* feat(proxy): tool policies - auto-discover tools, manage policies, guardrail enforcement
- New LiteLLM_ToolTable in schema.prisma to store discovered tools
- Auto-discovery: tools seen in LLM responses get upserted via ToolDiscoveryQueue
(hooks into DBSpendUpdateWriter, same pipeline as spend tracking)
- Management endpoints: GET /v1/tool/list, GET /v1/tool/{name}, POST /v1/tool/policy
- ToolPolicyGuardrail: blocks tool_calls in responses based on policy setting
- UI: Tool Policies page under Guardrails section with policy selector,
filters by policy/team/key, live tail, sortable table
- Unit tests for queue, writer, endpoints, guardrail
* feat(tool-policies): track call_count + discover tools from request body and /messages API
- Add call_count column to LiteLLM_ToolTable; incremented on every flush
- Extract tools from request body too (not just response tool_calls):
- OpenAI /chat/completions: tools[].function.name
- Anthropic /messages pass-through: request_body.tools[].name
- Show call_count column in UI table (sortable)
- UI: drop dual_llm option, keep only trusted/blocked
* fix: address greptile review feedback
- Remove redundant @@index([tool_name]) from schema.prisma (tool_name has @unique which already creates an index)
- Replace gen_random_uuid()::text with str(uuid.uuid4()) for portability
- Rewrite test_tool_registry_writer.py to mock execute_raw/query_raw (actual implementation) instead of Prisma model methods
- Fix test patches in test_tool_management_endpoints.py to target source modules since imports are inside function bodies
- Add "Tool Policies" page title to ToolPolicies.tsx
* fix: address greptile review round 2
- Replace NOW() with Python datetime parameter in tool_registry_writer (SQLite portability)
- Fix cache key collision in tool_policy_guardrail: use null-byte separator instead of colon
- Remove type==function filter from request-side tool extraction to match response-side behavior
- Clear seen_tool_names on flush so call_count increments per batch cycle not per pod lifetime
* fix: address greptile review round 3
- Fix test_seen_names_persist_across_flushes to match actual per-flush-cycle behavior
- Update module docstring in tool_discovery_queue.py to accurately describe flush behavior
- Add created_at/updated_at to raw SQL INSERT in batch_upsert_tools and update_tool_policy
* fix: cache tool policies per tool name not per combination
Previously the cache key was built from the full set of tool names in a
request, so each unique combination of tools got its own cold cache entry
and triggered a separate DB query. With N distinct tools across requests
this was effectively a DB hit on every request.
Now each tool name is cached individually. Cache hits are checked per
tool, only missing tools are fetched from DB in a single batch query,
and each result is cached separately. Once a tool's policy is warm,
any subsequent request using that tool benefits from the cache regardless
of what other tools are in the request.
* Update ui/litellm-dashboard/src/components/ToolPolicies.tsx
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix: feat: add litellm_system_prompt support
* feat: support new 'litellm_agent' model provider
* feat: ui/ - new agent builder ui
* fix(anthropic/chat/transformation.py): normalize max_tokens if decimal
* feat(agentbuilderview.tsx): run compliance datasets against litellm agent
* feat: new response rejection detector
* fix: multiple fixes
* feat: add mcp tools support to agent builder
create an agent with access to llm's + mcp servers
* feat: add competitor name blocker guardrail
* fix: fix batch test endpoint for compliance playground
* fix(airline.py): add list of all known airlines to airline competitor name detector
prevent competitor discussion on company chatbot
* feat: ui tweaks for prod
* Add keyword-based topic blocker implementation
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add embedding-based topic blocker using MiniLM
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add topic blocker package init with exports
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add synthetic engine eval set (34 cases)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add investment questions eval set (207 cases)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add engine eval synthetic policy config
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add engine keyword blocker eval results
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add investment keyword blocker eval results
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add investment embedding blocker eval results
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add investment embedding MiniLM eval results
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add investment embedding MPNet eval results (historical)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add investment TF-IDF eval results (historical)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add unified eval runner with confusion matrix reporting
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add benchmarks comparison table in markdown
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Clean up topic blocker: remove unused blockers, add phrase_patterns to content filter
- Remove embedding_blocker.py, api_embedding_blocker.py, nli_blocker.py,
tfidf_blocker.py, onnx_blocker.py (heavy deps not in Docker, inferior accuracy)
- Remove airline_off_topic_restriction policy template and its test
- Fix __init__.py to only export DeniedTopic and TopicBlocker (no eager import crash)
- Add phrase_patterns support to ContentFilterGuardrail for regex-based paraphrase detection
- Rewrite denied_financial_advice.yaml with conditional matching (identifier + block word),
always-block keywords, phrase patterns, and exception phrases
- Clean up test_eval.py: only keyword blocker + content filter tests remain (no network calls)
- All 207 eval cases pass at 100% F1, 0 FP, 0 FN, <0.1ms latency
Addresses all Greptile review comments:
- Eager import crash (embedding deps) → fixed
- Undeclared dependencies → fixed (files deleted)
- lru_cache memory leak → fixed (file deleted)
- Real network calls in tests → fixed (embedding tests removed)
- Unused Dict import → already fixed
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add LLM-as-judge eval and update BENCHMARKS.md
- Add TestInvestmentLlmJudgeGpt4oMini and TestInvestmentLlmJudgeClaude
test classes that use litellm.completion() to classify messages
- System prompt instructs LLM to act as airline chatbot content moderator
- Tests skip gracefully when API keys aren't set
- Update BENCHMARKS.md with production results table, historical comparison,
and instructions for running LLM judge evals
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Move evals and benchmarks to guardrail_benchmarks folder
Move eval runner, eval data (JSONL), and results from
tests/test_litellm/.../topic_blocker/ into the guardrail implementation
folder at litellm/.../litellm_content_filter/guardrail_benchmarks/.
This keeps benchmarks co-located with the guardrail code they test.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Remove standalone topic_blocker package, consolidate into content_filter
The standalone keyword_blocker.py was redundant with content_filter.py +
denied_financial_advice.yaml. Removed the entire topic_blocker/ package,
engine eval files, and old keyword blocker results. Simplified test_eval.py
to only test ContentFilter + LLM judge baselines.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Fix compliance playground batch scoring bug, add display_name support
The compliance playground was sending all texts in a single batch API call,
but the content filter raises HTTPException on the first blocked text. This
caused a single blocked/allowed result to be applied to all rows, producing
incorrect scores (e.g. 41% instead of 100%). Fix by sending each text
individually to get per-text results with progressive UI updates.
Also add display_name field support for category YAML files so
denied_financial_advice shows as "Denied Financial / Investment Advice"
in the UI dropdown.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Add block_investment CSV eval set and update benchmark result JSON
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* address greptile review feedback (greploop iteration 1)
Fix stale test path in denied_financial_advice.yaml comment.
Other comments were on files already deleted in prior commits.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat(schema.prisma): add object permissions for end users
allows controlling if end user can call specific mcp servers
* feat: cleanup for customer_endpoints support of object permission id
* fix: cleanup str
* feat(customers/): enforce end user can only call allowed mcps - if configured
* docs: document customer/end user object permission usage
* feat: enforce end user permissions on MCP tool calls
This commit implements end user permission enforcement for MCP servers:
1. Always add server prefixes to MCP tool names
- Removed conditional logic that only added prefixes when multiple servers existed
- Now always adds server prefix for consistent tool naming across all scenarios
- Updated 5 locations in server.py (list_tools, get_prompts, get_resources,
get_resource_templates, get_prompt)
2. Created MCP End User Permission Guardrail Hook
- New guardrail hook: litellm/proxy/guardrails/guardrail_hooks/mcp_end_user_permission.py
- Runs on post_call to validate tool calls in LLM responses
- Extracts MCP server name from tool names (splits on first '-')
- Checks if end_user_id has permissions for the MCP server
- Raises GuardrailRaisedException if end user lacks permission
- Supports both streaming and non-streaming responses
3. Added comprehensive tests
- Test file: tests/test_litellm/proxy/guardrails/guardrail_hooks/test_mcp_end_user_permission.py
- Tests cover: authorized/unauthorized tools, non-MCP tools, no end_user scenarios
- Tests permission checking logic and exception raising
The hook integrates with the existing MCPRequestHandler._get_allowed_mcp_servers_for_end_user
to fetch end user permissions and enforce access control at the response level.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
* refactor: remove redundant add_prefix variable assignments
Simplified the code by removing intermediate `add_prefix` variable
assignments and passing `True` directly to function calls since
we now always add server prefixes.
Changes:
- Removed `add_prefix = True` variable assignments in 5 locations
- Changed `add_prefix=add_prefix` to `add_prefix=True` in function calls
- Added inline comments to clarify the behavior
This makes the code more concise and clearer in intent.
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
* feat(auth_utils.py): support safety_identifier as a valid way of passing the end user id for responses api
* feat(llms): ensure 'tools' is correctly updated for responses api
* fix: fix greptile feedback
* feat: transformation.py
proper responses api tool handling for guardrail translation layer
---------
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
* Add MCP_SECURITY enum to SupportedGuardrailIntegrations
* Add MCP security guardrail initializer
* Add MCPSecurityGuardrail implementation
* Add MCP Security policy template
* Add Type filter to policy templates UI
* Add unit tests for MCP security guardrail
* fix(lint): remove unused Dict import from mcp_security_guardrail
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Add French language support for EU AI Act Article 5 guardrail (#21427)
* Add French language support for EU AI Act Article 5 template
- Create eu_ai_act_article5_fr.yaml with comprehensive French keywords
- Includes identifier words: concevoir, créer, développer, noter, classer, etc.
- Includes block words: crédit social, comportement social, émotion des employés, etc.
- Includes always-block keywords for explicit prohibited practices
- Includes exceptions for research, compliance, and legitimate use cases
- Catches circumvention attempts with phrase variations
* Add comprehensive tests for French EU AI Act guardrail
- Test 3 critical scenarios: blocked query, circumvention attempt, safe query
- Test edge cases: case-insensitive, mixed language, research exceptions
- All 7 tests passing
- Validates both blocking and allowing behavior
* Fix content filter to support conditional matching without inherit_from
- Enable conditional matching when identifier_words + additional_block_words are present
- Previously required inherit_from, but EU AI Act templates are self-contained
- Fixes Greptile feedback: conditional matching now works as documented
* Add pure conditional matching test for French guardrail
- Test identifier + block word combinations not in always_block_keywords
- Verifies conditional matching works independently
- Addresses Greptile feedback about test coverage gap
* Fix exception word bypass risk in French template
- Replace short words (film, jeu, juste) with context-specific phrases
- Prevents substring matching bypasses (e.g., enjeu matching jeu)
- Add tests for bypass prevention and legitimate game context
- Addresses Greptile security feedback
* Make conditional match assertion more robust
- Use getattr to safely access exception detail field
- Check if detail is dict before calling .get()
- Addresses Greptile feedback about brittle string assertion
* Add French EU AI Act Article 5 policy template to registry
- Add eu-ai-act-article5-fr template for French language support
- Includes French description and guardrail info
- Matches structure of English template
* Address greptile review feedback (greploop iteration 1)
- Use status_code=400 instead of 403 to match guardrail logging convention
- Use prefix stripping instead of split('/')[-1] for robust server name extraction
* remove French EU AI Act template from policy_templates.json
---------
Co-authored-by: Julio Quinteros Pro <jquinter@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Use importlib.import_module + reload uniformly in both code paths
to ensure fresh module state regardless of whether litellm was
previously in sys.modules. This fixes the inconsistency where the
"not in sys.modules" branch didn't reload the module.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- test_pillar_guardrails.py: Fix fixture to properly update module-level
litellm reference using global keyword and assignment from reload
- test_anthropic_experimental_pass_through_messages_handler.py: Add missing
assert keywords to kwargs comparison statements (lines 36, 60-62)
- test_proxy_server.py: Replace silent pytest.skip with explicit assertion
to catch router initialization regressions
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* Add 6 new EU PII patterns for GDPR compliance
- fr_nir: French Social Security Number (NIR/INSEE) with validation
- eu_iban_enhanced: Enhanced IBAN detection with specific format
- fr_phone: French phone numbers (+33, 0033, 0 formats)
- eu_vat: EU VAT identification numbers (all 27 member states)
- eu_passport_generic: Generic EU passport format
- fr_postal_code: French postal codes with contextual keywords
* Add GDPR Art. 32 EU PII Protection policy template
- Comprehensive GDPR Article 32 compliance policy
- 4 guardrail groups: National IDs, Financial, Contact Info, Business IDs
- Masks French NIR/INSEE, EU IBANs, French phones, EU VAT numbers
- Includes EU passport numbers and email addresses
- Medium complexity template with indigo icon
* Add comprehensive tests for EU PII patterns
- Test French NIR validation (sex digit, month range)
- Test enhanced IBAN detection (French, German)
- Test French phone number formats
- Test EU VAT numbers
- Test generic EU passport format
- Test French postal code pattern
* Add EU pattern loading and category validation tests
- Verify all 6 EU PII patterns are loaded correctly
- Verify patterns are categorized as 'EU PII Patterns'
- Ensure pattern loading consistency
* Add end-to-end tests for GDPR policy template
- 4 tests for PII that should be masked (NIR, IBAN, phone, VAT)
- 4 tests for text that should pass through (invalid patterns, no PII)
- 1 bonus test for multiple PII types in same message
- All tests verify correct masking behavior
* Add region field to policy templates
- Added region field to all 6 templates (EU, AU, Global)
- Updated both main and backup JSON files
- Enables region-based filtering in UI
* Add region filter to policy templates UI
- Added Radio.Group filter for regions (All, AU, EU, Global)
- Efficient filtering with useMemo hooks
- Clean button-based UI matching existing design
- Defaults missing regions to Global
* Apply suggestion from @greptile-apps[bot]
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Address Greptile review: add contextual guards and negative tests
- Added keyword_pattern to eu_vat (VAT, tax number, fiscal code, etc.)
- Added keyword_pattern to eu_passport_generic (passport, travel document, etc.)
- Added 3 negative unit tests for false positive prevention
- Added 2 E2E tests verifying no masking without keyword context
- All patterns now require contextual keywords to prevent false positives
* address greptile review feedback (greploop iteration 1)
- Remove unused HTTPException import from test file
- Add keyword_pattern to eu_vat for contextual VAT matching
- Add allow_word_numbers: false to eu_passport_generic
- Add negative test cases for EU VAT false positives
- All 5 Greptile comments addressed
* Address Greptile feedback: fix patterns and sync backup
- Fix fr_phone pattern: use negative lookbehind (?<!\d) to prevent false matches in longer digit strings
- Add keyword_pattern to eu_passport_generic to reduce false positives on version strings/SKUs
- Sync policy_templates_backup.json with main file (add GDPR template)
- Add keyword_pattern to eu_vat (was auto-added by formatter)
All pattern tests passing
* address greptile review feedback (greploop iteration 2)
- Update test to document that eu_vat raw pattern is intentionally broad
- Test verifies pattern DOES match common words (by design)
- Documents that keyword_pattern guard prevents false positives in production
- Addresses Greptile's false positive risk concern
* address greptile review feedback (greploop iteration 3)
- Fix test_eu_vat_masked: change gap from 2 words to 1 word (VAT number: FR...)
- This ensures keyword matching works within MAX_KEYWORD_VALUE_GAP_WORDS=1 limit
- fr_phone pattern already works correctly (verified with tests)
- test_pattern_requires_keyword_context already updated in iteration 2
Addresses final issues from Greptile 2/5 review
* fix: remove country-specific passport patterns from GDPR template
- Remove passport_france, passport_germany, passport_netherlands from template
- These patterns lack keyword guards and cause false positives
- Only eu_passport_generic remains (has keyword_pattern guard)
- Sync policy_templates_backup.json with main file
- Update test setup to match template
All 11 E2E tests now passing ✅
* Update tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_gdpr_policy_e2e.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Update litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
The setup_and_teardown fixture was failing with "ImportError: module
litellm not in sys.modules" during parallel test execution. This occurs
because another worker might have removed/modified litellm from
sys.modules before this test tries to reload it.
Fix: Check if litellm is in sys.modules before attempting reload.
* fix(content_filter.py): fix filter on toxic keywords
* feat: improve toxic/abusive language detection
* fix: additional improvements to nsfw filters
* feat: more improvements to nsfw filter
* feat(content_filter.json): add new australia specific nsfw content filter
ensure complete coverage for australia nsfw
* fix: cleanup policy templates
* fix(index.tsx): alert notice
* fix(index.tsx): add disclaimer notice
* feat(harmful_child_safety.yaml): new child safety content filter
ensure we catch inappropriate, child-specific content
* feat(policy_templates.json): add child safety and self harm filters
* fix(content_filter.py): improve racial bias filter to use a similar identifier + block word pattern and cover a wider range of ethnicities
* feat(policy_templates.json): add racial bias to nsfw policy template
* feat: add json content viewer
* fix: empty guardrails/policies arrays should not trigger enterprise license check (#20304)
The UI sends empty arrays for enterprise-only fields (guardrails, policies,
logging) even when the user has not configured these features. The backend
`is not None` check treated `[]` as a truthy intent to use the feature,
falsely requiring an enterprise license for basic team operations.
Backend: Add `and updated_kv[field] != [] and updated_kv[field] != {}`
guards in `_update_metadata_fields` so empty collections are skipped.
UI: Conditionally omit guardrails, logging, and policies from the
payload when empty instead of defaulting to `[]`.
Fixes#20304
* fix: allow clearing fields with empty collections while skipping enterprise check
Address PR review feedback:
1. Move the empty-collection guard into _update_metadata_field (singular)
so that empty lists/dicts skip only the premium license check but still
get written into metadata. This lets users intentionally clear a
previously-set field (e.g. guardrails: []) without being blocked, while
the UI's default empty arrays still don't trigger a false enterprise
error.
2. Remove sys.path hack from test file; use standard imports that work
with pytest discovery.
3. Add tests verifying that empty collections are moved into metadata
(field clearing works) even though they bypass the premium check.
Fixes#20304
* fix(proxy): add regression tests for #20441 - ensure <script> tags in LLM messages are not blocked
The 403 Forbidden error when sending messages containing `<script>` is caused
by external WAF/reverse proxy infrastructure (confirmed by the standard nginx
HTML 403 response format), not by LiteLLM's own content filtering. However,
these regression tests ensure that:
1. The content filter guardrail's built-in patterns do not match HTML tags
2. Messages containing <script> and other HTML tags pass through the content
filter unchanged when no explicit HTML-blocking rules are configured
3. The HTTP request body parser correctly handles JSON payloads containing
HTML content without modification
These tests guard against accidentally introducing HTML/XSS filtering that
would break legitimate LLM API usage (e.g., discussing HTML/JavaScript code).
Closes#20441
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add opus 4.5 and 4.6 to use outout_format param
* generate poetry lock with 2.3.2 poetry
* restore poetry lock
* e2e tests, key delete, update tpm rpm, and regenerate
* Split e2e ui testing for browser
* new login with sso button in login page
* option to hide usage indicator
* fix(cloudzero): update CBF field mappings per LIT-1907 (#20906)
* fix(cloudzero): update CBF field mappings per LIT-1907
Phase 1 field updates for CloudZero integration:
ADD/UPDATE:
- resource/account: Send concat(api_key_alias, '|', api_key_prefix)
- resource/service: Send model_group instead of service_type
- resource/usage_family: Send provider instead of hardcoded 'llm-usage'
- action/operation: NEW - Send team_id
- resource/id: Send model name instead of CZRN
- resource/tag:organization_alias: Add if exists
- resource/tag:project_alias: Add if exists
- resource/tag:user_alias: Add if exists
REMOVE:
- resource/tag:total_tokens: Removed
- resource/tag:team_id: Removed (team_id now in action/operation)
Fixes LIT-1907
* Update litellm/integrations/cloudzero/transform.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix: define api_key_alias variable, update CBFRecord docstring
- Fix F821 lint error: api_key_alias was used but not defined
- Update CBFRecord docstring to reflect LIT-1907 field mappings
- Remove unused Optional import
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Add banner notifying of breaking change
* Add semgrep & Fix OOMs (#20912)
* [Feat] Policies - Allow connecting Policies to Tags, Simulating Policies, Viewing how many keys, teams it applies on (#20904)
* init schema with TAGS
* ui: add policy test
* resolvePoliciesCall
* add_policy_sources_to_metadata + headers
* types Policy
* preview Impact
* def _describe_match_reason(
* match based on TAGs
* TestTagBasedAttachments
* test fixes
* add policy_resolve_router
* add_guardrails_from_policy_engine
* TestMatchAttribution
* refactor
* fix
* fix: address Greptile review feedback on policy resolve endpoints
- Track unnamed keys/teams as separate counts instead of inflating
affected_keys_count with duplicate "(unnamed key)" placeholders.
Added unnamed_keys_count and unnamed_teams_count to response.
- Push alias pattern matching to DB via _build_alias_where() which
converts exact patterns to Prisma "in" and suffix wildcards to
"startsWith" filters.
- Gate sync_policies_from_db/sync_attachments_from_db behind
force_sync query param (default false) to avoid 2 DB round-trips
on every /policies/resolve request.
- Remove worktree-only conftest.py that cleared sys.modules at import
time — no longer needed since code moved to main repo.
- Rename MAX_ESTIMATE_IMPACT_ROWS → MAX_POLICY_ESTIMATE_IMPACT_ROWS.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: eliminate duplicate DB queries and fix header delimiter ambiguity
- Fetch teams table once in estimate_attachment_impact and reuse for
both tag-based and alias-based lookups (was querying teams twice when
both tag_patterns and team_patterns were provided).
- Convert tag/team filter functions from async DB queries to sync
filters that operate on pre-fetched data (_filter_keys_by_tags,
_filter_teams_by_tags).
- Fix comma ambiguity in x-litellm-policy-sources header: use '; '
as entry delimiter since matched_via values can contain commas.
- Use '+' as the within-value separator in matched_via reason strings
(e.g. "tag:healthcare+team:health-team") to avoid conflict with
header delimiters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update litellm/proxy/policy_engine/policy_resolve_endpoints.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix: type error & better error handling (#20689)
* [Docs] Add docs guide for using policies (#20914)
* init schema with TAGS
* ui: add policy test
* resolvePoliciesCall
* add_policy_sources_to_metadata + headers
* types Policy
* preview Impact
* def _describe_match_reason(
* match based on TAGs
* TestTagBasedAttachments
* test fixes
* add policy_resolve_router
* add_guardrails_from_policy_engine
* TestMatchAttribution
* refactor
* fix
* fix: address Greptile review feedback on policy resolve endpoints
- Track unnamed keys/teams as separate counts instead of inflating
affected_keys_count with duplicate "(unnamed key)" placeholders.
Added unnamed_keys_count and unnamed_teams_count to response.
- Push alias pattern matching to DB via _build_alias_where() which
converts exact patterns to Prisma "in" and suffix wildcards to
"startsWith" filters.
- Gate sync_policies_from_db/sync_attachments_from_db behind
force_sync query param (default false) to avoid 2 DB round-trips
on every /policies/resolve request.
- Remove worktree-only conftest.py that cleared sys.modules at import
time — no longer needed since code moved to main repo.
- Rename MAX_ESTIMATE_IMPACT_ROWS → MAX_POLICY_ESTIMATE_IMPACT_ROWS.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: eliminate duplicate DB queries and fix header delimiter ambiguity
- Fetch teams table once in estimate_attachment_impact and reuse for
both tag-based and alias-based lookups (was querying teams twice when
both tag_patterns and team_patterns were provided).
- Convert tag/team filter functions from async DB queries to sync
filters that operate on pre-fetched data (_filter_keys_by_tags,
_filter_teams_by_tags).
- Fix comma ambiguity in x-litellm-policy-sources header: use '; '
as entry delimiter since matched_via values can contain commas.
- Use '+' as the within-value separator in matched_via reason strings
(e.g. "tag:healthcare+team:health-team") to avoid conflict with
header delimiters.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* docs v1 guide with UI imgs
* docs fix
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add dashscope/qwen3-max model with tiered pricing (#20919)
Add support for Alibaba Cloud's Qwen3-Max model with:
- 258K input tokens, 65K output tokens
- Tiered pricing based on context window usage (0-32K, 32K-128K, 128K-252K)
- Function calling and tool choice support
- Reasoning capabilities enabled
Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
* fix linting
* docs: add Greptile review requirement to PR template (#20762)
* fix(azure): preserve content_policy_violation error details from Azure OpenAI
Closes#20811
Azure OpenAI returns rich error payloads for content policy violations
(inner_error with ResponsibleAIPolicyViolation, content_filter_results,
revised_prompt). Previously these details were lost when:
1. The top-level error code was not "content_policy_violation" but the
inner_error.code was "ResponsibleAIPolicyViolation" -- the structured
check only examined the top-level code.
2. The DALL-E image generation polling path stringified the error JSON
into the message field instead of setting the structured body, making
it impossible for exception_type() to extract error details.
3. The string-based fallback detector used "invalid_request_error" as a
content-policy indicator, which is too broad and could misclassify
regular bad-request errors.
Changes:
- exception_mapping_utils.py: Check inner_error.code for
ResponsibleAIPolicyViolation when top-level code is not
content_policy_violation. Replace overly broad "invalid_request_error"
string match with specific Azure safety-system messages.
- azure.py: Set structured body on AzureOpenAIError in both async and
sync DALL-E polling paths so exception_type() can inspect error details.
- test_azure_exception_mapping.py: Add regression tests covering the
exact error payloads from issue #20811.
- Fix pre-existing lint: duplicate PerplexityResponsesConfig dict key,
unused RouteChecks top-level import.
---------
Co-authored-by: Kelvin Tran <kelvin-tran@users.noreply.github.com>
Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: shin-bot-litellm <shin-bot-litellm@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: Alexsander Hamir <alexsanderhamirgomesbaptista@gmail.com>
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Harshit Jain <48647625+Harshit28j@users.noreply.github.com>
Co-authored-by: ken <122603020@qq.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
* Generic Guardrails: Forward request headers + litellm_version to generic guardrail API
* Generic Guardrail: Change the request headers addition to be with allowlist instead denylist
The unified guardrail's streaming iterator hook processes every Nth
chunk (sampling_rate, default 5). On each sampled chunk it calls
process_output_streaming_response, which combines all accumulated text
into the first chunk and clears all subsequent chunks to "".
The hook then yielded `processed_items[-1]` — the last item, whose
content had been cleared to "". This permanently lost every Nth
chunk's content, causing random missing words/tokens in the client
output (observed in Roo Code, Open WebUI, etc.).
Fix: deep-copy the current chunk before guardrail processing runs,
then yield the original (unmodified) chunk. The guardrail validation
still executes and can block if it detects a problem, but the stream
content is preserved.
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
* fix fail-open for grayswan; pass metadata to cygnal api endpoint; update docs
* pass litellm_metadata to cygnal in payload
* switch error msg to const, and clean exception handling.
* update pyproject.toml as requested
* Revert "update pyproject.toml as requested"
This reverts commit 4eece154d0.
Previously, when Model Armor guardrail blocked a request/response,
the `applied_guardrails` field was not populated in the logs because
`add_guardrail_to_applied_guardrails_header()` was called after the
HTTPException was raised.
This fix moves the `add_guardrail_to_applied_guardrails_header()` call
to before the blocking check in all hooks:
- async_pre_call_hook (pre_call mode)
- async_moderation_hook (during_call mode)
- async_post_call_success_hook (post_call mode)
- async_post_call_streaming_iterator_hook (streaming)
This ensures that even when a guardrail blocks content, the guardrail
name is properly recorded in the logs for observability.
Added regression tests to verify applied_guardrails is populated when
content is blocked.
Co-authored-by: Cursor <cursoragent@cursor.com>
* [Feat] Add model parameter to Generic Guardrail API
Add model information to guardrail requests, allowing guardrails to make
model-specific security decisions.
Changes:
- Add `model` field to GenericGuardrailAPIInputs TypedDict
- Add `model` field to GenericGuardrailAPIRequest Pydantic model
- Update OpenAI and Anthropic handlers to pass model from request/response
- Add unit tests for model parameter handling
* [Feat] Add model parameter to all guardrail_translation handlers
Extend model parameter support to all guardrail handlers for consistent
implementation across all endpoint types:
- OpenAI Responses API (input/output + streaming)
- OpenAI Image Generation (input only)
- OpenAI Text Completion (input/output)
- OpenAI Text-to-Speech (input only)
- OpenAI Audio Transcription (output only)
- Cohere Rerank (input only)
- Pass-through Endpoints (input/output)
- MCP Server (input only)
This addresses the review feedback requesting consistent model parameter
handling across all guardrail_translation/handler.py files.
---------
Co-authored-by: Igal Boxerman <igal@pillar.security>
* Consolidated change
* fix(prompt_security): update message processing to persist sanitized files and filter for API calls
* fix per krrishdholakia suggestion
