The default version for Cloud Run deployment is changed to the version in the dev environment instead of the latest version.
PiperOrigin-RevId: 759767654
Copybara import of the project:
--
9cefcdde97685bc6966a13019bfb80cc232a399b by Jack Wotherspoon <jackwoth@google.com>:
chore: delete toolbox_tool.py
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b2607eb0397e72b6b616ac592920f74d42a8ee5d by jackwotherspoon <jackwoth@google.com>:
feat: expose toolbox
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a4a0859997af9a68e240f78ff351f0fded6a52e2 by Jack Wotherspoon <jackwoth@google.com>:
chore: update formatting
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070dc93cdc289a5ee5935bd5995d3005bf8396a0 by jackwotherspoon <jackwoth@google.com>:
chore: add base toolbox tests
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ab84a0f49eccc9b993317b3ffe2b5b6cad278d70 by jackwotherspoon <jackwoth@google.com>:
chore: remove ToolboxTool
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87f4f909acc294468bcb3053e300f4df252bdb27 by Jack Wotherspoon <jackwoth@google.com>:
chore: update formatting
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ddc1a11c0ce45fe34e5f2dd43c808d88a7d6af0b by Jack Wotherspoon <jackwoth@google.com>:
chore: Update pyproject.toml
--
aee173d8df40ffefe535b266e1bd6528c9aeb1b9 by Jack Wotherspoon <jackwoth@google.com>:
chore: Update pyproject.toml
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22fc95922b500ddb0ec4901dccbe2fbfcc53b35f by Jack Wotherspoon <jackwoth@google.com>:
chore: Update __init__.py
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/763 from jackwotherspoon:expose-toolbox 3cb629a9d0d18aaeeeed59fb0d0d1e1b225b7437
PiperOrigin-RevId: 759744557
--
aa863ca851d4c689fbdb431d91189d5ebbc59932 by Jack Wotherspoon <jackwoth@google.com>:
chore: fix variable name in test
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/769 from jackwotherspoon:test-fix aa863ca851d4c689fbdb431d91189d5ebbc59932
PiperOrigin-RevId: 759731577
When the user provides instruction provider, we assume that they will inject the session state parameters if needed. This assumption allows users to return code snippets in the instruction provider without any template replacement.
PiperOrigin-RevId: 759705471
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636685818a512e9de30d5119f0244261cf16af27 by Adrian Cole <64215+codefromthecrypt@users.noreply.github.com>:
fix: corrects typo in bigquery sample
Noticed while reading that google was spelled wrong.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/709 from codefromthecrypt:patch-1 b30e819979f81962a2e44922bfd2dadd539fe3ea
PiperOrigin-RevId: 759641065
Copybara import of the project:
--
cec6f5044307e3ecdd20a513e0d8202b0854ff4c by ZhiNing <574775237@qq.com>:
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/705 from czn574775237:fix/issue-655 c15f116d4bc0328e8a90a437958042ef1b087c14
PiperOrigin-RevId: 759381068
Also included a token_usage sample that showcases the token usage of subagents with different models under a parent agent.
PiperOrigin-RevId: 759347015
Copybara import of the project:
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dcfb9ff720562802f9ce15c90a71b4cd40f77280 by Eugen-Bleck <eugenbleck@gmail.com>:
fix: Display full help text for adk create CLI command when arguments are missing
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f2eea8d1e5947b1631ac2de3824d8bf0cf833cbc by Eugen-Bleck <eugenbleck@gmail.com>:
fix: Display full help text for adk run and eval CLI command when arguments are missing
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/443 from bl3ck:fix/cli-help-display c2bca420b00aa3391ab8cfb6baedc9ea0dbfb7e1
PiperOrigin-RevId: 758498242
Add a `task_completed` function to the agent so when a model finished the task, it can send a signal and the program knows it can go to next agent.
This cl include:
* Implements the `_run_live_impl` in `sequential_agent` so it can handle live case.
* Add an example for sequential agent.
* Improve error message for unimplemented _run_live_impl in other agents.
Note:
1. Compared to non-live case, live agents process a continuous streams of audio
or video, so it doesn't have a native way to tell if it's finished and should pass
to next agent or not. So we introduce a task_compelted() function so the
model can call this function to signal that it's finished the task and we
can move on to next agent.
2. live agents doesn't seems to be very useful or natural in parallel or loop agents so we don't implement it for now. If there is user demand, we can implement it easily using similar approach.
PiperOrigin-RevId: 758315430
The old implementation:
1. We only started transcription at the beginning of agent transferring.
2. The transcription service we used is not as good / fast as the model/native transcription.
In the current implementation, the live agent will rely on the llm's transcription, instead of our transcription when llm support audio transcription in the input. And in that case, the live agent won't use our own audio transcriber. This reduces the latency from 5secs to 2 secs during agent transferring. It also improves the transcription quality.
When the llm doesn't support audio transcription, we still use our audio transcriber to transcribe audio input.
PiperOrigin-RevId: 758296647
Details:
- Add a in-memory SpanExporter to capture all trace information.
- Add /debug/trace/session/{session_id} endpoint to retrieve traces from the in-memory exporter.
- Add Session ID in Telemetry spans.
PiperOrigin-RevId: 757984565
--
d481e0604a79470e2c1308827b3ecb78bfb5327e by Alan B <alan@nerds.ai>:
feat: đźš§ catch user transcription
--
bba436bb76d1d2f9d5ba969fce38ff8b8a443254 by Alan B <alan@nerds.ai>:
feat: ✨ send user transcription event as llm_response
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ad2abf540c60895b79c50f9051a6289ce394b98d by Alan B <death1027@outlook.com>:
style: đź’„ update lint problems
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744703c06716300c0f9f41633d3bafdf4cb180a1 by Hangfei Lin <hangfeilin@gmail.com>:
fix: set right order for input transcription
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31a5d42d6155b0e5caad0c73c8df43255322016f by Hangfei Lin <hangfeilin@gmail.com>:
remove print
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59e5d9c72060f97d124883150989315401a4c1b5 by Hangfei Lin <hangfeilin@gmail.com>:
remove api version
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/590 from BloodBoy21:feat/api-version-vertex 1ed855249cae398b40691b91c6e468bccec07a3a
PiperOrigin-RevId: 757840099
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84abc07d2342e88e601949faa67a3014c0f491e8 by mukundjha-mj <mukundjha204@gmail.com>:
Fix spelling mistakes, reorder sections, and improve readability in pyproject.toml
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122c19d4be0fad394fbd02720734aa4625877637 by mukundjha-mj <mukundjha204@gmail.com>:
fix(pyproject): correct spelling and reorder config sections for clarity
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/669 from mukundjha-mj:fix/typos-pyproject 122c19d4be0fad394fbd02720734aa4625877637
PiperOrigin-RevId: 757318920
Copybara import of the project:
--
ade1d98e030a966183f56cb5c9c1b04cf51f5337 by Thiago Neves <thiagohneves@gmail.com>:
fix(tests): use mock GCS client in artifact service tests to avoid real credentials
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becd2925feebf60196129b029a0ab8d490f7b19e by Thiago Neves <thiagohneves@gmail.com>:
test(agents): add unit tests for live_request_queue, readonly_context, and run_config
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/641 from thiagoneves:feature/increase-test-coverage 0f7a9fc55d97902e190a394f099324fbeb1541af
PiperOrigin-RevId: 756798390
Copybara import of the project:
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f60707a22905f30040808b41b7e3510a47a80fc6 by K <51281148+K-dash@users.noreply.github.com>:
test(cli): Add unit tests for CLI functionality
This commit introduces unit tests for the following CLI-related components:
- cli_deploy.py: Tests for the cloud deployment feature.
- cli_create.py: Tests for the agent creation feature.
- cli.py: Tests for the main CLI execution logic.
- cli_tools_click.py: Tests for the Click-based CLI tools.
--
7be2159a475d0785619fea5e40c70e6461a7f4e1 by K <51281148+K-dash@users.noreply.github.com>:
fix test_cli_eval_success_path
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/577 from K-dash:test/add-unit-tests-for-cli 69f12d3a27d9c50a46ef269075e050f498dee67a
PiperOrigin-RevId: 756602765
(before/after) tool callbacks are invoked throughout the provided chain until one callback does not return None. Callbacks can be async and sync.
PiperOrigin-RevId: 756526507
--
0723b0915550a0af9d1eb2952ee193238eee8178 by Thiago Neves <thiagohneves@gmail.com>:
fix(tests): use mock GCS client in artifact service tests to avoid real credentials
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/601 from thiagoneves:fix/mock-gcs-client-in-tests e7d16719b9c3116b652988d2ed1b1f8893686f3e
PiperOrigin-RevId: 756381115
Copybara import of the project:
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a4a998d5418af47a4f263823810e8ab85a9ae4d6 by éŹč¶… <nneverwei@gmail.com>:
fix(cli): Disable auto-reload feature on Windows system
Fixed the issue caused by the auto-reload feature when running the CLI tool on Windows system. By detecting the operating system type, the auto-reload is disabled on Windows system to avoid potential errors: When mcp is asynchronously loaded, it will enter the _make_subprocess_transport NotImplementedError logic due to uvicorn reload=True in fastapi.
--
46c9bb600e4530d3f9c22369c4a99774efa024c9 by éŹč¶… <nneverwei@gmail.com>:
add an option in the CLI to enable or disable the reload feature. So users(esp. windows) can disable this if they come across the '_make_subprocess_transport NotImplementedError' bug on windows.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/415 from nneverwei:win-subprocess-NotImplError-with-mcp fbb9ab03350bb0a98769cf1a4cf930983ba9fa78
PiperOrigin-RevId: 756360981
(before/after) agent callbacks are invoked throughout the provided chain until one callback does not return None. Callbacks can be async and sync.
PiperOrigin-RevId: 756359693
--session_id : The session ID to save the session to on exit when --save_session is set to true. User will be prompted to enter a session ID if not set.
PiperOrigin-RevId: 756335619
--
5eabc6c1fe339e87637b9ed6d0516a3edcbcb060 by kavinkumarbaskar <kavinkumarbaskar@gmail.com>:
fix readme pip install
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bb21018aea7a4b8d8a60e6ef42b084dae51d7845 by kavinkumarbaskar <kavinkumarbaskar@gmail.com>:
fix: added build and local testing command
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aa1f2305b098b79480eab9ab37b744d0273a5fcf by kavinkumarbaskar <kavinkumarbaskar@gmail.com>:
fix: added example
--
69b649d81e6757d6305c481e3415ec8f017a75ac by kavinkumarbaskar <kavinkumarbaskar@gmail.com>:
fix: updated the windows command
--
bd5202308bf08b9b44099c4cd016af23f2e2350e by kavinkumarbaskar <kavinkumarbaskar@gmail.com>:
fix: removed redundant code
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/529 from kavinkumar807:fix-readme-pip-install a49d82d49a0cecb4cee399620c62ae10c1f3370a
PiperOrigin-RevId: 756122021
--
8d5e7f017d975d4ecd5ad6004079fec0f6b417e1 by Mrigank Khandelwal <mrigankkhandelwal300@gmail.com>:
fix: Fixed incorrect difinition of MCP in function docstring
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/594 from Mrigankkh:main 0d52a8059a7c3438207a86c03cfd3f25204d4b2e
PiperOrigin-RevId: 755698357
--
09b10cd96fc095061c6891a0d3cc3cc83948a126 by pratikmahajan <pmahajan@redhat.com>:
fix: change litellm request log level to debug
Litellm was previously logging every request at the info level,
which could clutter the logs with unnecessary detail in production environments.
This commit changes the log statement to use the debug level instead,
ensuring that request details are only logged when debug mode is active.
This helps keep the standard logs focused on more critical information.
Co-authored-by: pratikmahajan<pmahajan@redhat.com>
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/583 from PratikMahajan:litellm-log-levels 04fcd7247693e0c733318789f1ea47ecec81add4
PiperOrigin-RevId: 755691209
Copybara import of the project:
--
93cc9c0b71a92991a888c93675ddc8aee11f21dc by luaifei <lu.aifei@thoughtworks.com>:
fix: Update skipped tests in test_auth_handlers
--
06ddf559c76c113231719bff549d41801a93daf4 by luaifei <lu.aifei@thoughtworks.com>:
fix: Update skipped & failed tests in test_connections_client and test_streaming
--
b8f2d357c1101c59ee9b65fa89a75f216e014a7c by luaifei <lu.aifei@thoughtworks.com>:
fix: Remove ignored test file from Python unit tests workflow
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/553 from luaifei:fix-tests d51e42841e71d388c16cc719a4798b029182084f
PiperOrigin-RevId: 755669644
(before/after) model callbacks are invoked throughout the provided chain until one callback does not return None. Callbacks can be async and sync.
PiperOrigin-RevId: 755565583
Copybara import of the project:
--
c1d0d649b5aae1322a02dbaa586822d69b8546f6 by allengour <allengour@google.com>:
fix: fix and test `config.after_timestamp` behavior in `InMemorySessionService.get_session()`
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/438 from allengour:fix/issue-437-after_timestamp-behavior 4b49a5e6509b5ad9dd9103a6dc357fd44c101f31
PiperOrigin-RevId: 755492201
Copybara import of the project:
--
d481e0604a79470e2c1308827b3ecb78bfb5327e by Alan B <alan@nerds.ai>:
feat: đźš§ catch user transcription
--
bba436bb76d1d2f9d5ba969fce38ff8b8a443254 by Alan B <alan@nerds.ai>:
feat: ✨ send user transcription event as llm_response
--
ad2abf540c60895b79c50f9051a6289ce394b98d by Alan B <death1027@outlook.com>:
style: đź’„ update lint problems
--
744703c06716300c0f9f41633d3bafdf4cb180a1 by Hangfei Lin <hangfeilin@gmail.com>:
fix: set right order for input transcription
--
31a5d42d6155b0e5caad0c73c8df43255322016f by Hangfei Lin <hangfeilin@gmail.com>:
remove print
--
59e5d9c72060f97d124883150989315401a4c1b5 by Hangfei Lin <hangfeilin@gmail.com>:
remove api version
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/495 from BloodBoy21:main ea29015af041f9785abaa8583e2c767f9d8c8bc8
PiperOrigin-RevId: 755401615
* Fix install command for Zsh compatibility. Wrapped extras list in quotes to prevent Zsh from expanding it as a glob pattern.
* Fix install command for Zsh compatibility. Wrapped extras list in quotes to prevent Zsh from expanding it as a glob pattern.
---------
Co-authored-by: Hangfei Lin <hangfei@google.com>
--
ec246aeee44156db8a94661b7e997cf2012f2e4e by Yuwei Fu <fuyuweiwill@gmail.com>:
Fix install command for Zsh compatibility. Wrapped extras list in quotes to prevent Zsh from expanding it as a glob pattern.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/520 from Imfuyuwei:main 6cd4ecc9216ea2f03c3fed43e37d18d1838cac05
PiperOrigin-RevId: 754625822
--
41329f091a31b3d32af3025000951295477c717b by Hangfei Lin <hangfei@google.com>:
doc: Update CONTRIBUTING.md to include testing requirements
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380b82e00fa8f16cbfd9e113ef45e1fc8e8c0932 by Hangfei Lin <hangfei@google.com>:
doc: Update CONTRIBUTING.md
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61e81d848c275d4be3da2cb60a93e84bc68b3b4b by Hangfei Lin <hangfei@google.com>:
doc: Update CONTRIBUTING.md
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/541 from google:hangfei-patch-1 63d5c56e663cdfe6f6e78be85d9686873aeb2a5a
PiperOrigin-RevId: 754541490
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1ca16aba5b7b869afa8e0a0cddaea539acd737f5 by bart.lee(ěť´ě˛ ëŻĽ)/kakao <bart.lee@kakaocorp.com>:
chore: Improves session update time validation message
Enhances the error message when a session's last update time is later than the storage update time.
This provides better readability by formatting the timestamps in the error message.
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/446 from kakao-bart-lee:main a2a0cff036429b61bd7cf1600fc4c2c0cf50089d
PiperOrigin-RevId: 754452381
Enhances the error message when a session's last update time is later than the storage update time.
This provides better readability by formatting the timestamps in the error message.
Co-authored-by: Hangfei Lin <hangfei@google.com>
--
ad923c2c8c503ba73c62db695e88f1a3ea1aeeea by YU MING HSU <abego452@gmail.com>:
docs: enhance Contribution process within CONTRIBUTING.md
--
8022924fb7e975ac278d38fce3b5fd593d874536 by YU MING HSU <abego452@gmail.com>:
fix: move _maybe_append_user_content from google_llm.py to base_llm.py,
so subclass can get benefit from it, call _maybe_append_user_content
from generate_content_async within lite_llm.py
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cf891fb1a3bbccaaf9d0055b23f614ce52449977 by YU MING HSU <abego452@gmail.com>:
fix: modify install dependencies cmd, and use pyink to format codebase
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/428 from hsuyuming:fix_litellm_error_issue_427 dbec4949798e6399a0410d1b8ba7cc6a7cad7bdd
PiperOrigin-RevId: 754124679
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709e1dd079d03d7eb4d742b9448ed3d1b946ff30 by joao.campista <joaocampista@proton.me>:
feat: add ordering to recent events in database session service
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/454 from lugui-co:main 912503f972c9cbd8982f2b7f8b210d4e0fe08b69
PiperOrigin-RevId: 753013663
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21736067f9 by Alankrit Verma <alankrit386@gmail.com>:
feat(llm_flows): support async before/after tool callbacks
Previously, callbacks were treated as purely synchronous,
so passing an async coroutine caused “was never awaited”
errors and Pydantic serialization failures.
Now we detect awaitable return values from
before_tool_callback and after_tool_callback,
and `await` them if necessary.
Fixes: #380
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08ac9a117e by Alankrit Verma <alankrit386@gmail.com>:
Refactor function callback handling and update type signatures
- Simplify variable names in `functions.py`: always use `function_response` and `altered_function_response`
- Update LlmAgent callback type aliases to support async:
- Import `Awaitable`
- Change `BeforeToolCallback` and `AfterToolCallback` signatures to return `Awaitable[Optional[dict]]`
- Ensure `after_tool_callback` uses `await` when necessary
--
fcbf57466e by Alankrit Verma <alankrit386@gmail.com>:
refactor: update callback type signatures to support sync and async responses
COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/434 from AlankritVerma01:support-async-tool-callbacks 926b0ef1a6
PiperOrigin-RevId: 753005846
--replay : a json file that contains the initial session state and user queries, adk run will create a new session based on the state and run the user queries against the session. Users cannot continue to interact with agent.
--resume : a json file that contains the previously saved session (by --save_session option), adk run will replay this session and then user can continue to interact with the agent.
PiperOrigin-RevId: 752923403
- Simplify variable names in `functions.py`: always use `function_response` and `altered_function_response`
- Update LlmAgent callback type aliases to support async:
- Import `Awaitable`
- Change `BeforeToolCallback` and `AfterToolCallback` signatures to return `Awaitable[Optional[dict]]`
- Ensure `after_tool_callback` uses `await` when necessary
Previously, callbacks were treated as purely synchronous,
so passing an async coroutine caused “was never awaited”
errors and Pydantic serialization failures.
Now we detect awaitable return values from
before_tool_callback and after_tool_callback,
and `await` them if necessary.
Fixes: #380
* Evaluation dataset schema is finalized with strong-type pydantic models.
(previously saved eval file needs re-generation, for both adk eval cli and
the eval tab in adk web UI).
*`BuiltInCodeExecutor` (in code_executors package) replaces
`BuiltInCodeExecutionTool` (previously in tools package).
* All methods in services are now async, including session service, artifact
service and memory service.
*`list_events` and `close_session` methods are removed from session service.
* agent.py file structure with MCP tools are now easier and simpler ([now](https://github.com/google/adk-python/blob/3b5232c14f48e1d5b170f3698d91639b079722c8/contributing/samples/mcp_stdio_server_agent/agent.py#L33) vs [before](https://github.com/google/adk-python/blob/a4adb739c0d86b9ae4587547d2653d568f6567f2/contributing/samples/mcp_agent/agent.py#L41)).
Old format is not working anymore.
*`Memory` schema and `MemoryService` is redesigned.
* Mark various class attributes as private in the classes in the `tools` package.
* Disabled session state injection if instruction provider is used.
(so that you can have `{var_name}` in the instruction, which is required for code snippets)
* Toolbox integration is revamped: tools/toolbox_tool.py → tools/toolbox_toolset.py.
* Removes the experimental `remote_agent.py`. We'll redesign it and bring it back.
### Features
* Dev UI:
* A brand new trace view for overall agent invocation.
* A revamped evaluation tab and comparison view for checking eval results.
* Introduced `BaseToolset` to allow dynamically add/remove tools for agents.
* Revamped MCPToolset with the new BaseToolset interface.
* Revamped GoogleApiTool, GoogleApiToolset and ApplicationIntegrationToolset with the new BaseToolset interface.
* Resigned agent.py file structure when needing MCPToolset.
* Allows evals to be extended for non-text modality.
* Allows for a structured interaction with the uber eval system.
* Redesigned Memory schema and MemoryService interfaces.
* Added token usage to LlmResponse.
* Allowed specifying `--adk_version` in `adk deploy cloud_run` cli. Default is the current version.
### Bug Fixes
* Fixed `adk deploy cloud_run` failing bug.
* Fixed logs not being printed due to `google-auth` library.
### Miscellaneous Chores
* Display full help text when adk cli receives invalid arguments.
*`adk web` now binds `127.0.0.1` by default, instead of 0.0.0.0.
*`InMemoryRunner` now takes `BaseAgent` in constructor.
* Various docstring improvements.
* Various UI tweaks.
* Various bug fixes.
* Update various contributing/samples for contributors to validate the implementation.
## 0.5.0
### âš BREAKING CHANGES
* Updated artifact and memory service interface to be async. Agents that
interact with these services through callbacks or tools will now need to
adjust their invocation methods to be async (using await), or ensure calls
are wrapped in an asynchronous executor like asyncio.run(). Any service that
extends the base interface must also be updated.
### Features
* Introduced the ability to chain model callbacks.
* Added support for async agent and model callbacks.
* Added input transcription support for live/streaming.
* Captured all agent code error and display on UI.
* Set param required tag to False by default in openapi_tool.
* Updated evaluation functions to be asynchronous.
### Bug Fixes
* Ensured a unique ID is generated for every event.
* Fixed the issue when openapi_specparser has parameter.required as None.
* Updated the 'type' value on the items/properties nested structures for Anthropic models to adhere to JSON schema.
* Fix litellm error issues.
### Miscellaneous Chores
* Regenerated API docs.
* Created a `developer` folder and added samples.
* Updated the contributing guide.
* Docstring improvements, typo fixings, GitHub action to enforce code styles on formatting and imports, etc.
## 0.4.0
### âš BREAKING CHANGES
* Set the max size of strings in database columns. MySQL mandates that all VARCHAR-type fields must specify their lengths.
* Extract content encode/decode logic to a shared util, resolve issues with JSON serialization, and update key length for DB table to avoid key too long issue in mysql.
* Enhance `FunctionTool` to verify if the model is providing all the mandatory arguments.
### Features
* Update ADK setup guide to improve onboarding experience.
* feat: add ordering to recent events in database session service.
* feat(llm_flows): support async before/after tool callbacks.
* feat: Added --replay and --resume options to adk run cli. Check adk run --help for more details.
* Created a new Integration Connector Tool (underlying of the ApplicationIntegrationToolSet) so that we do not force LLM to provide default value.
### Bug Fixes
* Don't send content with empty text to LLM.
* Fix google search reading undefined for `renderedContent`.
### Miscellaneous Chores
* Docstring improvements, typo fixings, github action to enfore code styles on formatting and imports, etc.
## 0.3.0
### âš BREAKING CHANGES
* Auth: expose `access_token` and `refresh_token` at top level of auth
[Google's Open Source Community Guidelines](https://opensource.google/conduct/).
## Contribution process
## Contribution workflow
### Finding Issues to Work On
- Browse issues labeled **`good first issue`** (newcomer-friendly) or **`help wanted`** (general contributions).
- For other issues, please kindly ask before contributing to avoid duplication.
### Requirement for PRs
- All PRs, other than small documentation or typo fixes, should have a Issue assoicated. If not, please create one.
- All PRs, other than small documentation or typo fixes, should have a Issue assoicated. If not, please create one.
- Small, focused PRs. Keep changes minimal—one concern per PR.
- For bug fixes or features, please provide logs or screenshot after the fix is applied to help reviewers better understand the fix.
- Please add corresponding testing for your code change if it's not covered by existing tests.
- Please include a `testing plan` section in your PR to talk about how you will test. This will save time for PR review. See `Testing Requirements` section for more details.
### Large or Complex Changes
For substantial features or architectural revisions:
@@ -38,6 +61,100 @@ For substantial features or architectural revisions:
- Open an Issue First: Outline your proposal, including design considerations and impact.
- Gather Feedback: Discuss with maintainers and the community to ensure alignment and avoid duplicate work
### Testing Requirements
To maintain code quality and prevent regressions, all code changes must include comprehensive tests and verifiable end-to-end (E2E) evidence.
#### Unit Tests
Please add or update unit tests for your change. Please include a summary of passed `pytest` results.
Requirements for unit tests:
- **Coverage:** Cover new features, edge cases, error conditions, and typical use cases.
- **Location:** Add or update tests under `tests/unittests/`, following existing naming conventions (e.g., `test_<module>_<feature>.py`).
- **Framework:** Use `pytest`. Tests should be:
- Fast and isolated.
- Written clearly with descriptive names.
- Free of external dependencies (use mocks or fixtures as needed).
- **Quality:** Aim for high readability and maintainability; include docstrings or comments for complex scenarios.
#### Manual End-to-End (E2E) Tests
Manual E2E tests ensure integrated flows work as intended. Your tests should cover all scenarios. Sometimes, it's also good to ensure relevant functionality is not impacted.
Depending on your change:
- **ADK Web:**
- Use the `adk web` to verify functionality.
- Capture and attach relevant screenshots demonstrating the UI/UX changes or outputs.
- Label screenshots clearly in your PR description.
- **Runner:**
- Provide the testing setup. For example, the agent definition, and the runner setup.
- Execute the `runner` tool to reproduce workflows.
- Include the command used and console output showing test results.
- Highlight sections of the log that directly relate to your change.
### Documentation
For any changes that impact user-facing documentation (guides, API reference, tutorials), please open a PR in the [adk-docs](https://github.com/google/adk-docs) repository to update relevant part before or alongside your code PR.
### Development Setup
1.**Clone the repository:**
```shell
git clone git@github.com:google/adk-python.git
cd adk-python
```
2. **Create and activate a virtual environment:**
```shell
python -m venv .venv
```
```shell
source .venv/bin/activate
```
**windows**
```shell
source .\.venv\Scripts\activate
```
3. **Install dependencies:**
```shell
pip install uv
uv sync --all-extras
```
4. **Run unit tests:**
```shell
uv run pytest ./tests/unittests
```
5. **Run pyink to format codebase:**
```shell
uv run pyink --config pyproject.toml ./src
```
6. **Build the package**
```shell
uv build
```
7. **Local Testing**
Have a simple testing folder setup as mentioned in the [quickstart](https://google.github.io/adk-docs/get-started/quickstart/)
then install the local package with changes after building it using the below command to test the changes.
Agent Development Kit (ADK) is designed for developers seeking fine-grained
control and flexibility when building advanced AI agents that are tightly
integrated with services in Google Cloud. It allows you to define agent
behavior, orchestration, and tool use directly in code, enabling robust
debugging, versioning, and deployment anywhere – from your laptop to the cloud.
Agent Development Kit (ADK) is a flexible and modular framework for developing and deploying AI agents. While optimized for Gemini and the Google ecosystem, ADK is model-agnostic, deployment-agnostic, and is built for compatibility with other frameworks. ADK was designed to make agent development feel more like software development, to make it easier for developers to create, deploy, and orchestrate agentic architectures that range from simple tasks to complex workflows.
This sample demonstrates how to use the `ApplicationIntegrationToolset` within an ADK agent to interact with external applications, specifically Jira in this case. The agent (`agent.py`) is configured to manage Jira issues using a pre-configured Application Integration connection.
## Prerequisites
1.**Set up Integration Connection:**
* You need an existing [Integration connection](https://cloud.google.com/integration-connectors/docs/overview) configured to interact with your Jira instance. Follow the [documentation](https://google.github.io/adk-docs/tools/google-cloud-tools/#use-integration-connectors) to provision the Integration Connector in Google Cloud and then use this [documentation](https://cloud.google.com/integration-connectors/docs/connectors/jiracloud/configure) to create an JIRA connection. Note the `Connection Name`, `Project ID`, and `Location` of your connection.
*
2.**Configure Environment Variables:**
* Create a `.env` file in the same directory as `agent.py` (or add to your existing one).
* Add the following variables to the `.env` file, replacing the placeholder values with your actual connection details:
```dotenv
CONNECTION_NAME=<YOUR_JIRA_CONNECTION_NAME>
CONNECTION_PROJECT=<YOUR_GOOGLE_CLOUD_PROJECT_ID>
CONNECTION_LOCATION=<YOUR_CONNECTION_LOCATION>
```
## How to Use
1. **Install Dependencies:** Ensure you have the necessary libraries installed (e.g., `google-adk`, `python-dotenv`).
2. **Run the Agent:** Execute the agent script from your terminal:
```bash
python agent.py
```
3. **Interact:** Once the agent starts, you can interact with it by typing prompts related to Jira issue management.
## Sample Prompts
Here are some examples of how you can interact with the agent:
* `Can you list me all the issues ?`
* `Can you list me all the projects ?`
* `Can you create an issue: "Bug in product XYZ" in project ABC ?`
You are an agent that helps manage issues in a JIRA instance.
Be accurate in your responses based on the tool response. You can perform any formatting in the response that is appropriate or if asked by the user.
If there is an error in the tool response, understand the error and try and see if you can fix the error and then and execute the tool again. For example if a variable or parameter is missing, try and see if you can find it in the request or user query or default it and then execute the tool again or check for other tools that could give you the details.
If there are any math operations like count or max, min in the user request, call the tool to get the data and perform the math operations and then return the result in the response. For example for maximum, fetch the list and then do the math operation.
This sample tests and demos the OAuth support in ADK via two tools:
* 1. bigquery_datasets_list:
List user's datasets.
* 2. bigquery_datasets_get:
Get a dataset's details.
* 3. bigquery_datasets_insert:
Create a new dataset.
* 4. bigquery_tables_list:
List all tables in a dataset.
* 5. bigquery_tables_get:
Get a table's details.
* 6. bigquery_tables_insert:
Insert a new table into a dataset.
## How to use
* 1. Follow https://developers.google.com/identity/protocols/oauth2#1.-obtain-oauth-2.0-credentials-from-the-dynamic_data.setvar.console_name. to get your client id and client secret.
Be sure to choose "web" as your client type.
* 2. Configure your .env file to add two variables:
* GOOGLE_CLIENT_ID={your client id}
* GOOGLE_CLIENT_SECRET={your client secret}
Note: done't create a separate .env , instead put it to the same .env file that stores your Vertex AI or Dev ML credentials
* 3. Follow https://developers.google.com/identity/protocols/oauth2/web-server#creatingcred to add http://localhost/dev-ui to "Authorized redirect URIs".
Note: localhost here is just a hostname that you use to access the dev ui, replace it with the actual hostname you use to access the dev ui.
* 4. For 1st run, allow popup for localhost in Chrome.
## Sample prompt
*`Do I have any datasets in project sean-dev-agent ?`
*`Do I have any tables under it ?`
*`could you get me the details of this table ?`
*`Can you help to create a new dataset in the same project? id : sean_test , location: us`
*`could you show me the details of this new dataset ?`
*`could you create a new table under this dataset ? table name : sean_test_table. column1 : name is id , type is integer, required. column2 : name is info , type is string, required. column3 : name is backup , type is string, optional.`
'hello world agent that can roll a dice of 8 sides and check prime'
' numbers.'
),
instruction="""
You roll dice and answer questions about the outcome of the dice rolls.
You can roll dice of different sizes.
You can use multiple tools in parallel by calling functions in parallel(in one request and in one round).
It is ok to discuss previous dice roles, and comment on the dice rolls.
When you are asked to roll a die, you must call the roll_die tool with the number of sides. Be sure to pass in an integer. Do not pass in a string.
You should never roll a die on your own.
When checking prime numbers, call the check_prime tool with a list of integers. Be sure to pass in a list of integers. You should never pass in a string.
You should not check prime numbers before calling the tool.
When you are asked to roll a die and check prime numbers, you should always make the following two function calls:
1. You should first call the roll_die tool to get a roll. Wait for the function response before calling the check_prime tool.
2. After you get the function response from roll_die tool, you should call the check_prime tool with the roll_die result.
2.1 If user asks you to check primes based on previous rolls, make sure you include the previous rolls in the list.
3. When you respond, you must include the roll_die result from step 1.
You should always perform the previous 3 steps when asking for a roll and checking prime numbers.
You should not rely on the previous history on prime results.
"""Returns: data science agent system instruction."""
return"""
# Guidelines
**Objective:** Assist the user in achieving their data analysis goals within the context of a Python Colab notebook, **with emphasis on avoiding assumptions and ensuring accuracy.** Reaching that goal can involve multiple steps. When you need to generate code, you **don't** need to solve the goal in one go. Only generate the next step at a time.
**Code Execution:** All code snippets provided will be executed within the Colab environment.
**Statefulness:** All code snippets are executed and the variables stays in the environment. You NEVER need to re-initialize variables. You NEVER need to reload files. You NEVER need to re-import libraries.
**Imported Libraries:** The following libraries are ALREADY imported and should NEVER be imported again:
```tool_code
import io
import math
import re
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
```
**Output Visibility:** Always print the output of code execution to visualize results, especially for data exploration and analysis. For example:
- To look a the shape of a pandas.DataFrame do:
```tool_code
print(df.shape)
```
The output will be presented to you as:
```tool_outputs
(49, 7)
```
- To display the result of a numerical computation:
```tool_code
x = 10 ** 9 - 12 ** 5
print(f'{{x=}}')
```
The output will be presented to you as:
```tool_outputs
x=999751168
```
- You **never** generate ```tool_outputs yourself.
- You can then use this output to decide on next steps.
- Print just variables (e.g., `print(f'{{variable=}}')`.
**No Assumptions:** **Crucially, avoid making assumptions about the nature of the data or column names.** Base findings solely on the data itself. Always use the information obtained from `explore_df` to guide your analysis.
**Available files:** Only use the files that are available as specified in the list of available files.
**Data in prompt:** Some queries contain the input data directly in the prompt. You have to parse that data into a pandas DataFrame. ALWAYS parse all the data. NEVER edit the data that are given to you.
**Answerability:** Some queries may not be answerable with the available data. In those cases, inform the user why you cannot process their query and suggest what type of data would be needed to fulfill their request.
"""
root_agent=Agent(
model="gemini-2.0-flash-001",
name="data_science_agent",
instruction=base_system_instruction()+"""
You need to assist the user with their queries by looking at the data and the context in the conversation.
You final answer should summarize the code and code execution relavant to the user query.
You should include all pieces of data to answer the user query, such as the table from code execution results.
If you cannot answer the question directly, you should follow the guidelines above to generate the next step.
If the question can be answered directly with writing any code, you should do that.
If you doesn't have enough data to answer the question, you should ask for clarification from the user.
You should NEVER install any package on your own like `pip install ...`.
When plotting trends, you should make sure to sort and order the data by the x-axis.
# 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.
from.importagent
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