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
Merge https://github.com/google/adk-python/pull/3430
**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
**1. Link to an existing issue (if applicable):**
- Closes: #3429
**2. Or, if no issue exists, describe the change:**
_If applicable, please follow the issue templates to provide as much detail as
possible._
**Problem:**
The existing `/list-apps` endpoint only returns the name of the folder that each agent is in
**Solution:**
This adds a new endpoint `/list-apps-detailed` which will load each agent using the existing `AgentLoader.load_agent` method, and then return the folder name, display name (with underscores replaced with spaces for a more readable version), description, and the agent type.
This does introduce overhead if you had multiple agents since they all need to be loaded, but by maintaining the existing `/list-apps` endpoint, users can choose which one to hit if they don't want to load all agents. Since the existing `load_agents` method will cache results, there's only a penalty on the first hit.
### Testing Plan
Created a unit test for this, similar to the `/list-apps`. Also tested this with my own ADK instance to verify it loaded correctly.
```
curl --location "localhost:8000/list-apps-detailed"
```
```json
{
"apps": [
{
"name": "agent_1",
"displayName": "Agent 1",
"description": "A test description for a test agent",
"agentType": "package"
},
{
"name": "agent_2",
"displayName": "Agent 2",
"description": "A test description for a test agent ",
"agentType": "package"
},
{
"name": "agent_3",
"displayName": "Agent 3",
"description": "A test description for a test agent",
"agentType": "package"
}
]
}
```
**Unit Tests:**
- [X] I have added or updated unit tests for my change.
- [X] All unit tests pass locally.
3054 passed, 2383 warnings in 46.96s
### 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/3430 from dylan-apex:more-detailed-list-apps e6864fd61a673da5fd2fb28d2d7d72cb90f5af0a
PiperOrigin-RevId: 834907771
This change loads the agent or app from the specified directory before creating the session. This allows using the correct application name (from the `App` object if applicable) when initializing the session, rather than always defaulting to the folder name. The variable `root_agent` is also renamed to `agent_or_app` to better reflect that it can be either an Agent or an App
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 833839070
pin crew ai to 3.13 at highest version as it uses chromadb and that uses onnxruntime which does not work yet with 3.14
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 831508884
Creates AdkFolderManager for creating/resetting the .adk layout, helper builders that return SQLite- and filesystembacked services for each agent
Co-authored-by: George Weale <gweale@google.com>
PiperOrigin-RevId: 831206377
The new Sqlite version has fixed schema and use a single column to store Event data, this should avoid DB migration for future add to the Event object.
- This change introduces `SqliteSessionService`, an asynchronous session service using `aiosqlite` that stores event data as JSON within SQLite.
- A migration script, `migrate_from_sqlalchemy_sqlite.py`, is included to transition data from the older SQLAlchemy-based SQLite schema to this new format.
- The CLI service registry is updated to use SqliteSessionService for sqlite:// URIs.
- Throw error when user trying to access a legacy DB and advice the user to do the migration.
Co-authored-by: Shangjie Chen <deanchen@google.com>
PiperOrigin-RevId: 829971174
Session input file contains fields that are needed to run evals and later be able retrieve the session generated by them.
Co-authored-by: Ankur Sharma <ankusharma@google.com>
PiperOrigin-RevId: 825742522
To register a custom service:
- Create a factory function that takes a URI and returns an instance of your custom service. This function will parse any details it needs from the URI.
- Register your factory with the global service registry. You need to define a unique URI scheme for your service (e.g., custom).
PiperOrigin-RevId: 822310466
This change removes the `convert_session_to_eval_format` function and its associated unit tests. New tests for `create_gcs_eval_managers_from_uri` are also added.
PiperOrigin-RevId: 819576620
- add a shared --structured_logs flag to adk web and adk api_server so users can opt into JSON-formatted output
- introduce CloudTraceJSONFormatter that emits structured entries and attaches current Cloud Trace/Span IDs when an OpenTelemetry context is active
- update CLI logging setup to clear duplicate stdout handlers when Cloud Logging is enabled and to reconfigure existing handlers (like from Uvicorn) so they also pick up the structured format and requested log level
With the flag disabled the CLIs keep their existing text logs; when enabled, the services now produce Cloud Logging–friendly JSON that can be correlated with distributed traces.
PiperOrigin-RevId: 818823818
This change removes the `run_evals` function and its helper `_get_evaluator` from `cli_eval.py`, as they were marked as deprecated. Corresponding test mocks and patches in `test_fast_api.py` are also removed.
PiperOrigin-RevId: 818719422
Agent developers can now create an eval set and add eval cases through command line itself. Adding an eval case is limited only to specifying conversation scenarios.
Sample comamnds:
- Create an eval set:
adk eval_set create \
contributing/samples/hello_world \
set_01
- Add an eval case with scenario file
Content of scenarios.json file:
'{"scenarios": [{"starting_prompt": "hello", "conversation_plan": "world"}]}'
adk eval_set add_eval_case \
contributing/samples/hello_world \
set_01 \
--scenarios scenarios.json
PiperOrigin-RevId: 817456117
The `agent_loader.load_agent` method can now return an `App` object. This change unwraps the `App` to get its `root_agent` before passing it to the graph builder, makes sure a `BaseAgent` instance is always used
PiperOrigin-RevId: 817209601
We updated the one of the public methods on AgentEvaluator to take in eval metric configurations using a more formal EvalConfig data model.
We also mark "criteria" field on the method as deprecated.
Updated some integration test cases.
PiperOrigin-RevId: 814314134
This is allow user to update session state without running the agent. e.g. if I want to test some case when session has certain state on adk web.
PiperOrigin-RevId: 814252851
Details:
- We plan on introducing Rubric based metrics in subsequent changes. This change introduces the data model needed that allows agent developer to provide rubrics.
- We also introduce a data model for the config that the eval system has been using for quite some time. It was loosely and informally described as a dictionary of metric names and expected thresholds. In this change, we actually formalize it using a pydantic data model, and extend it allow developers to specify rubrics as a part of their eval config.
What is a rubric based metric?
A rubric based metric is the assessment of a Agent's response (final or intermediate) along some rubric. This evaluation of agent's response significantly differs from the strategy where one has to provide a golden response.
PiperOrigin-RevId: 805488436
