This trimmed schema includes only the fields relevant to agent shells, tool wiring, and common generation parameters, improving efficiency and focus.
The default model for the assistant has also been updated to "gemini-2.5-pro"
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 829513627
Add `_to_litellm_response_format` to convert ADK's `response_schema` types (Pydantic models, JSON schema dicts) into the format needed by LiteLLM for JSON object/schema constraints
Close#1967
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 829037987
Merge https://github.com/google/adk-python/pull/3394
This PR corrects misspellings identified by the [check-spelling action](https://github.com/marketplace/actions/check-spelling)
Note: while I use tooling to identify errors, the tooling doesn't _actually_ provide the corrections, I'm picking them on my own. I'm a human, and I may make mistakes.
### Testing Plan
The misspellings have been reported at https://github.com/jsoref/adk-python/actions/runs/19056081305/attempts/1#summary-54426435973
The action reports that the changes in this PR would make it happy: https://github.com/jsoref/adk-python/actions/runs/19056081446/attempts/1#summary-54426436321
**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:**
_Please provide instructions on how to manually test your changes, including any
necessary setup or configuration. Please provide logs or screenshots to help
reviewers better understand the fix._
### 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.
- [ ] I have manually tested my changes end-to-end.
- [ ] Any dependent changes have been merged and published in downstream modules.
### Additional context
- https://github.com/google/adk-python/pull/3382#issuecomment-3488654110
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3394 from jsoref:spelling-contributing c3d5e342c4350f7cae9f8f0c6638b176f2e30e80
PiperOrigin-RevId: 828659867
Merge https://github.com/google/adk-python/pull/3411
## Summary
Fixes AgentTool cleanup to prevent MCP session errors by calling `runner.close()` after sub-agent execution.
## Problem
When using AgentTool with MCP tools, the runner cleanup happened during garbage collection in a different async task context, causing:
```
RuntimeError: Attempted to exit cancel scope in a different task than it was entered in
```
## Solution
- Call `await runner.close()` immediately after sub-agent execution in AgentTool
- This ensures MCP sessions and other resources are cleaned up in the correct async task context
- Updated test mock to include the close() method
## Demo Agents
Added two comprehensive demo agents showing how to use AgentTool with MCP tools:
### mcp_in_agent_tool_remote (SSE mode)
- Uses HTTP/SSE connection to remote MCP server
- Zero-installation setup with `uvx`
- Demonstrates server-side MCP deployment pattern
### mcp_in_agent_tool_stdio (stdio mode)
- Uses subprocess connection with automatic server launch
- Fully automatic setup with `uvx` subdirectory syntax
- Demonstrates embedded MCP deployment pattern
Both demos:
- Use Gemini 2.5 Flash
- Include example prompts for JSON Schema exploration
- Have comprehensive READMEs with architecture diagrams
- Follow ADK agent structure conventions
## Testing
- ✅ All existing unit tests pass
- ✅ Manual testing with both SSE and stdio modes
- ✅ Verified cleanup happens in correct async context
- ✅ No more cancel scope errors with MCP tools
## Related
- Fixes#1112
- Related to #929
Co-authored-by: Wei Sun (Jack) <weisun@google.com>
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3411 from google:fix/agent-tool-mcp-cleanup 9ae753b5a428e5a8d5039f079bc9b9faed65b467
PiperOrigin-RevId: 828651896
Replace the full JSON schema dump with a compact text summary of key AgentConfig components like LlmAgent, ToolConfig, and GenerateContentConfig
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 828627911
The sample agent now uses updated model names for Gemini Live, including a new Vertex model as the default and a new AI Studio model option.
Co-authored-by: Hangfei Lin <hangfei@google.com>
PiperOrigin-RevId: 828515811
Add support for MCP prompts via the McpInstructionProvider class, which can be specified as an agent's instruction.
Co-authored-by: Kathy Wu <wukathy@google.com>
PiperOrigin-RevId: 828166051
This fixes MCP authentication for gcloud service accounts. Previously it was failing to authenticate tool calls.
Co-authored-by: Kathy Wu <wukathy@google.com>
PiperOrigin-RevId: 826639044
Merge https://github.com/google/adk-python/pull/3345
Add run_debug() helper method to InMemoryRunner that reduces agent execution boilerplate from 7-8 lines to just 2 lines, making it ideal for quick experimentation, notebooks, and getting started with ADK.
**Key changes:**
• Introduce run_debug() to reduce boilerplate from 7-8 lines to 2 lines
• Enable quick testing in notebooks, REPL, and during development
• Support single or multiple messages with automatic session management
• Add verbose flag to show/hide tool calls and intermediate processing
• Add quiet flag to suppress console output while capturing events
• Extract event printing logic to reusable utility (utils/_debug_output.py)
• Include comprehensive test suite with 21 test cases covering all part types
• Provide complete working example with 8 usage patterns
• **This is a convenience method for experimentation, not a replacement for run_async()**
### Link to Issue or Description of Change
**1. Link to an existing issue (if applicable):**
* N/A - New feature to improve developer experience
**2. Or, if no issue exists, describe the change:**
**Problem:**
Developers need to write 7-8 lines of boilerplate code just to test a simple agent interaction during development. This creates friction for:
* New developers getting started with ADK
* Quick experimentation in Jupyter notebooks or Python REPL
* Debugging agent behavior during development
* Writing examples and tutorials
* Rapid prototyping of agent capabilities
**Solution:**
Introduce `run_debug()` as a convenience helper method specifically designed for quick experimentation and getting started scenarios. This method:
* **Is NOT a replacement for `run_async()`** - it's a developer convenience tool
* **Reduces boilerplate** from 7-8 lines to just 2 lines for simple testing
* **Handles session management automatically** with sensible defaults
* **Provides debugging visibility** with optional verbose flag for tool calls
* **Supports common patterns** like multiple messages and event capture
* **Type-safe implementation** using direct attribute access instead of getattr()
### Before vs After Comparison
**BEFORE - Current approach requires 7-8 lines of boilerplate:**
```python
from google.adk import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
# Define a simple agent
agent = Agent(
model="gemini-2.5-flash",
instruction="You are a helpful assistant"
)
# Need all this boilerplate just to test the agent
APP_NAME = "default"
USER_ID = "default"
session_service = InMemorySessionService()
runner = Runner(agent=agent, app_name=APP_NAME, session_service=session_service)
session = await session_service.create_session(
app_name=APP_NAME, user_id=USER_ID, session_id="default"
)
content = types.Content(role="user", parts=[types.Part.from_text("Hello")])
async for event in runner.run_async(
user_id=USER_ID, session_id=session.id, new_message=content
):
if event.content and event.content.parts:
print(event.content.parts[0].text)
```
**AFTER - With run_debug() helper, just 2 lines:**
```python
from google.adk import Agent
from google.adk.runners import InMemoryRunner
# Define the same agent
agent = Agent(
model="gemini-2.5-flash",
instruction="You are a helpful assistant"
)
# Test it with just 2 lines!
runner = InMemoryRunner(agent=agent)
await runner.run_debug("Hello")
```
### API Design
```python
async def run_debug(
self,
user_messages: str | list[str],
*,
user_id: str = 'debug_user_id',
session_id: str = 'debug_session_id',
run_config: RunConfig | None = None,
quiet: bool = False,
verbose: bool = False,
) -> list[Event]:
```
**Parameters:**
* `user_messages`: Single message string or list of messages (required)
* `user_id`: User identifier (default: 'debug_user_id')
* `session_id`: Session identifier for conversation continuity (default: 'debug_session_id')
* `run_config`: Optional advanced configuration
* `quiet`: Suppress console output (default: False)
* `verbose`: Show detailed tool calls and responses (default: False)
**Key Features:**
* **Always returns events** - Simplifies API, no conditional return type
* **Type-safe implementation** - Uses direct attribute access on Pydantic models
* **Text buffering** - Consecutive text parts printed without repeated author prefix
* **Smart truncation** - Long tool args/responses truncated for readability
* **Clean session management** - Get-then-create pattern, no try/except
* **Reusable printing logic** - Extracted to utils/_debug_output.py for other tools
### Implementation Highlights
**1. Event Printing Utility (utils/_debug_output.py):**
* Modular print_event() function for displaying events
* Text buffering to combine consecutive text parts
* Configurable truncation for different content types:
- Function args: 50 chars max
- Function responses: 100 chars max
- Code output: 100 chars max
* Supports all ADK part types (text, function_call, executable_code, inline_data, file_data)
**2. Session Management:**
```python
# Clean get-then-create pattern (no try/except)
session = await self.session_service.get_session(
app_name=self.app_name, user_id=user_id, session_id=session_id
)
if not session:
session = await self.session_service.create_session(
app_name=self.app_name, user_id=user_id, session_id=session_id
)
```
**3. Type-Safe Event Processing:**
* Direct attribute access on Pydantic models (no getattr() or hasattr())
* Proper handling of all part types
* Leverages `from __future__ import annotations` for duck typing
### Important Note on Scope
`run_debug()` is a **convenience method for experimentation only**. For production applications requiring:
* Custom session services (Spanner, Cloud SQL)
* Fine-grained event processing control
* Error recovery and resumability
* Performance optimization
* Complex authentication flows
Continue using the standard `run_async()` method. The `run_debug()` helper is specifically designed to lower the barrier to entry and speed up the development/testing cycle.
### Testing Plan
**Unit Tests (21 test cases in tests/unittests/runners/test_runner_debug.py):**
**Core functionality (7 tests):**
* ✅ Single message execution and event return
* ✅ Multiple messages in sequence
* ✅ Quiet mode (suppresses output)
* ✅ Custom session_id configuration
* ✅ Custom user_id configuration
* ✅ RunConfig passthrough
* ✅ Session persistence across calls
**Part type handling (8 tests):**
* ✅ Tool calls and responses (verbose mode)
* ✅ Executable code parts
* ✅ Code execution result parts
* ✅ Inline data (images)
* ✅ File data references
* ✅ Mixed part types in single event
* ✅ Long output truncation
* ✅ Verbose flag behavior (show/hide tools)
**Edge cases (6 tests):**
* ✅ None text filtering
* ✅ Existing session handling
* ✅ Empty parts list
* ✅ None event content
* ✅ Verbose=False hides tool calls
* ✅ Verbose=True shows tool calls
**All 21 tests passing in 3.8s** ✓
**Manual End-to-End (E2E) Tests:**
Tested all 8 example patterns in contributing/samples/runner_debug_example/main.py:
1. ✅ Minimal 2-line usage
2. ✅ Multiple sequential messages
3. ✅ Session persistence across calls
4. ✅ Multiple user sessions (Alice & Bob)
5. ✅ Verbose mode for tool visibility
6. ✅ Event capture with quiet mode
7. ✅ Custom RunConfig integration
8. ✅ Before/after comparison
### Files Changed
**Core implementation:**
* src/google/adk/runners.py - Added run_debug() method (~60 lines)
* src/google/adk/utils/_debug_output.py - Event printing utility (~106 lines)
**Tests:**
* tests/unittests/runners/test_runner_debug.py - Comprehensive test suite (21 tests)
**Examples:**
* contributing/samples/runner_debug_example/agent.py - Sample agent with tools
* contributing/samples/runner_debug_example/main.py - 8 usage examples
* contributing/samples/runner_debug_example/README.md - Complete documentation
### 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 (21/21 passing)
- [x] I have manually tested my changes end-to-end (8 examples tested)
- [x] Code follows ADK style guide (relative imports, type hints, 2-space indentation)
- [x] Ran ./autoformat.sh before committing
- [x] Any dependent changes have been merged and published in downstream modules
### Additional Context
**Example with Tools (verbose mode):**
```python
# Create agent with tools
agent = Agent(
model="gemini-2.5-flash",
instruction="You can check weather and do calculations",
tools=[get_weather, calculate]
)
# Test with verbose to see tool calls
runner = InMemoryRunner(agent=agent)
await runner.run_debug("What's the weather in SF?", verbose=True)
# Output:
# User > What's the weather in SF?
# agent > [Calling tool: get_weather({'city': 'San Francisco'})]
# agent > [Tool result: {'result': 'Foggy, 15°C (59°F)'}]
# agent > The weather in San Francisco is foggy, 15°C (59°F).
```
**Complete Example Included:**
The PR includes a full working example in `contributing/samples/runner_debug_example/` with:
* Agent with weather and calculator tools
* 8 different usage patterns
* Comprehensive README with troubleshooting
* Safe AST-based expression evaluation
**Breaking Changes:** None - this is purely additive.
**Security:** Example uses AST-based expression evaluation instead of eval().
**Code Quality:**
* Type-safe implementation (no getattr() or hasattr())
* Modular design (printing logic separated into utility)
* Follows ADK conventions (relative imports, from __future__ import annotations)
* Comprehensive error handling (gracefully handles None content, empty parts)
* Well-documented with docstrings and inline comments
END_PUBLIC
```
---
## Key Changes from Original:
1. ✅ Updated parameter name: `user_queries` → `user_messages`
2. ✅ Updated parameter name: `session_name` → `session_id`
3. ✅ Updated parameter name: `print_output` → `quiet`
4. ✅ Removed `return_events` parameter
5. ✅ Updated test count: 23 → 21
6. ✅ Changed "queries" → "messages" throughout
7. ✅ Added implementation highlights section
8. ✅ Added details about utils/_debug_output.py
9. ✅ Updated default values to debug_user_id/debug_session_id
10. ✅ Noted type-safe implementation
11. ✅ Added Code Quality section
12. ✅ Updated API signature to match final refactored version
13. ✅ Removed optional return type (always returns list[Event])
Co-authored-by: Wei Sun (Jack) <weisun@google.com>
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3345 from lavinigam-gcp:adk-runner-helper e0050b9f152d0f0e49e6501610d2c59a754fc571
PiperOrigin-RevId: 826607817
This change introduces a sample (`migration_session_db`) demonstrating how to load a session database created with an older version of ADK (e.g., 1.15.0) and make it compatible with the current version. It includes a script (`db_migration.sh`) to alter the SQLite schema automatically. to_event is updated to handle potential discrepancies in pickled `EventActions` by using `model_copy` to ensure compatibility with the latest `EventActions` model definition.
Related to #3272#3197, Closes#3197#3272
Co-authored-by: Dongyu Jia <dongyuj@google.com>
PiperOrigin-RevId: 826524368
This CL introduces a new tool, get_job_info, to the BigQuery toolset. This tool allows retrieving metadata about a BigQuery job, such as slot usage, job configuration, statistics, and job status.
Closes#2928
Co-authored-by: Dongyu Jia <dongyuj@google.com>
PiperOrigin-RevId: 825762399
Merge https://github.com/google/adk-python/pull/3037
fix: [#3036](https://github.com/google/adk-python/issues/3036)
- Fix FunctionTool parameter filtering to support CrewAI-style tools
- Functions with **kwargs now receive all parameters except 'self' and 'tool_context'
- Maintains backward compatibility with explicit parameter functions
- Add comprehensive tests for **kwargs functionality
Fixes parameter filtering issue where CrewAI tools using **kwargs pattern would receive empty parameter dictionaries, causing search_query and other parameters to be None.
#non-breaking
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3037 from omarcevi:fix/function-tool-kwargs-parameter-filtering 012bbfcfd68e83a29635ac74718a1bd1323c5187
PiperOrigin-RevId: 825275686
This change introduces a powerful pattern for customizing code execution environments by extending a base `CodeExecutor`. It showcases how to inject setup code to prepare the environment before a user's code is run, enabling advanced use cases that require specific configurations.
As a practical example, this change implements `CustomCodeExecutor`, a subclass of `VertexAiCodeExecutor`, to solve the problem of rendering non-standard characters in `matplotlib` plots (Issue #2993). The custom executor programmatically adds a Japanese font to the `matplotlib` font manager at runtime.
This is achieved by overriding the `execute_code` method to add font files to execution input and prepend the necessary font-loading logic. This approach is not limited to fonts and can be adapted for other setup tasks.
Fixes: #2993
PiperOrigin-RevId: 825240143
This change introduces a new sample agent and documentation to demonstrate the usage of the `VertexAiCodeExecutor`.
The new agent, located at `vertex_code_execution/agent.py`, is a direct counterpart to the existing sample at `code_execution/agent.py`. The key difference is that this new agent uses `VertexAiCodeExecutor` to execute code within the Vertex AI Code Interpreter Extension, whereas the original sample uses `BuiltInCodeExecutor` to run code in the model's built-in sandbox.
A `README.md` file is also included to provide an overview, setup instructions, and sample usage for the new agent.
Related: #2993
PiperOrigin-RevId: 825239758
This change introduces a new `detect_anomalies` tool in `query_tool.py` which uses BigQuery ML's `CREATE MODEL` with `ARIMA_PLUS` type and `ML.DETECT_ANOMALIES` to detect anomalies. The new function is also added to the `bigquery_toolset`.
PiperOrigin-RevId: 825181489
The computer_use sample now supports launching with a `user_data_dir` to maintain browser state across runs. The sample agent is updated to use a shared temporary directory for the browser profile, preserving login sessions and other data.
PiperOrigin-RevId: 823749082
While testing the bidi streaming sample agent, I noticed that the import was erroring and I think it's a typo -- the other bidi sample agents all import `from google.adk.agents.llm_agent`.
PiperOrigin-RevId: 823662586
## What's Added
- **PostgreSQL MCP Agent** ([mcp_postgres_agent/agent.py](cci:7://file:///Users/admin/git%20repos/adk-python/contributing/samples/mcp_postgres_agent/agent.py:0:0-0:0)): A fully functional agent that connects to PostgreSQL databases via the `postgres-mcp` MCP server
- **Comprehensive README** ([mcp_postgres_agent/README.md](cci:7://file:///Users/admin/git%20repos/adk-python/contributing/samples/mcp_postgres_agent/README.md:0:0-0:0)): Documentation with setup instructions, configuration details, and example queries
- **Environment Configuration**: Support for secure credential management via `.env` files
## Key Features
- **MCP Integration**: Demonstrates proper use of `MCPToolset` with `StdioConnectionParams`
- **Zero Installation**: Uses `uvx` to run the MCP server without manual installation
- **Secure Credentials**: Database connection strings passed via environment variables
- **Production-Ready**: Uses Gemini 2.0 Flash with unrestricted access mode for full database operations
## Technical Details
- **Model**: Gemini 2.0 Flash
- **MCP Server**: `postgres-mcp` (via `uvx`)
- **Connection**: StdioConnectionParams with 60-second timeout
- **Environment Variable**: Maps `POSTGRES_CONNECTION_STRING` to `DATABASE_URI`
## Testing
The agent has been tested with:
- PostgreSQL database connections (local and remote)
- Schema inspection queries
- Data querying operations
- Table listing and management
## Example Queries
Users can interact with the agent using natural language queries like:
- "What tables are in the database?"
- "Show me the schema for the users table"
- "Query the first 10 rows from the products table"
This sample serves as a reference implementation for developers looking to integrate PostgreSQL databases with ADK agents using MCP.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/3182 from Vrajesh-Babu:postgres-mcp f3b3846abae37ae376d3554624ac2b1be82f7adc
PiperOrigin-RevId: 822865931
George Weale