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…viderRegistry within CredentialManager The flow for integrating a new auth method will be as follows. The ADK framework contributor will 1. extend the `AuthScheme` to create their own within `adk/auth/auth_scheme.py` 2. implement `BaseAuthProvider` within their dedicated directory in `adk/integrations/auth` 3. do the static registration of the new scheme and provider with AuthProviderRegistry of CredentialManager. PiperOrigin-RevId: 881775983
PiperOrigin-RevId: 881782296
The _update_type_string function now recursively processes "properties" at any level of the schema, ensuring that all "type" fields within nested objects are correctly lowercased. This improves handling of complex Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 882050939
PiperOrigin-RevId: 882138997
Co-authored-by: Yifan Wang <wanyif@google.com> PiperOrigin-RevId: 882192675
This change adds logic to extract and re-embed the `thought_signature` field associated with function calls in Gemini models when converting between LiteLLM's ChatCompletionMessageToolCall and ADK's types.Part Close #4650 Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 882212223
Co-authored-by: Kathy Wu <wukathy@google.com> PiperOrigin-RevId: 882212923
The `can_use_output_schema_with_tools` function now checks if a model is a LiteLlm instance by inspecting its type's Method Resolution Order, rather than directly importing `LiteLlm` Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 882253446
PiperOrigin-RevId: 882270600
V4 still uses deprecated Node.js 20. Co-authored-by: Liang Wu <wuliang@google.com> PiperOrigin-RevId: 882275971
Co-authored-by: Liang Wu <wuliang@google.com> PiperOrigin-RevId: 882293566
Co-authored-by: Liang Wu <18244712+wuliang229@users.noreply.github.com>
Co-authored-by: Guoyi Lou <guoyilou@google.com> PiperOrigin-RevId: 882787811
This change introduces a new interceptor that adds the 'https://google.github.io/adk-docs/a2a/a2a-extension/' extension to the request headers in the A2A client from the RemoteAgent side. To send this extension along with requests, the RemoteAgent has to be instantiated with the `use_legacy` flag set to False. The AgentExecutor will default to the new implementation when this extension is requested by the client, but this behavior can be disabled via the `use_legacy` flag. The 'force_new' flag on the agent_executor side can be used to bypass the presence of the extension, and always activate the new version of the agent_executor. PiperOrigin-RevId: 883021792
Co-authored-by: Xuan Yang <xygoogle@google.com> PiperOrigin-RevId: 883246168
…creation due to missing version field Co-authored-by: Achuth Narayan Rajagopal <achuthr@google.com> PiperOrigin-RevId: 883336463
Co-authored-by: Xiang (Sean) Zhou <seanzhougoogle@google.com> PiperOrigin-RevId: 883401159
Merge #4718 ### Link to Issue or Description of Change **1. Link to an existing issue (if applicable):** - Closes: N/A - Related: N/A **2. Or, if no issue exists, describe the change:** **Problem** ADK’s MCP integration currently does not expose the MCP sampling callback capability. This prevents agent-side LLM sampling handlers from being used when interacting with MCP servers that support sampling. The MCP Python SDK supports sampling callbacks, but these parameters are not propagated through the ADK MCP integration layers. **Solution** Add sampling callback support by propagating the parameters through the MCP stack: - Add `sampling_callback` and `sampling_capabilities` parameters to `McpToolset` - Forward them to `MCPSessionManager` - Forward them to `SessionContext` - Pass them into `ClientSession` initialization This enables agent-side sampling handling when interacting with MCP servers. --- ### Testing Plan **Unit Tests** - [x] I have added or updated unit tests for my change. - [x] All unit tests pass locally. Added `test_mcp_sampling_callback.py` to verify that the sampling callback is correctly invoked. Example result: pytest tests/unittests/tools/mcp_tool/test_mcp_sampling_callback.py 1 passed **Manual End-to-End (E2E) Tests** Manual testing was performed using a FastMCP sampling example server where the sampling callback was invoked from the agent side and returned the expected response. --- ### Checklist - [x] I have read the CONTRIBUTING.md document. - [x] I have performed a self-review of my own code. - [x] I have commented my code where necessary. - [x] I have added tests proving the feature works. - [x] Unit tests pass locally. - [x] I have manually tested the change end-to-end. --- ### Additional context This change aligns ADK MCP support with the sampling capabilities available in the MCP Python SDK and enables agent implementations to handle sampling requests via a callback. Co-authored-by: Kathy Wu <wukathy@google.com> COPYBARA_INTEGRATE_REVIEW=#4718 from Piyushmrya:fix-mcp-sampling-callback 18f477f PiperOrigin-RevId: 883401178
Co-authored-by: Kathy Wu <wukathy@google.com> PiperOrigin-RevId: 883401885
Co-authored-by: Kathy Wu <wukathy@google.com> PiperOrigin-RevId: 883403479
Closes issue #4805 Co-authored-by: Liang Wu <wuliang@google.com> PiperOrigin-RevId: 883403628
…-2-preview The gemini-embedding-2-preview model requires the Vertex AI :embedContent endpoint instead of the legacy :predict endpoint used by older models (text-embedding-004, text-embedding-005). In google-genai <1.64.0, embed_content() unconditionally routed to :predict on Vertex AI, which returns FAILED_PRECONDITION for this model. v1.64.0 (googleapis/python-genai@af40cc6) introduced model-aware dispatch in embed_content(): models with "gemini" in the name are routed to :embedContent via t_is_vertex_embed_content_model(), while older text-embedding-* models continue to use :predict. This version also enforces a single-content-per-call limit for the embedContent API, which is why FilesRetrieval sets embed_batch_size=1. Co-authored-by: Xiang (Sean) Zhou <seanzhougoogle@google.com> PiperOrigin-RevId: 883689438
…provision an Agent Engine if neither agent_engine_resource_name nor sandbox_resource_name is provided The AgentEngineSandboxCodeExecutor now has three initialization modes: 1. Create both an Agent Engine and sandbox if neither resource name is provided. 2. Creating a new sandbox within a provided agent_engine_resource_name. 3. Using a provided sandbox_resource_name. PiperOrigin-RevId: 884088248
… execute_sql to support fine grained access controls PiperOrigin-RevId: 884166439
The metric takes into account all the turns of the multi-turn conversation. The class delegates the responsibility to Vertex Gen AI Eval SDK. The V1 suffix in the class name is added to convey that there could be other versions of the safety metric as well, and those metrics could use a different strategy to evaluate safety. Co-authored-by: Ankur Sharma <ankusharma@google.com> PiperOrigin-RevId: 884504910
Tool use: The class delegates the responsibility to Vertex Gen AI Eval SDK. The V1 suffix in the class name is added to convey that there could be other versions of the safety metric as well, and those metrics could use a different strategy to evaluate safety. Task trajectory: this metric is different from `Multi-Turn Overall Task Success`, in the sense that task success only concerns itself with the goal of whether the success was achieved or not. How that was achieved is not its concern. This metric on the other hand does care about the path that agent took to achieve the goal. Co-authored-by: Ankur Sharma <ankusharma@google.com> PiperOrigin-RevId: 884525532
Co-authored-by: Kathy Wu <wukathy@google.com> PiperOrigin-RevId: 884557773
When using OAuth2Session with `client_secret_post`, Authlib automatically includes the client_id and client_secret in the request body. Explicitly passing `client_id` again results in a duplicate parameter in the token exchange request, which can cause issues with some OAuth providers. Close #4782 Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 884574091
this sample can be used to test the latest gemini embedding model Co-authored-by: Xiang (Sean) Zhou <seanzhougoogle@google.com> PiperOrigin-RevId: 884574396
Merge #4818 **Please ensure you have read the [contribution guide](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) before creating a pull request.** ### Link to Issue or Description of Change **2. Or, if no issue exists, describe the change:** **Problem:** `src/google/adk/models/google_llm.py` includes a mitigation link for `429 RESOURCE_EXHAUSTED`, but the current URL points to a broken docs anchor: https://google.github.io/adk-docs/agents/models/#error-code-429-resource_exhausted <img width="1067" height="640" alt="image" src="https://github.com/user-attachments/assets/8aee07da-3007-4312-93d3-161321c01f2f" /> **Solution:** Update the link to the current Gemini-specific docs page so users are directed to the correct troubleshooting section: https://google.github.io/adk-docs/agents/models/google-gemini/#error-code-429-resource_exhausted <img width="1161" height="732" alt="image" src="https://github.com/user-attachments/assets/1badf7ab-9411-4f56-a719-6ba2a61ca7ce" /> ### Testing Plan This is a small string-only fix for a broken documentation link. No unit tests were added because there does not appear to be existing test coverage for this message and the change does not affect runtime behavior beyond the emitted URL. **Unit Tests:** - [ ] I have added or updated unit tests for my change. - [ ] All unit tests pass locally. _Please include a summary of passed `pytest` results._ **Manual End-to-End (E2E) Tests:** Confirmed the updated URL in the source points to the intended documentation page/anchor. ### Checklist - [x] I have read the [CONTRIBUTING.md](https://github.com/google/adk-python/blob/main/CONTRIBUTING.md) document. - [x] I have performed a self-review of my own code. - [ ] I have commented my code, particularly in hard-to-understand areas. - [ ] I have added tests that prove my fix is effective or that my feature works. - [ ] New and existing unit tests pass locally with my changes. - [x] I have manually tested my changes end-to-end. - [ ] Any dependent changes have been merged and published in downstream modules. ### Additional context This change only updates the documentation URL shown in the `RESOURCE_EXHAUSTED` guidance message. COPYBARA_INTEGRATE_REVIEW=#4818 from ftnext:fix-429-doc-link 1c53345 PiperOrigin-RevId: 884581120
PiperOrigin-RevId: 884597343
The previous test for unmapped LiteLLM finish_reason values was ineffective because LiteLLM's internal models normalize certain values (e.g., "eos" to "stop") before ADK processes them Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 884678119
Co-authored-by: Xuan Yang <xygoogle@google.com> PiperOrigin-RevId: 884686010
This change introduces a new `SpannerAdminToolset` with tools for managing Google Cloud Spanner resources. The toolset includes functions to list and get details of Spanner instances and instance configurations, and to create, list databases. The new toolset is marked as experimental. Unit tests for the new admin tools are also added PiperOrigin-RevId: 885116111
The Vertex AI session service does not natively support persisting usage_metadata. This change serializes usage_metadata into the custom_metadata field under the key '_usage_metadata' when appending events and deserializes it back when retrieving events. This allows usage information to be round-tripped through the Vertex AI session service. Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 885121070
…ntegration This change enables the LiteLLM adapter to correctly parse and generate Anthropic's structured "thinking_blocks" format, which includes a "signature" for each thought block. The "signature" is crucial for Anthropic models to maintain their reasoning state across multiple turns, particularly when tool calls are made Close #4801 Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 885131757
Co-authored-by: Xiang (Sean) Zhou <seanzhougoogle@google.com> PiperOrigin-RevId: 885138365
When PR #2872 migrated RestApiTool from requests (sync) to httpx.AsyncClient (async), the _request function was left without an explicit timeout parameter. Unlike requests which has no default timeout, httpx defaults to 5 seconds — causing ReadTimeout errors for any API call exceeding that limit. This regression blocked users from upgrading past 1.23.0. Set timeout=None on httpx.AsyncClient to restore parity with the previous requests-based behavior (no timeout). Fixes #4431 Co-authored-by: Xiang (Sean) Zhou <seanzhougoogle@google.com> PiperOrigin-RevId: 885287948
This change introduces optimistic concurrency control for session updates. Instead of automatically reloading and merging when an append is attempted on a session that has been modified in storage, the service now raises a ValueError. Close #4751 Co-authored-by: George Weale <gweale@google.com> PiperOrigin-RevId: 885334444
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This pull request introduces a new ADK Issue Monitoring Agent sample for GitHub spam detection, updates release configurations and changelogs to version 1.27.2, and refactors several core components. Key changes include updating dependency versions for google-genai and google-cloud-aiplatform, enhancing A2A agent executor and remote agent implementations with new integration extensions, and introducing new multi-turn evaluation metrics for Vertex AI. Additionally, the SpannerAdminToolset is added, and CrewaiTool and LangchainTool are moved to integrations modules. Review comments highlight potential issues with incorrect copyright years (2026 or 2025) in several new sample files, suggest updating an owner mapping in adk_triaging_agent/agent.py, recommend using a constant directly instead of a dictionary in a2a_agent_executor_impl.py, advise adding a comment to explain a new request interceptor in remote_a2a_agent.py, and point out a potential stack overflow risk in anthropic_llm.py due to unbounded recursion in JSON schema processing.
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| # Copyright 2026 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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The copyright header includes the year 2026. Ensure this is the correct and intended year.
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| # Copyright 2026 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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| # Copyright 2026 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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| # Copyright 2026 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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| # Copyright 2026 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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| # Copyright 2026 Google LLC | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. |
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| _get_adk_metadata_key('session_id'): executor_context.session_id, | ||
| # TODO: Remove this metadata once the new agent executor | ||
| # is fully adopted. | ||
| _get_adk_metadata_key('agent_executor_v2'): True, | ||
| _NEW_A2A_ADK_INTEGRATION_EXTENSION: {'adk_agent_executor_v2': True}, | ||
| } |
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This change replaces the metadata key with a constant. It would be better to use the constant directly instead of creating a dictionary.
_NEW_A2A_ADK_INTEGRATION_EXTENSION: {'adk_agent_executor_v2': True}
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| if not use_legacy: | ||
| if self._config.request_interceptors is None: | ||
| self._config.request_interceptors = [] | ||
| self._config.request_interceptors.append( | ||
| _new_integration_extension_interceptor | ||
| ) |
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This change adds a request interceptor when use_legacy is false. Consider adding a comment to explain why this interceptor is needed.
| <!doctype html> | ||
| <!-- | ||
| Copyright 2026 Google LLC | ||
| Copyright 2025 Google LLC |
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| def _update_type_string(value: Any): | ||
| """Lowercases nested JSON schema type strings for Anthropic compatibility.""" | ||
| if isinstance(value, list): | ||
| for item in value: | ||
| _update_type_string(item) | ||
| return | ||
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| if not isinstance(value, dict): | ||
| return | ||
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| schema_type = value.get("type") | ||
| if isinstance(schema_type, str): | ||
| value["type"] = schema_type.lower() | ||
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| for dict_key in ( | ||
| "$defs", | ||
| "defs", | ||
| "dependentSchemas", | ||
| "patternProperties", | ||
| "properties", | ||
| ): | ||
| child_dict = value.get(dict_key) | ||
| if isinstance(child_dict, dict): | ||
| for child_value in child_dict.values(): | ||
| _update_type_string(child_value) | ||
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| for single_key in ( | ||
| "additionalProperties", | ||
| "additional_properties", | ||
| "contains", | ||
| "else", | ||
| "if", | ||
| "items", | ||
| "not", | ||
| "propertyNames", | ||
| "then", | ||
| "unevaluatedProperties", | ||
| ): | ||
| child_value = value.get(single_key) | ||
| if isinstance(child_value, (dict, list)): | ||
| _update_type_string(child_value) | ||
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| for list_key in ( | ||
| "allOf", | ||
| "all_of", | ||
| "anyOf", | ||
| "any_of", | ||
| "oneOf", | ||
| "one_of", | ||
| "prefixItems", | ||
| ): | ||
| child_list = value.get(list_key) | ||
| if isinstance(child_list, list): | ||
| _update_type_string(child_list) |
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This function recursively updates the type strings in the JSON schema. This could potentially lead to unexpected behavior if the schema is very complex or deeply nested. Consider adding a limit to the recursion depth to prevent stack overflow errors.
…tracing into environment simulation PiperOrigin-RevId: 885759367
…ing to LiveConnectConfig RunConfig.response_modalities is typed as list[str] for backward compatibility, but LiveConnectConfig.response_modalities expects list[Modality] (an enum). Assigning strings directly causes a Pydantic serialization warning on every live streaming session: PydanticSerializationUnexpectedValue(Expected `enum` - serialized value may not be as expected [field_name='response_modalities', input_value='AUDIO', input_type=str]) Convert each modality value to types.Modality at the assignment point in basic.py using types.Modality(m), which is a no-op for values that are already Modality enums and converts plain strings like "AUDIO" to Modality.AUDIO. The public RunConfig interface remains list[str] so existing callers are unaffected. Fixes: #4869 Co-authored-by: Xiang (Sean) Zhou <seanzhougoogle@google.com> PiperOrigin-RevId: 885781014
Co-authored-by: Kathy Wu <wukathy@google.com> PiperOrigin-RevId: 885801247
Co-authored-by: Kathy Wu <wukathy@google.com> PiperOrigin-RevId: 885813216
Co-authored-by: Kathy Wu <wukathy@google.com> PiperOrigin-RevId: 885814460
Co-authored-by: Xuan Yang <xygoogle@google.com> PiperOrigin-RevId: 885829552
Merge #4780 ## Bug `AnthropicLlm.part_to_message_block()` only serializes `FunctionResponse.response` dicts that contain a `"content"` or `"result"` key. When neither key is present the variable `content` stays as `""` and an empty `ToolResultBlockParam` is sent to Claude. This silently drops the output of several `SkillToolset` tools: | Tool | Keys returned | Handled before this fix? | |---|---|---| | `load_skill` (success) | `skill_name`, `instructions`, `frontmatter` | **No** | | `run_skill_script` (success) | `skill_name`, `script_path`, `stdout`, `stderr`, `status` | **No** | | Any skill tool (error) | `error`, `error_code` | **No** | | `load_skill_resource` (success) | `skill_name`, `path`, `content` | Yes (`"content"` key) | Because `load_skill` is the entry-point for skill instructions, Claude models using `SkillToolset` **never received skill instructions**, making the feature completely non-functional with Anthropic models. ## Fix Added an `else` branch in `part_to_message_block()` that JSON-serializes the full response dict when neither `"content"` nor `"result"` is present: ```python elif response_data: # Fallback: serialize the entire response dict as JSON so that tools # returning arbitrary key structures (e.g. load_skill returning # {"skill_name", "instructions", "frontmatter"}) are not silently # dropped. content = json.dumps(response_data) ``` This is consistent with how Gemini handles it — the Gemini integration passes `types.Part` objects directly to the Google GenAI SDK which serializes them natively, so there is no key-based filtering at all. ## Testing plan Added 4 new unit tests to `tests/unittests/models/test_anthropic_llm.py`: - `test_part_to_message_block_arbitrary_dict_serialized_as_json` — covers the `load_skill` response shape - `test_part_to_message_block_run_skill_script_response` — covers the `run_skill_script` response shape - `test_part_to_message_block_error_response_not_dropped` — covers error dict responses - `test_part_to_message_block_empty_response_stays_empty` — ensures empty dict still produces empty content (no regression) All 35 tests in `test_anthropic_llm.py` pass: ``` 35 passed in 7.32s ``` Run with: ```bash uv sync --extra test pytest tests/unittests/models/test_anthropic_llm.py -v ``` Co-authored-by: Kathy Wu <wukathy@google.com> COPYBARA_INTEGRATE_REVIEW=#4780 from akashbangad:fix/anthropic-llm-skill-toolset-fallback c23ad37 PiperOrigin-RevId: 885831845
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Automated sync of v1 changes from main into v2. The oncall is responsible for reviewing and merging this PR. Resolve conflicts in favor of the v2 implementation.