### Overview
Major restructuring from monolithic `arcade-ai` package to modular
library architecture with standardized uv-based dependency management.
### New Package Structure
- **`arcade-tdk`** - Lightweight toolkit development kit (core
decorators, auth)
- **`arcade-core`** - Core execution engine and catalog functionality
- **`arcade-serve`** - FastAPI/MCP server components
- **`arcade-ai`** - Meta package that includes CLI functionality.
Optionally include evals via the `evals` extra. Optionally include all
packages via the `all` extra.
### Key Benefits
- **Lighter Dependencies**: Toolkits now depend only on `arcade-tdk` (~2
deps) vs full `arcade-ai` (~30+ deps)
- **Faster Builds**: uv provides 10-100x faster dependency resolution
and installation
- **Better Modularity**: Clear separation of concerns, consumers import
only what they need
- **Standard Tooling**: Eliminates custom poetry scripts, uses standard
Python packaging
### Migration Impact
- All 20 toolkits converted from poetry → uv with `arcade-tdk`
dependencies plus `arcade-ai[evals]` and `arcade-serve` dev
dependencies. When developing locally, devs should install toolkits via
`make install-local`.
- Modern Python 3.10+ type hints throughout
- Standardized build system with hatchling backend
- Enhanced Makefile with robust toolkit management commands
- Removed `arcade dev` CLI command
- Reduce the number of files created by `arcade new` and add an option
to not generate a tests and evals folder.
This foundation enables faster development cycles and cleaner dependency
chains for the growing toolkit ecosystem.
### Todo After this PR is merged
- [ ] Post-merge workflow(s) (release & publish containers, etc)
- [ ] Release order plan. @EricGustin suggests releasing in the
following order:
1. `arcade-core` version 0.1.0
2. `arcade-serve` version 0.1.0 and `arcade-tdk` version 0.1.0
3. `arcade-ai` version 2.0.0
4. Patch release for all toolkits (all changes in toolkits are internal
refactors)
- [ ] [Update docs](https://github.com/ArcadeAI/docs/pull/318)
---------
Co-authored-by: Eric Gustin <eric@arcade.dev>
Co-authored-by: Eric Gustin <34000337+EricGustin@users.noreply.github.com>
Addresses general improvements to all toolkits including changing ruff
from python 3.9 to python 3.10 which is the reason for the removal of
Optional[] among others.
Also, turns out that our `make install` for toolkits wasn't correctly
checking for whether poetry was installed (&> /dev/null syntax isn't
supported by our check-toolkits GitHub action, so we were installing
poetry twice. I replaced with the more portable >/dev/null 2>&1)
Question: Should we also change ruff to py310 for the `arcade/` package
in a later PR?
-------------------
CU-86b4gzyp6
~~Note: Don't merge until the correct secrets have been added to Arcade
Cloud.~~
Ready to merge, the feature is already on its way to prod.
---------
Co-authored-by: Eric Gustin <eric@arcade.dev>
# PR Description
* This PR updates code in `examples/` to be compatible with version
1.0.0
* This PR removes the Spotify examples since the Arcade hosted worker
doesn't currently cataloge the Spotify toolkit. We can reintroduce these
examples when it does.
* This PR performs various renames across the codebase for
`arcade-ai.com` --> `arcade.dev` and `Arcade AI` --> `Arcade`
# PR Description
Poetry released v2 with many breaking changes a couple days ago. The
`install-poetry` action that our workflows use default to that v2
version, so many of our workflows are failing. This PR forces that
action to use poetry version 1.8.5 and also uses 1.8.5 for toolkits
A ticket to migrate to 2.0.0 has been filed for future work
# PR Description
* Adds/updates the following files to all toolkits:
- `.pre-commit-config.yaml`
- `.ruff.toml`
- `LICENSE`
- `Makefile`
- `pyproject.toml`
* Lint all toolkits such that they pass `make check` and `make test` (a
total doozy). This includes adding some unit tests and evals.
* Github workflow for testing toolkits before merge into main (courtesy
of @sdreyer)
* Added a QOL improvement for tool developers for when they need to get
the context's auth token.
* Minor updates to `arcade new` template.
# PR Description
This PR renames `ExpectedToolCall` to `NamedExpectedToolCall` and then
creates a new dataclass called `ExpectedToolCall`. `ExpectedToolCall`
can be passed to the `EvalSuite.add_case` and `EvalSuite.extend_case`
methods.
1. Enhance `EvalSuite.add_case` and `EvalSuite.extend_case` by accepting
a list of `ExpectedToolCall` as their `expected_tool_calls` input
parameter. This helps create a scaffolding for developers. Previously,
the expected type was `list[tuple[Callable, dict[str, Any]]]`, which is
still valid for backward compatibility.
```python
# Before (still valid for backward compatibility)
expected_tool_calls=[
(
adjust_playback_position,
{
"absolute_position_ms": 10000,
},
)
]
# After
expected_tool_calls=[
ExpectedToolCall(
func=adjust_playback_position,
args={"absolute_position_ms": 10000},
)
]
```
2. Removed any references to arcade.core in toolkits directory.
3. Some linting for import organization.
# PR Description
This PR creates a new toolkit called CodeSandbox. This toolkit has two
tools:
1. `RunCode`: Creates an E2B sandbox and runs the provided code in that
sandbox. Returns the execution logs, result, and errors. Supports
Python, JavaScript, R, Java, and Bash code.
2. `CreateStaticMatplotlibChart`: Creates a sandbox, runs the provided
python code that uses matplotlib, and returns the base64 encoded image
of the chart along with any logs or errors.
- I recommend not using `tool_choice="generate"` since the return object
contains a base64 image can be a lot of tokens that will not provide
much value to a generate's response.
Example of creating a pie chart:
```python
import base64
import json
import os
from openai import OpenAI
def call_tool_with_openai(client: OpenAI) -> dict:
response = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "There are 17 red apples, 4 green apples, and 10 yellow apples. Create a pie chart for this data.",
},
],
model="gpt-4o-mini",
user="you@example.com",
tools=["CodeSandbox.CreateStaticMatplotlibChart"],
tool_choice="execute",
)
return response
arcade_api_key = os.environ.get("ARCADE_API_KEY")
cloud_host = "http://localhost:9099/v1"
openai_client = OpenAI(
api_key=arcade_api_key,
base_url=cloud_host,
)
chat_result = call_tool_with_openai(openai_client)
tool_call_id = chat_result.choices[0].message.tool_calls[0].id
content = json.loads(chat_result.choices[0].message.content)
base64_image = content[tool_call_id]["value"]["base64_image"]
image_data = base64.b64decode(base64_image)
with open("output_image.png", "wb") as image_file:
image_file.write(image_data)
```
