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chore(ci): promote internal staging to main - #33868

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Jul 18, 2026
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chore(ci): promote internal staging to main#33868
yuneng-berri merged 112 commits into
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litellm_internal_staging

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🆕 New Feature
🐛 Bug Fix
🧹 Refactoring
📖 Documentation
🚄 Infrastructure
✅ Test

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  • The tests check the right things, including the edge cases, and regressions in the respective real-world customer use-cases are not possible after this PR

tin-berri and others added 30 commits July 14, 2026 20:03
…single-server REST statuses

The aggregate MCP tools/list absorbed every per-server failure (upstream 401/403/5xx, timeouts,
network errors) into that server contributing zero tools, making a broken upstream indistinguishable
from a healthy server with no tools; the single-server REST list masked the same failures as
{"tools": [], "error": null, "message": "Successfully retrieved tools"}

Phase 2 of the MCP error-handling framework (LIT-4419): the manager fetch hops now raise a
classified MCPServerListError (faults/list_outcomes.py: total classifier, frozen outcome values)
instead of returning [], and each boundary applies the relay-vs-absorb policy matrix. The aggregate
keeps serving the healthy subset but records each server's outcome, surfaced on the tools/list
result _meta under litellm.ai/server_outcomes (the SDK passes a ListToolsResult through unwrapped)
and in spend logs as per_server_list_outcomes. Single-server REST requests relay truthful statuses
(unreachable/upstream_error 502, timeout 504, internal 500) and access denials now surface as real
403s instead of 200 unexpected_error bodies; upstream 403s surface through MCPUpstreamAuthError
like 401s. Outcome wire values carry category and status code only, never upstream prose

Resolves LIT-4421
…a healthy empty server

A cancelled fetch absorbed to [] made that server contribute ServerListOk(tool_count=0), the exact
healthy-but-empty impostor this change removes. Cancellation stays suppressed (the pre-existing
choice); it now carries an internal fault so outcomes stay truthful
…m listing failures

Both review findings shared one root cause: two exception-tree walkers with drifted semantics.
_extract_upstream_auth_failure walked the incidental __context__ chain before explicit causes, so a
403 raised while handling the causal 401 could shadow it; and the generic _get_tools_from_server arm
classified without extracting the challenge, so a nested 401 at client-build time surfaced without
the WWW-Authenticate the client needs. upstream_auth_challenge and raise_classified_list_failure in
faults/list_outcomes.py are now the single traversal and the single choice-point; both fetch arms
and _extract_upstream_auth_failure (also serving tool calls and the connect-time probe) delegate to
them, with dcr_bridge challenge suppression as a parameter so it holds on every path. The stale
_fetch_tools_with_timeout docstring describing the pre-change 403 absorb is rewritten to the actual
contract: 403 relays with its own status, an upstream-sent challenge relays verbatim per RFC 6750
insufficient_scope, and a challenge is only ever fabricated for a challenge-less 401
…che_control injection

Anthropic only caches a prompt when the request carries explicit cache_control
breakpoints, unlike OpenAI where prompt caching is automatic and needs no
configuration. Today litellm can inject those breakpoints server-side, but only
when an admin hand-writes cache_control_injection_points into a model's
litellm_params (or router_settings.default_litellm_params). Clients such as
Claude Code and Claude Desktop never set cache_control themselves, and the
admin recipe is easy to miss, so Anthropic traffic through the proxy silently
pays full price on every repeated prefix.

This adds an opt-in litellm_settings flag, enable_anthropic_prompt_caching. When
it is on and the request has no injection points configured and no
client-supplied cache_control, litellm synthesizes a default pair of breakpoints
(the system prompt and the trailing turn) so the stable prefix is cached while
the breakpoint advances with the conversation. It is wired into both surfaces:
/chat/completions seeds the points before the existing prompt-management gate, and
/v1/messages resolves them in maybe_inject_cache_control, so the existing
AnthropicCacheControlHook applies them unchanged and keeps its four-block cap and
its refusal to overwrite client breakpoints.

The default is off, so no existing deployment changes behavior. Injection is
gated to providers that actually consume cache_control markers (anthropic and
bedrock) and to models the cost map flags as supporting prompt caching; note that
supports_prompt_caching alone is not a sufficient gate, since OpenAI, Azure and
Gemini models report it as well but never take cache_control markers. The default
ttl is Anthropic's 5 minute ephemeral cache, with an optional
anthropic_prompt_caching_ttl of "5m" or "1h"; ttl is also added to
ChatCompletionCachedContent, which the bedrock and anthropic transforms already
read at runtime but the type never declared

Resolves LIT-4478
Both enable_anthropic_prompt_caching and anthropic_prompt_caching_ttl are
now read from LITELLM_ENABLE_ANTHROPIC_PROMPT_CACHING and
LITELLM_ANTHROPIC_PROMPT_CACHING_TTL at import, so the flag can be turned on
without a config file. An unsupported ttl falls back to the provider default
rather than reaching the provider verbatim
_request_has_cache_control only looked at messages and system, so a client that
marks cache_control on tools alone did not suppress auto-injection. Tool
breakpoints count toward the provider's four-block limit, so three of them plus
the two injected here is five, which Anthropic rejects. Thread tools through
both entry points and treat a client-marked tool as the stand-down signal it
already is for messages and system.
… 1024

MINIMUM_PROMPT_CACHE_TOKEN_COUNT was a flat 1024 described as "minimum number of
tokens to cache a prompt by Anthropic". Anthropic's minimum cacheable prefix is
per-model and ranges from 512 to 4096, and it can differ per platform for the same
model, so one constant is wrong in both directions

is_prompt_caching_valid_prompt gates PromptCachingDeploymentCheck, which is what
optional_pre_call_checks: ["prompt_caching"] turns on. When it believes a prompt is
cacheable, async_filter_deployments pins routing to whichever deployment previously
served that prefix. For a prompt between 1024 and 4096 tokens on Opus 4.6, Opus 4.5
or Haiku 4.5, litellm judged it cacheable and constrained routing while the provider
never cached it, so the pin cost load balancing for nothing. In the other direction
Fable 5 caches from 512 tokens, so a 512 to 1024 token prefix was refused a pin it
had earned

The minimum now resolves from prompt_cache_min_tokens in the model cost map, which
keeps it current with new models and lets the Bedrock override for Fable 5 fall out
of the existing per-entry keys with no special casing. MINIMUM_PROMPT_CACHE_TOKEN_COUNT
stays as a global escape hatch when explicitly set, and as the fallback for models the
cost map has no entry for

async_filter_deployments only ever receives the model group alias, never a model name,
so it resolves the threshold from healthy_deployments instead. A group may mix models
with different minimums, so it takes the max: a prompt is only treated as cacheable when
it clears every member's minimum, because an unnecessary pin is the defect being fixed
while a missed pin only forfeits an optimization

Gemini context caching shares this gate and has the same defect; its entries are left
unset so they keep today's behavior, tracked separately in LIT-4525
get_model_info is lru_cached, so swapping litellm.model_cost is not enough on its
own. An earlier test that resolved these models against the remote map, which does
not carry prompt_cache_min_tokens yet, leaves cached entries without it, and the
stale hit resolves to the default. The assertions would then pass for the wrong
reason or fail depending on execution order

Clear on teardown as well, so entries these tests warm against the local map do not
leak into later tests, matching the fixture already used in test_utils.py

Also pin that a wildcard route resolves the underlying model's minimum. That works
only because pattern_match_deployments substitutes the real model name into
litellm_params before the deployment reaches the check; without the assertion that
claim is unpinned and the threshold would silently fall back to the default
…sage (#33533)

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
…ghest

The read gate cannot cause a wrong pin. A deployment is only pinned when the cache
already holds an entry for the prefix, and async_log_success_event writes entries
against the deployment's real model rather than the group alias, so a model that
will not cache a prefix never records one and there is nothing to pin it to

That makes this gate purely a cheap short-circuit deciding whether the cache lookup
is worth doing, so the threshold must be the lowest minimum in the group. Taking the
highest skipped the lookup for a prefix a lower-minimum member had genuinely cached,
losing a hit it earned, and protected against nothing. It also broke the Fable 5
direction this ticket is meant to fix: its real minimum is 512, so a group gate stuck
at a higher value would skip the lookup for a prefix Fable 5 had actually cached
…t) (#33634)

* test(e2e): harness fixes for long_context, complexity router, UI, and unit coverage

Point long_context_1m at 1M-capable models, harden complexity-smart-router
registration and spend-log assertions, fix key models dropdown selectors, and
add gateway/lifecycle/transport and claude_code unit tests

* test(e2e): harden remaining stage failures in harness

Register complexity-smart-router via create_model + callable probe, fix
create-key UI navigation race, retry management writes and budget ALB
502s, mark Vertex count_tokens N/A when unsupported, and tighten
tool_search model lists for Azure/Bedrock capability gaps

* test(e2e): drop claude_code and harness unit tests from this PR

Keep management, router, budget, and shared conftest harness fixes only

* test(e2e): restore E2E_RESULT pytest_runtest_makereport hook

Accidentally dropped in an earlier harness commit; Grafana status history
depends on these structured log lines

* test(e2e): drop management control-plane write retries

Transient 500 retries do not fix the underlying control plane failures

* test(e2e): skip stage-red claude_code cells; fix multi-window budget latency

Mark the twelve failing claude_code matrix cells skip until product/config
lands. Multi-window budget polls gpt-5.5 with max_tokens=1 instead of
Claude so the reset wait stays under ALB target idle timeout rather than
masking awselb 502s

* test(e2e): require exactly one LLM-tier spend row for complexity router

Keep alias membership for compose vs stage model names, but assert
len(served) == 1 so a leaked classifier sub-call cannot pass. Also pin
LIT-4521 skip and align LIT-4522/23/24 skip reasons

* test(e2e): harden router callable probe and multi-window budget exhaustion

_router_is_callable treated any non-success chat whose body lacked "Invalid
model name" as callable, so an unpropagated probe key (401), a generic 502, or
a connection reset let the session proceed and hit real "Invalid model name"
failures inside the tests. Require a Success outcome instead; the reload-race
400 and every infra/auth error now correctly read as not-callable.

The multi-window budget test capped the tight window at 3e-6, which gpt-5.5
exhausts on the first call but a cheaper CHEAP_OPENAI_MODEL might not within the
20-call loop, turning a reset test into a spurious "window never enforced"
failure. Drop the tight cap to 1e-9 so the first billed call exhausts it
regardless of model price; the roomy 1m window stays at 1.0 and never blocks.

* test(e2e): use a tradeoff-decision prompt for the complexity router classifier

"Is P equal to NP?" reads to the LLM classifier as a short yes/no question, so
gpt-5.5 classified it SIMPLE and the request routed to the openai backend, which
made the test fail even though the classifier was running. The tier definitions
key on what the request demands, not how hard the answer is, and a short direct
question maps to SIMPLE regardless of subject.

Swap in "Should I pay off my mortgage early or invest the extra money instead?".
It carries none of the heuristic scorer's reasoning/technical/code keywords and
stays short, so heuristic scoring still lands SIMPLE (openai), but the LLM reads
it as a decision that has to weigh tradeoffs and lands it above SIMPLE, which the
config routes to anthropic. Any non-SIMPLE tier serves anthropic, so the classifier
only has to avoid SIMPLE for the test to distinguish a real classifier run from the
heuristic fallback.
fix(router): resolve prompt cache minimum per model instead of a flat 1024
Team-scoped wildcard deployments like openai/* are indexed separately from
global router models, so vector store file requests failed with api_key=None
when a team also had other yaml/db models. Pass team_id into credential
lookup and consult team model indexes and pattern routers.

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The consolidation regressed the pre-existing walker semantics: _extract_upstream_auth_failure used
to keep scanning until it found a 401/403, while the consolidated helper took the first response of
any status and then tested it, so a causal 401 sitting behind an unrelated 5xx (retry attempts,
multi-stream task groups) was misclassified as upstream_error and its challenge lost on the listing,
tool-call, and probe paths. The traversal is now an iterator in deliberate order and each consumer
applies its predicate over the stream: the auth scan takes the first 401/403 even behind non-auth
responses, generic classification takes the first response, and classify_list_exception derives its
auth arm from the same scan so the carrier choice and the classification can never disagree
…l_name (#33691)

* fix(router): tag-aware pre-routing strategy selection for shared model_name

Complexity/auto/adaptive/quality router registries were keyed by model_name
alone, so a second deployment sharing a model_name but carrying different tags
was rejected and every request used the first config. This made tag-based
routing to distinct provider configs behind one alias impossible, surfacing as
401 'Not allowed to access model due to tags configuration' for the second tag.

Each registry now holds a list of tag-scoped strategies and async_pre_routing_hook
selects the entry whose tags match the request before classification, falling
back to a default-tagged then first-registered entry. A repeat of the same
(model_name, tags) pair is still rejected.

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* test(router): cover tag-scoped pre-routing strategy registry helpers

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* chore: re-trigger CI

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

---------

Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
…auge (#32441)

* fix(proxy): enforce max_parallel_requests as a per-slot concurrency gauge

The v3 rate limiter tracked max_parallel_requests with the same
sliding-window machinery as RPM/TPM. A concurrency gauge cannot live on a
windowed counter: every window roll reset the counter to 1 while requests
were still in flight, the completion decrements for those forgotten
requests then drove the counter negative, and rejected requests left
stranded increments that nothing released. Under sustained load a key with
max_parallel_requests=5 let backend concurrency climb to the full client
concurrency (observed 60 on a live proxy) while the proxy kept returning
429s for everyone else

Replace the windowed counter with a per-slot registry (Redis sorted set of
slot ids scored by acquire time, with an asyncio-locked in-memory fallback):
admission atomically prunes expired slots and registers a new slot id only
when in_flight + 1 <= limit, so rejected requests never occupy a slot;
success, failure, and client-disconnect paths release exactly the slot id
this request acquired (stashed in the request metadata channels), so a
release without a matching acquire or a double-fired callback can never
free another request's slot; and a slot leaked by a crashed worker is
pruned individually after its TTL even under continuous traffic

Resolves LIT-4259
Fixes #16011

* fix(proxy): release every acquired gauge and respect mirrored counts in the in-memory fallback

Address review findings on the slot-registry gauge: the acquisition stash
now carries the gauge counter keys alongside the slot id, so the release
paths free the slot from every gauge it was registered under instead of
hardcoding the api_key scope, and the disconnect release keys off the
stashed acquisition instead of the key object's current
max_parallel_requests configuration (which can change mid-request). The
in-memory fallback now treats a cached integer (the count mirrored from
the last successful Redis script call) as real occupancy, carrying it
forward as a floored counter during a Redis outage instead of restarting
from an empty registry

* fix(proxy): release the parallel slot on proxy-level rejections

async_post_call_failure_hook is the only callback that fires when a
downstream hook (guardrail, budget check) rejects a request after the rate
limiter's pre-call hook acquired a slot; async_log_failure_event is a
completion-level callback and never runs for proxy-side rejections.
Release the stashed acquisition at the top of the hook, before the TPM
reservation guard, so those slots do not linger for the full slot TTL and
wedge the key at its limit under moderate rejection rates. Clearing the
acquisition marker keeps the release idempotent when a later failure
callback runs in the same flow

* test(proxy): cover success release, read-only count, Redis release mirror, and TPM rejection release

Four behaviors of the slot-registry gauge had no direct test: a successful
completion releasing exactly its acquired slot, read_only callers counting
in-flight slots through the count script (and degrading to the local
mirror when the script fails) without acquiring, the Redis release script
mirroring returned counts into the local cache, and the TPM reservation
rejection releasing the already-acquired slot before raising

* style(proxy): use builtin generics and union syntax in new rate limiter annotations

The slot-gauge code added Tuple/List/Dict and Optional[...] annotations, pushing
the UP006 and UP045 strict-rule totals past their ceilings in ruff-strict-budget.json.
Convert only the annotations this branch introduces to builtin generics and PEP 604
unions, leaving the rest of the module untouched.
…l on auth-enforced pass-through routes (#33710)

* fix(proxy): stop treating upstream model body field as a LiteLLM model on auth-enforced pass-through routes

An auth: true user-defined pass-through endpoint runs full virtual-key auth, and get_model_from_request unconditionally extracted the request body model field, so key/team/user/project model allowlist checks rejected requests whose model only exists upstream (key_model_access_denied), even when the key was explicitly granted the route via allowed_passthrough_routes.

The pass-through route registry moves to a leaf module (route_registry.py) that the auth layer can import without re-entering the pass_through_endpoints -> user_api_key_auth -> auth_utils import cycle. get_model_from_request now returns None for routes registered as user-defined pass-through endpoints (exact and subpath), which skips model allowlist and per-model budget enforcement on those routes while key auth, allowed_passthrough_routes, and spend/budget checks stay intact. Built-in provider passthrough routes (/vertex_ai, /gemini, ...) keep model enforcement.

Resolves LIT-4299

* fix(proxy): key pass-through model-access skip on the dispatched endpoint, not the request path

Addresses a model-authorization bypass: the first version decided whether to skip
model-allowlist extraction by matching the request path against the pass-through
route registry. That ignored the HTTP method and, more importantly, whether the
request was actually dispatched to a pass-through handler. A custom pass-through
whose path collides with a built-in route (e.g. /v1/chat/completions, or an
include_subpath prefix of one) still writes a registry entry even though FastAPI
serves the built-in handler, so a normal request to that route had its model checks
skipped and could reach a model outside the key/team/user/project allowlist.

The skip is now keyed off the FastAPI-resolved endpoint. create_pass_through_route
tags its handler with LITELLM_PASS_THROUGH_ENDPOINT_MARKER, and get_model_from_request
returns None only when request.scope["endpoint"] carries that marker. Because routing
runs before auth dependencies, this reflects the handler that actually serves the
request: on a collision the built-in handler is dispatched and carries no marker, so
model enforcement stays on. This also removes the need for the separate route_registry
module, so that extraction is reverted.

Regression tests cover a pass-through-dispatched request (model suppressed), a
built-in-dispatched request on the same path (model still enforced), and the no-request
budget path.

Resolves LIT-4299
…rants

fix(mcp): expand toolset grants in shared permission primitives so tools/call honors them
…omes

Append-append conflict at the end of test_mcp_server.py between this branch's aggregate-outcome
tests and the mode-aware preemptive-401 tests from staging; both kept
* feat(complexity-router): user-triggered escalation keywords

Add an escalation_keywords config option to the complexity router so a user
can force a bump to the next-higher complexity tier by including a phrase in
their message (a stronger model, but not one they get to choose). Defaults to
['LITELLM ESCALATE'] when unset, case-sensitive so it only fires on the
deliberate shouted form; admins can override the list or set [] to disable.

Escalation applies across every routing path: heuristic/LLM classification,
literal and semantic keyword_tier_rules overrides, adaptive routing, and
session affinity (where it bumps relative to the pinned model and persists the
higher tier for the rest of the session). Capped at the highest configured
tier and skips unconfigured intermediate tiers.

Expose it in the Auto-Router v2 UI as an Escalation Keywords field wired into
the complexity_router_config payload.

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(complexity-router): validate escalation keywords and pin at tier ceiling

Strip blank/whitespace escalation keywords so an empty phrase can't match every message and escalate all traffic. Keep the exact pinned model when a session escalates at the highest configured tier instead of randomly hopping to a peer in a multi-model pool.

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

---------

Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
yassin-berriai and others added 19 commits July 18, 2026 18:41
…coped fixture (#33750)

The shared proxy wrapper in tests/e2e/e2e_gateway.py was misnamed: Gateway is
not a gateway server, it is the client every suite uses to talk to the proxy
(keys, models, chat/embed/ocr, spend read-backs, poll helpers). Rename the
module to proxy_client.py and the class to ProxyClient, with build_gateway
becoming build_proxy_client and the GatewayProvider protocol becoming
ProxyClientProvider. The .gateway attribute suites held is now .proxy. Only
identifiers changed; prose and string literals that use the word gateway for the
proxy-server concept were left alone.

Each suite previously built its own instance through a per-suite build_client()
that called build_gateway() inside, duplicating the proxy wiring across suites.
There is now one session-scoped proxy fixture in tests/e2e/conftest.py; every
suite's client fixture depends on it and injects it, so the wiring lives in one
place. claude_code keeps building its own client directly since it has its own
harness and does not use the shared fixtures.

Behavior is unchanged: shared transport, data-plane/control-plane split routing,
poll budget, typed request/response models, and resource cleanup all go through
the same object.
…ust:true (#33616)

* feat(messages): route Azure Anthropic /messages through Rust behind rust:true

Adds an opt-in Rust path for non-streaming Azure Anthropic Messages. A
deployment sets rust: true in litellm_params to route litellm.messages()
and the proxy /v1/messages endpoint through the native Rust bridge; a
missing flag or rust: false keeps the existing Python path, and non-Azure
providers, streaming, an unavailable bridge, or a None result all fall
back to Python. Rust-backed responses carry an x-litellm-rust: true
response header so callers can see which path served the request.

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* test(docs): exclude LITELLM_USE_RUST_MESSAGES rollout flag from env-doc check

Mirrors the existing LITELLM_USE_RUST_OCR entry; the flag is an internal
rollout toggle that is intentionally not in the public environment settings
docs yet.

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* fix(rust_bridge): isolate OCR enable flag and drop dead messages global toggle

use_litellm_rust only mutates the OCR enabled flag when configuring OCR (or
called with no bridge kwargs, preserving the legacy contract), so configuring
only the messages bridge no longer flips OCR state.

Remove the vestigial global enabled/env state from the messages bridge. Routing
is controlled per deployment by rust:true in the shared handler gate, so the
messages module never consulted the global toggle; drop it rather than leave a
no-op switch.

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* refactor(rust/messages): split Anthropic config into its own provider file and type the request/response contract

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* feat(messages): route eligible Azure Anthropic streaming through Rust via buffered fake-stream

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* fix(messages): fold system-role messages for Azure Anthropic and fall back to Python on Rust bridge errors

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* fix(rust_bridge): use Python::attach for amessages after pyo3 bump

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* test(proxy): mock get_configured_token_limits in model_info tests

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* ci: run rust_bridge unit tests in misc shard

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* Revert "ci: run rust_bridge unit tests in misc shard"

This reverts commit c86d861.

* test(anthropic): move rust messages bridge tests into misc-shard dir

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
CodSpeed benchmarks the SDK with no IO, so it can't catch regressions that
only appear under real concurrent load through the full proxy stack (auth,
routing, logging, spend, Postgres, Redis). This adds a Locust load test under
tests/e2e/load that drives concurrent POST /chat/completions traffic against a
mock deployment (litellm_params.mock_response), so the measured throughput
reflects proxy overhead rather than a provider's latency, and asserts an
aggregate RPS SLO with a failure-ratio guard. The test is marked load and the
parent conftest sorts load-marked items last so it never perturbs
latency-sensitive suites. Covers reliability.perf.throughput.under_slo.
fix(proxy): resolve team wildcard credentials for vector store files
…ds (#33760)

* refactor(e2e): fold claude_code HTTP probes onto shared Gateway methods

Migrate tests/e2e/claude_code/http_probe.py off its own httpx client onto the
shared transport, and promote count_tokens and native anthropic messages to
first-class Gateway methods (Gateway.count_tokens / Gateway.messages) with typed
request/response models in the shared models.py so other suites reuse them.

The probes now take an injected Gateway and issue their request through the
shared count_tokens/messages methods, reusing the split control/data-plane
routing, timeout, and typed Result handling the rest of tests/e2e uses. The wire
shape is preserved: the pydantic bodies serialize byte-for-byte to what the old
httpx probes sent, and the anthropic-version header is carried by a small
AnthropicHeaders model. httpx is gone from the module.

* test(e2e): drop unit-level probe harness test

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): harden stage flakes for batches, UI, and MCP

Unique batch model names avoid load-balancing onto stale azure-batch
deployments that still pointed at the retired gpt-4.1-mini-batch, which
only the managed/unified path was hitting. Retry batch retrieve on 500
and /ui/api-keys navigation on ERR_ABORTED. Skip the MCP key-access suite
when the compose-only mcp-upstream is unreachable on stage k8s

* test(e2e): cover Datadog remote MCP via search_datadog_logs

Register the regional Datadog MCP endpoint with DD-API-KEY /
DD-APPLICATION-KEY static headers (CI-safe header auth; browser OAuth is
not headless-automatable). Seed a chat completion marked e2e-datadog-mcp-*,
assert the proxy shipped it, list tools, call search_datadog_logs for the
marker, and delete the server on teardown. Math-upstream key-access tests
only skip when that compose service is unreachable

* test(e2e): drop compose math MCP upstream; use Datadog only

Key-access denial and happy-path MCP e2e both register the real regional
Datadog remote MCP server with DD-API-KEY / DD-APPLICATION-KEY headers.
Remove the mcp-upstream compose service and FastMCP add/multiply fixture

* docs(e2e): require real Datadog MCP for all mcp suite tests

Document that tests/e2e/mcp must register via datadog_mcp helpers against
mcp.<site>/v1/mcp and must not introduce compose or fake MCP upstreams

* chore: restore mcp_e2e_upstream_server.py

Keep the FastMCP fixture file; e2e no longer wires it in compose, but the
module itself is not part of the Datadog-only cleanup

* fix(e2e): load tests/e2e/.env and fix datadog_reader importlib load

pytest on the host never inherited compose env_file keys, so DD_API_KEY
stayed empty. load_dotenv tests/e2e/.env in e2e_config. Register the
dynamically loaded datadog_reader module in sys.modules so dataclasses
do not crash under Python 3.12

* test(e2e/batches): harden azure/vertex unified lifecycle flakes

Put the provider deployment name in every JSONL body so Azure does not
depend on a perfect model rewrite. Retry create/retrieve/cancel on
transient statuses with backoff. Drop cancel assertions for azure and
vertex (registry only has a shared basic cell; create+retrieve prove
routing, cancel stays best-effort cleanup)

* test(e2e/ui): treat api-keys shell as success after SPA ERR_ABORTED

Post-login client redirects abort the first /ui/api-keys/ goto on stage.
Wait off /ui/login after cookie set, then accept the page once Create New
Key is visible even if goto raised ERR_ABORTED

* test(e2e): drop flaky key models dropdown Playwright suite

API management e2e already covers key generate/update persistence. The
UI Models-dropdown sentinel cases only added SPA ERR_ABORTED noise and
no unique product signal. Remove the suite and unused browser fixtures
* test(e2e): harden stage flakes for batches, UI, and MCP

Unique batch model names avoid load-balancing onto stale azure-batch
deployments that still pointed at the retired gpt-4.1-mini-batch, which
only the managed/unified path was hitting. Retry batch retrieve on 500
and /ui/api-keys navigation on ERR_ABORTED. Skip the MCP key-access suite
when the compose-only mcp-upstream is unreachable on stage k8s

* test(e2e): cover Datadog remote MCP via search_datadog_logs

Register the regional Datadog MCP endpoint with DD-API-KEY /
DD-APPLICATION-KEY static headers (CI-safe header auth; browser OAuth is
not headless-automatable). Seed a chat completion marked e2e-datadog-mcp-*,
assert the proxy shipped it, list tools, call search_datadog_logs for the
marker, and delete the server on teardown. Math-upstream key-access tests
only skip when that compose service is unreachable

* test(e2e): drop compose math MCP upstream; use Datadog only

Key-access denial and happy-path MCP e2e both register the real regional
Datadog remote MCP server with DD-API-KEY / DD-APPLICATION-KEY headers.
Remove the mcp-upstream compose service and FastMCP add/multiply fixture

* docs(e2e): require real Datadog MCP for all mcp suite tests

Document that tests/e2e/mcp must register via datadog_mcp helpers against
mcp.<site>/v1/mcp and must not introduce compose or fake MCP upstreams

* chore: restore mcp_e2e_upstream_server.py

Keep the FastMCP fixture file; e2e no longer wires it in compose, but the
module itself is not part of the Datadog-only cleanup

* fix(e2e): load tests/e2e/.env and fix datadog_reader importlib load

pytest on the host never inherited compose env_file keys, so DD_API_KEY
stayed empty. load_dotenv tests/e2e/.env in e2e_config. Register the
dynamically loaded datadog_reader module in sys.modules so dataclasses
do not crash under Python 3.12

* test(e2e/batches): harden azure/vertex unified lifecycle flakes

Put the provider deployment name in every JSONL body so Azure does not
depend on a perfect model rewrite. Retry create/retrieve/cancel on
transient statuses with backoff. Drop cancel assertions for azure and
vertex (registry only has a shared basic cell; create+retrieve prove
routing, cancel stays best-effort cleanup)

* test(e2e/ui): treat api-keys shell as success after SPA ERR_ABORTED

Post-login client redirects abort the first /ui/api-keys/ goto on stage.
Wait off /ui/login after cookie set, then accept the page once Create New
Key is visible even if goto raised ERR_ABORTED

* test(e2e): drop flaky key models dropdown Playwright suite

API management e2e already covers key generate/update persistence. The
UI Models-dropdown sentinel cases only added SPA ERR_ABORTED noise and
no unique product signal. Remove the suite and unused browser fixtures

* test(e2e/batches): fail clearly when OPENAI/AZURE provider is missing

Replace bare next() over PROVIDERS with _model_for that raises ValueError
naming the missing provider and the known list, instead of StopIteration

* fix(e2e): migrate load suite from e2e_gateway to ProxyClient

Stage collection failed with ModuleNotFoundError: e2e_gateway after the
Gateway rename. Wire load/conftest and LoadClient to the shared
ProxyClient fixture like every other suite

* fix(e2e): drop duplicate datadog_mcp_url and CLAUDE section after merge
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
)

* test(e2e): cover /v1/responses openai basic nonstream and stream

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* test(e2e): assert responses stream ends on final raw completed event

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* test(e2e): centralize responses stream event models

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
* test(e2e): cover /v1/responses openai basic nonstream and stream

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* test(e2e): assert responses stream ends on final raw completed event

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* test(e2e): cover /v1/responses openai cost_logged and tool_use

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

* test(e2e): centralize responses stream event models

Co-Authored-By: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>

---------

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
)

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
…dge (#33753)

Add a live spend-tracking e2e that drives a streaming anthropic-format
/v1/messages request through litellm's anthropic-messages -> OpenAI Responses
adapter and asserts the consumed stream writes exactly one SpendLogs row with
nonzero cost and token counts, attributed to the calling key under
custom_llm_provider openai and the /v1/messages call_type.

The deployment is a Responses-only OpenAI model (gpt-5.3-codex), so a served,
costed row proves the Responses path was taken; the chat-completions bridge
would have failed at OpenAI on an endpoint the model does not expose. Adds a
streaming /v1/messages method to the shared Gateway and the suite client, the
model to the inline compose config and driver-model registration, a coverage
registry row (quota_management.spend_tracking.messages_bridge.logs_cost), and
the matching variant vocab entry. The _summarize spend-row detail also gains
call_type and custom_llm_provider so a failed assertion prints the fields it
asserts on.

Resolves LIT-4546
…migrations work for any uid offline (#33853)

* fix(docker): bake prisma CLI and engines at a fixed path so fresh-DB migrations work for any uid offline

The runtime image shipped the prisma CLI and engines under /root/.cache, the
default HOME-derived prisma-python cache location. Any deployment whose
runtime HOME is not /root (kubernetes runAsUser, docker --user, HOME
overrides) missed that cache on a fresh database, fell back to a nodeenv
Node download that crashes on Wolfi (libatomic.so.1), and started the proxy
with zero tables while every DB-backed endpoint returned 500

The bake now lives at /opt/prisma, a path no HOME resolution or cache
volume mount can shadow. The builder records the engine paths there at
generate time, and the runtime stage pins PRISMA_BINARY_CACHE_DIR,
PRISMA_CLI_PATH, PRISMA_CLI_QUERY_ENGINE_TYPE=binary and
PRISMA_OFFLINE_MODE so both litellm-proxy-extras and prisma-python resolve
the baked CLI and engines directly. prisma migrate deploy on a fresh
database now needs no npm and no network access for any runtime uid,
including readOnlyRootFilesystem deployments

Verified against live containers: fresh and existing databases as root,
uid 12345, HOME overridden, on an internal-only docker network, and with
a read-only root filesystem all migrate and serve /team/new successfully

Fixes #33650, #24554

* chore(docker): fail the image build if the baked prisma CLI layout drifts

Asserts the baked CLI shim is executable and its entrypoint exists in the
runtime stage after the COPY and chmod, so a layout change in a future
prisma-python release breaks the image build loudly instead of silently
degrading the migration path at container startup
)

* feat(chat-ui): add personal Logs view scoped to the current user

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(chat-ui): show request payload from proxy_server_request in logs detail

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

* fix(chat-ui): address logs panel review feedback (stable detail key, error state)

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

---------

Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
…les (#33867)

Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
…/models (#33864)

A deployment whose model_info carried a non-numeric max_input_tokens or
max_output_tokens (for example "128,000" or an empty string) made the
bare int() in get_configured_token_limits raise inside the per-model
/v1/models loop, so one misconfigured deployment turned the entire
listing into a 500. Coerce each configured limit safely and treat
malformed values as absent, matching the graceful degradation the
listing had before the cost-map switch
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Ishaan Jaffer <155045088+ishaan-berri@users.noreply.github.com>
@greptile-apps

greptile-apps Bot commented Jul 18, 2026

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Too many files changed for review. (1099 files found, 100 file limit)

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CLAassistant commented Jul 18, 2026

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CLA assistant check
Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.
1 out of 3 committers have signed the CLA.

✅ yuneng-berri
❌ devin-ai-integration[bot]
❌ yassin-berriai
You have signed the CLA already but the status is still pending? Let us recheck it.

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}
@codspeed-hq

codspeed-hq Bot commented Jul 18, 2026

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Merging this PR will not alter performance

✅ 31 untouched benchmarks


Comparing litellm_internal_staging (366ec6f) with main (dc9297d)

Open in CodSpeed

@yuneng-berri
yuneng-berri merged commit 5d4c4d0 into main Jul 18, 2026
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