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Extend existing test modules with coverage for the instructions merge
logic, upstream cache, ContextVar-based injection, and client-side
capture — following each file's established patterns.
Made-with: Cursor
Remove the gateway-specific initialize fetch path and reuse instructions captured during existing MCP calls (list_tools/health_check/call_tool), while keeping YAML/DB instructions as immediate overrides.
Made-with: Cursor
Block cross-team key update/regenerate operations by raising when the caller is not a member of the target key's team, and add unit coverage for deny/allow team membership paths.
Made-with: Cursor
- drop unused Any annotation on register_extra_ui_setting's field
param; type it as FieldInfo and have enterprise callers construct
FieldInfo directly (pydantic.Field's stub reports the default's
type, which doesn't match FieldInfo)
- cache the effective UISettings class and invalidate it inside
register_extra_ui_setting so GET /get/ui_settings does not rebuild
a pydantic model on every request
- annotate _EXTRA_UI_SETTINGS_FIELDS with a concrete
Dict[str, Tuple[Any, FieldInfo]] instead of bare Dict[str, tuple];
the annotation remains Any because pydantic field annotations
include generics (Optional[X], List[X]) that are not instances of
type
- workflow proxy-config matrix: drop test_project*.py glob now that the
test lives under tests/enterprise/
- update uv.lock to match bumped litellm version
- fix mypy: loosen FieldInfo annotation on register_extra_ui_setting
(pydantic.Field stubs report the default's type) and silence
create_model overload resolution when passing **tuple_dict
- fix inline imports in moved test_project_endpoints_prisma.py to
target litellm_enterprise.proxy.management_endpoints.project_endpoints
Remove the /project/* management endpoints and the enable_projects_ui
admin-settings flag from the OSS litellm package. Project endpoints now
live under litellm_enterprise and are wired through the existing
enterprise router; OSS builds return 404 for every /project/* route.
The enable_projects_ui UI flag is registered back onto UISettings via a
small extension registry when the enterprise package is imported, so the
admin toggle and downstream key/sidebar gating continue to work in
enterprise builds. On OSS, explicit PATCH attempts with the flag return
403 with a clear enterprise-only message instead of being silently
dropped.
Pydantic request/response types (NewProjectRequest, UpdateProjectRequest,
DeleteProjectRequest, NewProjectResponse) stay in litellm/proxy/_types.py
because management_endpoints/common_utils.py and pydantic-shape tests
import them. LiteLLM_ProjectTable and all FK columns in schema.prisma
are unchanged.
- Omit messages whose list content is empty after stripping thinking blocks
- Retry only on HTTP 400 plus invalid-signature body match
- Return response inline from retry loop; drop unreachable None guard
- Tests: thinking-only turn dropped, non-400 no retry
Made-with: Cursor
Strip thinking blocks from the request body and retry once when Anthropic returns an invalid thinking signature error (e.g. after credential or deployment change). Applies to all BaseAnthropicMessagesConfig providers (direct Anthropic, Bedrock, Vertex, Azure AI).
Made-with: Cursor
The data-testid attributes added to React components are not present
in the CI-built UI output. Switch to using getByRole and getByText
selectors which work with the rendered DOM regardless of build cache.
Add E2E tests covering:
- Test connection with bad credentials shows failure modal
- Adding a specific model and verifying it appears in All Models table
- Adding a wildcard route and verifying it appears in All Models table
- Verifying model dropdown shows provider-specific models (existing test updated)
Added data-testid attributes to UI components to support stable test selectors.
Tests verified passing 3/3 consecutive runs with zero flakiness.
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Reviewer flagged that cleanup failures were silently swallowed and
suggested asserting `delete.ok()`. While thinking through the fix, the
actual question turned out to be "does the cleanup matter at all?" —
and the answer is no.
The e2e runner (`run_e2e.sh`) spins up a fresh postgres container per
invocation and tears it down at the end, so every local and CI run
starts with an empty DB. Playwright retries share the same DB but each
attempt creates a new model with a unique `Date.now()` name and only
queries its own model, so orphans from failed attempts never collide
with later attempts or other tests. Nothing else in the suite reads
the all-models table.
Keeping the cleanup would also turn every write test into an implicit
delete test, coupling responsibilities and inflating runtime — which
is probably why `teams.spec.ts` (create a team), `keys.spec.ts`
(update key limits), etc. all leave their entities in place. Matching
that convention, drop the try/finally block and the `createdModelId`
tracking. 12 lines removed, no behavior change.
Covers the full write-path flow for team-scoped models on the Models +
Endpoints page: create via /model/new, click the row to open the detail
view, click Edit Settings, change TPM/RPM, click Save Changes, assert
the new values render back. Cleans up via /model/delete in finally so
reruns stay deterministic.
Requires store_model_in_db: true in the fixture general_settings so the
proxy accepts /model/new and /model/delete. No existing test in the
dashboard e2e suite reads the all-models table or hits the model CRUD
endpoints, so enabling the flag has no cross-test impact.
The suite was superseded by ui/litellm-dashboard/e2e_tests/ on 2026-04-08
and is no longer referenced by CircleCI, docs, or Makefile targets. Drop
the directory wholesale and remove the orphaned e2e:psql npm script that
pointed at its runner.
* feat: add litellm.compress() for BM25-based context compression
Adds a compress() utility that reduces context size for LLM calls using
BM25 relevance scoring (with optional semantic embeddings via
litellm.embedding()). Messages below a token threshold pass through
unchanged; messages above are scored, ranked, and the lowest-relevance
ones replaced with stubs. Originals are cached and a retrieval tool is
injected so the model can recover dropped content on demand.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(compress): truncate high-scoring messages instead of fully stubbing them
When a relevant message was too large to fit in the token budget it was
replaced with a stub, leaving the LLM with no real content to work with.
Now the highest-scoring overflow message is truncated (first 70% + last 30%
of words) to fill the remaining budget, so the LLM always receives actual
content rather than just a retrieval pointer.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(bm25): add prefix expansion so query terms match inflected doc tokens
"cook" now matches "cooking", "auth" matches "authentication", etc.
Without this, short query terms scored 0 against longer inflected forms
in documents, causing the wrong message to be kept.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: add routing correctness test and eval harness for litellm.compress()
- test_simple_compression: parametrized test verifying BM25 routes the
right message based on query ("How to cook?" keeps cooking, "Fix auth"
keeps auth content)
- eval_compression.py: end-to-end eval harness comparing baseline vs
compressed model performance on HumanEval-style coding problems
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat(eval): add SWE-bench Lite compression eval harness
Uses princeton-nlp/SWE-bench_Lite_bm25_27K which bundles ~27k tokens of
BM25-retrieved repo context per problem — large enough to meaningfully
stress litellm.compress() without Docker or GitHub API calls.
Proxy eval metrics (no test runner needed):
- has_diff: model produced a valid unified diff
- file_overlap: fraction of gold-patch files in generated patch
- exact_file_match: generated patch touches exactly the right files
Run: python tests/eval_swe_bench.py --model gpt-4o --problems 10
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(eval): robust dataset loading + sys.path fix for worktree imports
- Add HuggingFace API fallback so the SWE-bench loader doesn't need
the `datasets` library (avoids pyarrow/numpy binary compat issues)
- Insert repo root into sys.path so compression module resolves
from worktrees
- Use direct import of litellm_compress to avoid __getattr__ issues
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* improve compression quality: line-based truncation, multi-message budget, 70% default target
- Switch truncate_message from word-based to line-based splitting to
preserve code structure (function boundaries, indentation)
- Allow multiple messages to be truncated instead of burning entire
budget on one overflow message
- Raise default compression target from 50% to 70% of trigger for
better quality/cost tradeoff
- Add --compression-target CLI arg to SWE-bench eval harness
- Move tests to canonical locations (tests/test_litellm/, scripts/)
- Add docs page and sidebar entries for compress()
Eval results (5 problems, Opus, trigger=10k):
Hunk overlap delta improved from -0.417 to -0.221
Content similarity now matches baseline (+0.006)
Cost savings: 72%
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* docs: add SWE-bench performance results to compress() docs
Include benchmark table from Opus eval (5 problems, trigger=10k)
showing 72% cost savings with file-level quality fully preserved.
Add metric explanations and eval runner examples.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(eval): use tolerance-based hunk overlap metric
The exact line-number matching was too brittle — LLM-generated patches
often target the right code region but with slightly offset line numbers.
Switch to hunk-level overlap with a 10-line tolerance window so nearby
edits count as matches. This better reflects actual patch quality.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add compression_interception callback for LiteLLM Proxy
Add a proxy callback that automatically compresses incoming /v1/messages
payloads above a configurable token threshold, runs the retrieval tool
loop server-side, and returns the final response. This brings compress()
support to proxy deployments (e.g. Claude Code via /v1/messages).
- New callback: litellm/integrations/compression_interception/
- Proxy config: compression_interception_params in litellm_settings
- Support for input_type param in compress() (openai vs anthropic)
- Docs: proxy setup instructions with YAML config example
- Tests: 139-line unit test suite for the interception handler
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Revert "feat: add compression_interception callback for LiteLLM Proxy"
This reverts commit 72bd5cb152.
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Updates the expected header text to "Guardrails Settings" to match
GuardrailSettingsView's rendering, and moves the mock guardrails
from team_info.guardrails (legacy top-level path that nothing
reads) to team_info.metadata.guardrails where the component
actually looks. Also tightens the assertion to verify the
individual guardrail names appear, not just the section header.
Previously these were silently dropped with a verbose warning, which
could break observability integrations without surfacing a clear error.
Now raises ValueError with remediation steps (configure server-side
or pass the resolved value) so callers get immediate, actionable feedback.