* attempt to implement the passthrough feature
* Formatting and small change
* Fix formatting
* feat: grayswan guardrail overwrite ModelResponse in passthrough mode
* fix missing exception error catching on certain
endpoints
* fix wrong call site
* fix: patch anthropic endpoint internal error on streaming obj
* fix grayswan testcase
* feat: update the violation response to more natural
* Formatting
* move passthrough exception definition to custom_guardrail.
* Enhancement: show whether the blocked at input or output
* update exception name
* fix a typo in testing unit.
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Co-authored-by: Xiaohan Fu <xiaohan@grayswan.ai>
* refactor: remove api-key conversion logic for Azure Anthropic
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* fix(passthrough): pass custom_llm_provider to completion_cost for Azure AI Anthropic
The passthrough logging for Anthropic was failing when using Azure AI Anthropic
because the completion_cost function was not receiving the custom_llm_provider
parameter, causing it to fail with "LLM Provider NOT provided" error.
This fix:
- Retrieves custom_llm_provider from logging_obj.model_call_details
- Prepends provider prefix to model name for cost calculation
- Passes both formatted model and custom_llm_provider to completion_cost
- Centralizes provider prefix logic in _create_anthropic_response_logging_payload
This ensures cost calculation works correctly for Azure AI Anthropic requests
with models like azure_ai/claude-sonnet-4-5_gb_20250929.
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
* test: add unit tests for Azure AI Anthropic fixes
- Add tests for custom_llm_provider cost calculation in passthrough logging
- Add tests for ProviderConfigManager returning AzureAnthropicMessagesConfig
- Update existing tests to reflect removal of api-key to x-api-key conversion
Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
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Co-authored-by: Erdem Halil <erdemhalil@users.noreply.github.com>
The async_post_call_streaming_iterator_hook function was broken:
1. Was a sync function (def) not async generator
2. Returned AsyncGenerator without iterating it
3. Callback generators were chained but never consumed
This fix:
1. Makes the function an async generator (async def + yield)
2. Actually iterates through the chained callbacks with 'async for'
3. Properly yields chunks to the caller
Fixes#9639
* feat(mcp): preserve tool metadata and full CallToolResult in MCP gateway
This PR fixes two issues that prevented ChatGPT from rendering MCP UI widgets
when proxied through LiteLLM:
1. Preserve Tool Metadata in tools/list
- Modified _create_prefixed_tools() to mutate tools in place instead of
reconstructing them, preserving all fields including metadata/_meta
- This ensures ChatGPT can see 'openai/outputTemplate' URIs in tools/list
and will call resources/read to fetch widgets
2. Preserve Full CallToolResult (structuredContent + metadata)
- Changed call_mcp_tool() and _handle_managed_mcp_tool() to return full
CallToolResult objects instead of just content
- Updated error handlers to return CallToolResult with isError flag
- Wrapped local tool results in CallToolResult objects
- This preserves structuredContent and metadata fields needed for widget rendering
Files changed:
- litellm/proxy/_experimental/mcp_server/mcp_server_manager.py
- litellm/proxy/_experimental/mcp_server/server.py
Fixes issues where ChatGPT could not render MCP UI widgets when using
LiteLLM as an MCP gateway.
* feat(mcp): Preserve tool metadata and return full CallToolResult for ChatGPT UI widgets
- Preserve metadata and _meta fields when creating prefixed tools
- Return full CallToolResult instead of just content list
- Ensures ChatGPT can discover and render UI widgets via openai/outputTemplate
- Fixes metadata stripping that prevented widget rendering in ChatGPT
Changes:
- mcp_server_manager.py: Mutate tools in place to preserve all fields including metadata
- server.py: Return CallToolResult with structuredContent and metadata preserved
- Added test to verify metadata preservation
* fix: guard cost calculator when BaseModel lacks _hidden_params
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Co-authored-by: Afroz Ahmad <aahmad@Afrozs-MacBook-Pro.local>
Co-authored-by: Afroz Ahmad <aahmad@KNDMCPTMZH3.sephoraus.com>
- Skip empty/whitespace text before calling Presidio API
- Handle error dict responses gracefully (e.g., {'error': 'No text provided'})
- Add defensive error handling for invalid result items
- Add comprehensive test coverage for empty content scenarios
Fixes crash in tool/function calling where assistant messages have empty content.
Fixes#17552
- Change Prisma include from 'users' to 'members'
- Use LiteLLM_OrganizationTableWithMembers type for membership validation
- Access organization.members instead of organization.users
- Add tests for membership validation
* fix async_log_success_event for _PROXY_DynamicRateLimitHandlerV3
* test_async_log_success_event_increments_by_actual_tokens
* fix redis TTL
* Potential fix for code scanning alert no. 3873: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
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Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* fix(unified_guardrail.py): support during_call event type for unified guardrails
allows guardrails overriding apply_guardrails to work 'during_call'
* feat(generic_guardrail_api.py): support new 'tool_calls' field for generic guardrail api
returns the tool calls emitted by the LLM API to the user
* fix(generic_guardrail_api.py): working anthropic /v1/messages tool call response
send llm tool calls to guardrail api when called via `/v1/messages` API
* fix(responses/): run generic_guardrail_api on responses api tool call responses
* fix: fix tests
* test: fix tests
* fix: fix tests
Fixes#17517
- Fixed bug where only the first matching blocked keyword was masked
- Now iterates through ALL blocked keywords and masks each one
- Added 3 regression tests for multiple keyword masking
* init schema.prisma
* init LiteLLM_ObjectPermissionTable with agents and agent_access_groups
* TestAgentRequestHandler
* refatctor agent list
* add AgentRequestHandler
* fix agent access controls by key/team
* feat - new migration for LiteLLM_AgentsTable
* fix add LiteLLM_ObjectPermissionBase with agent and agent groups
* add agent routes to llm api routes
* add agent routes as llm route
yuneng-jiang