### 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>
~~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
* 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 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)
```
