* feat(batches/): fix batch cost calculation - ensure it's accurate
use the correct cost value - prev. defaulting to non-batch cost
* feat(batch_utils.py): log batch models to spend logs + standard logging payload
makes it easy to understand how cost was calculated
* fix: fix stored payload for test
* test: fix test
* feat: initial commit - enable dev to see translated request
* feat(utils.py): expose new endpoint - `/utils/transform_request` to see the raw request sent by litellm
* feat(transform_request.tsx): allow user to see their transformed request
* refactor(litellm_logging.py): return raw request in 3 parts - api_base, headers, request body
easier to render each individually on UI vs. extracting from combined string
* feat: transform_request.tsx
working e2e raw request viewing
* fix(litellm_logging.py): fix transform viewing for bedrock models
* fix(litellm_logging.py): don't return sensitive headers in raw request headers
prevent accidental leak
* feat(transform_request.tsx): style improvements
* fix(anthropic_claude3_transformation.py): fix amazon anthropic claude 3 tool calling transformation on invoke route
move to using anthropic config as base
* fix(utils.py): expose anthropic config via providerconfigmanager
* fix(llm_http_handler.py): support json mode on async completion calls
* fix(invoke_handler/make_call): support json mode for anthropic called via bedrock invoke
* fix(anthropic/): handle 'response_format: {"type": "text"}` + migrate amazon claude 3 invoke config to inherit from anthropic config
Prevents error when passing in 'response_format: {"type": "text"}
* test: fix test
* fix(utils.py): fix base invoke provider check
* fix(anthropic_claude3_transformation.py): don't pass 'stream' param
* fix: fix linting errors
* fix(converse_transformation.py): handle response_format type=text for converse
* fix(o_series_transformation.py): fix optional param check for o-series models
o3-mini and o-1 do not support parallel tool calling
* fix(utils.py): support 'drop_params' for 'thinking' param across models
allows switching to older claude versions (or non-anthropic models) and param to be safely dropped
* fix: fix passing thinking param in optional params
allows dropping thinking_param where not applicable
* test: update old model
* fix(utils.py): fix linting errors
* fix(main.py): add param to acompletion
* feat(bedrock/rerank): infer model region if model given as arn
* test: add unit testing to ensure bedrock region name inferred from arn on rerank
* feat(bedrock/rerank/transformation.py): include search units for bedrock rerank result
Resolves https://github.com/BerriAI/litellm/issues/7258#issuecomment-2671557137
* test(test_bedrock_completion.py): add testing for bedrock cohere rerank
* feat(cost_calculator.py): refactor rerank cost tracking to support bedrock cost tracking
* build(model_prices_and_context_window.json): add amazon.rerank model to model cost map
* fix(cost_calculator.py): bedrock/common_utils.py
get base model from model w/ arn -> handles rerank model
* build(model_prices_and_context_window.json): add bedrock cohere rerank pricing
* feat(bedrock/rerank): migrate bedrock config to basererank config
* Revert "feat(bedrock/rerank): migrate bedrock config to basererank config"
This reverts commit 84fae1f167.
* test: add testing to ensure large doc / queries are correctly counted
* Revert "test: add testing to ensure large doc / queries are correctly counted"
This reverts commit 4337f1657e.
* fix(migrate-jina-ai-to-rerank-config): enables cost tracking
* refactor(jina_ai/): finish migrating jina ai to base rerank config
enables cost tracking
* fix(jina_ai/rerank): e2e jina ai rerank cost tracking
* fix: cleanup dead code
* fix: fix python3.8 compatibility error
* test: fix test
* test: add e2e testing for azure ai rerank
* fix: fix linting error
* test: mark cohere as flaky
* feat(litellm_pre_call_utils.py): support `x-litellm-tags` request header
allow tag based routing + spend tracking via request headers
* docs(request_headers.md): document new `x-litellm-tags` for tag based routing and spend tracking
* docs(tag_routing.md): add to docs
* fix(utils.py): only pass str values for openai metadata param
* fix(utils.py): drop non-str values for metadata param to openai
preview-feature, otel span was being sent in
* fix(main.py): fix key leak error when unknown provider given
don't return passed in args if unknown route on embedding
* fix(main.py): remove instances of {args} being passed in exception
prevent potential key leaks
* test(code_coverage/prevent_key_leaks_in_codebase.py): ban usage of {args} in codebase
* fix: fix linting errors
* fix: remove unused variable
* fix(azure/chat/gpt_transformation.py): add 'prediction' as a support azure param
Closes https://github.com/BerriAI/litellm/issues/8500
* build(model_prices_and_context_window.json): add new 'gemini-2.0-pro-exp-02-05' model
* style: cleanup invalid json trailing commma
* feat(utils.py): support passing 'tokenizer_config' to register_prompt_template
enables passing complete tokenizer config of model to litellm
Allows calling deepseek on bedrock with the correct prompt template
* fix(utils.py): fix register_prompt_template for custom model names
* test(test_prompt_factory.py): fix test
* test(test_completion.py): add e2e test for bedrock invoke deepseek ft model
* feat(base_invoke_transformation.py): support hf_model_name param for bedrock invoke calls
enables proxy admin to set base model for ft bedrock deepseek model
* feat(bedrock/invoke): support deepseek_r1 route for bedrock
makes it easy to apply the right chat template to that call
* feat(constants.py): store deepseek r1 chat template - allow user to get correct response from deepseek r1 without extra work
* test(test_completion.py): add e2e mock test for bedrock deepseek
* docs(bedrock.md): document new deepseek_r1 route for bedrock
allows us to use the right config
* fix(exception_mapping_utils.py): catch read operation timeout
* fix(utils.py): fix vertex ai optional param handling
don't pass max retries to unsupported route
Fixes https://github.com/BerriAI/litellm/issues/8254
* fix(get_supported_openai_params.py): fix linting error
* fix(get_supported_openai_params.py): default to openai-like spec
* test: fix test
* fix: fix linting error
* Improved wildcard route handling on `/models` and `/model_group/info` (#8473)
* fix(model_checks.py): update returning known model from wildcard to filter based on given model prefix
ensures wildcard route - `vertex_ai/gemini-*` just returns known vertex_ai/gemini- models
* test(test_proxy_utils.py): add unit testing for new 'get_known_models_from_wildcard' helper
* test(test_models.py): add e2e testing for `/model_group/info` endpoint
* feat(prometheus.py): support tracking total requests by user_email on prometheus
adds initial support for tracking total requests by user_email
* test(test_prometheus.py): add testing to ensure user email is always tracked
* test: update testing for new prometheus metric
* test(test_prometheus_unit_tests.py): add user email to total proxy metric
* test: update tests
* test: fix spend tests
* test: fix test
* fix(pagerduty.py): fix linting error
* (Bug fix) - Using `include_usage` for /completions requests + unit testing (#8484)
* pass stream options (#8419)
* test_completion_streaming_usage_metrics
* test_text_completion_include_usage
---------
Co-authored-by: Kaushik Deka <55996465+Kaushikdkrikhanu@users.noreply.github.com>
* fix naming docker stable release
* build(model_prices_and_context_window.json): handle azure model update
* docs(token_auth.md): clarify scopes can be a list or comma separated string
* docs: fix docs
* add sonar pricings (#8476)
* add sonar pricings
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window_backup.json
* update load testing script
* fix test_async_router_context_window_fallback
* pplx - fix supports tool choice openai param (#8496)
* fix prom check startup (#8492)
* test_async_router_context_window_fallback
* ci(config.yml): mark daily docker builds with `-nightly` (#8499)
Resolves https://github.com/BerriAI/litellm/discussions/8495
* (Redis Cluster) - Fixes for using redis cluster + pipeline (#8442)
* update RedisCluster creation
* update RedisClusterCache
* add redis ClusterCache
* update async_set_cache_pipeline
* cleanup redis cluster usage
* fix redis pipeline
* test_init_async_client_returns_same_instance
* fix redis cluster
* update mypy_path
* fix init_redis_cluster
* remove stub
* test redis commit
* ClusterPipeline
* fix import
* RedisCluster import
* fix redis cluster
* Potential fix for code scanning alert no. 2129: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* fix naming of redis cluster integration
* test_redis_caching_ttl_pipeline
* fix async_set_cache_pipeline
---------
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
* Litellm UI stable version 02 12 2025 (#8497)
* fix(key_management_endpoints.py): fix `/key/list` to include `return_full_object` as a top-level query param
Allows user to specify they want the keys as a list of objects
* refactor(key_list.tsx): initial refactor of key table in user dashboard
offloads key filtering logic to backend api
prevents common error of user not being able to see their keys
* fix(key_management_endpoints.py): allow internal user to query `/key/list` to see their keys
* fix(key_management_endpoints.py): add validation checks and filtering to `/key/list` endpoint
allow internal user to see their keys. not anybody else's
* fix(view_key_table.tsx): fix issue where internal user could not see default team keys
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* fix: fix linting error
* test_supports_tool_choice
* test_async_router_context_window_fallback
* fix: fix test (#8501)
* Litellm dev 02 12 2025 p1 (#8494)
* Resolves https://github.com/BerriAI/litellm/issues/6625 (#8459)
- enables no auth for SMTP
Signed-off-by: Regli Daniel <daniel.regli1@sanitas.com>
* add sonar pricings (#8476)
* add sonar pricings
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window.json
* Update model_prices_and_context_window_backup.json
* test: fix test
---------
Signed-off-by: Regli Daniel <daniel.regli1@sanitas.com>
Co-authored-by: Dani Regli <1daniregli@gmail.com>
Co-authored-by: Lucca Zenóbio <luccazen@gmail.com>
* test: fix test
* UI Fixes p2 (#8502)
* refactor(admin.tsx): cleanup add new admin flow
removes buggy flow. Ensures just 1 simple way to add users / update roles.
* fix(user_search_modal.tsx): ensure 'add member' button is always visible
* fix(edit_membership.tsx): ensure 'save changes' button always visible
* fix(internal_user_endpoints.py): ensure user in org can be deleted
Fixes issue where user couldn't be deleted if they were a member of an org
* fix: fix linting error
* add phoenix docs for observability integration (#8522)
* Add files via upload
* Update arize_integration.md
* Update arize_integration.md
* add Phoenix docs
* Added custom_attributes to additional_keys which can be sent to athina (#8518)
* (UI) fix log details page (#8524)
* rollback changes to view logs page
* ui new build
* add interface for prefetch
* fix spread operation
* fix max size for request view page
* clean up table
* ui fix column on request logs page
* ui new build
* Add UI Support for Admins to Call /cache/ping and View Cache Analytics (#8475) (#8519)
* [Bug] UI: Newly created key does not display on the View Key Page (#8039)
- Fixed issue where all keys appeared blank for admin users.
- Implemented filtering of data via team settings to ensure all keys are displayed correctly.
* Fix:
- Updated the validator to allow model editing when `keyTeam.team_alias === "Default Team"`.
- Ensured other teams still follow the original validation rules.
* - added some classes in global.css
- added text wrap in output of request,response and metadata in index.tsx
- fixed styles of table in table.tsx
* - added full payload when we open single log entry
- added Combined Info Card in index.tsx
* fix: keys not showing on refresh for internal user
* merge
* main merge
* cache page
* ca remove
* terms change
* fix:places caching inside exp
---------
Signed-off-by: Regli Daniel <daniel.regli1@sanitas.com>
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Co-authored-by: Kaushik Deka <55996465+Kaushikdkrikhanu@users.noreply.github.com>
Co-authored-by: Lucca Zenóbio <luccazen@gmail.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Co-authored-by: Dani Regli <1daniregli@gmail.com>
Co-authored-by: exiao <exiao@users.noreply.github.com>
Co-authored-by: vivek-athina <153479827+vivek-athina@users.noreply.github.com>
Co-authored-by: Taha Ali <123803932+tahaali-dev@users.noreply.github.com>
* fix(litellm_logging.py): support saving applied guardrails in logging object
allows list of applied guardrails to be logged for proxy admin's knowledge
* feat(spend_tracking_utils.py): log applied guardrails to spend logs
makes it easy for admin to know what guardrails were applied on a request
* ci(config.yml): uninstall posthog from ci/cd
* test: fix tests
* test: update test
* fix(azure.py): ensure max_retries=0 is respected
Fixes https://github.com/BerriAI/litellm/issues/6129
* fix(test_openai.py): add unit test to ensure openai sdk calls always respect max_retries = 0
* test(test_azure_openai.py): add unit testing for azure_text/ route
* fix(azure.py): fix passing max retries on streaming
* fix(azure.py): fix azure max retries on async completion + streaming
* fix(completion/handler.py): fix azure text async completion + streaming
* test(test_azure_openai.py): ensure azure openai max retries always respected
* test(test_azure_o_series.py): add testing to ensure max retries always respected
* Added gemini providers for 2.0-flash and 2.0-flash lite (#8321)
* Update model_prices_and_context_window.json
added gemini providers for 2.0-flash and 2.0-flash light
* Update model_prices_and_context_window.json
fixed URL
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* Convert tool use arguments to string before counting tokens (#6989)
In at least some cases the `messages["tool_calls"]["function"]["arguments"]` is a dict, not a string. In order to tokenize it properly it needs to be a string. In the case that it is already a string this is a noop, which is also fine.
* build(model_prices_and_context_window.json): add gemini 2.0 flash lite pricing
* build(model_prices_and_context_window.json): add gemini commercial rate limits
* fix(utils.py): fix linting error
* refactor(utils.py): refactor to maintain function size
---------
Co-authored-by: Bardia Khosravi <bardiakhosravi95@gmail.com>
Co-authored-by: Josh Morrow <josh@jcmorrow.com>
* fix(utils.py): handle key error in msg validation
* Support running Aim Guard during LLM call (#7918)
* support running Aim Guard during LLM call
* Rename header
* adjust docs and fix type annotations
* fix(timeout.md): doc fix for openai example on dynamic timeouts
---------
Co-authored-by: Tomer Bin <117278227+hxtomer@users.noreply.github.com>
* initial transform for invoke
* invoke transform_response
* working - able to make request
* working get_complete_url
* working - invoke now runs on llm_http_handler
* fix unused imports
* track litellm overhead ms
* working stream request
* sign_request transform
* sign_request update
* use has_async_custom_stream_wrapper property
* use get_async_custom_stream_wrapper in base llm http handler
* fix make_call in invoke handler
* fix invoke with streaming get_async_custom_stream_wrapper
* working bedrock async streaming with invoke
* fix make call handler for bedrock
* test_all_model_configs
* fix test_bedrock_custom_prompt_template
* sync streaming for bedrock invoke
* fix _add_stream_param_to_request_body
* test_async_text_completion_bedrock
* fix transform_request
* fix get_supported_openai_params
* fix test supports tool choice
* fix test_supports_tool_choice
* add unit test coverage for bedrock invoke transform
* fix location of transformation files
* update import loc
* fix bedrock invoke unit tests
* fix import for max completion tokens
* fix(o_series_transformation.py): add 'reasoning_effort' as o series model param
Closes https://github.com/BerriAI/litellm/issues/8182
* fix(main.py): ensure `reasoning_effort` is a mapped openai param
* refactor(azure/): rename o1_[x] files to o_series_[x]
* refactor(base_llm_unit_tests.py): refactor testing for o series reasoning effort
* test(test_azure_o_series.py): have azure o series tests correctly inherit from base o series model tests
* feat(base_utils.py): support translating 'developer' role to 'system' role for non-openai providers
Makes it easy to switch from openai to anthropic
* fix: fix linting errors
* fix(base_llm_unit_tests.py): fix test
* fix(main.py): add missing param
* fix: support azure o3 model family for fake streaming workaround (#8162)
* fix: support azure o3 model family for fake streaming workaround
* refactor: rename helper to is_o_series_model for clarity
* update function calling parameters for o3 models (#8178)
* refactor(o1_transformation.py): refactor o1 config to be o series config, expand o series model check to o3
ensures max_tokens is correctly translated for o3
* feat(openai/): refactor o1 files to be 'o_series' files
expands naming to cover o3
* fix(azure/chat/o1_handler.py): azure openai is an instance of openai - was causing resets
* test(test_azure_o_series.py): assert stream faked for azure o3 mini
Resolves https://github.com/BerriAI/litellm/pull/8162
* fix(o1_transformation.py): fix o1 transformation logic to handle explicit o1_series routing
* docs(azure.md): update doc with `o_series/` model name
---------
Co-authored-by: byrongrogan <47910641+byrongrogan@users.noreply.github.com>
Co-authored-by: Low Jian Sheng <15527690+lowjiansheng@users.noreply.github.com>
* add support for using llama spec with bedrock
* fix get_bedrock_invoke_provider
* add support for using bedrock provider in mappings
* working request
* test_bedrock_custom_deepseek
* test_bedrock_custom_deepseek
* fix _get_model_id_for_llama_like_model
* test_bedrock_custom_deepseek
* doc DeepSeek-R1-Distill-Llama-70B
* test_bedrock_custom_deepseek
* Litellm dev 01 29 2025 p4 (#8107)
* fix(key_management_endpoints.py): always get db team
Fixes https://github.com/BerriAI/litellm/issues/7983
* test(test_key_management.py): add unit test enforcing check_db_only is always true on key generate checks
* test: fix test
* test: skip gemini thinking
* Litellm dev 01 29 2025 p3 (#8106)
* fix(__init__.py): reduces size of __init__.py and reduces scope for errors by using correct param
* refactor(__init__.py): refactor init by cleaning up redundant params
* refactor(__init__.py): move more constants into constants.py
cleanup root
* refactor(__init__.py): more cleanup
* feat(__init__.py): expose new 'disable_hf_tokenizer_download' param
enables hf model usage in offline env
* docs(config_settings.md): document new disable_hf_tokenizer_download param
* fix: fix linting error
* fix: fix unsafe comparison
* test: fix test
* docs(public_teams.md): add doc showing how to expose public teams for users to join
* docs: add beta disclaimer on public teams
* test: update tests
* docs: cleanup doc
* feat(bedrock/): initial commit adding bedrock/converse_like/<model> route support
allows routing to a converse like endpoint
Resolves https://github.com/BerriAI/litellm/issues/8085
* feat(bedrock/chat/converse_transformation.py): make converse config base config compatible
enables new 'converse_like' route
* feat(converse_transformation.py): enables using the proxy with converse like api endpoint
Resolves https://github.com/BerriAI/litellm/issues/8085
* refactor _add_callbacks_from_db_config
* fix check for _custom_logger_exists_in_litellm_callbacks
* move loc of test utils
* run ci/cd again
* test_add_custom_logger_callback_to_specific_event_with_duplicates_callbacks
* fix _custom_logger_class_exists_in_success_callbacks
* unit testing for test_add_callbacks_from_db_config
* test_custom_logger_exists_in_callbacks_individual_functions
* fix config.yml
* fix test test_stream_chunk_builder_openai_audio_output_usage - use direct dict comparison
* docs(reliability.md): add doc on disabling fallbacks per request
* feat(litellm_pre_call_utils.py): support reading request timeout from request headers - new `x-litellm-timeout` param
Allows setting dynamic model timeouts from vercel's AI sdk
* test(test_proxy_server.py): add simple unit test for reading request timeout
* test(test_fallbacks.py): add e2e test to confirm timeout passed in request headers is correctly read
* feat(main.py): support passing metadata to openai in preview
Resolves https://github.com/BerriAI/litellm/issues/6022#issuecomment-2616119371
* fix(main.py): fix passing openai metadata
* docs(request_headers.md): document new request headers
* build: Merge branch 'main' into litellm_dev_01_27_2025_p3
* test: loosen test
* fix(utils.py): handle failed hf tokenizer request during calls
prevents proxy from failing due to bad hf tokenizer calls
* fix(utils.py): convert failure callback str to custom logger class
Fixes https://github.com/BerriAI/litellm/issues/8013
* test(test_utils.py): fix test - avoid adding mlflow dep on ci/cd
* fix: add missing env vars to test
* test: cleanup redundant test
* fix(utils.py): initial commit fixing custom cost tracking
refactors out provider specific model info from `get_model_info` - this was causing custom costs to be registered incorrectly
* fix(utils.py): cleanup `_supports_factory` to check provider info, if model info is None
some providers support features like vision across all models
* fix(utils.py): refactor to use _supports_factory
* test: update testing
* fix: fix linting errors
* test: fix testing
* test(test_completion_cost.py): add sdk test to ensure base model is used for cost tracking
* test(test_completion_cost.py): add sdk test to ensure custom pricing works
* fix(main.py): add base model cost tracking support for embedding calls
Enables base model cost tracking for embedding calls when base model set as a litellm_param
* fix(litellm_logging.py): update logging object with litellm params - including base model, if given
ensures base model param is always tracked
* fix(main.py): fix linting errors
* fix(http_handler.py): support passing ssl verify dynamically and using the correct httpx client based on passed ssl verify param
Fixes https://github.com/BerriAI/litellm/issues/6499
* feat(llm_http_handler.py): support passing `ssl_verify=False` dynamically in call args
Closes https://github.com/BerriAI/litellm/issues/6499
* fix(proxy/utils.py): prevent bad logs from breaking all cost tracking + reset list regardless of success/failure
prevents malformed logs from causing all spend tracking to break since they're constantly retried
* test(test_proxy_utils.py): add test to ensure bad log is dropped
* test(test_proxy_utils.py): ensure in-memory spend logs reset after bad log error
* test(test_user_api_key_auth.py): add unit test to ensure end user id as str works
* fix(auth_utils.py): ensure extracted end user id is always a str
prevents db cost tracking errors
* test(test_auth_utils.py): ensure get end user id from request body always returns a string
* test: update tests
* test: skip bedrock test- behaviour now supported
* test: fix testing
* refactor(spend_tracking_utils.py): reduce size of get_logging_payload
* test: fix test
* bump: version 1.59.4 → 1.59.5
* Revert "bump: version 1.59.4 → 1.59.5"
This reverts commit 1182b46b2e.
* fix(utils.py): fix spend logs retry logic
* fix(spend_tracking_utils.py): fix get tags
* fix(spend_tracking_utils.py): fix end user id spend tracking on pass-through endpoints
* fix(bedrock/converse_handler.py): fix bedrock region name on async calls
* fix(utils.py): fix split model handling
Fixes bedrock cost calculation when region name is given
* feat(_health_endpoints.py): support health checking datadog integration
Closes https://github.com/BerriAI/litellm/issues/7921
* feat(router.py): add retry headers to response
makes it easy to add testing to ensure model-specific retries are respected
* fix(add_retry_headers.py): clarify attempted retries vs. max retries
* test(test_fallbacks.py): add test for checking if max retries set for model is respected
* test(test_fallbacks.py): assert values for attempted retries and max retries are as expected
* fix(utils.py): return timeout in litellm proxy response headers
* test(test_fallbacks.py): add test to assert model specific timeout used on timeout error
* test: add bad model with timeout to proxy
* fix: fix linting error
* fix(router.py): fix get model list from model alias
* test: loosen test restriction - account for other events on proxy
