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11 commits
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Yassin Kortam
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89c87ae59a
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test(e2e): mcp suite for key-without-access denial (#33752)
Add an e2e suite at tests/e2e/mcp/ that proves MCP authorization over the api_key auth family. An admin registers an upstream MCP server through the management API (POST /v1/mcp/server, persisted in the DB and picked up without a restart) and queues its deletion. Two keys are created against that one server: one granted access through object_permission.mcp_servers and one with no MCP grant. The permitted key is a live control proving the upstream is reachable and the tool is callable, so a denial on the ungranted key is an authorization decision rather than a dead server. The denied key then sees none of the server's tools on tools/list and is refused a tools/call with a 403 access_denied. A deterministic self-hosted FastMCP upstream (add/multiply over streamable-http) is added to the e2e compose stack so the suite runs offline with a known tool set. KeyGenerateBody gains an optional typed object_permission so the shared gateway can create a key with an MCP grant. |
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mubashir1osmani
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224fe67f10
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test: e2e staging leftovers (#33613)
* 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> |
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mubashir1osmani
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3f5ed5a9c8
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fix(e2e/claude_code): unblock stage collection, align proxy env names, register compat models (#33433)
* refactor(e2e/claude_code): align proxy env names with the rest of tests/e2e
Every claude_code compat cell used to read its own `LITELLM_PROXY_BASE_URL` and `LITELLM_PROXY_API_KEY` and duplicate the same 12-line "missing env, hard fail" block. The rest of `tests/e2e/` reads `LITELLM_PROXY_URL` and `LITELLM_MASTER_KEY` from `e2e_config.py`, so anyone standing up a live proxy for one suite had to export a second spelling for claude_code, and every cell repeated the same boilerplate.
Centralize the resolution in `claude_code/_env.py`. `resolve_proxy()` prefers the suite-wide `LITELLM_PROXY_URL` / `LITELLM_MASTER_KEY` names and falls back to the legacy pair so existing CI wiring on stage keeps working during the roll-out. `require_proxy(compat_result)` is the one-liner cells call to bind `(base_url, api_key)` or hard-fail with a message that names both spellings.
55 cell files, `_basic_messaging.py`, and the driver's own unit-test fixture now go through the helper. `run_compat.sh` accepts either spelling and normalizes to the primary names before invoking pytest. `cron_vm/run_daily.sh` exports the primary names when launching pytest.
`_pr_gate_unit_tests/test_env_resolution.py` pins the resolution rules so a future edit cannot silently reintroduce the drift: primary names win on tie, legacy names still resolve when primary is unset, mixed URL-primary key-legacy still resolves, empty-string exports are treated as unset, `require_proxy` names both spellings in its error message.
Net diff: 71 files, +370/-1240.
* fix(e2e): anchor claude_code Bash pin at parents[1] so container run collects
`test_bash_tool_restrictions.py` derived `REPO_ROOT = Path(__file__).resolve().parents[4]` and then joined `tests/e2e/claude_code/<feature>`. That works locally, but the stage container mounts tests/e2e/ at /app/e2e/, so parents[4] resolves to filesystem root and the `_bash_cells()` assertion looks for `/tests/e2e/claude_code/tool_use` — a path that doesn't exist. Collection interrupts before any test runs, so the entire e2e suite appears broken.
Fix: `CLAUDE_CODE_DIR = Path(__file__).resolve().parents[1]` resolves to the sibling `claude_code/` dir in either layout, and the `relative_to(REPO_ROOT)` calls become `relative_to(CLAUDE_CODE_DIR)` so test IDs and error messages read the same.
Adds `test_claude_code_dir_anchor_is_layout_independent` as a regression pin: it checks the anchor lands on a directory named `claude_code` that contains this test file, which would fail under the old parents[4] anchor when run from /app/e2e/.
* feat(e2e/claude_code): register compat deployments via /model/new from a session fixture
Every compat cell hardcodes a virtual model name like `claude-sonnet-4-6` or `claude-sonnet-4-6-bedrock-invoke` and hits the proxy expecting it to be routable. On stage those live in the deployed model_list; locally the `docker-config.yaml` under tests/e2e/ only declares one of them, so anything past haiku 400s with `Invalid model name`.
`claude_code/test_config.yaml` is the ground-truth compat matrix config the deployment already uses. `_compat_models.py` loads it, normalizes the yaml keys pydantic would silently drop (vertex_ai_* → vertex_*), and selects the subset whose provider credentials are present in the environment. An autouse session fixture in `conftest.py` POSTs each selected deployment to `/model/new`, blocks until it is servable on the data plane, and tears them all down on session exit. Skips silently when the proxy env is unset so pure-unit runs stay hermetic.
`test_compat_models.py` pins the invariants that keep this safe. Every cell-referenced name must have a yaml entry (drift check catches a cell probing a name the fixture never registered); the yaml has no unused declarations; the fixture registers exactly 15 deployments (3 tiers × 5 provider surfaces); vertex_ai_* yaml keys populate the pydantic body's vertex_* fields (they got silently dropped historically); Azure needs both AZURE_FOUNDRY_* env vars; Bedrock lifts creds from the ambient AWS chain; Vertex needs both the yaml refs AND ambient GCP credentials.
* refactor(e2e/claude_code): inject env + runner instead of monkeypatching
`require_proxy` and `_basic_messaging.run_basic_messaging_cell` now take the env mapping (and the CLI runner) as constructor-style arguments with `os.environ` and `run_claude_models_parallel` as defaults. Tests exercise the branching by passing dicts and callables directly, so `monkeypatch.setenv` and `monkeypatch.setattr(_basic_messaging, "run_claude_models_parallel", ...)` are gone from every unit test in this refactor's blast radius.
`test_env_resolution.py` drops the `monkeypatch.setenv`/`delenv` fixtures and passes `env={...}` dicts to `require_proxy`. Added a new pinned check that a successful resolution leaves `compat_result` untouched, and split the "unset env" test into three explicit shapes (empty, primary-only, legacy-only) so a regression that swaps the precedence rule can no longer hide behind a single monkeypatched fixture.
`test_basic_messaging.py` (driver) replaces the `_install_fake_runner(monkeypatch, ...)` helper with `_make_fake_runner(...)` that returns a `(callable, captured_dict)` pair the test passes in via the helper's new `runner=` kwarg. Also drops the autouse `_proxy_env` fixture in favor of a module-level `_PROXY_ENV` dict each test wires through the helper's new `env=` kwarg. Added a regression pin that a missing-env call hard-fails without ever invoking the runner (so the guard order stays correct).
`test_run_daily_pytest_scrubs_env.py` updates its pin to assert the new suite-wide env spellings (`LITELLM_PROXY_URL` / `LITELLM_MASTER_KEY`) instead of the legacy `LITELLM_PROXY_BASE_URL` / `LITELLM_PROXY_API_KEY` that `run_daily.sh` used to export.
* handwrote rules
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yucheng-berri
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edc30ea515
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test(e2e): datadog log delivery for successful chat, messages, and responses (LIT-4447) (#33415)
* test(e2e): datadog log delivery for successful chat, messages, and responses Covers logging.datadog.success.exports_metric on all three routes: one successful non-streaming call must reach the DataDog logs intake as exactly one log event whose StandardLoggingPayload message carries the model group, real token counts, and a response cost equal to the x-litellm-response-cost header of the same response. Delivery is judged at the intake: the compose stack gains a dd-sink service recording every batch the datadog callback ships via the DD_BASE_URL testing override, and a typed reader replays it. Writing these caught a live product bug: /v1/messages double-logs every success (two byte-identical events per call), filed as LIT-4447; the messages test tolerates byte-identical duplicates of the one event until it lands, while a second differing event still fails * test(e2e): address review findings on the datadog delivery suite Consolidates the fresh-key first_ok helper into logging_client now that the otel PR it mirrored has merged (both test files use the shared copy), moves intake batch parsing into a helper so no path can leave the batch unbound, and gives the sink's /health endpoint a truthful text/plain content type * test(e2e): tolerate same-logical-event duplicates by call id, not byte identity A clean LIT-4447 repro showed the duplicated payload is built twice and can mint a fresh synthetic completion id per emission, arriving as two separate intake POSTs with the same litellm_call_id and identical substantive fields. Byte-identity was therefore a flaky criterion; duplicates now qualify only when they share the call id, call type, model group, tokens, and cost, and a second differing event still fails * test(e2e): assert the scenario strictly; the messages test is the LIT-4447 regression pin Per review direction the tests now assert exactly what the scenario promises: exactly one DataDog log event per successful call, on every route. The /v1/messages test therefore fails on current code against the known double-log (LIT-4447) and is its regression pin; it goes green when the fix lands. The duplicate-tolerance machinery is removed * Simplify docstrings for DataDog log tests Removed redundant phrasing about cost cross-checking in docstrings. * Update test_datadog_log_e2e.py |
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devin-ai-integration[bot]
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fac43df9b9
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fix(complexity_router): return empty dict from _classifier_call_metadata when metadata is absent (#33452)
* fix(complexity_router): return empty dict from _classifier_call_metadata when metadata is absent
The LLM classifier reads request_kwargs.get("litellm_metadata"), but the proxy stores request metadata under "metadata", so this returned None. _classifier_call_metadata then passed None straight through to the classifier acompletion call, which assumes a dict and blows up with 'NoneType' object has no attribute 'update'; the router swallowed it and silently fell back to heuristic scoring, so the configured LLM classifier never ran. Returning an empty dict keeps the classifier call well-formed.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(e2e): cover complexity-router LLM classifier routes over the proxy
Add a live e2e regression for the complexity auto-router: a lexically simple but hard prompt ("Is P equal to NP?") is routed by the LLM classifier to the higher-tier anthropic backend, read back from the spend log's model. Before the metadata fix the classifier silently crashed and the router fell back to heuristic SIMPLE scoring on the openai backend, so this test fails pre-fix and passes post-fix.
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>
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yucheng-berri
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d6f498ff5c
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test(e2e): failed request error span carries the full untruncated message and status (LIT-4179) (#33304)
* test(e2e): failed request error span carries the full untruncated message and status Covers logging.otel.failure.exports_metric on chat_completions: a request that fails at the provider (invalid upstream key deployment) must export one complete trace whose gen-AI span carries the LIT-4179 error contract, declared as one reviewable payload (EXPECTED_ERROR_SPAN_ATTRIBUTES) plus an untruncated error.message proven by parsing the embedded provider error JSON back out of the attribute. The root SERVER span must record the 401 the client received. Adds STORE_MODEL_IN_DB to the compose stack so /model/new works locally, which the suite's model-registering tests already assume * test(e2e): clean failure diagnostics on the error-span contract per review A truncated error.message with missing braces now fails with a readable assertion instead of an unhandled ValueError, an unparseable embedded JSON fails via pytest.fail with the truncation context, and the retry loop now asserts the upstream provider failure was actually observed so a fresh-key propagation deadline cannot masquerade as a trace-export failure * test(e2e): pin the full error attribute set including the litellm.provider.error keys The LIT-4179 fix restored error.message/code/stack_trace/llm_provider; a later refactor (#32591) moved the litellm-specific keys under litellm.provider.error.*, which the initial contract missed. The payload now pins error, error.type, otel.status_code, litellm.provider.error.code=401, and litellm.provider.error.llm_provider=anthropic exactly, plus non-empty litellm.provider.error.stack_trace and the untruncated error.message * test(e2e): author the error-span test docstring |
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yucheng-berri
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948a43cd64
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test(e2e): otel trace completeness on /chat/completions (#33132)
* test(e2e): OTEL trace completeness on /chat/completions against a local Jaeger destination Adds the logging-suite infrastructure for LIT-3787 trace-completeness coverage: a jaeger service in the compose stack as the OTEL v2 destination (arize_phoenix preset pointed at it via PHOENIX_COLLECTOR_HTTP_ENDPOINT, so gen-AI spans export through a preset-owned provider - the code path where trace splits happen), a typed Jaeger query read-back client, and the first test: one successful non-streaming /chat/completions call exports ONE complete trace (root SERVER span + auth/db/cost children + gen-AI CLIENT span, no dangling parents). * test(e2e): harden the otel trace read-back per review Jaeger reads now query server-side by the litellm.call_id span tag instead of paging recent traces and filtering client-side; the compose stack's background jobs alone can push a request trace past the page. A failed query hard-fails instead of reading as an empty result, the settle predicate now also waits for the prefix-matched db span the assertion demands, parent-chain walking follows CHILD_OF references only, the zero-trace and split-trace failures get distinct messages, jaeger gets a healthcheck so the depends_on condition is accurate, and the chat docstring names the route the code actually asserts * test(e2e): author the chat trace test docstring * Update logging section in CLAUDE.md Removed mention of OTEL trace-tree completeness from logging integration section. |
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mubashir1osmani
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54d404ef2c
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fix(e2e): batch credentials wiring and compose harness for live proxy suite (#32744)
* fix(e2e): wire batch provider secrets for docker and k8s
Point batch deployments at the credential field names and os.environ refs
the gateway actually resolves from process env (compose .env or EKS secret
mounts). Missing secrets skip instead of failing red so a red run means a
product bug. Mirror S3 bucket env aliases in docker-compose for provider_fallback
* fix(e2e): drop batch provider_env unit tests
The batches suite is live e2e only; no monkeypatch or unit-level tests
* fix: batch credentials, provider list, and team db lookup
Keep object-storage fields through CredentialLiteLLMParams and resolve
os.environ/ refs when reading deployment credentials so Vertex/Bedrock
batch file uploads see bucket and AWS keys from K8s/docker env
Skip managed batch list when the request is provider-scoped so
/{provider}/v1/batches list works instead of 500
Force DB on check_db_only team lookups and stop masking non-404 errors
as "team doesn't exist"
Drop e2e runner-side skip helpers; hard-fail on missing gateway secrets
* fix: tag reseed, team window spend, and remaining e2e flakes
Reseed spend:tag counters from LiteLLM_TagTable so cold redis still
enforces after the spend writer flushes
When applying post-call cost to team multi-window counters, load the
team from the DB if it is missing from the management cache so window
spend is not dropped on cache misses
Harden cold-counter reseed e2e (namespace-aware keys, burst success,
poll). Give tag budget more headroom. Retry /key/update on redis DNS
blips. Ensure NLTK punkt_tab is present for pipecat realtime audio
* revert: drop product code changes; e2e-only scope
Reverts all litellm/ and unit-test product edits. This branch is limited
to tests/e2e per contributor instruction
* fix(e2e): harden batch list and team member setup races
provider_fallback list falls back when managed batches reject provider
filtering. Team create waits for /team/info and member_add retries on
transient team-not-found so split control-plane lag does not red the suite
* fix(e2e): remove .env.example
Leave local .env and docker-compose env wiring as the secret source
* fix(e2e): wire files_settings and faster budget rescheduler for compose
OpenAI/Azure batch file uploads need files_settings; budget reset e2e needs a
short rescheduler window. Drop unsupported bedrock-encoded create_batch cells,
tolerate bedrock file.bytes=0, and surface team-info wait failures instead of
hanging silently
* chore(e2e): strip verbose comments from batch capabilities
* fix(e2e): assert managed list fallback before provider_fallback skip
When provider-scoped list is rejected, still fetch the unfiltered list and
check the envelope. Only skip membership when the id is a raw
provider_fallback batch that managed list cannot index
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mubashir1osmani
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8519d7fc24
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test: litellm fix failing tests (#32577)
* fix: rust ocr tests finally pass * fix: move realtime dir * fix(realtime): normalize azure realtime api_base to host for Foundry endpoints The azure realtime handler appended the realtime path to api_base verbatim, so a Foundry base carrying a project path (.../api/projects/<name>) produced an invalid realtime URL and the websocket handshake hung. Normalize api_base to scheme and host before building the realtime path so both Azure OpenAI and Foundry bases connect Point the e2e realtime azure deployment at the GA gpt-realtime model and stop passing the os.environ refs the realtime path never unwraps, resolving them from the gateway env by name instead. Drop the local docker-compose scaffolding from the tree * test(e2e): add Gateway.list_files and list_fine_tuning_jobs for the discovery suite The discovery endpoints suite calls client.gateway.list_files and list_fine_tuning_jobs, which did not exist on Gateway, so both tests errored with AttributeError before reaching the proxy. Add the two GET wrappers using the existing FileListResponse / FineTuningJobsResponse models * revert(realtime): drop azure realtime api_base host-normalization The azure realtime handshake failure was a config issue, not a litellm bug: the realtime base was set to the Azure AI Foundry project endpoint (.../api/projects/<p>), but the OpenAI-compatible realtime route lives at the resource root. litellm correctly appends the realtime path to whatever base it is given, so pointing the realtime deployment at the resource root is the fix and no core change is needed * fix(ocr): route azure_ai doc-intelligence to its own endpoint at the source get_llm_provider inherits AZURE_AI_API_BASE into api_base for every azure_ai/* OCR model, but Azure Document Intelligence is a separate resource reached via AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT, so doc-intelligence requests went to the wrong host. Stop inheriting the azure_ai base for doc-intelligence models so api_base stays unset and both the rust bridge and the python get_complete_url fall back to the document-intelligence endpoint. This drops the earlier _rust_bridge_api_base reorder, which only covered the rust path and let the env silently override an explicit api_base * refactor(ocr): consolidate azure doc-intelligence detection; keep explicit api_base Extract is_azure_document_intelligence_model as the single source of truth for the azure_ai doc-intelligence sub-route so the check is no longer duplicated across _prepare_ocr_request and _rust_bridge_api_base, and gate the dynamic_api_base suppression on the caller not supplying an api_base so an explicit endpoint is always honoured. Restore xai to the realtime PROVIDERS as a documented disabled entry instead of dropping it silently, and add a regression test pinning doc-intelligence api_base resolution. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Mubashir Osmani <mubashir@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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mubashir1osmani
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a05a1eef94
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fix(ui): scope key models dropdown options to the key's team (#32382)
* fix(ui): scope key models dropdown options to the key's team A teamless key no longer offers the all-team-models option in the create and edit forms; the backend expands that sentinel to the full proxy model list when no team is attached, which is rarely what the user intended. A team key no longer surfaces the all-proxy-models sentinel that leaks in verbatim when the team's own model list carries it; the dropdown keeps All Team Models plus the team's individual models. Adds browser coverage to the management e2e suite: playwright (an optional dependency behind importorskip) drives the proxy-served dashboard at /ui, asserts the dropdown options a real user sees for teamless and team keys on both create and edit, and walks the create modal end to end, reading the persisted key back through /key/info. * fix(ui): offer all-proxy-models on teamless keys in the models dropdown A teamless key has no team allowlist to inherit, so the dropdown now offers All Proxy Models in place of All Team Models on both the create and edit forms, with the same exclusive-selection handling. Component and browser e2e tests updated to pin the swapped option pair; the teamless create case now also walks the modal end to end and reads the persisted key back through /key/info. * test(ui): update no-team key creation spec to pick All Proxy Models The create modal no longer offers All Team Models without a team; the teamless path now offers All Proxy Models, which is what this spec exercises * fix(ui): gate All Team Models on the team object being loaded When a key has a team_id but the teams prop does not yet include the matching team, availableModels stays empty and the models dropdown rendered All Team Models on its own with nothing to compare against. Gate the option on the team object being present so it only appears once team models are known, and add a regression test for the loading state Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(ui): filter all-proxy-models from teamless model fetch in key edit form The teamless fetch path stored modelAvailableCall results without excludeProxyWideSentinel, so an all-proxy-models entry in the response rendered a second option colliding with the hardcoded All Proxy Models sentinel. Apply the same filter used on the team path and add a regression test asserting the sentinel option is not duplicated Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Mubashir Osmani <mubashir@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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mubashir1osmani
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a86dc4c15e
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chore(e2e): untrack gateway config and document e2e test location (#31914)
* chore(e2e): untrack gateway config and document e2e test location Stop tracking tests/e2e/gateway/litellm-config.yml so the local proxy config stays on the machine Add a note to CLAUDE.md that new e2e tests belong in tests/e2e/ and must follow that directory's conventions * chore(e2e): add self-contained docker compose stack for local runs Ship a docker-compose.yml that starts the proxy with a throwaway Postgres and Redis and inlines the proxy config with example models, so contributors can bring up a local gateway with nothing but a .env. Update CONTRIBUTING.md to match the inline-config flow * chore(e2e): drop the second gemini deployment; one key is enough locally * docs(e2e): make pre-commit steps ordered and require flagging internally found issues |