The check `content.get("thinking", None) is not None` incorrectly
drops thinking blocks when the `thinking` key is explicitly null or
absent. Changed to `content.get("type") == "thinking"` to match
the fix already applied in the experimental pass-through path (PR #15501).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Populate container_id on streaming code_interpreter_results by
re-emitting at message_delta when container info arrives
- Reconstruct Pydantic OutputCodeInterpreterCall objects from plain
dicts in _extract_tool_result_output_items so responses_output
has uniform types across streaming and non-streaming paths
- test_non_bash_tool_result_skipped: verifies text_editor results produce
zero code_interpreter_call items
- test_end_to_end_streaming_chunks_to_code_interpreter_output: exercises
full path from Anthropic SSE chunks through ModelResponseIterator,
stream_chunk_builder, and _extract_tool_result_output_items without
a live server
- Empty stdout/stderr now produces outputs=None (matching OpenAI parity)
instead of outputs=[{logs:""}], in both streaming and non-streaming paths
- Fix test fixture to use real Anthropic type "bash_code_execution_tool_result"
instead of "code_execution_tool_result"
- Add test for empty-output → outputs=None behavior
- Add unit tests for _extract_tool_result_output_items: Pydantic objects,
plain dicts (post-model_dump), empty/missing provider_specific_fields,
and in-place substitution preserving output ordering
stream_chunk_builder uses "last value wins" for list-valued
provider_specific_fields keys. _build_code_interpreter_results was
emitting only new items (incremental), so earlier results were silently
dropped when multiple sequential code executions occurred.
- Emit cumulative list from _build_code_interpreter_results, matching
web_search_results pattern
- Assemble server_tool_use input from input_json_delta deltas at
content_block_stop (Anthropic streams input: {} in start block)
- Handle dict items in _extract_tool_result_output_items after
model_dump() serialization in stream_chunk_builder
- Simplify _merge_provider_specific_fields to last-value-wins for lists,
matching stream_chunk_builder semantics
PR #18945 added support for capturing Anthropic server-side tool results
(bash_code_execution_tool_result, etc.) in provider_specific_fields, but
the data never reached the Responses API output because:
1. Non-streaming: provider_specific_fields wasn't copied into _hidden_params
2. Streaming: chunk delta's provider_specific_fields wasn't accumulated
3. Tool results weren't mapped to standard output items
This fix:
- Copies provider_specific_fields to _hidden_params in transform_response()
- Accumulates provider_specific_fields from streaming chunk deltas
- Maps bash_code_execution_tool_result to code_interpreter_call output items
with code and outputs (matching OpenAI's native shape)
- Removes redundant function_call items for server-side tools
- Adds OutputCodeInterpreterCall type to the output union
- Keep Anthropic-native tools (tool_search_tool_regex, web_search, bash, etc.) in original format when translating to OpenAI format for guardrails
- Convert guardrail-returned tools back from OpenAI to Anthropic format (type=custom for user tools)
- Add TOOL_SEARCH_TOOL to ANTHROPIC_HOSTED_TOOLS enum; use prefix matching for native tool detection
- Set type=custom explicitly when mapping OpenAI function tools to AnthropicMessagesTool
- Add test for Anthropic native tools with guardrails
Made-with: Cursor
1. Add missing __init__.py files in tests/test_litellm/llms/gemini/ and
subdirectories (realtime/, image_edit/) to fix ModuleNotFoundError
with pytest-xdist parallel workers.
2. Update test_transform_request_uses_dynamic_max_tokens to use
claude-3-7-sonnet-20250219 (max_output_tokens=64000) since
claude-3-5-sonnet-20241022 was removed from model_prices JSON
during deprecated model cleanup. The test assertion was outdated.
3. Update context caching TTL tests to use gemini-2.5-pro instead of
gemini-1.5-pro. The old model was removed from model_prices JSON,
causing supports_system_messages to return False, which prevented
system_instruction from appearing in the transformation output.
Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com>
Lint fixes (check_code_and_doc_quality job):
- Remove unused variable reasoning_effort in gpt_5_transformation.py (F841)
- Remove unused timezone imports in mcp_server rest_endpoints.py and server.py (F401)
- Remove unused ProxyBaseLLMRequestProcessing import in realtime endpoints.py (F401)
- Add BaseRealtimeHTTPConfig to TYPE_CHECKING block in utils.py (F821)
- Add PLR0915 per-file-ignore for mcp_server/rest_endpoints.py in ruff.toml
Test fixes (litellm_mapped_tests_llms job):
- Gemini video cost tests: pass explicit model_info to video_generation_cost()
instead of relying on gemini/veo-3.0-generate-preview being in model_prices JSON
- Anthropic max_tokens tests: mock get_max_tokens() to return expected values
instead of depending on claude-3-5-sonnet-20241022 being in model_prices JSON
- Vertex AI pydantic obj test: update from removed gemini-1.5-pro to gemini-2.5-flash,
update expected request body to use response_json_schema format
- Vertex AI/Bedrock file_content integration tests: update mocks to target
base_llm_http_handler.retrieve_file_content (the new code path via
ProviderConfigManager) instead of the old vertex_ai_files_instance/
bedrock_files_instance paths
Co-authored-by: yuneng-jiang <yuneng-jiang@users.noreply.github.com>
Addresses Greptile review:
- dict(_input_schema) before mutation prevents cross-provider state leakage
- Test asserts original tool parameters dict is unchanged after call
Anthropic's API requires all tool input_schema to have type:'object'
at the root level. When OpenAI-format tools have parameters with a
missing or non-'object' type field (common with MCP tool servers),
the schema was passed through unchanged, causing Anthropic to reject
with: 'tools.N.custom.input_schema.type: Input should be object'.
The existing default handles the case where parameters is entirely
missing, but does not normalize schemas that ARE provided with a
wrong or absent type field.
Fix: After extracting _input_schema in _map_tool_helper(), ensure
type is set to 'object' and properties exists. This matches the
normalization already done implicitly by the Bedrock handler.
Added 4 unit tests covering: missing type, wrong type, valid schema
(no-op), and entirely missing parameters.
Related issues: #12020, #64, #1671
* fix(anthropic): populate output_config when reasoning_effort is used on Claude 4.6
When reasoning_effort is passed for Claude 4.6 models, _map_reasoning_effort
returns {type: 'adaptive'} but the effort level is silently dropped. Per
the Anthropic docs, effort on 4.6 models is controlled via output_config,
not thinking budget_tokens.
Map reasoning_effort to output_config.effort for 4.6 models so the effort
guidance is sent to the API.
Fixes#22212
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
* test: add coverage for "max" effort level in Claude 4.6 reasoning test
---------
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Claude 4.6 models use output_config as a stable API feature. This commit:
- Maps reasoning_effort to output_config for 4.6 models (minimal → low)
- Restricts effort="max" to Opus 4.6 only
- Skips beta header injection for 4.6 models
- Updates docs for Claude 4.6 effort support
* 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
The production error message was expanded when Sonnet 4.6 was also added as
a supported model for effort='max'. The test's match regex still referenced
the old "Claude Opus 4.6"-only message; update it to match the new
"Claude 4.6 models" wording.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Empty system messages were skipped for Anthropic's system param but
not removed from the messages list, causing BadRequestError when
anthropic_messages_pt encountered the unsupported "system" role.
Fixes#21622
* fix: check for model_response_choices before guardrail input
* test: add tests for responses api translation
* fix: protect other guardrail translations
* refactor: remove type ignores
* anthropic request body got mutated fix
* add warning when extra_body is provided but user is non premium
* fix: resolve mypy union-attr errors in anthropic guardrail handler
Cast choices[0] to Choices type before accessing .message attribute
to satisfy mypy's union type checking for Choices | StreamingChoices.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
* add logger when model response has no choices for streaming /response and /messages
* update pyproject.toml as requested
* Revert "update pyproject.toml as requested"
This reverts commit 541a2b075a.
* update pyproject.toml as requested
* Revert "update pyproject.toml as requested"
This reverts commit 716ea0caa1.
---------
Co-authored-by: Xiaohan Fu <xiaohan@grayswan.ai>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Fixes#18137
Similar to the fix for web_search_tool_result (#17746, #17798), this PR
preserves web_fetch_tool_result blocks in multi-turn conversations.
Changes:
- Add handling for web_fetch_tool_result in transformation.py (non-streaming)
- Add capture of web_fetch_tool_result in handler.py (streaming)
- Fix streaming tool arguments bug where empty input {} was prepended to
actual arguments by using empty string instead of str({})
- Add unit tests for web_fetch_tool_result handling
Claude 3.7 Sonnet's default max_output_tokens is 64000, not 128000.
The 128K output limit requires the beta header 'output-128k-2025-02-19'.
This fixes the integration test failure where requests with max_tokens=128000
were being rejected by the Anthropic API.
Fixes test_multiturn_tool_calls in test_anthropic_responses_api.py
* fix(anthropic): use dynamic max_tokens based on model
When users don't specify max_tokens in requests to Anthropic models,
LiteLLM now uses the correct max_output_tokens value from the model
pricing JSON instead of a hardcoded 4096.
This fixes truncated responses for Claude 3.5+ models which support
higher output limits (8192 for Claude 3.5, 128k for Claude 3.7, etc.)
Fixes#8835
* fix(anthropic): restore env var support for backwards compatibility
Keep DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS as fallback when model is not
found in JSON, allowing users to configure via environment variable.
This fix addresses two issues with Anthropic web search streaming:
1. Fix trailing {} in tool call arguments
- web_search_tool_result blocks have input_json_delta events that were
incorrectly emitted as tool calls
- Added current_content_block_type tracking to only emit tool calls for
tool_use and server_tool_use blocks
2. Capture web_search_tool_result for multi-turn
- The web_search_tool_result content comes ALL AT ONCE in content_block_start
- Now captured in provider_specific_fields.web_search_results
- stream_chunk_builder combines these for final message
- Allows multi-turn conversations to work with streaming web search
- Extract web_search_tool_result blocks in extract_response_content()
- Store web_search_results in provider_specific_fields for round-trip
- Detect srvtoolu_ prefix to reconstruct as server_tool_use (not tool_use)
- Add corresponding web_search_tool_result after server_tool_use blocks
This ensures multi-turn conversations with Anthropic web search + custom
tools work correctly without Anthropic expecting tool_result for server-
side tool executions.
Fixes#17473 - Anthropic streaming fails with JSONDecodeError when
network fragmentation causes SSE data to arrive in partial chunks.
Changes:
- Add accumulated_json buffer and chunk_type to ModelResponseIterator
- Add _handle_accumulated_json_chunk() to accumulate partial JSON
- Add _parse_sse_data() to handle both complete and partial chunks
- Modify __next__ and __anext__ to use accumulation logic
- Add unit tests for partial chunk handling
When translating system messages for the Anthropic API, empty text
content blocks cause the error "messages: text content blocks must be
non-empty". This fix skips empty string content and empty text blocks
in list content to prevent this error.
Fixes issue with Vertex AI Anthropic API calls.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude <noreply@anthropic.com>
* Added tool search support for anthropic
* Add programtic tool calling support
* Add tool use input examples support
* Add anthropic effort param support
* Add anthropic effort param support
* Add blog for new features
* fix mypy and lint errors
* fix mypy and lint errors
* fix mypy and lint errors
* fix mypy and lint errors
* Add better handling
* Add better handling
* feat(anthropic/chat/transformations): for claude-4-5-sonnet and opus-4-1 support passing structured output to anthropic api
* docs: document new feature
* fix: fix output format
* fix: cleanup
* fix(transformation.py): conditionally pass in json tool call
* fix: support ARIZE_SPACE_ID instead of ARIZE_SPACE_KEY
* docs(arize_integration.md): cleanup arize docs
* feat(callback_info_helpers.tsx): allow setting arize space id via ui
* fix: fix linting error
* fix(opentelemetry.py): working arize phoenix root span tracing
