In live mode, when the model calls an unregistered tool, ADK now runs on_tool_error_callback before failing. If the callback returns a response, ADK emits
that function response and continues; otherwise it keeps the old ValueError
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 872996178
Previously we resolved tools sequentially by awaiting _convert_tool_union_to_tools() in a loop -- reduce the latency by resolving tools concurrently.
Co-authored-by: Kathy Wu <wukathy@google.com>
PiperOrigin-RevId: 872979105
Merge https://github.com/google/adk-python/pull/4171
**Problem:**
The BigQuery ADK tools currently lack the ability to search for and discover BigQuery assets using the Dataplex Catalog. Users cannot leverage Dataplex's search capabilities within the ADK to find relevant data assets before querying them.
**Solution:**
This PR integrates a new search_catalog_tool into the BigQuery ADK. This tool utilizes the dataplex catalog client library to interact with the Dataplex API, allowing users to search the catalog.
**Unit Tests:**
- [x] I have added or updated unit tests for my change.
- [x] All unit tests pass locally.
Added the screenshots of the manual adk web UI tests - https://docs.google.com/document/d/1c_lMW7NYGKuLAvPFmSkLehbqySeNyXQIhzQlvo3ixmQ/edit?usp=sharing
### 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.
- [x] I have commented my code, particularly in hard-to-understand areas.
- [x] I have added tests that prove my fix is effective or that my feature works.
- [x] New and existing unit tests pass locally with my changes.
- [x] I have manually tested my changes end-to-end.
- [x] Any dependent changes have been merged and published in downstream modules.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/4171 from sahaajaaa:sahaajaaa-bq-adk 3dbbaa4f909cb25259e8e7d73a00a58fbe9c2f09
PiperOrigin-RevId: 872951141
This change allows `add_memory` to use the `memories.generate` API with `direct_memories_source` when `custom_metadata["enable_consolidation"]` is set to True. This enables server-side consolidation of the provided memories
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 872554004
The `add_memory` methods in `Context` and `BaseMemoryService` now accept `MemoryEntry` objects in addition to strings. The Vertex AI Memory Bank service implementation is updated to handle these new types
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 872108561
* use last-release-sha to locate the previous release commit in main for CHANGELOG generating.
* release tag can now use the commit sha in release branch
Co-authored-by: Wei Sun (Jack) <weisun@google.com>
PiperOrigin-RevId: 871909085
## Problem
When performing authentication flows via `OAUTH2` or `OPEN_ID_CONNECT`, the native `OAuth2Token` response from identity providers, like Google OAuth, often includes an `id_token` alongside the `access_token` and `refresh_token`. [MCP Toolbox](https://googleapis.github.io/genai-toolbox/resources/authservices/google/) implements authentication through ID Tokens and [integrates with ADK](https://google.github.io/adk-docs/integrations/mcp-toolbox-for-databases/) to provide easy tools management for the end-users.
However, the ADK's `update_credential_with_tokens` utility explicitly drops the `id_token`, preventing agents and tools from verifying user identity or extracting OIDC claims securely. Furthermore, the `OAuth2Auth` model does not have a designated field for `id_token`.
## Solution
1. Added an `id_token: Optional[str] = None` field to the `OAuth2Auth` pydantic model in `auth_credential.py`.
2. Updated `update_credential_with_tokens` in `oauth2_credential_util.py` to correctly extract and map `tokens.get("id_token")` into the `OAuth2Auth` credential object.
3. Updated the relevant unit tests to ensure `id_token` is asserted and preserved during credential updates.
### Testing Plan
- I have added or updated unit tests for my change.
- All unit tests pass locally.
PiperOrigin-RevId: 871801313
The list_skills method is not for model tool listing, but for giving the developer flexibility to load the skill name/description at runtime (from discussion in go/orcas-rfc-555)
Co-authored-by: Kathy Wu <wukathy@google.com>
PiperOrigin-RevId: 871406905
This CL fixes several bugs in the BigQuery Agent Analytics plugin and refactors the internal data-passing pattern for better type safety and maintainability.
- **Stale Loop State Validation:** Use `loop.is_closed()` — a public, reliable API — to detect and clean up stale asyncio loop states in `_batch_processor_prop`, `_get_loop_state`, and `flush`. The previous approach used `asyncio.Queue._loop` which is `None` on Python 3.10+, causing the check to always treat states as stale.
- **Quota Project ID Fallback:** Remove the `or project_id` fallback when setting `quota_project_id` on `BigQueryWriteAsyncClient`. This fixes Workload Identity Federation flows where the federated identity lacks `serviceusage.services.use` on the quota project.
- **Kwargs Passthrough:** Pass `**kwargs` through to `_log_event` in all callbacks. Previously only model callbacks forwarded them, causing custom attributes (e.g. `customer_id`) to silently drop for agent, tool, run, and error events.
- **State Delta Logging:** Replace the dead `on_state_change_callback` (never invoked by the framework) with `on_event_callback`, which is already dispatched by the runner for every event. Remove duplicate `STATE_DELTA` logging from `after_tool_callback`.
- **EventData Dataclass:** Replace the `**kwargs`-as-data-bus pattern in `_log_event` with an explicit `EventData` dataclass. This makes the interface self-documenting, catches typos at construction time, and eliminates shared dict mutation across `_resolve_span_ids`, `_extract_latency`, and `_enrich_attributes`. All 12 callback call sites now construct typed `EventData` instances.
- **Multi-Subagent Tool Logging Tests:** Add `TestMultiSubagentToolLogging` (6 tests) verifying that tool events are correctly attributed to subagents in multi-turn, multi-agent scenarios. Total tests: 111 (up from 60).
Co-authored-by: Haiyuan Cao <haiyuan@google.com>
PiperOrigin-RevId: 871381533
Liang Wu
Google Team Member
George Weale
Kathy Wu
Sahaja Reddy Pabbathi Reddy
Xiang (Sean) Zhou
Wei Sun (Jack)
Haiyuan Cao