING_SECONDS: Final = 1.0
+
+
+class _PoolWaitContext(SimpleNamespace):
+    def __init__(self, trace_request_ctx: object = None) -> None:
+        super().__init__()
+        self.host: str | None = None
+        self.port: int | None = None
+        self.started_at: float | None = None
+        self.waited: float = 0.0
+        self.warning: asyncio.TimerHandle | None = None
+        self.warned: bool = False
+
+    def set_destination(self, params: TraceRequestStartParams) -> None:
+        self.host = params.url.host
+        self.port = params.url.port
+
+    def redirect(self, params: TraceRequestRedirectParams) -> None:
+        location: Final = params.response.headers.get("Location") or params.response.headers.get("URI")
+        if location is None:
+            return
+        try:
+            destination: Final = params.url.join(URL(location))
+            host: Final = destination.host
+            port: Final = destination.port
+        except ValueError:
+            return
+        self.host = host
+        self.port = port
+
+    def start(self, session: ClientSession) -> None:
+        loop: Final = asyncio.get_running_loop()
+        self.started_at = loop.time()
+        if not self.warned:
+            self.warning = loop.call_later(max(0.0, _POOL_WAIT_WARNING_SECONDS - self.waited), self.warn, session)
+
+    def elapsed(self) -> float:
+        return self.waited + (
+            asyncio.get_running_loop().time() - self.started_at if self.started_at is not None else 0.0
+        )
+
+    def warn(self, session: ClientSession) -> None:
+        if self.warned or self.started_at is None:
+            return
+        self.warned = True
+        connector: Final = session.connector
+        if connector is None:
+            return
+        verbose_logger.warning(
+            "aiohttp connection pool saturated: host=%s port=%s waited=%.3fs for a connection slot "
+            "(acquired_total=%s, limit=%s, limit_per_host=%s). "
+            "Wait occurred before sending the request upstream.",
+            self.host,
+            self.port,
+            self.elapsed(),
+            len(connector._acquired),  # pyright: ignore[reportPrivateUsage]  # aiohttp exposes no public acquired count
+            connector.limit,
+            connector.limit_per_host,
+        )
+
+    def finish(self, session: ClientSession, failed: bool = False) -> None:
+        if self.warning is not None:
+            self.warning.cancel()
+            self.warning = None
+        if self.started_at is None:
+            return
+        if failed or self.elapsed() >= _POOL_WAIT_WARNING_SECONDS:
+            self.warn(session)
+        self.waited = self.elapsed()
+        self.started_at = None
+
+
+async def _on_request_start(session: ClientSession, context: SimpleNamespace, params: TraceRequestStartParams) -> None:
+    if isinstance(context, _PoolWaitContext):
+        context.set_destination(params)
+
+
+async def _on_queued_start(
+    session: ClientSession, context: SimpleNamespace, params: TraceConnectionQueuedStartParams
+) -> None:
+    if isinstance(context, _PoolWaitContext):
+        context.start(session)
+
+
+async def _on_queued_end(
+    session: ClientSession, context: SimpleNamespace, params: TraceConnectionQueuedEndParams
+) -> None:
+    if isinstance(context, _PoolWaitContext):
+        context.finish(session)
+
+
+async def _on_request_exception(
+    session: ClientSession, context: SimpleNamespace, params: TraceRequestExceptionParams
+) -> None:
+    if isinstance(context, _PoolWaitContext):
+        context.finish(session, failed=True)
+
+
+async def _on_request_redirect(
+    session: ClientSession, context: SimpleNamespace, params: TraceRequestRedirectParams
+) -> None:
+    if isinstance(context, _PoolWaitContext):
+        context.redirect(params)
+
+
+def build_pool_wait_trace_config() -> TraceConfig:
+    trace: Final = TraceConfig(trace_config_ctx_factory=_PoolWaitContext)
+    trace.on_request_start.append(_on_request_start)
+    trace.on_connection_queued_start.append(_on_queued_start)
+    trace.on_connection_queued_end.append(_on_queued_end)
+    trace.on_request_exception.append(_on_request_exception)
+    trace.on_request_redirect.append(_on_request_redirect)
+    return trace

litellm/llms/custom_httpx/aiohttp_transport.py

@@ -110,6 +110,15 @@ def map_aiohttp_exceptions() -> Generator[None, None, None]:
         raise mapped_exc(message) from exc
 
 
+def _unread_payload(content: object) -> bytes:
+    """Bytes the upstream sent before the connection dropped that aiohttp buffered but never handed out, since its
+    reads raise the stored payload error ahead of the buffer."""
+    if not isinstance(content, aiohttp.StreamReader):
+        return b""
+    buffered: Final = b"".join(content._buffer)  # pyright: ignore[reportPrivateUsage]  # no public read drains it
+    return buffered[content._buffer_offset :]  # pyright: ignore[reportPrivateUsage]  # see above
+
+
 def _release_transport_anchor(_transport: object) -> None:
     """Release a transport anchored to a response stream. See ``_anchor_transport_to``."""
 
@@ -144,6 +153,12 @@ class AiohttpResponseStream(httpx.AsyncByteStream):
             raise httpx.ReadError(str(e)) from e
         except aiohttp.http_exceptions.TransferEncodingError as e:
             raise httpx.ReadError(str(e)) from e
+        except aiohttp.ClientPayloadError:
+            unread: Final = _unread_payload(self._aiohttp_response.content)
+            if unread:
+                yield unread
+            with map_aiohttp_exceptions():
+                raise
         except Exception:
             # For other exceptions, use the normal mapping
             with map_aiohttp_exceptions():

litellm/llms/custom_httpx/http_handler.py

@@ -44,6 +44,7 @@ from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
 from litellm.litellm_core_utils.request_timeout_resolver import (
     get_configured_request_timeout,
 )
+from litellm.llms.custom_httpx.aiohttp_pool_diagnostics import build_pool_wait_trace_config
 from litellm.llms.custom_httpx.http2_connection_spread import AsyncHTTP2ConnectionSpread
 from litellm.types.llms.custom_http import *
 
@@ -1456,6 +1457,7 @@ class AsyncHTTPHandler:
                 connector=TCPConnector(**transport_connector_kwargs),
                 cookie_jar=DummyCookieJar(),
                 trust_env=trust_env,
+                trace_configs=[build_pool_wait_trace_config()],
             )
 
         # Use shared session if provided and valid

litellm/llms/custom_httpx/llm_http_handler.py

@@ -131,6 +131,8 @@ from litellm.responses.streaming_iterator import (
     ResponsesAPIStreamingIterator,
     ResponsesWebSocketStreaming,
     SyncResponsesAPIStreamingIterator,
+    TokenLimitCallback,
+    with_token_limit_checks,
 )
 from litellm.types.containers.main import (
     ContainerFileListResponse,
@@ -598,10 +600,10 @@ def _decoded_body_headers(response: httpx.Response) -> httpx.Headers:
     )
 
 
-def _collect_ws_project_quota_callbacks() -> tuple[ProjectQuotaCallback, ...]:
-    """Duck-type discover proxy hooks exposing per-frame project ITPM/OTPM
-    enforcement, so the Responses WebSocket loop can charge every
-    ``response.create`` frame, not just the connection's first one.
+def _ws_callbacks_exposing(method_name: str) -> tuple[object, ...]:
+    """Duck-type discover proxy hooks exposing a per-frame check, so the
+    Responses WebSocket loop can run it on every ``response.create`` frame,
+    not just the connection's first one.
 
     Uses duck-typing on ``litellm.callbacks`` (rather than importing the
     proxy hook directly) to avoid a layering violation (SDK importing from
@@ -612,13 +614,28 @@ def _collect_ws_project_quota_callbacks() -> tuple[ProjectQuotaCallback, ...]:
     callbacks: Final = cast(  # cast-ok: callback registry is inspected before protocol use
         Sequence[object], _litellm.callbacks
     )
+    return tuple(callback for callback in callbacks if callable(getattr(callback, method_name, None)))
+
+
+def _collect_ws_project_quota_callbacks() -> tuple[ProjectQuotaCallback, ...]:
     return tuple(
         cast(ProjectQuotaCallback, callback)  # cast-ok: required callback method is callable
-        for callback in callbacks
-        if callable(getattr(callback, "enforce_project_io_token_quota_for_frame", None))
+        for callback in _ws_callbacks_exposing("enforce_project_io_token_quota_for_frame")
     )
 
 
+def _collect_ws_token_limit_callbacks() -> tuple[TokenLimitCallback, ...]:
+    return tuple(
+        cast(TokenLimitCallback, callback)  # cast-ok: required callback method is callable
+        for callback in _ws_callbacks_exposing("enforce_token_limits_for_frame")
+    )
+
+
+def _ws_model_group(litellm_metadata: Mapping[str, object] | None, model: str) -> str:
+    model_group: Final = (litellm_metadata or {}).get("model_group")
+    return model_group if isinstance(model_group, str) and model_group else model
+
+
 def _unwrap_provider_error(error: Exception) -> Exception:
     if not isinstance(error, httpx.TransportError):
         return error
@@ -7285,6 +7302,12 @@ class BaseLLMHTTPHandler:
         - Forwards events over the websocket connection
         """
         _ws_quota_callbacks: Final = _collect_ws_project_quota_callbacks()
+        _ws_turn_admission: Final = with_token_limit_checks(
+            turn_admission,
+            _collect_ws_token_limit_callbacks(),
+            user_api_key_dict,
+            _ws_model_group(litellm_metadata, model),
+        )
 
         _ws_guardrail_callbacks: list = []
         _ws_output_guardrail_callbacks: list = []
@@ -7332,7 +7355,7 @@ class BaseLLMHTTPHandler:
                 custom_llm_provider=custom_llm_provider,
                 first_message=first_message,
                 quota_callbacks=_ws_quota_callbacks,
-                turn_admission=turn_admission,
+                turn_admission=_ws_turn_admission,
                 guardrail_callbacks=_ws_guardrail_callbacks,  # pyright: ignore[reportUnknownArgumentType]  # duck-typed callbacks from litellm.callbacks
                 frame_guard=frame_guard,
                 **kwargs,
@@ -7429,7 +7452,7 @@ class BaseLLMHTTPHandler:
                     authorized_model=model,
                     custom_llm_provider=custom_llm_provider,
                     request_defaults=request_defaults,
-                    turn_admission=turn_admission,
+                    turn_admission=_ws_turn_admission,
                     frame_guard=frame_guard,
                 )
                 return await streaming.bidirectional_forward()

litellm/llms/databricks/embed/handler.py

@@ -26,6 +26,7 @@ class DatabricksEmbeddingHandler(OpenAILikeEmbeddingHandler, DatabricksBase):
         aembedding=None,
         custom_endpoint: bool | None = None,
         headers: dict | None = None,
+        drop_params: bool = False,
     ) -> EmbeddingResponse:
         # Check for custom user agent in optional_params or environment
         # This allows partners building on LiteLLM to set their own telemetry
@@ -58,4 +59,5 @@ class DatabricksEmbeddingHandler(OpenAILikeEmbeddingHandler, DatabricksBase):
             aembedding=aembedding,
             custom_endpoint=True,
             headers=headers,
+            drop_params=drop_params,
         )

litellm/llms/deepgram/common_utils.py

@@ -1,16 +1,21 @@
+import json
 import math
+import re
 from collections.abc import Mapping, Sequence
+from dataclasses import dataclass
 from types import MappingProxyType
 from typing import Final
-from urllib.parse import parse_qs, urlparse
+from urllib.parse import parse_qs, urlparse, urlsplit, urlunsplit
 
 import httpx
 
 import litellm
-from litellm.constants import DEEPGRAM_DEFAULT_API_BASE, DEEPGRAM_LISTEN_DEFAULT_MODEL
+from litellm.constants import DEEPGRAM_DEFAULT_API_BASE, DEEPGRAM_LISTEN_DEFAULT_MODEL, DEEPGRAM_SPEAK_DEFAULT_MODEL
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.passthrough_endpoints.pass_through_endpoints import WebSocketSessionUsage
 from litellm.types.utils import LlmProviders
+from litellm.utils import ModelInfoLookupMiss, lookup_model_info_for_registration
 
 _WEBSOCKET_SCHEMES: Final = MappingProxyType({"https": "wss", "http": "ws", "wss": "wss", "ws": "ws"})
 DEEPGRAM_LISTEN_CALLBACK_PARAMS: Final = frozenset({"callback", "callback_method"})
@@ -199,3 +204,71 @@ def deepgram_listen_transcript(websocket_messages: Sequence[Mapping[str, object]
         if frame.get("type") == "Results"
         if (transcript := _final_transcript(frame)) is not None
     )
+
+
+_AURA_GENERATION: Final = re.compile(r"aura(?:-\d+)?(?=-[a-z])")
+
+
+def deepgram_speak_websocket_target(api_base: str | None, query_string: str) -> str:
+    speak_url: Final = urlsplit(join_api_base(api_base or DEEPGRAM_DEFAULT_API_BASE, "speak"))
+    scheme: Final = _WEBSOCKET_SCHEMES.get(speak_url.scheme, speak_url.scheme)
+    params: Final = _first_occurrences(query_string)
+    query: Final = params if params.get("model") else params.remove("model").add("model", DEEPGRAM_SPEAK_DEFAULT_MODEL)
+    return urlunsplit((scheme, speak_url.netloc, speak_url.path, str(query), ""))
+
+
+def deepgram_speak_model(upstream_url: str) -> str:
+    models: Final = parse_qs(urlparse(upstream_url).query).get("model")
+    return models[0] if models else DEEPGRAM_SPEAK_DEFAULT_MODEL
+
+
+def deepgram_speak_requested_model(query_string: str) -> str:
+    """Read off the upstream target, so the voice a key is authorized for is the voice Deepgram receives"""
+    return deepgram_speak_model(deepgram_speak_websocket_target(None, query_string))
+
+
+def deepgram_speak_registry_key(upstream_url: str) -> str:
+    """Deepgram bills every voice of an Aura generation at one rate, so ``aura-2-thalia-en`` is priced at the
+    ``deepgram/aura-2`` row"""
+    model: Final = deepgram_speak_model(upstream_url)
+    generation: Final = _AURA_GENERATION.match(model)
+    return f"{LlmProviders.DEEPGRAM.value}/{generation.group() if generation else model}"
+
+
+def deepgram_speak_character_rate(upstream_url: str) -> float | None:
+    """Only an exact registry hit counts, so a voice is never billed at a neighbouring row's rate"""
+    registry_key: Final = deepgram_speak_registry_key(upstream_url)
+    model_info: Final = lookup_model_info_for_registration(registry_key, LlmProviders.DEEPGRAM.value)
+    if isinstance(model_info, ModelInfoLookupMiss) or model_info["key"] != registry_key:
+        return None
+    return model_info.get("input_cost_per_character")
+
+
+@dataclass(frozen=True, slots=True)
+class DeepgramSpeakMeter:
+    """Holds the rate checked when the socket opened, so a cost-map reload mid-session can't change or drop the price"""
+
+    upstream_url: str
+    character_rate: float
+
+    def measure(self, client_text: str) -> int:
+        """Counts a Speak message's text as it is sent. Deepgram also speaks, and bills, unflushed text on its own (a
+        long buffer, Close, a close frame), so counting only flushed text would let a client get audio free, while
+        counting on send can only over-bill text Deepgram discards, such as a Clear or an oversized Speak. Never
+        raises, so a malformed frame can't end the session"""
+        try:
+            message: Final[object] = json.loads(client_text)
+        except (ValueError, RecursionError):
+            return 0
+        match message:
+            case {"type": "Speak", "text": str() as text}:
+                return len(text)
+            case _:
+                return 0
+
+    def price(self, total: int) -> WebSocketSessionUsage:
+        return WebSocketSessionUsage(
+            model=deepgram_speak_model(self.upstream_url),
+            custom_llm_provider=LlmProviders.DEEPGRAM.value,
+            response_cost=total * self.character_rate,
+        )

litellm/llms/deepinfra/chat/transformation.py

@@ -130,7 +130,8 @@ class DeepInfraConfig(OpenAIGPTConfig):
         - Input:  {"role": "tool", "content": [{"type": "text", "text": "result"}]}
         - Output: {"role": "tool", "content": "[{\"type\": \"text\", \"text\": \"result\"}]"}
         """
-        for message in messages:
+        copied_messages: Final = [message.copy() for message in messages]
+        for message in copied_messages:
             if message.get("role") == "tool":
                 content = message.get("content")
 
@@ -149,7 +150,7 @@ class DeepInfraConfig(OpenAIGPTConfig):
                         # For complex content, serialize the entire array as JSON string
                         message["content"] = json.dumps(content)
 
-        return messages
+        return copied_messages
 
     @overload
     def _transform_messages(

litellm/llms/fireworks_ai/chat/transformation.py

@@ -23,6 +23,7 @@ from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionToolParam,
+    ChatCompletionToolParamFunctionChunk,
     OpenAIChatCompletionToolParam,
 )
 from litellm.types.utils import (
@@ -45,6 +46,7 @@ from ...openai.chat.gpt_transformation import (
     OpenAIChatCompletionStreamingHandler,
     OpenAIGPTConfig,
 )
+from ...openai.common_utils import without_cache_control
 from ..common_utils import (
     FIREROUTER,
     FireworksAIException,
@@ -480,15 +482,23 @@ class FireworksAIConfig(FireworksAIMixin, OpenAIGPTConfig):
         return (("response_format", _json_schema_response_format(choice_schema, "choice")),)
 
     def _transform_tools(self, tools: list[OpenAIChatCompletionToolParam]) -> list[OpenAIChatCompletionToolParam]:
-        for tool in tools:
-            if tool.get("type") != "function":
-                continue
-            function = tool["function"]
-            function.pop("strict", None)
-            params = function.get("parameters")
-            if isinstance(params, dict):
-                unpack_legacy_defs(params)
-        return tools
+        return [self._fireworks_tool(tool) for tool in tools]
+
+    @staticmethod
+    def _fireworks_tool(tool: OpenAIChatCompletionToolParam) -> OpenAIChatCompletionToolParam:
+        if tool.get("type") != "function":
+            return tool
+        function: Final = tool["function"]
+        params: Final = function.get("parameters")
+        unstrict_function: Final = cast(  # cast-ok: the same function definition minus `strict`
+            ChatCompletionToolParamFunctionChunk, {key: value for key, value in function.items() if key != "strict"}
+        )
+        new_function: Final[ChatCompletionToolParamFunctionChunk] = (
+            {**unstrict_function, "parameters": unpack_legacy_defs(params, copy=True)}
+            if isinstance(params, dict)
+            else unstrict_function
+        )
+        return {**tool, "function": new_function}
 
     def _transform_messages_helper(
         self, messages: list[AllMessageValues], model: str, litellm_params: dict
@@ -496,10 +506,6 @@ class FireworksAIConfig(FireworksAIMixin, OpenAIGPTConfig):
         """
         Strip fields not permitted by FireworksAI from messages.
         """
-        from litellm.litellm_core_utils.prompt_templates.common_utils import (
-            filter_value_from_dict,
-        )
-
         supports_vision_value: Final = self._get_model_cost_capability_exact(model=model, capability="supports_vision")
         for message in messages:
             if message["role"] == "user":
@@ -530,9 +536,11 @@ class FireworksAIConfig(FireworksAIMixin, OpenAIGPTConfig):
                                 model=model,
                                 llm_provider="fireworks_ai",
                             )
-            filter_value_from_dict(cast(dict, message), "cache_control")
 
-        return [strip_litellm_response_only_fields(message) for message in messages]
+        return [
+            strip_litellm_response_only_fields(cast(AllMessageValues, without_cache_control(message)))
+            for message in messages
+        ]
 
     # Cached index of fireworks_ai/* entries from litellm.model_cost. Building
     # this index requires a full scan of model_cost (tens of thousands of

litellm/llms/groq/chat/transformation.py

@@ -140,7 +140,8 @@ class GroqChatConfig(OpenAILikeChatConfig):
     def _transform_messages(
         self, messages: list[AllMessageValues], model: str, is_async: bool = False
     ) -> list[AllMessageValues] | Coroutine[object, object, list[AllMessageValues]]:
-        for idx, message in enumerate(messages):
+        transformed_messages: Final = list(messages)
+        for idx, message in enumerate(transformed_messages):
             """
             1. Don't pass 'null' function_call assistant message to groq - https://github.com/BerriAI/litellm/issues/5839
             """
@@ -154,12 +155,12 @@ class GroqChatConfig(OpenAILikeChatConfig):
                 for k, v in _message.items():
                     if v is not None:
                         new_message[k] = v
-                messages[idx] = strip_litellm_internal_message_fields(new_message)
+                transformed_messages[idx] = strip_litellm_internal_message_fields(new_message)
 
         if is_async:
-            return super()._transform_messages(messages=messages, model=model, is_async=True)
+            return super()._transform_messages(messages=transformed_messages, model=model, is_async=True)
         else:
-            return super()._transform_messages(messages=messages, model=model, is_async=False)
+            return super()._transform_messages(messages=transformed_messages, model=model, is_async=False)
 
     def transform_messages(
         self,

litellm/llms/hosted_vllm/chat/transformation.py

@@ -2,6 +2,7 @@
 Translate from OpenAI's `/v1/chat/completions` to VLLM's `/v1/chat/completions`
 """
 
+import copy
 import json
 from collections.abc import Coroutine
 from typing import Final, Literal, cast, overload
@@ -88,7 +89,7 @@ class HostedVLLMChatConfig(OpenAIGPTConfig):
     ) -> dict:
         _tools = non_default_params.pop("tools", None)
         if _tools is not None:
-            _tools = remove_additional_properties(_tools)
+            _tools = remove_additional_properties(copy.deepcopy(_tools))  # pyright: ignore[reportUnknownArgumentType]  # tools arrive untyped through the bare-dict params
             _tools = remove_strict_from_schema(_tools)
             if isinstance(_tools, list):
                 _tools = self._convert_custom_tools_to_function_tools(_tools)

litellm/llms/langgraph/chat/sse_iterator.py

@@ -41,8 +41,9 @@ class LangGraphSSEStreamIterator:
         return self
 
     def __aiter__(self):
-        """Initialize async iteration."""
-        self.async_line_iterator = aiter_sse_lines(self.response.aiter_text())
+        """Initialize async iteration once; CustomStreamWrapper re-enters `async for` on every chunk."""
+        if self.async_line_iterator is None:
+            self.async_line_iterator = aiter_sse_lines(self.response.aiter_text())
         return self
 
     def _parse_sse_line(self, line: str) -> ModelResponseStream | None:

litellm/llms/litellm_proxy/skills/skill_search.py

@@ -131,7 +131,7 @@ async def search_skills(
     user_api_key_dict: UserAPIKeyAuth,
     proxy_logging_obj: ProxyLogging,
 ) -> SkillSearchOutcome:
-    if embedding_model is None:
+    if embedding_model is None or not embedding_model.strip():
         return SkillSearchNotConfigured(
             reason="skill search needs litellm_settings.skill_search_embedding_model set to an embedding model from model_list"
         )

litellm/llms/mistral/chat/transformation.py

@@ -25,16 +25,16 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
     strip_litellm_internal_message_fields,
     strip_none_values_from_message,
 )
+from litellm.litellm_core_utils.reasoning_effort_capability import (
+    declared_reasoning_efforts_for_model,
+    nearest_declared_reasoning_effort,
+)
 from litellm.litellm_core_utils.reasoning_effort_utils import OPAQUE_VALUE, warn_dropped_reasoning
 from litellm.litellm_core_utils.url_utils import ensure_api_route
 from litellm.llms.openai.chat.gpt_transformation import (
     OpenAIChatCompletionStreamingHandler,
     OpenAIGPTConfig,
 )
-from litellm.router_utils.reasoning_effort_capability import (
-    declared_reasoning_efforts_for_model,
-    nearest_declared_reasoning_effort,
-)
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.llms.mistral import (
     MistralCacheKeyParams,
@@ -755,7 +755,8 @@ class MistralConfig(OpenAIGPTConfig):
         if _name is not None:
             # Remove name if not a tool message
             if message["role"] != "tool" or isinstance(_name, str) and len(_name.strip()) == 0:
-                message.pop("name", None)
+                message_without_name: Final = {k: v for k, v in message.items() if k != "name"}
+                return cast(AllMessageValues, message_without_name)  # cast-ok: the same message minus name
 
         return message
 

litellm/llms/oci/chat/transformation.py

@@ -20,7 +20,12 @@ import litellm
 from litellm._logging import verbose_logger
 from litellm.constants import DEFAULT_OCI_CHAT_MAX_TOKENS
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
-from litellm.litellm_core_utils.prompt_templates.common_utils import messages_with_file_parts_as_image_urls
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    merge_consecutive_system_messages,
+    messages_with_file_parts_as_image_urls,
+    system_messages_first,
+)
+from litellm.litellm_core_utils.reasoning_effort_capability import declared_reasoning_efforts_for_model
 from litellm.litellm_core_utils.reasoning_effort_utils import warn_dropped_reasoning
 from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm.litellm_core_utils.url_utils import join_api_base
@@ -54,7 +59,6 @@ from litellm.llms.oci.common_utils import (
     sign_oci_request,
     validate_oci_environment,
 )
-from litellm.router_utils.reasoning_effort_capability import declared_reasoning_efforts_for_model
 from litellm.types.llms.oci import (
     CohereChatRequest,
     OCIChatRequestPayload,
@@ -693,14 +697,18 @@ class OCIChatConfig(BaseConfig):
                 chatRequest=chat_request,
             )
         else:
+            generic_messages: Final = list(messages_with_file_parts_as_image_urls(messages))
+            normalized_messages: Final = (
+                merge_consecutive_system_messages(system_messages_first(generic_messages))
+                if model.lower().removeprefix("oci/").startswith("google.gemini")
+                else generic_messages
+            )
             data = OCICompletionPayload(
                 compartmentId=oci_compartment_id,
                 servingMode=serving_mode,
                 chatRequest=OCIChatRequestPayload(
                     apiFormat=vendor.value,
-                    messages=adapt_messages_to_generic_oci_standard(
-                        list(messages_with_file_parts_as_image_urls(messages))
-                    ),
+                    messages=adapt_messages_to_generic_oci_standard(normalized_messages),
                     **self._get_optional_params(vendor, optional_params, model),
                 ),
             )

litellm/llms/openai/chat/gpt_transformation.py

@@ -14,7 +14,7 @@ from pydantic import TypeAdapter, ValidationError
 
 import litellm
 from litellm.constants import OPENAI_SYSTEM_MESSAGES_FIRST_PROVIDERS
-from litellm.litellm_core_utils.core_helpers import map_finish_reason
+from litellm.litellm_core_utils.core_helpers import drops_unsupported_params, map_finish_reason
 from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
     extract_reasoning_content,
     handle_invalid_parallel_tool_calls,
@@ -64,7 +64,7 @@ from litellm.types.utils import (
 )
 from litellm.utils import convert_to_model_response_object, declared_mid_conversation_system_support
 
-from ..common_utils import OpenAIError
+from ..common_utils import OpenAIError, without_cache_control
 from ..workload_identity import get_workload_identity_bearer_token, resolve_openai_workload_identity_config
 
 if TYPE_CHECKING:
@@ -128,6 +128,29 @@ def _litellm_params_str(litellm_params: Mapping[str, object] | None, key: str) -
     return value if isinstance(value, str) else None
 
 
+def _drops_params(litellm_params: Mapping[str, object]) -> bool:
+    return drops_unsupported_params(litellm_params.get("drop_params"))
+
+
+def _names_claude(model: str, litellm_params: Mapping[str, object]) -> bool:
+    base_model: Final = litellm_params.get("base_model")
+    return "claude" in model.lower() or (isinstance(base_model, str) and "claude" in base_model.lower())
+
+
+def _claude_messages_within_cache_control_cap(
+    model: str,
+    messages: Sequence[AllMessageValues],
+    tools: Sequence["ChatCompletionToolParam"] | None,
+    litellm_params: Mapping[str, object],
+    request_params: Mapping[str, object],
+) -> Sequence[AllMessageValues]:
+    if not _names_claude(model, litellm_params) or not _drops_params(litellm_params):
+        return messages
+    from litellm.integrations.anthropic_cache_control_hook import messages_within_cache_control_cap
+
+    return list(messages_within_cache_control_cap(messages, tools, request_params.get("cache_control"), request_params))
+
+
 def _prompt_cache_options(optional_params: Mapping[str, object]) -> object | None:
     try:
         extra_body: Final = _OBJECT_MAPPING.validate_python(optional_params.get("extra_body"))
@@ -311,52 +334,58 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
         return content_copy
 
     async def _async_handle_pdf_url(self, content_item: ChatCompletionFileObjectFile) -> ChatCompletionFileObjectFile:
-        file_id: Final = content_item.get("file_id")
+        content_copy: Final = content_item.copy()
+        file_id: Final = content_copy.get("file_id")
         if file_id is not None:  # check for file id being url done in _handle_pdf_url
             base64_data: Final = await async_convert_url_to_base64(file_id)
-            content_item["file_data"] = base64_data
-            content_item["filename"] = "my_file.pdf"
-            content_item.pop("file_id")
-        return content_item
+            content_copy["file_data"] = base64_data
+            content_copy["filename"] = "my_file.pdf"
+            content_copy.pop("file_id")
+        return content_copy
 
     def _common_file_data_check(self, content_item: ChatCompletionFileObjectFile) -> ChatCompletionFileObjectFile:
         file_data: Final = content_item.get("file_data")
         filename: Final = content_item.get("filename")
         if file_data is not None and filename is None:
-            content_item["filename"] = "my_file.pdf"
+            return {**content_item, "filename": "my_file.pdf"}
         return content_item
 
+    @staticmethod
+    def _image_item_for_openai(content_item: ChatCompletionImageObject) -> ChatCompletionImageObject:
+        image_url: Final = content_item["image_url"]
+        if isinstance(image_url, str):
+            return {**content_item, "image_url": {"url": image_url}}
+        if not isinstance(image_url, dict):
+            return content_item
+        new_image_url_obj: Final = cast(
+            ChatCompletionImageUrlObject,
+            {k: v for k, v in image_url.items() if k not in {"format", "video_metadata"}},
+        )
+        return {**content_item, "image_url": new_image_url_obj}
+
+    @staticmethod
+    def _file_item_for_openai(content_item: ChatCompletionFileObject) -> ChatCompletionFileObject:
+        litellm_specific_params: Final = {"format", "detail", "video_metadata"}
+        file_obj: Final = content_item.get("file")
+        if file_obj is None:
+            raise litellm.BadRequestError(
+                message="Content block has type='file' but is missing the required 'file' field",
+                model=None,
+                llm_provider="openai",
+            )
+        new_file_obj: Final = ChatCompletionFileObjectFile(
+            **{k: v for k, v in file_obj.items() if k not in litellm_specific_params}
+        )
+        return {**content_item, "file": new_file_obj}
+
     def _apply_common_transform_content_item(
         self,
         content_item: OpenAIMessageContentListBlock,
     ) -> OpenAIMessageContentListBlock:
-        litellm_specific_params: Final = {"format", "detail", "video_metadata"}
         if content_item.get("type") == "image_url":
-            content_item = cast(ChatCompletionImageObject, content_item)
-            if isinstance(content_item["image_url"], str):
-                content_item["image_url"] = {
-                    "url": content_item["image_url"],
-                }
-            elif isinstance(content_item["image_url"], dict):
-                new_image_url_obj: Final = cast(
-                    ChatCompletionImageUrlObject,
-                    {k: v for k, v in content_item["image_url"].items() if k not in {"format", "video_metadata"}},
-                )
-                content_item["image_url"] = new_image_url_obj
-        elif content_item.get("type") == "file":
-            content_item = cast(ChatCompletionFileObject, content_item)
-            file_obj: Final = content_item.get("file")
-            if file_obj is None:
-                raise litellm.BadRequestError(
-                    message="Content block has type='file' but is missing the required 'file' field",
-                    model=None,
-                    llm_provider="openai",
-                )
-            new_file_obj: Final = ChatCompletionFileObjectFile(
-                **{k: v for k, v in file_obj.items() if k not in litellm_specific_params}
-            )
-            content_item["file"] = new_file_obj
-
+            return self._image_item_for_openai(cast(ChatCompletionImageObject, content_item))
+        if content_item.get("type") == "file":
+            return self._file_item_for_openai(cast(ChatCompletionFileObject, content_item))
         return content_item
 
     def _transform_content_item(
@@ -419,31 +448,34 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
         hoisted_messages: Final = hoist_images_from_tool_messages(stripped_messages)
 
         async def _async_transform():
-            for message in hoisted_messages:
-                message_content = message.get("content")
-                message_role = message.get("role")
-
-                if message_role == "user" and message_content and isinstance(message_content, list):
-                    message_content_types = cast(list[OpenAIMessageContentListBlock], message_content)
-                    for i, content_item in enumerate(message_content_types):
-                        message_content_types[i] = await self._async_transform_content_item(
-                            cast(OpenAIMessageContentListBlock, content_item),
-                        )
-            return hoisted_messages
+            return [await self._async_user_message_for_openai(message) for message in hoisted_messages]
 
         if is_async:
             return _async_transform()
-        else:
-            for message in hoisted_messages:
-                message_content = message.get("content")
-                message_role = message.get("role")
-                if message_role == "user" and message_content and isinstance(message_content, list):
-                    message_content_types = cast(list[OpenAIMessageContentListBlock], message_content)
-                    for i, content_item in enumerate(message_content):
-                        message_content_types[i] = self._transform_content_item(
-                            cast(OpenAIMessageContentListBlock, content_item)
-                        )
-            return hoisted_messages
+        return [self._user_message_for_openai(message) for message in hoisted_messages]
+
+    @staticmethod
+    def _user_content_parts(message: AllMessageValues) -> Sequence[OpenAIMessageContentListBlock] | None:
+        message_content: Final = message.get("content")
+        if message.get("role") != "user" or not message_content or not isinstance(message_content, list):
+            return None
+        return cast(Sequence[OpenAIMessageContentListBlock], message_content)
+
+    def _user_message_for_openai(self, message: AllMessageValues) -> AllMessageValues:
+        parts: Final = self._user_content_parts(message)
+        if parts is None:
+            return message
+        return cast(  # cast-ok: the same user message with each content part rebuilt for OpenAI
+            AllMessageValues, {**message, "content": [self._transform_content_item(part) for part in parts]}
+        )
+
+    async def _async_user_message_for_openai(self, message: AllMessageValues) -> AllMessageValues:
+        parts: Final = self._user_content_parts(message)
+        if parts is None:
+            return message
+        return cast(  # cast-ok: the same user message with each content part rebuilt for OpenAI
+            AllMessageValues, {**message, "content": [await self._async_transform_content_item(part) for part in parts]}
+        )
 
     def transform_messages(
         self,
@@ -462,23 +494,15 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
         messages: list[AllMessageValues],
         tools: list["ChatCompletionToolParam"] | None = None,
     ) -> tuple[list[AllMessageValues], list["ChatCompletionToolParam"] | None]:
-        from litellm.litellm_core_utils.prompt_templates.common_utils import (
-            filter_value_from_dict,
-        )
         from litellm.types.llms.openai import ChatCompletionToolParam
 
-        for i, message in enumerate(messages):
-            messages[i] = cast(
-                AllMessageValues,
-                filter_value_from_dict(message, "cache_control"),
-            )
-        if tools is not None:
-            for i, tool in enumerate(tools):
-                tools[i] = cast(
-                    ChatCompletionToolParam,
-                    filter_value_from_dict(tool, "cache_control"),
-                )
-        return messages, tools
+        new_messages: Final = [cast(AllMessageValues, without_cache_control(message)) for message in messages]
+        new_tools: Final = (
+            [cast(ChatCompletionToolParam, without_cache_control(tool)) for tool in tools]
+            if tools is not None
+            else None
+        )
+        return new_messages, new_tools
 
     def translate_developer_role_to_system_role(
         self,
@@ -527,6 +551,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
         tools: Sequence["ChatCompletionToolParam"] | None,
         prompt_cache_options: object,
         litellm_params: Mapping[str, object],
+        request_params: Mapping[str, object],
     ) -> tuple[Sequence[AllMessageValues], Sequence["ChatCompletionToolParam"] | None]:
         """
         A deployment that reads OpenAI's ``prompt_cache_breakpoint`` (gpt-5.6+ on
@@ -543,10 +568,11 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
             model, custom_llm_provider, api_base, prompt_cache_options
         )
         if not reads_breakpoints and self._should_preserve_cache_control_for_endpoint(custom_llm_provider, api_base):
-            return messages, tools
-        drops_breakpoints: Final = not reads_breakpoints and (
-            litellm.drop_params is True or litellm_params.get("drop_params") is True
-        )
+            return (
+                _claude_messages_within_cache_control_cap(model, messages, tools, litellm_params, request_params),
+                tools,
+            )
+        drops_breakpoints: Final = not reads_breakpoints and _drops_params(litellm_params)
         renamed: Final = (
             [AnthropicCacheControlHook.cache_control_as_prompt_cache_breakpoints(message) for message in messages]
             if reads_breakpoints
@@ -561,7 +587,13 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
             if drops_breakpoints
             else list(tools)
         )
-        return self.remove_cache_control_flag_from_messages_and_tools(model=model, messages=renamed, tools=kept_tools)
+        sent_messages, sent_tools = self.remove_cache_control_flag_from_messages_and_tools(
+            model=model, messages=renamed, tools=kept_tools
+        )
+        return (
+            _claude_messages_within_cache_control_cap(model, sent_messages, sent_tools, litellm_params, request_params),
+            sent_tools,
+        )
 
     def _sanitized_tools_update_for_openai(
         self,
@@ -623,6 +655,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
             tools=optional_params.get("tools", []),
             prompt_cache_options=_prompt_cache_options(optional_params),
             litellm_params=litellm_params,
+            request_params=optional_params,
         )
         if tools is not None and len(tools) > 0:
             optional_params["tools"] = tools
@@ -654,6 +687,7 @@ class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
             tools=optional_params.get("tools", []),
             prompt_cache_options=_prompt_cache_options(optional_params),
             litellm_params=litellm_params,
+            request_params=optional_params,
         )
         if tools is not None and len(tools) > 0:
             optional_params["tools"] = tools

litellm/llms/openai/chat/o_series_transformation.py

@@ -153,12 +153,13 @@ class OpenAIOSeriesConfig(OpenAIGPTConfig):
         - role: system ==> translate to role 'user'
         """
         _supports_system_messages: Final = supports_system_messages(model, "openai")
-        for i, message in enumerate(messages):
+        transformed_messages: Final = list(messages)
+        for i, message in enumerate(transformed_messages):
             if message["role"] == "system" and not _supports_system_messages:
                 new_message = ChatCompletionUserMessage(content=message["content"], role="user")
-                messages[i] = new_message  # Replace the old message with the new one
+                transformed_messages[i] = new_message  # Replace the old message with the new one
 
         if is_async:
-            return super()._transform_messages(messages, model, is_async=cast(Literal[True], True))
+            return super()._transform_messages(transformed_messages, model, is_async=cast(Literal[True], True))
         else:
-            return super()._transform_messages(messages, model, is_async=cast(Literal[False], False))
+            return super()._transform_messages(transformed_messages, model, is_async=cast(Literal[False], False))