* refactor(e2e): replace bespoke result reporter with standard JUnit report
tests/e2e/e2e_result_reporter.py hand-rolled a per-test logfmt emitter that
reimplemented outcome mapping, logfmt escaping, and node-id parsing to print one
E2E_RESULT line per finished test. Outcome, duration, and node id are all things
a standard pytest reporter already produces, so the only genuinely custom data is
the covers marker ids and the normalized package label
Delete the module and emit a standard pytest JUnit XML report (--junitxml)
instead, carrying the two custom signals as user_properties (JUnit <property>
entries) attached at collection time in pytest_collection_modifyitems, so they
land on every test on every outcome including skips and setup errors. The small
package/covers extraction lives in junit_properties.py and is unit tested plus
checked end to end against a real JUnit artifact in test_junit_properties.py
Shipping the JUnit report to Loki is a thin infra-side transform, documented in
grafana/status_history_panels.md
* chore(e2e): remove grafana status history panels doc and junit properties e2e 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>
Outcome keys in the tools/list _meta, the spend-log outcome and count maps, and the REST error
messages now all use get_server_prefix (alias, or the short prefix when that mode is enabled), the
same naming the caller already sees on tool names. Keying them by canonical server_name let an
authenticated caller enumerate internal server names and their health or auth state that the alias
and short-prefix schemes deliberately hide (Veria finding). One helper decides the key for every
surface; exception messages reaching the multi-server REST error list are mapped to their fault tag
with the display prefix instead of relaying exception text carrying canonical names. Server-side
logs keep the real names
The e2e docs claimed `e2e`-marked tests skip when no proxy answers the
liveness probe, but the harness has always hard-failed: conftest.py's
pytest_runtest_setup calls pytest.fail, its module docstring states
"hard failures only ... never skip", and logging/conftest.py forbids
skipping outright. Align the docs to the code so the single most
important contract reads the same everywhere; a dead proxy turns a run
red instead of being silently skipped and mistaken for a pass. The
per-suite conftest docstrings that described the shared hook as a
"proxy liveness skip" are corrected to "liveness gate" for the same
reason.
Also scope the no-unit-tests hard rule to what it means: never
substitute a unit test for e2e feature coverage, while explicitly
allowing tests that cover the harness itself (e.g.
coverage_registry/test_collector.py), which carry no e2e marker and
run whether or not a proxy is up.
No product code and no harness logic changed.
Resolves LIT-4554
tests/e2e/grafana/status_history_panels.md was prose describing Loki/Grafana
status-history panels and LogQL queries. Nothing in the tree imports, reads, or
links to it; the e2e suite only emits the E2E_RESULT lines those panels consume
(tests/e2e/conftest.py, tests/e2e/e2e_result_reporter.py) and never depends on
this file. Dashboards drift when versioned as prose in the repo, so remove it;
if we want them versioned it should be dashboard-as-code in the observability
repo, not markdown here.
* fix(proxy): bill partial streamed spend when the client disconnects mid-stream
* fix(router): guard FallbackStreamWrapper chunks alias for non-CSW streams
* fix(proxy): await disconnect billing dispatch instead of unrooted create_task
* fix(proxy): make disconnect slot release single-owner to avoid double release
* fix(proxy): use union syntax for disconnect cleanup params (UP045 budget)
Conflict in _list_mcp_tools: staging (#33612) moved toolset-grant expansion into the shared
permission primitives and removed the _merge_toolset_permissions call; resolution applies that
removal to this branch's AggregateToolListing structure
* test(e2e): assert bare-key budget refusal is 429 and /key/info spend reaches the cap
* test(e2e): keep the bare-key budget assertion to the 429 refusal shape
* test(e2e): assert a team's max_budget blocks every key on the team
* test(e2e): focus the team budget case on the 429 blocking behavior
* test(e2e): assert an org budget block is a 429 naming the organization
Rather than mixing the flag and its ttl into the generic General settings table
(which also surfaced the confusing Not Set / In Config / In DB provenance badges),
give prompt caching a dedicated tab with a purpose-built toggle and ttl dropdown.
Each registry field gains an optional tab, surfaced as ConfigList.field_tab, so
the General tab renders the ungrouped fields and the caching fields render on
their own tab. The update, persist and reset endpoints are unchanged.
_should_auto_execute_tools returned True as soon as any MCP reference set
require_approval="never", so a request that mixed a "never" reference with an
"always" or "manual" one auto-executed every tool call the model produced,
including the approval-gated ones. A prompt could name the approval-required
tool and have it run with no approval.
Make the gate fail closed: auto-execute only when every reference opts in with
"never". A single approval-required reference (including the object form or an
unset value) returns the model's tool calls to the caller instead of running
them, so an approval-gated tool can never be auto-invoked. This is the shared
decision behind /chat/completions, /responses, the streaming iterator and the
new /v1/messages path, so all four fail closed from one change. The common case,
every reference "never", is unchanged.
The alternative, executing the "never" calls and returning only the
approval-required ones, needs partial execution that the Anthropic tool loop
cannot express without fabricating tool_result blocks for the calls it withheld,
so the whole-request fail-closed gate is the safe minimum. A future change can
add per-call partial execution if a caller needs it.
Test covers the mixed and manual cases; reverting to "any never" fails it.
* test(e2e): assert bare-key budget refusal is 429 and /key/info spend reaches the cap
* test(e2e): keep the bare-key budget assertion to the 429 refusal shape
* test(e2e): assert a team's max_budget blocks every key on the team
* test(e2e): focus the team budget case on the 429 blocking behavior
Register enable_anthropic_prompt_caching and anthropic_prompt_caching_ttl on the
General Settings table so caching can be turned on without hand-writing config.
The registry could not express either field: validation was hardcoded to a float in
(0, 1], reset set every field to None (not a bool for a boolean flag), and the listing
reported any non-None value as 'In Config', which a False default would always trip.
Validation now dispatches on the declared type and reset restores each field's own
default. ConfigList carries field_options so the table can render a Select for enums
instead of no editor at all.
The gpt-realtime family (OpenAI and Azure) only serves /v1/realtime and is rejected by /v1/chat/completions with "This is not a chat model", but the cost map tagged them mode=chat. Retag them mode=realtime (a value already used by gemini-live and handled by the health-check realtime handler) and add realtime to the ModelInfoBase mode literal.
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Two review findings, both a chat-vs-messages divergence.
transform_mcp_tool_to_anthropic_tool sent the MCP inputSchema to Anthropic almost
as-is, while the chat path (_map_tool_helper) coerces the type to object, inlines
legacy definitions with unpack_legacy_defs, and allow-lists keys to
AnthropicInputSchema. So a tool whose schema carried $schema, legacy definitions
or oneOf worked on /chat/completions and 400d on /v1/messages; a clean-schema
server hid it. Both paths now run the same sanitize_input_schema_for_anthropic,
extracted next to unpack_legacy_defs so they cannot drift again, and the chat
path is refactored onto it rather than keeping its own copy.
buildMcpToolBlocks percent-encoded the server and toolset names inside
litellm_proxy/mcp/... urls, but the gateway resolves the name with a raw
server_url.split("/")[-1] and never url-decodes, so a name with a space failed
lookup. The already-working chat path does not encode; the shared builder now
matches it.
Tests pin both: reverting the transform to the unfiltered schema fails, and
re-adding encodeURIComponent fails the builder test.
* 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>
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
* 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
* 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.
* 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>
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
* 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.
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
* fix(e2e): make the datadog read-back find what DataDog actually indexes
Live verification of the merged #33604 against real DataDog (us5) exposed
three read-back defects that the local-sink tests could never see; all
three fixes are verified against the real API:
- Marker search: DataDog consumes the shipped JSON message into the
event's attributes and leaves the indexed message EMPTY, so the
full-text '"marker"' query matched nothing and every test failed with
zero events. The query is now '*:*marker*', which scans all attributes
(the marker sits in messages.content); verified to return exactly the
event for the call.
- Rate limit: the Logs Search API budget is 2 requests per 10s org-wide
(x-ratelimit-name logs_public_search_api). Polling at POLL_INTERVAL=5s
sat exactly at the limit and the reader hard-failed on the first 429.
Searches now pace at DD_SEARCH_INTERVAL (10s default) and a 429 backs
off and retries up to 5 times; only non-429 failures stay hard fails.
- Envelope status: DataDog re-derives the indexed event status from the
parsed payload's status attribute ('success') and normalizes it to its
OK severity, so the assertion expects 'ok', not the shipped 'info'.
Live run: chat_completions and responses pass every assertion including
the exact response-cost cross-check; messages red-pins the LIT-4447
duplicate for real (one call -> two sync-sweep copies + one async batch
copy, same request id, confirmed in proxy debug logs). The duplicate is
race-dependent, so the pin flickers until #33589 lands.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(e2e): datadog log delivery for streamed chat, messages, and responses
Rewritten from the dd-sink version (original #33566) to judge delivery on
what real DataDog ingested, matching the merged #33604 conversion: the
dd_logs reader searches events back through the Logs Search API and the
assertions validate the indexed envelope (source:litellm tag, ok status)
and the StandardLoggingPayload fields under the event's attributes.
Each streamed test drives one STREAMED call per route, asserts the stream
actually streamed (event-stream content type, >0 chunks, no upstream error
event), then pins exactly one DataDog event whose payload records
stream=true, the aggregated token count, and a response_cost equal to the
/spend/logs row for the call - a stream's headers ship before its cost
exists, so the spend row is the cross-check anchor, and the spend row and
DataDog event must also agree on total_tokens.
Coverage registry: adds logging.datadog.stream.exports_metric exercised on
chat_completions, messages, and responses.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* Update test_datadog_log_e2e.py
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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
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
The /v1/messages handler resolved only the auth object and the trace id, so tool
listing and tool execution ran without the caller's MCP auth headers. That fails
quietly rather than loudly: the tool still executes, just with no credentials, so
every server behind interactive OAuth, a bearer token or per-user env vars returns
nothing while the model reports it has no access. Only a no-auth server looks
healthy, which is exactly what the first proof used.
Threading the missing arguments would have left the real problem in place. Each
gateway surface rebuilds the same context by hand (responses/main.py twice,
chat_completions_handler, mcp_streaming_iterator), which is why a new surface
drops fields; this adds a fifth that dropped six of eight. Resolve it once into a
frozen MCPRequestContext and have the handlers take that, so a field cannot be
forgotten at a call site. chat_completions_handler now uses it too, and the
resolver reads user_api_key_auth from both metadata keys because
LITELLM_METADATA_ROUTES carry it in litellm_metadata while chat uses metadata.
Also stop the loop when every tool call was skipped. tool_results is empty then,
and the tool_result message built from it has empty content, which Anthropic
rejects; the caller saw a 400 from mid-loop instead of the model's own answer.
Tests pin both: dropping the headers from either listing or execution fails, and
so does removing the empty-results guard.
MCP tool calling worked on /v1/chat/completions and /v1/responses but not on
/v1/messages. Those are the only two surfaces with an MCP gateway entry point,
so a litellm_proxy MCP reference reached Anthropic verbatim inside tools and the
API rejected the request with "Input tag 'mcp' found using 'type' does not match
any of the expected tags". The playground never surfaced this because it dropped
the reference before sending, and disabled the MCP selector for the endpoint.
Add the third entry point in anthropic_messages_handler, ahead of the provider
branch so it covers the native path and both bridges from one place. The gateway
expands the reference against the caller's own credentials and access control,
which is the whole point of routing it through litellm rather than handing the
url to the provider.
/v1/messages needs Anthropic's own tool shape, so transform_mcp_tool_to_anthropic_tool
joins the OpenAI chat and Responses transforms alongside it. The tool loop speaks
tool_use and tool_result rather than OpenAI tool_calls, and reuses the existing
FakeAnthropicMessagesStreamIterator to re-stream the result, the same pattern the
websearch interception already uses on this route. Argument extraction moves into
the shared extractor: an Anthropic tool_use block carries its arguments under
`input`, and reading only `arguments` failed silently, executing the tool with
every argument dropped.
On the frontend the request builder declared selectedMCPTools and never read it,
so no tools key was ever sent. Wire it through a shared block builder and add the
endpoint to MCP_SUPPORTED_ENDPOINTS, which is what greys the selector out.
Resolves LIT-4517
Resolves LIT-4518
_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.
* test(e2e): read datadog log delivery back from the real datadog api (#33604)
* test(e2e): read datadog log delivery back from the real datadog api
* test(e2e): compare datadog-read cost with math.isclose, not bit-equality
The response_cost now round-trips through DataDog's attribute indexing
pipeline, whose float serialization is not guaranteed to preserve the
exact bit pattern the proxy shipped. rel_tol=1e-9 (equal to 9 significant
digits) still fails on any real cost discrepancy while tolerating
representation drift. Addresses the Greptile P2 on this PR.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* test(e2e): widen the duplicate-settle window to 30s for real DataDog
Against the local sink one poll interval (5s) after the first hit was
enough to catch a same-call duplicate, because both events arrived in the
same flush batch. Against real DataDog, ingestion jitter can make one
call's two events searchable tens of seconds apart, so a 5s settle could
let the LIT-4447 duplicate slip past the exactly-one assertion. The reader
now keeps re-reading for DD_SETTLE_SECONDS (default 30s, env-overridable
via E2E_DD_SETTLE_SECONDS) after the first event appears, returning early
only when a duplicate is already visible - more waiting cannot clear it.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
* fix(e2e): point UI tests at dashboard service; register complexity router
Stage gateway 404s /ui; the Next.js dashboard is litellm-ui:3000. Drive
playwright against E2E_UI_BASE_URL and wait on login placeholders after
client render. Register complexity-smart-router via /model/new when the
proxy does not already list it so stage matches compose config
* docs(e2e): clarify E2E_UI_BASE_URL should be ALB when ingress splits UI
* docs(e2e): prefer single path-routing host for control plane and UI
CONTROL_PLANE and UI already default to PROXY_BASE_URL; clarify that
stage should set one ALB host rather than three endpoints
* fix(e2e): always capture complexity router model_id for teardown
Split /model/new from the data-plane wait so a propagation timeout still
deletes the control-plane registration (greptile orphan-model concern)
* fix(e2e): click exact Login button so SSO control is not matched
Playwright strict mode matched both Login and Login with SSO
* fix(router): score complexity by difficulty not request length
The LLM classifier prompt treated short wording as SIMPLE, so probes like
"Is P equal to NP?" stayed on the SIMPLE backend even though the classifier
ran. Judge intellectual difficulty so short hard questions route higher
* fix(e2e): open key edit via Key ID and wait for team models
Key Alias text is not the row open control on the virtual keys table;
KeyInfoView opens from the Key ID button in that row. Also wait for a
real team model in the edit Models dropdown so we do not race the async
availableModels fetch that only has All Team Models on first paint
* fix(e2e): keep settled DD events on empty search; bump mcp for OSV
Do not let a transient empty DataDog search wipe events already seen in
the settle window (Greptile P1). Make the logs-search from window
env-overridable via E2E_DD_SEARCH_FROM (Greptile P2). Prefer the mono
Key ID button when opening key edit. Bump mcp 1.26.0 -> 1.28.1 so OSV
clears the three high GHSA findings on the staging PR
* revert: drop mcp lock bump from e2e staging PR
OSV mcp upgrade is unrelated to the e2e fixes; leave the dep pin alone
---------
Co-authored-by: yucheng-berri <yucheng@berri.ai>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
A `lite login` token 429'd with "Budget has been exceeded! Max budget:
0.25" even when no budget was configured anywhere. cli_poll_key stamped
the minted CLI session token with litellm.max_ui_session_budget ($0.25)
as a fallback whenever the user and team had no budget of their own. That
cap was designed for the Admin UI "Test Key" chat pane; the CLI reused
the same session-token machinery, so it inherited a playground-sized
budget baked into the encrypted token at login (unchangeable without
re-login), which trips fast under real CLI/agent use.
The cap is also redundant: the token already carries user_id and team_id,
so the real user/team budgets are enforced independently at request time.
Pass max_budget=None so the CLI token is governed only by those real
budgets, and drop the now-dead user/team budget lookups. The UI login
token's guard (get_experimental_ui_login_jwt_auth_token) is untouched.
Grafted from PR #33582 (closing as superseded by this PR): drives
handle_streamable_http_mcp with real MCPServer objects, parametrized over a
stamped client_credentials row and a legacy unstamped M2M-shape row; both must
reach the session manager without the per-user token store being consulted
The preemptive-401 gate for auth_type=oauth2 MCP servers keyed the challenge
on whether an Authorization header was present (not oauth2_headers). Because
the header parser classifies any Authorization bearer as an OAuth token before
the target server is resolved, a LiteLLM virtual key presented as
Authorization: Bearer sk-... suppressed the challenge on a gateway-managed
authorization_code server; the session then opened with no upstream token and
tools/list masked the failure as 200 with an empty tool list. The same gate
also wrongly challenged client_credentials (M2M) servers, which the gateway
authenticates by minting its own token at egress.
The decision is per oauth2 sub-mode, not per header. Gateway-managed modes
never receive a client-supplied upstream token: client_credentials mints at
egress so it is never challenged, and gateway-managed interactive
(authorization_code, non-delegate) is challenged whenever no stored per-user
token exists, regardless of any bearer. Only the delegate/upstream-PKCE mode,
where a present bearer genuinely is the upstream token, keeps keying on the
Authorization header. oauth2_headers itself is left untouched so the
delegate/passthrough egress paths that forward the client bearer are
unchanged.
