* feat(proxy): add max_iterations limiter for agent session loops (#22058)
Adds a new proxy hook that enforces a per-session cap on the number of
LLM calls an agentic loop can make. Callers send a session_id with each
request, and the hook counts calls per session, returning 429 when the
configured max_iterations limit is exceeded.
- Uses Redis Lua script for atomic increment (multi-instance safe)
- Falls back to in-memory cache when Redis unavailable
- Follows parallel_request_limiter_v3 pattern
- Configurable via key metadata: {"max_iterations": 25}
- Session counters auto-expire via TTL (default 1hr)
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add new code execution dataset
* feat(agent_endpoints/): allow giving agents keys
* fix: ui fixes
* feat: allow assigning mcp servers to agents
* fix: eliminate duplicate DB queries in MCP agent auth and N+1 in agent listing (#22110)
- Extract _get_agent_object_permission helper so _get_allowed_mcp_servers_for_agent
and _get_agent_tool_permissions_for_server share a single DB fetch instead of
each independently querying the same agent row (was 1+N queries per MCP request)
- Use include={"object_permission": True} on find_many in get_all_agents_from_db
to eagerly load permissions in one query instead of N+1
- Use include={"object_permission": True} on create/update/find_unique in all
agent CRUD operations, removing attach_object_permission_to_dict follow-up calls
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Add a `request_duration_ms` column to `LiteLLM_SpendLogs` to track request
duration. New rows are computed at write time. Legacy rows use a COALESCE
fallback in the `/spend/logs/ui` query to compute duration on the fly from
`endTime - startTime`. The field is also sortable in the UI endpoint.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(agents): assign virtual keys to agents
- Add agent_id field to LiteLLM_VerificationToken (schema.prisma + _types.py)
- Pass agent_id through key generation endpoint so keys can be scoped to an agent
- Refactor Add Agent wizard to 3-step flow (Configure → Assign Key → Ready)
- Configure: all agent fields, custom/other type with just name+description
- Assign Key: create new key or reassign existing key to agent
- URL is now optional for easy discovery
- Add "Agent" ownership option to Create Key modal on Virtual Keys page
with agent selector dropdown
- Extract CreatedKeyDisplay into shared component, reused in both flows
- Add keyCreateForAgentCall networking helper
- Add test for agent_id key generation
* fix(agents): code quality fixes from self-review
- Fix test_generate_key_helper_fn_agent_id: remove bare except clause,
use explicit assert mock_insert.called, use .kwargs for clean arg access
- Remove no-op conditional in handleNext (both branches were identical)
- Validate selectedExistingKey before calling keyUpdateCall
- Validate selectedAgentId before setting on formValues in create_key_button
* fix(ui): replace deprecated Tremor Button with Ant Design Button in CreatedKeyDisplay
Failure spend logs were missing key metadata (key alias, user ID, team ID,
team alias) in two scenarios:
1. Auth errors (401 ProxyException): auth_exception_handler creates a
minimal UserAPIKeyAuth with only api_key and request_route set — all
other fields are null. The failure hook now looks up the full key object
from cache/DB using the key hash to populate the missing fields.
2. Post-auth failures (provider errors, rate limits): key fields are
present but team_alias is always null because LiteLLM_VerificationTokenView
SQL view does not include team_alias. The failure hook now looks up the
team object from cache to populate team_alias.
Both lookups are non-fatal and wrapped in try/except.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Add Ask AI chat component to Usage page
- Create UsageAIChatModal component with streaming chat interface
- Integrate with existing model hub for model selection
- Pass usage data context (spend, models, providers, keys) to AI
- Add Ask AI button next to Export Data button in global view
- Add tests for the new component and integration
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Convert Ask AI from modal to right-side sliding panel
- Replace UsageAIChatModal with UsageAIChatPanel
- Panel slides in from right side, usage page stays visible
- Full-height panel with header, model selector, chat area, and input
- Smooth CSS transition for open/close animation
- Update tests for new panel component (34 tests passing)
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Remove build output directory from tracking
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Add backend AI usage chat endpoint with tool calling
Backend:
- New /usage/ai/chat SSE streaming endpoint
- AI agent has get_usage_data tool that queries /user/daily/activity/aggregated
- Follows same architecture as policy AI suggest (litellm.acompletion + tools)
- Non-admin users are restricted to their own data
- 12 backend unit tests
Frontend:
- Panel now calls /usage/ai/chat backend endpoint via SSE
- Removed direct OpenAI client calls from frontend
- Added usageAiChatStream networking function following enrichPolicyTemplateStream pattern
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Make model selection optional, default to gpt-4o-mini on backend
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Add team/tag tools, status indicators, and improved AI agent
- AI agent now has 3 tools: get_usage_data, get_team_usage_data, get_tag_usage_data
- Stream status events (Thinking... Fetching... Analyzing...) to UI
- Frontend shows spinner + status text during tool execution
- Better system prompt guiding tool selection
- Entity summariser for team/tag data with ranked breakdowns
- 13 backend tests, 34 frontend tests passing
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Fix: inject today's date into system prompt so AI resolves relative dates correctly
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Show tool calls as distinct steps + render markdown in responses
- Backend emits tool_call events with tool_name, label, args, and status
- Frontend shows each tool call as a step with ✓/spinner/✗ indicator
- Tool call steps show icon, label, date range, and filters
- AI responses rendered with ReactMarkdown (bold, lists, tables, code)
- Cursor-like UX: Thinking → tool calls → Analyzing → streamed answer
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Refactor backend for code quality: proper types, constants, all functions ≤50 LOC
- TypedDict for SSE events (SSEStatusEvent, SSEToolCallEvent, etc.) and ToolHandler
- Constants for table names, entity fields, temperature, page sizes, top-N limits
- Shared _query_activity() eliminates duplicated fetch logic
- _accumulate_breakdown() + _ranked_lines() replace inline aggregation loops
- Extracted _process_tool_call() and _stream_final_response() from main stream fn
- Black + Ruff clean, all 15 functions verified ≤50 LOC
- Replaced Tremor Button with Antd Button in panel (Tremor deprecated per AGENTS.md)
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Address greptile review: security fixes and input validation
- Restrict team/tag tools to admin-only users (non-admins only get get_usage_data)
- Constrain ChatMessage.role to Literal['user', 'assistant'] to prevent system prompt injection
- Add test for base tools restriction (non-admin gets 1 tool, admin gets 3)
- Issues 3 (unused imports) and 4 (inline datetime) were already fixed in prior commit
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Address greptile round 2: sanitize errors, defense-in-depth allowlist, revert tsconfig
- Sanitize error messages: generic 'An internal error occurred' sent to client,
full exception logged server-side via verbose_proxy_logger
- Defense-in-depth: _process_tool_call validates fn_name against role-based
allowlist before dispatch (even though LLM only receives allowed tools)
- Revert tsconfig.json jsx back to 'preserve' (Next.js recommended default)
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Role-scoped system prompt + additional test coverage
- System prompt is now role-aware: admin sees all 3 tool descriptions,
non-admin only sees get_usage_data (consistent with tool filtering)
- Added tests: non-admin prompt excludes team/tag tools, date injection
- 15 backend tests, 34 frontend tests passing
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* Fix LLM arg validation + cap conversation size at 20 messages
- _resolve_fetch_kwargs uses .get() with ValueError for missing dates
(handles malformed LLM tool arguments gracefully)
- MAX_CHAT_MESSAGES = 20 constant; backend truncates to last 20
- Frontend also sends only last 20 messages per request
- Prevents excessive token usage and context-length errors
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>
* 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>
- Resolve merge conflict in pass_through_endpoints.py
- Add .copy() to proxy_server_request headers to prevent cache corruption
- Add test for request.state unavailable fallback path
* fix(router): preserve _hidden_params in FallbackStreamWrapper so x-litellm-overhead-duration-ms is emitted for streaming requests
* test(router): add regression test for FallbackStreamWrapper _hidden_params preservation
* auth_with_role_name add region_name arg for cross-account sts
* update tests to include case with aws_region_name for _auth_with_aws_role
* Only pass region_name to STS client when aws_region_name is set
* Add optional aws_sts_endpoint to _auth_with_aws_role
* Parametrize ambient-credentials test for no opts, region_name, and aws_sts_endpoint
* consistently passing region and endpoint args into explicit credentials irsa
* fix env var leakage
* fix: bedrock openai-compatible imported-model should also have model arn encoded
* feat: show proxy url in ModelHub (#21660)
* fix(bedrock): correct modelInput format for Converse API batch models (#21656)
* fix(proxy): add model_ids param to access group endpoints for precise deployment tagging (#21655)
POST /access_group/new and PUT /access_group/{name}/update now accept an
optional model_ids list that targets specific deployments by their unique
model_id, instead of tagging every deployment that shares a model_name.
When model_ids is provided it takes priority over model_names, giving
API callers the same single-deployment precision that the UI already has
via PATCH /model/{model_id}/update.
Backward compatible: model_names continues to work as before.
Closes#21544
* feat(proxy): add custom favicon support\n\nAdd ability to configure a custom favicon for the litellm proxy UI.\n\n- Add favicon_url field to UIThemeConfig model\n- Add LITELLM_FAVICON_URL env var support\n- Add /get_favicon endpoint to serve custom favicons\n- Update ThemeContext to dynamically set favicon\n- Add favicon URL input to UI theme settings page\n- Add comprehensive tests\n\nCloses #8323 (#21653)
* fix(bedrock): prevent double UUID in create_file S3 key (#21650)
In create_file for Bedrock, get_complete_file_url is called twice:
once in the sync handler (generating UUID-1 for api_base) and once
inside transform_create_file_request (generating UUID-2 for the
actual S3 upload). The Bedrock provider correctly writes UUID-2 into
litellm_params["upload_url"], but the sync handler unconditionally
overwrites it with api_base (UUID-1). This causes the returned
file_id to point to a non-existent S3 key.
Fix: only set upload_url to api_base when transform_create_file_request
has not already set it, preserving the Bedrock provider's value.
Closes#21546
* feat(semantic-cache): support configurable vector dimensions for Qdrant (#21649)
Add vector_size parameter to QdrantSemanticCache and expose it through
the Cache facade as qdrant_semantic_cache_vector_size. This allows users
to use embedding models with dimensions other than the default 1536,
enabling cheaper/stronger models like Stella (1024d), bge-en-icl (4096d),
voyage, cohere, etc.
The parameter defaults to QDRANT_VECTOR_SIZE (env var or 1536) for
backward compatibility. When creating new collections, the configured
vector_size is used instead of the hardcoded constant.
Closes#9377
* fix(utils): normalize camelCase thinking param keys to snake_case (#21762)
Clients like OpenCode's @ai-sdk/openai-compatible send budgetTokens
(camelCase) instead of budget_tokens in the thinking parameter, causing
validation errors. Add early normalization in completion().
* feat: add optional digest mode for Slack alert types (#21683)
Adds per-alert-type digest mode that aggregates duplicate alerts
within a configurable time window and emits a single summary message
with count, start/end timestamps.
Configuration via general_settings.alert_type_config:
alert_type_config:
llm_requests_hanging:
digest: true
digest_interval: 86400
Digest key: (alert_type, request_model, api_base)
Default interval: 24 hours
Window type: fixed interval
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat: add blog_posts.json and local backup
* feat: add GetBlogPosts utility with GitHub fetch and local fallback
Adds GetBlogPosts class that fetches blog posts from GitHub with a 1-hour
in-process TTL cache, validates the response, and falls back to the bundled
blog_posts_backup.json on any network or validation failure.
* test: add cache reset fixture and LITELLM_LOCAL_BLOG_POSTS test
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add GET /public/litellm_blog_posts endpoint
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: log fallback warning in blog posts endpoint and tighten test
* feat: add disable_show_blog to UISettings
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add useUISettings and useDisableShowBlog hooks
* fix: rename useUISettings to useUISettingsFlags to avoid naming collision
* fix: use existing useUISettings hook in useDisableShowBlog to avoid cache duplication
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add BlogDropdown component with react-query and error/retry state
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: enforce 5-post limit in BlogDropdown and add cap test
* fix: add retry, stable post key, enabled guard in BlogDropdown
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: add BlogDropdown to navbar after Docs link
* feat: add network_mock transport for benchmarking proxy overhead without real API calls
Intercepts at httpx transport layer so the full proxy path (auth, routing,
OpenAI SDK, response transformation) is exercised with zero-latency responses.
Activated via `litellm_settings: { network_mock: true }` in proxy config.
* Litellm dev 02 19 2026 p2 (#21871)
* feat(ui/): new guardrails monitor 'demo
mock representation of what guardrails monitor looks like
* fix: ui updates
* style(ui/): fix styling
* feat: enable running ai monitor on individual guardrails
* feat: add backend logic for guardrail monitoring
* fix(guardrails/usage_endpoints.py): fix usage dashboard
* fix(budget): fix timezone config lookup and replace hardcoded timezone map with ZoneInfo (#21754)
* fix(budget): fix timezone config lookup and replace hardcoded timezone map with ZoneInfo
* fix(budget): update stale docstring on get_budget_reset_time
* fix: add missing return type annotations to iterator protocol methods in streaming_handler (#21750)
* fix: add return type annotations to iterator protocol methods in streaming_handler
Add missing return type annotations to __iter__, __aiter__, __next__, and __anext__ methods in CustomStreamWrapper and related classes.
- __iter__(self) -> Iterator["ModelResponseStream"]
- __aiter__(self) -> AsyncIterator["ModelResponseStream"]
- __next__(self) -> "ModelResponseStream"
- __anext__(self) -> "ModelResponseStream"
Also adds AsyncIterator and Iterator to typing imports.
Fixes issue with PLR0915 noqa comments and ensures proper type checking support.
Related to: BerriAI/litellm#8304
* fix: add ruff PLR0915 noqa for files with too many statements
* Add gollem Go agent framework cookbook example (#21747)
Show how to use gollem, a production Go agent framework, with
LiteLLM proxy for multi-provider LLM access including tool use
and streaming.
* fix: avoid mutating caller-owned dicts in SpendUpdateQueue aggregation (#21742)
* fix(vertex_ai): enable context-1m-2025-08-07 beta header (#21870)
* server root path regression doc
* fixing syntax
* fix: replace Zapier webhook with Google Form for survey submission (#21621)
* Replace Zapier webhook with Google Form for survey submission
* Add back error logging for survey submission debugging
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* Revert "Merge pull request #21140 from BerriAI/litellm_perf_user_api_key_auth"
This reverts commit 0e1db3f7e4, reversing
changes made to 7e2d6f2355.
* test_vertex_ai_gemini_2_5_pro_streaming
* UI new build
* fix rendering
* ui new build
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* docs fix
* release note docs
* docs
* adding image
* fix(vertex_ai): enable context-1m-2025-08-07 beta header
The `context-1m-2025-08-07` Anthropic beta header was set to `null` for vertex_ai,
causing it to be filtered out when users set `extra_headers: {anthropic-beta: context-1m-2025-08-07}`.
This prevented using Claude's 1M context window feature via Vertex AI, resulting in
`prompt is too long: 460500 tokens > 200000 maximum` errors.
Fixes#21861
---------
Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* Revert "fix(vertex_ai): enable context-1m-2025-08-07 beta header (#21870)" (#21876)
This reverts commit bce078a796.
* docs(ui): add pre-PR checklist to UI contributing guide
Add testing and build verification steps per maintainer feedback
from @yjiang-litellm. Contributors should run their related tests
per-file and ensure npm run build passes before opening PRs.
* Fix entries with fast and us/
* Add tests for fast and us
* Add support for Priority PayGo for vertex ai and gemini
* Add model pricing
* fix: ensure arrival_time is set before calculating queue time
* Fix: Anthropic model wildcard access issue
* Add incident report
* Add ability to see which model cost map is getting used
* Fix name of title
* Readd tpm limit
* State management fixes for CheckBatchCost
* Fix PR review comments
* State management fixes for CheckBatchCost - Address greptile comments
* fix mypy issues:
* Add Noma guardrails v2 based on custom guardrails (#21400)
* Fix code qa issues
* Fix mypy issues
* Fix mypy issues
* Fix test_aaamodel_prices_and_context_window_json_is_valid
* fix: update calendly on repo
* fix(tests): use counter-based mock for time.time in prisma self-heal test
The test used a fixed side_effect list for time.time(), but the number
of calls varies by Python version, causing StopIteration on 3.12 and
AssertionError on 3.14. Replace with an infinite counter-based callable
and assert the timestamp was updated rather than checking for an exact
value.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(tests): use absolute path for model_prices JSON in validation test
The test used a relative path 'litellm/model_prices_and_context_window.json'
which only works when pytest runs from a specific working directory.
Use os.path based on __file__ to resolve the path reliably.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update tests/test_litellm/test_utils.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* fix(tests): use os.path instead of Path to avoid NameError
Path is not imported at module level. Use os.path.join which is already
available.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* clean up mock transport: remove streaming, add defensive parsing
* docs: add Google GenAI SDK tutorial (JS & Python) (#21885)
* docs: add Google GenAI SDK tutorial for JS and Python
Add tutorial for using Google's official GenAI SDK (@google/genai for JS,
google-genai for Python) with LiteLLM proxy. Covers pass-through and
native router endpoints, streaming, multi-turn chat, and multi-provider
routing via model_group_alias. Also updates pass-through docs to use the
new SDK replacing the deprecated @google/generative-ai.
* fix(docs): correct Python SDK env var name in GenAI tutorial
GOOGLE_GENAI_API_KEY does not exist in the google-genai SDK.
The correct env var is GEMINI_API_KEY (or GOOGLE_API_KEY).
Also note that the Python SDK has no base URL env var.
* fix(docs): replace non-existent GOOGLE_GENAI_BASE_URL env var in interactions.md
The Python google-genai SDK does not read GOOGLE_GENAI_BASE_URL.
Use http_options={"base_url": "..."} in code instead.
* docs: add network mock benchmarking section
* docs: tweak benchmarks wording
* fix: add auth headers and empty latencies guard to benchmark script
* refactor: use method-level import for MockOpenAITransport
* fix: guard print_aggregate against empty latencies
* fix: add INCOMPLETE status to Interactions API enum and test
Google added INCOMPLETE to the Interactions API OpenAPI spec status enum.
Update both the Status3 enum in the SDK types and the test's expected
values to match.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Guardrail Monitor - measure guardrail reliability in prod (#21944)
* fix: fix log viewer for guardrail monitoring
* feat(ui/): fix rendering logs per guardrail
* fix: fix viewing logs on overview tab of guardrail
* fix: log viewer
* fix: fix naming to align with metric
* docs: add performance & reliability section to v1.81.14 release notes
* fix(tests): make RPM limit test sequential to avoid race condition
Concurrent requests via run_in_executor + asyncio.gather caused a race
condition where more requests slipped through the rate limiter than
expected, leading to flaky test failures (e.g. 3 successes instead of 2
with rpm_limit=2).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: Singapore guardrail policies (PDPA + MAS AI Risk Management) (#21948)
* feat: Singapore PDPA PII protection guardrail policy template
Add Singapore Personal Data Protection Act (PDPA) guardrail support:
Regex patterns (patterns.json):
- sg_nric: NRIC/FIN detection ([STFGM] + 7 digits + checksum letter)
- sg_phone: Singapore phone numbers (+65/0065/65 prefix)
- sg_postal_code: 6-digit postal codes (contextual)
- passport_singapore: Passport numbers (E/K + 7 digits, contextual)
- sg_uen: Unique Entity Numbers (3 formats)
- sg_bank_account: Bank account numbers (dash format, contextual)
YAML policy templates (5 sub-guardrails):
- sg_pdpa_personal_identifiers: s.13 Consent
- sg_pdpa_sensitive_data: Advisory Guidelines
- sg_pdpa_do_not_call: Part IX DNC Registry
- sg_pdpa_data_transfer: s.26 overseas transfers
- sg_pdpa_profiling_automated_decisions: Model AI Governance Framework
Policy template entry in policy_templates.json with 9 guardrail definitions
(4 regex-based + 5 YAML conditional keyword matching).
Tests:
- test_sg_patterns.py: regex pattern unit tests
- test_sg_pdpa_guardrails.py: conditional keyword matching tests (100+ cases)
* feat: MAS AI Risk Management Guidelines guardrail policy template
Add Monetary Authority of Singapore (MAS) AI Risk Management Guidelines
guardrail support for financial institutions:
YAML policy templates (5 sub-guardrails):
- sg_mas_fairness_bias: Blocks discriminatory financial AI (credit/loans/insurance by protected attributes)
- sg_mas_transparency_explainability: Blocks opaque/unexplainable AI for consequential financial decisions
- sg_mas_human_oversight: Blocks fully automated financial decisions without human-in-the-loop
- sg_mas_data_governance: Blocks unauthorized sharing/mishandling of financial customer data
- sg_mas_model_security: Blocks adversarial attacks, model poisoning, inversion on financial AI
Policy template entry in policy_templates.json with 5 guardrail definitions.
Aligned with MAS FEAT Principles, Project MindForge, and NIST AI RMF.
Tests:
- test_sg_mas_ai_guardrails.py: conditional keyword matching tests (100+ cases)
* fix: address SG pattern review feedback
- Update NRIC lowercase test for IGNORECASE runtime behavior
- Add keyword context guard to sg_uen pattern to reduce false positives
* docs: clarify MAS AIRM timeline references
- Explicitly mark MAS AIRM as Nov 2025 consultation draft
- Add 2018 qualifier for FEAT principles in MAS policy descriptions
- Update MAS guardrail wording to avoid release-year ambiguity
* chore: commit resolved MAS policy conflicts
* test:
* chore:
* Add OpenAI Agents SDK tutorial with LiteLLM Proxy to docs (#21221)
* Add OpenAI Agents SDK tutorial to docs
* Update OpenAI Agents SDK tutorial to use LiteLLM environment variables
* Enhance OpenAI Agents SDK tutorial with built-in LiteLLM extension details and updated configuration steps. Adjust section headings for clarity and improve the flow of information regarding model setup and usage.
* adjust blog posts to fetch from github first
* feat(videos): add variant parameter to video content download (#21955)
openai videos models support the features to download variants.
See more details here: https://developers.openai.com/api/docs/guides/video-generation#use-image-references.
Plumb variant (e.g. "thumbnail", "spritesheet") through the full
video content download chain: avideo_content → video_content →
video_content_handler → transform_video_content_request. OpenAI
appends ?variant=<value> to the GET URL; other providers accept
the parameter in their signature but ignore it.
* fixing path
* adjust blog post path
* Revert duplicate issue checker to text-based matching, remove duplicate PR workflow
Remove the Claude Code-powered duplicate PR detection workflow and revert
the duplicate issue checker back to wow-actions/potential-duplicates with
text similarity matching.
* ui changes
* adding tests
* adjust default aggregation threshold
* fix(videos): pass api_key from litellm_params to video remix handlers (#21965)
video_remix_handler and async_video_remix_handler were not falling back
to litellm_params.api_key when the api_key parameter was None, causing
Authorization: Bearer None to be sent to the provider. This matches the
pattern already used by async_video_generation_handler.
* adding testing coverage + fixing flaky tests
* fix(ollama): thread api_base through get_model_info and add graceful fallback
When users pass api_base to litellm.completion() for Ollama, the model
info fetch (context window, function_calling support) was ignoring the
user's api_base and only reading OLLAMA_API_BASE env var or defaulting
to localhost:11434. This caused confusing errors in logs when Ollama
runs on a remote server.
Thread api_base from litellm_params through the get_model_info call
chain so OllamaConfig.get_model_info() uses the correct server. Also
return safe defaults instead of raising when the server is unreachable.
Fixes#21967
---------
Co-authored-by: An Tang <ta@stripe.com>
Co-authored-by: janfrederickk <75388864+janfrederickk@users.noreply.github.com>
Co-authored-by: Zhenting Huang <3061613175@qq.com>
Co-authored-by: Darien Kindlund <darien@kindlund.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: yuneng-jiang <yuneng.jiang@gmail.com>
Co-authored-by: Ryan Crabbe <rcrabbe@berkeley.edu>
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
Co-authored-by: LeeJuOh <56071126+LeeJuOh@users.noreply.github.com>
Co-authored-by: Monesh Ram <31161039+WhoisMonesh@users.noreply.github.com>
Co-authored-by: Trevor Prater <trevor.prater@gmail.com>
Co-authored-by: The Mavik <179817126+themavik@users.noreply.github.com>
Co-authored-by: Edwin Isac <33712823+edwiniac@users.noreply.github.com>
Co-authored-by: milan-berri <milan@berri.ai>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
Co-authored-by: Harshit Jain <harshitjain0562@gmail.com>
Co-authored-by: Harshit Jain <48647625+Harshit28j@users.noreply.github.com>
Co-authored-by: Ephrim Stanley <ephrim.stanley@point72.com>
Co-authored-by: TomAlon <tom@noma.security>
Co-authored-by: Julio Quinteros Pro <jquinter@gmail.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Co-authored-by: ryan-crabbe <128659760+ryan-crabbe@users.noreply.github.com>
Co-authored-by: Ron Zhong <ron-zhong@hotmail.com>
Co-authored-by: Arindam Majumder <109217591+Arindam200@users.noreply.github.com>
Co-authored-by: Lei Nie <lenie@quora.com>
* auth_with_role_name add region_name arg for cross-account sts
* update tests to include case with aws_region_name for _auth_with_aws_role
* Only pass region_name to STS client when aws_region_name is set
* Add optional aws_sts_endpoint to _auth_with_aws_role
* Parametrize ambient-credentials test for no opts, region_name, and aws_sts_endpoint
* consistently passing region and endpoint args into explicit credentials irsa
* fix env var leakage
* fix: bedrock openai-compatible imported-model should also have model arn encoded
* fix: team assignment fails for keys with special model names (#21880)
---------
Co-authored-by: An Tang <ta@stripe.com>
Co-authored-by: Sameer Kankute <sameer@berri.ai>
The test used a fixed side_effect list for time.time(), but the number
of calls varies by Python version, causing StopIteration on 3.12 and
AssertionError on 3.14. Replace with an infinite counter-based callable
and assert the timestamp was updated rather than checking for an exact
value.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix(budget): fix timezone config lookup and replace hardcoded timezone map with ZoneInfo
* fix(budget): update stale docstring on get_budget_reset_time
* 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
Replace Click CliRunner with standalone_mode=False to avoid
"I/O operation on closed file" errors caused by Click's stream
isolation in CI environments.
test_vertex_passthrough_with_default_credentials and
test_view_spend_logs_with_date_range_summarized fail intermittently when a
prior xdist worker sets master_key — auth then rejects the unauthenticated
test requests before the code under test is reached.
- mock user_api_key_auth in test_vertex_passthrough_with_default_credentials
(same pattern used for test_vertex_passthrough_with_no_default_credentials
in #21810)
- wrap test_view_spend_logs_with_date_range_summarized in
app.dependency_overrides[ps.user_api_key_auth] with try/finally cleanup
(same pattern used for the other spend log tests in #21810)
- TestSpendLogsPayload: save/restore litellm.cache in setup_method/teardown_method
so tests that run after a cache-setting test don't see a non-None cache and get
a hash instead of "Cache OFF" in the cache_key field
- test_use_prisma_db_push_flag_behavior: apply clean_env pattern (strip DATABASE_URL/DIRECT_URL,
then set DATABASE_URL to test value) inside the with block instead of using @patch.dict
decorator, matching the pattern from test_skip_server_startup to avoid Click 8.3.x
StreamMixer stream lifecycle issues in CI
Tests hardcode expected api_base as https://api.anthropic.com/v1/messages but
if ANTHROPIC_BASE_URL is set in the environment the recorded api_base changes,
causing a mismatch. Clear both env vars via monkeypatch at the start of each test.
* fix(tests): add app.dependency_overrides for auth in spend logs tests
test_ui_view_spend_logs_with_status, test_ui_view_spend_logs_with_model,
test_ui_view_spend_logs_with_model_id, and test_view_spend_logs_summarize_parameter
all send Bearer sk-test without mocking user_api_key_auth. When a prior test
in the same xdist worker sets master_key, the auth check fails for sk-test
and the test fails intermittently.
Fix: use app.dependency_overrides[ps.user_api_key_auth] to bypass auth,
same pattern as other tests in the same file.
* fix(tests): mock user_api_key_auth in test_vertex_passthrough_with_no_default_credentials
vertex_proxy_route calls user_api_key_auth internally. When a prior test in the
same xdist worker sets master_key, the auth check fails for the test request
and create_pass_through_route is never called, causing assert_called_once_with to fail.
Fix: patch user_api_key_auth as an AsyncMock in the with mock.patch() block.
yuneng-jiang