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| Author | SHA1 | Message | Date | |
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Sameer Kankute
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e8fcb01215
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Litellm OSS Staging (#29161)
* Cato Networks guardrail, based on Aim (#26597) * Aim was acquired by Cato Networks, creating Cato Networks guardrail based on Aim * Add more tests * Move test so they are reached by codecov coverage * base URL trailing slashes * Support Lemonade runtime context metadata (#28135) * Support Lemonade runtime context metadata * Add provider hook for runtime model metadata * Address provider model info review feedback Keep the runtime model info hook duck-typed instead of extending the base model-info class, and avoid importing ModelInfoBase from Ollama common utilities to reduce CodeQL cyclic-import noise. Co-authored-by: openhands <openhands@all-hands.dev> * Fix CI after staging rebase Relax the Ollama runtime metadata return annotation to match the provider-hook dict response and update the Google Interactions OpenAPI status expectation for the current live spec. Co-authored-by: openhands <openhands@all-hands.dev> * Normalize Lemonade runtime model metadata * Avoid leaking Ollama metadata auth * Avoid leaking Lemonade metadata auth --------- Co-authored-by: Graham Neubig <398875+neubig@users.noreply.github.com> Co-authored-by: openhands <openhands@all-hands.dev> * fix(cato): address guardrail review feedback Use proxy-authenticated user identity, forward moderation hook return values, and ensure streaming sender tasks are cancelled and awaited on exit. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(vertex_ai): route google/gemma-*-maas through partner-models OpenAI path - clone of #28010 (#28846) * fix(vertex_ai): route google/gemma-*-maas through partner-models OpenAI path Fixes #26083 vertex_ai/google/gemma-4-26b-a4b-it-maas previously fell through to the NON_GEMINI route. Per owtaylor's plan on #26083: add the google/gemma- prefix to PartnerModelPrefixes so is_vertex_partner_model picks it up and should_use_openai_handler routes it to the OpenAI-compatible /endpoints/openapi/chat/completions URL. No gemma-detection exclusion needed (the "gemma/" check uses a slash, which google/gemma-... doesn't match). No OpenAIGPTConfig subclass needed — works with the base handler. * fix(vertex_ai): mark gemma-4-26b-a4b-it-maas as vision-capable (empirically verified) * fix(vertex_ai): address greptile feedback — provider category, canonical URL, sync backup * test(vertex_ai): add function-calling and vision pass-through tests for Gemma MaaS Addresses oss-pr-review-agent-shin feedback on PR #28010: supports_function_calling, supports_tool_choice, and supports_vision were marked true but had no tests proving the payloads actually reached the OpenAI-compatible endpoint. Added: - test_gemma_maas_supports_function_calling — verifies the utility returns True when the model_cost entry carries supports_function_calling=true - test_gemma_maas_supports_vision — same for supports_vision - test_vertex_ai_gemma_function_calling_passthrough — verifies tools + tool_choice appear in the JSON body POSTed to /endpoints/openapi/chat/completions - test_vertex_ai_gemma_vision_passthrough — verifies image_url content parts survive transformation and reach the global endpoint URL * fix: Delete uv.lock * test(vertex_ai): add function-calling and vision pass-through tests for Gemma MaaS Addresses oss-pr-review-agent-shin feedback on PR #28010: P1 (patch target): Added a comment explaining why patching litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler is correct — get_async_httpx_client() (defined in http_handler.py) instantiates AsyncHTTPHandler within that module's scope, so the definition-site patch intercepts it. Without the mock the test raises AuthenticationError, confirming it never silently passes. P2 (partner-provider regression guard): Added test_gemma_routes_through_openai_handler() which calls VertexAIPartnerModels.should_use_openai_handler() directly, so if Gemma's routing to VertexPartnerProvider.llama ever changes the URL-shape tests below it become a real regression guard rather than an unanchored unit test. Also added: - test_gemma_maas_supports_function_calling / supports_vision — capability flag checks via patch.dict(litellm.model_cost) - test_vertex_ai_gemma_function_calling_passthrough — tools + tool_choice forwarded in the request body - test_vertex_ai_gemma_vision_passthrough — image_url part survives transformation to the global endpoint Added: - test_gemma_maas_supports_function_calling — verifies the utility returns True when the model_cost entry carries supports_function_calling=true - test_gemma_maas_supports_vision — same for supports_vision - test_vertex_ai_gemma_function_calling_passthrough — verifies tools + tool_choice appear in the JSON body POSTed to /endpoints/openapi/chat/completions - test_vertex_ai_gemma_vision_passthrough — verifies image_url content parts survive transformation and reach the global endpoint URL * fix: proper patch for unit tests --------- Co-authored-by: Iana <iana@Shivakumars-MacBook-Pro.local> * fix(cato): guardrail all completion choices on output When n > 1, only choices[0] was analyzed and redacted. Iterate every Choices entry so block and anonymize actions apply to all completions. Co-authored-by: Cursor <cursoragent@cursor.com> * Fix review * fix(cato_networks): harden output anonymize handling and restructure nested UI routes Guard against empty redacted_output and empty all_redacted_messages from Cato. Restructure nested admin UI HTML exports to index.html so extensionless routes work. Co-authored-by: Cursor <cursoragent@cursor.com> * Fix mypy * fix(cato): guard missing policy_drill_down and all_redacted_messages keys * fix(cato): avoid KeyError bypassing block action on missing analysis_result * fix(cato): preserve non-text message fields during anonymize Rebuild redacted messages from the original messages, overwriting only content, so tool_calls, tool_call_id, name and multimodal fields survive the anonymize action. * fix(cato): preserve trailing messages when fewer redacted messages returned Avoid silently truncating the conversation in _anonymize_request when Cato returns fewer redacted messages than were sent, and isolate the no-api-key config test from a pre-existing CATO_API_KEY environment variable. * fix(cato,model-info): preserve stream block signal on sender teardown; forward api_key in dynamic model-info lookup Suppress ConnectionClosed (alongside CancelledError) when tearing down the Cato streaming sender task so a backend ConnectionClosed cannot mask the original StreamingCallbackError (e.g. a guardrail block) raised by the receive loop. Thread api_key through get_model_info -> _get_model_info_helper so an explicit key reaches a provider's dynamic get_model_info for a caller-supplied api_base. Previously only api_base was forwarded, so authenticated Ollama and Lemonade servers at a custom base could only be queried unauthenticated. * fix(cato): surface mid-stream forwarding errors instead of blocking on recv If the upstream LLM stream errors mid-flight, the sender task dies before sending the terminal done frame, so the consumer would block on websocket.recv() until Cato closes the connection. Race recv against the sender task and raise the stored sender exception promptly as a StreamingCallbackError. * fix(cato): drop spoofable end_user_id from guardrail user identity Only the key/JWT-bound user_email is a trusted identity. end_user_id is resolved from caller-supplied request fields (OpenAI user param, headers, metadata), so an authenticated caller with no bound user_email could set it to another user's email and have LiteLLM forward x-cato-user-email for that victim, poisoning Cato audit and policy attribution. Forward only user_email and omit the header otherwise. * fix(cato): harden output anonymize path against missing content key * fix(cato): fall back to original message when redacted content key is missing * refactor(model-info): drop unused api_key from cached model-info helper _cached_get_model_info_helper is only called by the cost-tracking hot path, which never authenticates, so the api_key parameter was never populated. Keeping it in the lru_cache key offered no benefit and risked fragmenting the high-RPS cache and retaining credential strings per entry. * fix(cato): preserve None content on tool-call-only choices in output hook * fix(ollama): respect static-model guard in OllamaConfig.get_model_info Delegate to OllamaModelInfo.get_model_info so statically-priced Ollama models short-circuit before the /api/show network call instead of hitting the server unconditionally. * fix(lemonade,ollama): treat empty api_key as unset to avoid leaking server creds An empty-string api_key was treated as an explicit key, so it passed the guard meant to keep server-side credentials off caller-supplied bases and then fell back through the env/global key chain. A caller could point api_base at a server they control and send api_key="" to receive the configured provider key in the Authorization header. Gate the credential fallback on the api_key being truthy instead of merely not-None. * fix(cato): inspect and redact Responses-API input, not just messages The guardrail only read data["messages"], so /v1/responses requests, which carry their text in data["input"], reached Cato as an empty message list and bypassed inspection entirely. Send build_inspection_messages(data) so both shapes are analyzed, and write anonymized results back with apply_redacted_messages_back when the request used input. * perf(utils): keep api_key out of get_model_info lru_cache key * fix(cato): propagate ssl_verify to streaming WebSocket connection The streaming hook applied ssl_verify only to the HTTP handler; the websockets.connect() call used default verification, so a custom Cato instance behind TLS with a self-signed cert worked for non-streaming calls but failed every streaming request. Resolve the ssl_verify setting into the connect() ssl argument, mirroring the HTTP handler. * refactor(utils): rename shadowing local in _get_model_info_helper * fix(cato): flatten multimodal chat content before inspection Chat Completions requests whose message content is a multimodal parts array were posted to Cato as the raw OpenAI parts, so text inside content: [{"type":"text", ...}] reached the model without Cato ever inspecting the string. Flatten each message's list content to plain text while keeping the list 1:1 with the request so the index-based redaction write-back stays valid; Responses-API input requests still go through build_inspection_messages. * test(lemonade): clear get_model_info cache around api_base test * fix(cato): inspect and redact Responses-API input even when messages present _inspection_messages returned early once messages was non-empty, so a /v1/responses caller could place benign text in messages and disallowed text in input and have only messages reach Cato while the model used input. Inspect both fields and write anonymize redactions back to input as well as the index-aligned messages. * test(log_db_metrics): assert table_name event_metadata contract log_db_metrics now emits minimal event_metadata via _safe_db_event_metadata (table_name only, function_name/function_kwargs/function_args dropped as redundant with call_type and unsafe to stamp on a span). The success-path test still asserted function_name membership and crashed with TypeError on the None metadata returned when no table_name is passed. Pass a table_name and assert the surfaced contract instead. * fix(cato): inspect and redact completion prompt and Responses-API instructions The Cato guardrail only inspected chat messages and the Responses-API input field, so blocked text placed in the legacy /v1/completions prompt or the /v1/responses instructions field reached the model without ever being sent to Cato. Both fields are now appended as synthetic inspection messages, and the anonymize path slices Cato's redactions back to the field they came from. * fix(cato): serialize non-str/bytes websocket chunks before forwarding * fix(cato): inspect tool descriptions and tool-call arguments * fix(cato): map redacted output by assistant index; restore get_model_info.cache_info * fix(cato): block output even when detection_message is null/empty A block_action returned by Cato on the output hook whose detection_message was null or empty was let through to the caller: the truthiness guard on detection_message skipped the HTTPException and the unblocked response was returned. Raise the HTTPException directly in _handle_block_action_on_output so the output path blocks unconditionally, mirroring the input path. * fix(cato): inspect and redact nested tool param and legacy function descriptions Tool/function parameter descriptions and the legacy functions[] array are forwarded to the model but were not seen by Cato, so blocked text hidden there bypassed inspection and anonymization. Recursively walk every description string in tools[].function and functions[] schemas for both the analyze payload and the anonymize write-back. * fix(cato): traverse schema descriptions iteratively to satisfy recursive detector The nested walk() generator recursed over tool/function JSON schemas with no depth bound, which the recursive_detector code-quality gate rejects. Replace it with an explicit-stack DFS that yields the same (container, key) refs in the same pre-order, so schema description redaction is unchanged. * fix(cato): inspect and redact response_format JSON schema descriptions response_format json_schema descriptions are forwarded to the model, so blocked text hidden in nested schema descriptions could bypass Cato inspection and redaction. Extend the schema-description walk to cover response_format alongside tools and legacy functions. * fix(cato): skip output rewrite when Cato returns no redaction Return None from call_cato_guardrail_on_output on monitor/no-action so the post-call hook only mutates the message when there is an actual redaction, instead of redundantly re-writing the original content. * refactor(utils): resolve explicit api_key model info without the cache Move the model-info build into a non-cached _build_model_info helper and drop api_key from the lru-cached _cached_get_model_info signature. Both cached helpers now take the same (model, provider, api_base) key and never forward api_key, while explicit per-caller keys are resolved through the builder directly instead of reaching into the cache wrapper's __wrapped__. * fix(cato): inspect and redact non-description schema string values Tool, function and response_format JSON schemas forward more than just description text to the model. enum, const, default, examples and title values are sent verbatim, so blocked content hidden in any of them bypassed Cato inspection and redaction. Walk those schema string values alongside descriptions on both the inspection and anonymize paths. * fix(model-info): surface swallowed dynamic model-info errors The provider-specific get_model_info dispatch falls back to the static cost map when a provider's dynamic lookup raises, which is intentional graceful degradation. Previously the exception was discarded with a bare debug line, so a real failure (e.g. a provider whose get_model_info signature does not accept api_key) was invisible. Log the exception at warning level with the model and provider context so the fallback is diagnosable. * fix(cato): inspect and redact Responses API output in post-call hook The post-call success hook only handled ModelResponse, so /v1/responses (which returns a ResponsesAPIResponse) bypassed the Cato output guardrail. Extract and inspect/redact every output_text content block and function-call arguments string, blocking on a block action, so generated text cannot escape inspection by using the Responses API. * chore: reset _experimental/out folder * chore(ui): remove orphaned prebuilt dashboard chunk files The _experimental/out manifests are byte-identical to the base branch, so the served dashboard already matches base. 436 unreferenced Next.js chunk files had accumulated in the directory and are not loaded by any manifest; removing them restores the committed UI artifacts to the base build and drops the artifact churn from this PR's diff. * fix(guardrails,ollama): forward ssl_verify to Cato init and raise_for_status on /api/show --------- Co-authored-by: Alex Yaroslavsky <trexinc@gmail.com> Co-authored-by: Graham Neubig <neubig@gmail.com> Co-authored-by: Graham Neubig <398875+neubig@users.noreply.github.com> Co-authored-by: openhands <openhands@all-hands.dev> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Piotr Placzko <piotr@icep-design.com> Co-authored-by: Iana <iana@Shivakumars-MacBook-Pro.local> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Sameer Kankute
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bbeb094d00
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Litellm agent oss staging 05 11 2026 (#27733)
* fix(ollama): Include provider in model list for ollama (#26135) * Include provider in model names for ollama * Fix unit tests * fix(ollama): process both thinking and content in same streaming chunk (#26098) * fix(health_check): skip max_tokens for image_generation mode (#26417) * fix(health_check): skip max_tokens for image_generation mode `_update_litellm_params_for_health_check` injected `max_tokens` for every deployment. OpenAI `/v1/images/generations` strictly rejects unknown fields, so health checks for dall-e-* and gpt-image-1 always failed with `400 "Unknown parameter: 'max_tokens'"` even though the actual image endpoint calls succeed. Skip the `max_tokens` injection when `model_info.mode == "image_generation"`. `messages` still gets injected (downstream `_filter_model_params` already strips it for non-chat handlers). * Switch to allow-list with per-deployment override Per @krrishdholakia review: deny-listing image_generation only re-introduces the same bug for every other non-chat mode (embedding, audio_*, rerank, video_generation, ocr, search, moderation, ...). Replace the single image_generation skip with `_MAX_TOKEN_SUPPORT_MODES = {chat, completion, responses}`. Missing `mode` is treated as chat for backward compatibility. New modes are safe by default. Add `model_info.health_check_supports_max_tokens` as an operator escape hatch — True forces injection on a non-listed deployment (operator wants to bound probe tokens), False suppresses it on a chat-style deployment behind a strict-schema provider. Tests: parametrize over 3 chat-style + 10 non-chat modes, plus override on/off and the no-mode legacy path. * fix(http_handler): handle RequestNotRead in MaskedHTTPStatusError for multipart uploads (#26718) Squash-merged by litellm-agent from dawidkulpa's PR. * fix(ollama): guard against double 'ollama/' prefix in live model listing Greptile flagged that Ollama servers can return names that already start with 'ollama/'. Check the prefix before prepending so we don't produce 'ollama/ollama/...'. Adds a regression test. * Fix Ollama empty reasoning stream chunks Co-authored-by: Yassin Kortam <yassin@berri.ai> --------- Co-authored-by: James Myatt <james@jamesmyatt.co.uk> Co-authored-by: VHash <225398745+vhash0@users.noreply.github.com> Co-authored-by: hayden <sewhan.kim+@a-bly.com> Co-authored-by: dawidkulpa <84176950+dawidkulpa@users.noreply.github.com> Co-authored-by: Cursor <cursoragent@cursor.com> Co-authored-by: Claude <claude@anthropic.com> Co-authored-by: Yassin Kortam <yassin@berri.ai> |
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Michael Verrilli
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c014bfa683
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fix(ollama): forward tool_calls and tool_call_id in transform_request (#26122)
tool_calls on assistant messages were translated to OllamaToolCall format
but never copied into the outgoing OllamaChatCompletionMessage, so Ollama
received {role: assistant, content: ''} with no tool_calls. The model
then had no record of having made a tool call, causing it to re-issue
the identical call on every turn (infinite loop).
Similarly, tool_call_id on role:tool messages was silently dropped.
Ollama uses this field to resolve the tool name from conversation history.
Also add tool_call_id to OllamaChatCompletionMessage TypedDict.
Fixes #26094
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Ishaan Jaffer
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e8461b5b97
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style: run black formatter on files from main merge | ||
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Jared Everett
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3cbb36aa13
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fix(ollama): propagate done_reason='length' as finish_reason for max_tokens truncation (#25824)
* fix(ollama): propagate done_reason='length' as finish_reason for max_tokens truncation
Ollama returns done_reason='length' when a response is cut off by num_predict
(the max_tokens limit). Previously, non-streaming responses hardcoded
finish_reason='stop', and streaming used chunk.get('done_reason', 'stop')
which also defaulted to 'stop' when done_reason was absent.
This meant callers (e.g. the Anthropic pass-through adapter, which maps
OpenAI 'length' -> Anthropic 'max_tokens') could never detect truncation,
making stop_reason always appear as 'end_turn' even for cut-off responses.
Fix: read done_reason from the response JSON in the non-streaming path and
use `chunk.get('done_reason') or 'stop'` in the streaming path, so Ollama's
actual done_reason passes through to the caller unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Update test_ollama_chat_transformation.py
* Update litellm/llms/ollama/chat/transformation.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
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Cesar Garcia
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9495f4e941
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fix(ollama): thread api_base to get_model_info + graceful fallback (#21970)
* 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 |
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DenisStefanAndrei
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2e680ca62b |
[Critical] - Fix ollama_chat reasoning content (#20750)
* Fix ollama_chat reasoning_context. For ollama_chat models, reasoning context is ignored after 2 consecutive thinking chunks. * add test |
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Ryan Malloy
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f76938af5e
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fix(ollama): set finish_reason to tool_calls and remove broken capability check (#18924)
* Update CLAUDE.md with qwen3 tool_calls bug fix instructions (#18922) * fix(ollama): set finish_reason to "tool_calls" when tool_calls present When qwen3 models return tool_calls through Ollama, the finish_reason was incorrectly left as "stop" instead of being set to "tool_calls". This caused clients to miss the tool_calls in the response. Added _get_finish_reason helper method following OpenAI provider's pattern, and fixed both streaming and non-streaming response paths. Fixes: https://github.com/BerriAI/litellm/issues/18922 * fix(ollama): pass tools directly without model capability check The previous code tried to check model capability via get_model_info() which made network calls to localhost:11434. When Ollama is remote, this fails and falls back to JSON format, breaking tool calling. Ollama 0.4+ supports native tool calling - let Ollama handle model capability detection instead of LiteLLM. Fixes #18922 * fix(ollama): transform tool_calls response to OpenAI format Ollama returns tool_calls with arguments as dict, but OpenAI format requires arguments to be a JSON string. Also ensures 'type': 'function' field is present. Completes the fix for #18922 * fix(ollama): set finish_reason to "tool_calls" when tool_calls present Fixes #18922 Two issues addressed: 1. Remove broken model capability check - get_model_info() fails when Ollama runs on remote server - Broken fallback triggered JSON prompt injection - Now passes tools directly - Ollama 0.4+ handles detection 2. Set finish_reason correctly - Was hardcoded to "stop" even with tool_calls present - Clients use this to know how to process the response - Now returns "tool_calls" when tool_calls are in response Both streaming and non-streaming responses are fixed. Tests: - All 14 existing Ollama tests pass - Added 3 focused tests for the fixes |
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Sameer Kankute
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c0f74c994a | Fix: test_transform_request_image_extraction | ||
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Chris Lapa
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9c5f2ea827
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Fixes #13652 - auth not working with ollama.com (#17191)
* ollama: adds missing auth headers if set * ollama: sets ollama as openai compatible provider. * ollama: adds tests for ollama auth |
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wenhua
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b0ccc35a9c
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fix(ollama): Enhance chunk parsing for empty responses without 'thinking' and improve error logging (#13333) (#15717) | ||
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Alexsander Hamir
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eaa04cd8ce
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fix: use fastuuid helper (#14903)
* fix: use fastuuid helper across the codebase First batch of changes, simple drop in replacement. * second batch of changes * fixed: script mistake on helper file |
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Krrish Dholakia
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8ec02a5bdc | test: add unit tests for ollama chat request transformation | ||
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Krish Dholakia
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884cca7da9
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Merge pull request #12984 from darashenka/main
honor OLLAMA_API_KEY for ollama_chat |
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Krrish Dholakia
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2ad77d9bf6 |
feat(ollama/completion): output parse thinking content on streaming + non-streaming for ollama completion calls
Completes 'thinking' param support for ollama |
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Cole McIntosh
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d5d7e00d34 |
Enhance chunk parsing for Ollama streaming responses
Updated the chunk_parser method to return a ModelResponseStream when handling 'thinking' field content, allowing UIs to render reasoning information. Adjusted tests to verify the new behavior, ensuring that reasoning content is correctly returned in the response. |
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Cole McIntosh
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938e9ace54 |
Fix unclosed aiohttp client session warnings during concurrent requests
Fixed MyPy type error in Ollama completion transformation where finish_reason was set to None instead of expected string type. Changed finish_reason=None to finish_reason="" to match GenericStreamingChunk TypedDict requirements. Also updated corresponding test to expect empty string instead of None. |
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Cole McIntosh
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e1a2bfb63a |
Fix Ollama GPT-OSS streaming with 'thinking' field
- Handle chunks containing 'thinking' field with empty 'response' - Treat these as intermediate chunks that don't contain user content - Add comprehensive tests for chunk parsing scenarios - Resolves APIConnectionError for GPT-OSS model streaming Fixes #13340 |
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Andrei Darashenka
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ddac3d5386 | honor OLLAMA_API_KEY for ollama_chat | ||
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Ciprian Tomoiaga
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b3b4c65ac4
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Fix default parameters for ollama-chat (#12201) | ||
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Krish Dholakia
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1a4ad8bf18
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Update mistral 'supports_response_schema' field + Fix ollama embedding (#12024)
* build(model_prices_and_context_window.json): update all mistral models (besides codestral-mamba) to indicate support for response schema Closes https://github.com/BerriAI/litellm/issues/12012 * fix(route_llm_request.py): if llm router is not initialized, go straight through to litellm sdk Fixes https://github.com/BerriAI/litellm/issues/12008 * test: add unit test * fix(ollama_embeddings): fix unecessary await Fixes https://github.com/BerriAI/litellm/issues/11997 * test: update ollama embedding tests |
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Abinand P
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99c2a7fb70
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feat: update the feature of ollama_embeddings to work on a sync api (#11746)
* feat: update the feature of ollama_embeddings to work on a sync api Signed-off-by: Abinand P <abinand0911@gmail.com> * lint:fixing of the lint file Signed-off-by: Abinand P <abinand0911@gmail.com> * fix:test Signed-off-by: Abinand P <abinand0911@gmail.com> * chore: added test for ollama embedding and refactored handler Signed-off-by: Abinand P <abinand0911@gmail.com> * fix:lint error Signed-off-by: Abinand P <abinand0911@gmail.com> --------- Signed-off-by: Abinand P <abinand0911@gmail.com> |
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Krish Dholakia
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4c82dd9b27
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Ollama Chat - parse tool calls on streaming (#11171)
* fix(user_api_key_auth.py): fix else block Fixes https://github.com/BerriAI/litellm/issues/11170 * refactor(ollama/chat): refactor to base config pattern easier to maintain fixes * fix(ollama/chat): support tool call parsing on streaming Closes https://github.com/BerriAI/litellm/issues/11104 * test: update import location * fix: cleanup unused import * fix: fix ruff check error * test: update import * test: update test on ci * ci: cleanup * fix: fix chekc * fix: fix api key check order * test: fix import * ci: fix script * test: fix imports * fix: fix tests |
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Krish Dholakia
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ef42461c1e
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Litellm fix GitHub action testing (#11163)
* test: add __init__.py files * refactor: rename test folder to avoid naming conflict * test: update workflows * test: update tests * test: update imports * test: update tests * test: remove unused import * ci(test-litellm.yml): add pytest retry to github workflow * test: fix test |