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
Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Waiting to run
Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py, 30, 8) (push) Waiting to run
The suite was superseded by ui/litellm-dashboard/e2e_tests/ on 2026-04-08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>
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.
- Only use_chat_completions_api and openai/chat_completions/ opt into the bridge
- Extract MCP gateway and file_search emulation dispatch to cut responses() size
- Update docs and tests
Made-with: Cursor
- Add use_chat_completions_api (keep use_responses_api_bridge as deprecated alias)
- Support openai/chat_completions/<model> model prefix for the same behavior
- Forward use_chat_completions_api in file_search emulation inner calls
- Update response_api.md and extend unit tests
Made-with: Cursor
- Add automatic conversion of Vertex AI batch prediction JSONL to OpenAI format
- Preserve custom_id via Vertex AI labels for request correlation
- Fix Content-Length header mismatch in transformed responses
- Add comprehensive tests for batch output transformation
Made-with: Cursor
Brings the date-range branch in line with the non-date-range branch which
already hashes sk- prefixed tokens before querying. Adds coverage for
filter-combination behavior in view_spend_logs.
- Log a warning when dropping callback params that carry os.environ/
references so operators notice the misconfiguration.
- Require absolute paths in oidc/file/ and correct the documented
example to use the leading-slash form.
- Drop the unused return value from _reject_os_environ_references.
- Reject os.environ/ references supplied via /health/test_connection
request params instead of resolving them; config-sourced values are
already resolved before reaching the endpoint.
- Skip os.environ/ references in dynamic callback params loaded from
per-request metadata.
- Constrain oidc/file/ to an allowed credential directory allowlist
(defaults to /var/run/secrets and /run/secrets, overridable via
LITELLM_OIDC_ALLOWED_CREDENTIAL_DIRS).
Budget table entries (team members, end-users) used duration_in_seconds()
for a sliding-window reset, while keys/users/teams used calendar-aligned
get_budget_reset_time(). This made "30d" and "1mo" mean different things
depending on entity type. Now both paths use get_budget_reset_time() for
consistent calendar-aligned resets (e.g. "30d" → 1st of next month).
Fixes#25432
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The model_max_budget limiter tracks spend in one code path
(async_log_success_event) and enforces budget limits in another
(is_key_within_model_budget via user_api_key_auth). These two paths
used different model name formats to build cache keys:
- Tracking used standard_logging_payload["model"], which is the
deployment-level model name (e.g. "vertex_ai/claude-opus-4-6@default")
- Enforcement used request_data["model"], which is the model group
alias (e.g. "claude-opus-4-6")
Because the cache keys never matched, the enforcement path always read
None for current spend, silently allowing all requests through even
after the budget was exceeded. This affected any provider that decorates
model names with provider prefixes or version suffixes (Vertex AI,
Bedrock, etc.).
Fix: use model_group (the user-facing alias) from StandardLoggingPayload
for spend tracking, falling back to model when model_group is None.
This aligns the tracking cache key with the enforcement cache key.
Fixes the same root cause reported in #15223 and #10052.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Two code paths in key_management_endpoints.py call hash_token()
unconditionally when invalidating the user_api_key_cache after a key
update. When the caller passes a pre-hashed token ID (not an sk-
prefixed key), hash_token() double-hashes it, producing a cache key
that does not match the actual cached entry. Cache invalidation
silently fails.
This is compounded by update_cache() which writes the stale cached key
object back with a fresh 60s TTL after every successful request,
preventing natural TTL expiry. The stale entry (with outdated fields
like max_budget=None) persists indefinitely under load.
PR #24969 fixed this in update_key_fn but missed two other call sites:
- _process_single_key_update (bulk update path)
- _execute_virtual_key_regeneration (key rotation path)
Fix: replace hash_token() with _hash_token_if_needed() in both
locations, matching the pattern already used elsewhere in the file.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Renames the new per-guardrail opt-out field from `disabled_global_guardrails`
to `opted_out_global_guardrails` to eliminate the one-character collision with
the legacy `disable_global_guardrails` boolean kill switch. Adds a type guard
on the new gate so a misnamed bool can't crash the guardrail check. Filters
duplicates out of the team-edit guardrail display for legacy teams that have a
global name persisted in `metadata.guardrails` from before this PR. Drops the
unused `isGuardrailsLoading` and `guardrailsError` destructures left in
AddModelForm after the hook refactor.
Adds Python tests for the new gate behavior (root, litellm_metadata, metadata,
non-matching name, empty list, malformed bool value, opt-in coexistence) and
extends useGuardrails.test.ts to exercise the global / optional partition
logic that the rebuilt hook performs in its `select` transform.
Wires the legacy kill switch and the new opt-out list together in the team
edit form so they can never fall out of sync:
- Toggling the kill switch reactively updates the Guardrails Select via
`onValuesChange` — switch on strips all globals from the selection, switch
off re-adds them. Existing opt-in extras are preserved either way.
- When the switch is on, global options in the Select are individually
disabled (greyed out) so the user can still manage opt-in guardrails but
cannot accidentally re-enable a global the kill switch is bypassing.
- The save handler writes both fields together: `disable_global_guardrails`
reflects the switch, and `opted_out_global_guardrails` is set to either
every global (when the switch is on) or the user's explicit opt-outs.
- `effectiveGuardrails` for the form's initialValues honors the kill switch
on legacy teams so the form opens in a state that matches what the runtime
gate is actually doing — fixes the visual lie where chips appeared active
while the switch was bypassing them.
The backend gate already reads the list as the primary path with the bool
as a fallback, so untouched legacy teams keep working until they get edited,
at which point they migrate naturally.
Rename disable_global_guardrail → disable_global_guardrails to match
the key name used by litellm_pre_call_utils.py, the API endpoints,
and the UI when propagating key/team metadata.
The singular form was introduced in PR #16983 and has never matched
the plural form written by the rest of the codebase, so the feature
silently did nothing.
Re-applies fix originally from #25488. Original commit could not be
merged due to missing signature.
Co-Authored-By: Remi Mabon <remi.mabon@redcare-pharmacy.com>
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The test expected "stream": false in the serialized request body, but
stream is only included in optional_params when explicitly passed by
the caller. litellm.completion() defaults stream=None which is excluded
from non_default_params. Assert individual fields instead of the full
serialized JSON to avoid brittleness around optional field inclusion.
usage-based-routing tracks RPM in log_success_event which runs in a
background ThreadPoolExecutor. The cache update races with the next
call's routing check in both sync (tight loop) and async (concurrent
gather) modes, making over-limit detection non-deterministic.
Skip the over-limit parametrization (num_try_send > num_allowed_send).
The under-limit cases (2 sent, 3 allowed) still verify the routing
strategy works. Rate-limit enforcement is properly tested with mocks
in tests/test_litellm/test_router/test_enforce_model_rate_limits.py.
- Add _verify_org_access to deprecated POST /organization/info endpoint
- Move get_user_object to module-level import in organization_endpoints.py
- Add tests for _verify_team_access 403 denial path
- Introduced a new method in `FileContentStreamingHandler` to resolve streaming request parameters, enhancing the routing logic based on credentials.
- Updated the `should_stream_file_content` method to check against supported providers.
- Cleaned up type hints and imports across multiple files for better organization and clarity.
- Added comprehensive tests to validate the new routing behavior and ensure original data integrity during streaming requests.
