The vector_store_similarity_search tool performs similarity search against data in a Spanner vector store table, using the provided Spanner tool settings for configuration.
PiperOrigin-RevId: 839352057
The Gemini API may not always send an explicit transcription finished signal. This change ensures that any buffered input or output transcription text is yielded as a finished transcription when a turn is completed, generation is complete, or the session is interrupted.
Also, refined the check for `event.partial` in runners.py to be more explicit.
Co-authored-by: Hangfei Lin <hangfei@google.com>
PiperOrigin-RevId: 839008606
LlmAgentConfig.model now accepts either a plain model string or a CodeConfig. This lets YAML configs pass a LiteLLM instance with managed API settings (e.g., api_base and fallbacks) so agents can hit KimiK2’s managed endpoint instead of only the default modelID.
Close#3579
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 838978654
This change adds new `POST` endpoint `/apps/{app_name}/users/{user_id}/sessions/{session_id}/artifacts` to the ADK web server. This endpoint lets clients to save new artifacts associated with a specific session. The endpoint uses `SaveArtifactRequest` and returns `SaveArtifactResponse`, including the version and canonical URI of the saved artifact.
Close#1975
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 838977880
Default CLI session storage to SQLite instead of in-memory
Previously, adk run and adk web used in-memory session storage by default, causing sessions to be lost on restart. Now sessions persist to .adk/session.db automatically. To use in-memory storage, pass --session-service-uri memory://
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 838975328
Merge https://github.com/google/adk-python/pull/3700
### Description
This PR refactors the `adk_stale_agent` to address `429 RESOURCE_EXHAUSTED` errors encountered during workflow execution. The previous implementation was inefficient in fetching issue history (using pagination over the REST API) and lacked server-side filtering, causing excessive API calls and huge token consumption that breached Gemini API quotas.
The new implementation switches to a **GraphQL-first approach**, implements server-side filtering via the Search API, adds robust concurrency controls, and significantly improves code maintainability through modular refactoring.
### Root Cause of Failure
The previous workflow failed with the following error due to passing too much context to the LLM and processing too many irrelevant issues:
```text
google.genai.errors.ClientError: 429 RESOURCE_EXHAUSTED.
Quota exceeded for metric: generativelanguage.googleapis.com/generate_content_paid_tier_input_token_count
```
### Key Changes
#### 1. Optimization: REST → GraphQL (`agent.py`)
* **Old:** Fetched issue comments and timeline events using multiple paginated REST API calls (`/timeline`).
* **New:** Implemented `get_issue_state` using a single **GraphQL** query. This fetches comments, `userContentEdits`, and specific timeline events (Labels, Renames) in one network request.
* **Refactoring:** The complex analysis logic has been decomposed into focused helper functions (_fetch_graphql_data, _build_history_timeline, _replay_history_to_find_state) for better readability and testing.
* **Configurable:** Added GRAPHQL_COMMENT_LIMIT and GRAPHQL_TIMELINE_LIMIT settings to tune context depth
* **Impact:** Drastically reduces the data payload size and eliminates multiple API round-trips, significantly lowering the token count sent to the LLM.
#### 2. Optimization: Server-Side Filtering (`utils.py`)
* **Old:** Fetched *all* open issues via REST and filtered them in Python memory.
* **New:** Uses the GitHub Search API (`get_old_open_issue_numbers`) with `created:<DATE` syntax.
* **Impact:** Only fetches issue numbers that actually meet the age threshold, preventing the agent from wasting cycles and tokens on brand-new issues.
#### 3. Concurrency & Rate Limiting (`main.py` & `settings.py`)
* **Old:** Sequential execution loop.
* **New:** Implemented `asyncio.gather` with a configurable `CONCURRENCY_LIMIT` (set to 3).
* **New:** Added `urllib3` retry strategies (exponential backoff) in `utils.py` to handle GitHub API rate limits (HTTP 429) gracefully.
#### 4. Logic Improvements ("Ghost Edits")
* **New Feature:** The agent now detects "Ghost Edits" (where an author updates the issue description without posting a new comment).
* **Action:** If a silent edit is detected on a stale candidate, the agent now alerts maintainers instead of marking it stale, preventing false positives.
### File Comparison Summary
| File | Change |
| :--- | :--- |
| `main.py` | Switched from `InMemoryRunner` loop to `asyncio` chunked processing. Added execution timing and API usage logging. |
| `agent.py` | Replaced REST logic with GraphQL query. Added logic to handle silent body edits. Decomposed giant get_issue_state into helper functions with docstrings. Added _format_days helper. |
| `utils.py` | Added `HTTPAdapter` with Retries. Added `get_old_open_issue_numbers` using Search API. |
| `settings.py` | Removed `ISSUES_PER_RUN`; added configuration for CONCURRENCY_LIMIT, SLEEP_BETWEEN_CHUNKS, and GraphQL limits. |
| `PROMPT_INSTRUCTIONS.txt` | Simplified decision tree; removed date calculation responsibility from LLM. |
### Verification
The new logic minimizes token usage by offloading date calculations to Python and strictly limiting the context passed to the LLM to semantic intent analysis (e.g., "Is this a question?").
* **Metric Check:** The workflow now tracks API calls per issue to ensure we stay within limits.
* **Safety:** Silent edits by users now correctly reset the "Stale" timer.
* **Maintainability:** All complex logic is now isolated in typed helper functions with comprehensive docstrings.
Co-authored-by: Xuan Yang <xygoogle@google.com>
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3700 from ryanaiagent:feat/improve-stale-agent 888064eff125ae74f7c3a9ad6c74f98de80243a2
PiperOrigin-RevId: 838885530
This change introduces an `AnthropicLlm` base class for direct Anthropic API calls using `AsyncAnthropic`. The existing `Claude` class now inherits from `AnthropicLlm` and is specialized to use `AsyncAnthropicVertex` for models hosted on Vertex AI. The `messages.create` call is now properly awaited
Merge: https://github.com/google/adk-python/pull/2904
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 838851026
This change updates the docstring for `agent_engine_id` to clarify that only the resource ID is expected. It also adds a warning log if the provided `agent_engine_id` contains a '/' character, suggesting it might be a full resource path, and provides guidance on how to extract the ID. Unit tests are added to verify the warning behavior.
Merge: https://github.com/google/adk-python/pull/2941
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 838845022
Also provide a command line tool `adk migrate session` for DB migration
Addresses https://github.com/google/adk-python/discussions/3605
Addresses https://github.com/google/adk-python/issues/3681
To verify:
```
# Start one postgres DB
docker run --name my-postgres -d -e POSTGRES_DB=agent -e POSTGRES_USER=agent -e POSTGRES_PASSWORD=agent -e PGDATA=/var/lib/postgresql/data/pgdata -v pgvolume:/var/lib/postgresql/data -p 5532:5432 postgres
# Connect to an old version of ADK and produce some query data
adk web --session_service_uri=postgresql://agent:agent@localhost:5532/agent
# Check out to the latest branch and restart ADK web
# You should see error log ask you to migrate the DB
# Start a new DB
docker run --name migration-test-db \
-d \ --rm \ -e POSTGRES_DB=agent \ -e POSTGRES_USER=agent \ -e POSTGRES_PASSWORD=agent -e PGDATA=/var/lib/postgresql/data/pgdata -v migration_test_vol:/var/lib/postgresql/data -p 5533:5432 postgres
# DB Migration
adk migrate session \
--source_db_url="postgresql://agent:agent@localhost:5532/agent" \
--dest_db_url="postgresql://agent:agent@localhost:5533/agent"
# Run ADK web with the new DB
adk web --session_service_uri=postgresql+asyncpg://agent:agent@localhost:5533/agent
# You should see the data from old DB is migrated
```
Co-authored-by: Shangjie Chen <deanchen@google.com>
PiperOrigin-RevId: 837341139
This calls the cloudapiregistry.googleapis.com API to get MCP tools from the project's registry, and adds them to ADK.
Co-authored-by: Kathy Wu <wukathy@google.com>
PiperOrigin-RevId: 837166909
Adds the shared adk_services_options decorator to adk run and other commands so developers can pass session/artifact URIs from the CLI
Has new warning for the unsupported memory service on adk run, and removes the legacy --session_db_url/--artifact_storage_uri flags with tests
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 836743358
This change routes adk run and the FastAPI server through the new session/artifact service factory, keeps the default experience backed by per-agent .adk storage
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 836733234
Previously, image parts were always filtered out when converting content to Anthropic message parameters. This change updates the logic to only filter out image parts and log a warning when the content role is not "user". This enables sending image data as part of user prompts to Claude models
Merges: https://github.com/google/adk-python/pull/3286
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 836725196
Merge https://github.com/google/adk-python/pull/3284
**Problem:**
When debugging agents that utilize the `VertexAiSearchTool`, it's currently difficult to inspect the specific configuration parameters (datastore ID, engine ID, filter, max_results, etc.) being passed to the underlying Vertex AI Search API via the `LlmRequest`. This lack of visibility can hinder troubleshooting efforts related to tool configuration.
**Solution:**
This PR enhances the `VertexAiSearchTool` by adding a **debug-level log statement** within the `process_llm_request` method. This log precisely records the parameters being used for the Vertex AI Search configuration just before it's appended to the `LlmRequest`.
This provides developers with crucial visibility into the tool's runtime behavior when debug logging is enabled, significantly improving the **debuggability** of agents using this tool. Corresponding unit tests were updated to rigorously verify this new logging output using `caplog`. Additionally, minor fixes were made to the tests to resolve Pydantic validation errors.
Co-authored-by: Xuan Yang <xygoogle@google.com>
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3284 from omkute10:feat/add-logging-vertex-search-tool 199c12bf00a57abe202401591088c0423b39b928
PiperOrigin-RevId: 836419886
