semvec/pypi-packages huggingface dataset:
pebble-tool 5.0.27
Tool for interacting with pebbles.
Requires Python: <3.14,>3.9

# Pebble Tool

The command-line tool for the Pebble SDK.

## About

The Pebble SDK now runs in Python 3. This includes:
1. The command-line tool to build and install apps and watchfaces (this repository).
2. The SDK code in PebbleOS (https://github.com/coredevices/PebbleOS/tree/main/sdk). This isn't fully working yet, so pebble-tool currently uses a patched version of the existing SDK core (version 4.3) that has been modified for Python 3.
3. pypkjs (https://github.com/coredevices/pypkjs), which allows PebbleKitJS code to run in the QEMU emulator.

Previously, the Pebble SDK was installed by downloading a tar file containing pebble-tool, the toolchain, and executables for PebbleOS QEMU and pebble-tool. Users had to decide where to extract the file, add the binaries to their PATH, and configure a virtualenv.

Now, pebble-tool is a standalone command-line tool that can be installed through pip/uv. The toolchain (arm-none-eabi) and QEMU binary are no longer bundled, but instead installed when `pebble sdk install` is run.

## Installation

Instructions are at https://developer.repebble.com/sdk

It's super simple: install a few platform-specific dependencies, then install pebble-tool via `uv`.

For developers of `pebble-tool`, use:
```sh
uv run pebble.py
```

## Testing
Test coverage can be run locally with:
```sh
uv run pytest
```

## Troubleshooting

If you run into issues, try uninstalling and re-installing. You can remove the latest SDK with
```shell
pebble sdk uninstall 4.4
```

You can also delete pebble-tool's entire data directory, located at ~/.pebble-sdk on Linux and ~/Library/Application Support/Pebble SDK on Mac.
llama-index-tools-igpt-email 0.1.0
llama-index tools igpt_email integration
Requires Python: <4.0,>=3.10
Keywords: email, igpt, intelligence, rag

# LlamaIndex Tools Integration: iGPT Email Intelligence

This tool connects to [iGPT](https://igpt.ai/) to give your agent
structured, reasoning-ready context from connected email threads.

iGPT handles thread reconstruction, participant role detection, temporal
reasoning, and intent extraction before returning results — so agents
receive clean, structured JSON instead of raw message data.

To begin, you need to obtain an API key at [docs.igpt.ai](https://docs.igpt.ai).

## Usage

```python
# %pip install llama-index llama-index-core llama-index-tools-igpt-email

from llama_index.tools.igpt_email import IGPTEmailToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

tool_spec = IGPTEmailToolSpec(api_key="your-key", user="user-id")

agent = FunctionAgent(
    tools=tool_spec.to_tool_list(),
    llm=OpenAI(model="gpt-4.1"),
)

print(await agent.run("What tasks were assigned to me this week?"))
```

`ask`: Ask a question about email context using iGPT's reasoning engine. Returns structured context including tasks, decisions, owners, sentiment, deadlines, and citations.

`search`: Search email context for relevant messages and threads. Returns matching email context as Documents with thread metadata (subject, participants, date, thread ID).

This tool is designed to be used as a way to load data as a Tool in an Agent.
llama-index-readers-igpt-email 0.1.0
llama-index readers igpt_email integration
Requires Python: <4.0,>=3.10
Keywords: email, igpt, intelligence, rag

# LlamaIndex Readers Integration: iGPT Email Intelligence

```bash
pip install llama-index-readers-igpt-email
```

The iGPT Email Intelligence Reader loads structured, reasoning-ready email
context from the iGPT API as LlamaIndex Documents for indexing and retrieval.

Unlike raw email connectors that return unprocessed message data, iGPT handles
thread reconstruction, participant role detection, and intent extraction before
returning results — so each Document contains clean, structured content ready
for a RAG pipeline.

To begin, you need to obtain an API key at [docs.igpt.ai](https://docs.igpt.ai).

## Usage

Here's an example usage of the IGPTEmailReader.

```python
from llama_index.readers.igpt_email import IGPTEmailReader
from llama_index.core import VectorStoreIndex

reader = IGPTEmailReader(api_key="your-key", user="user-id")
documents = reader.load_data(query="project Alpha", date_from="2025-01-01")
index = VectorStoreIndex.from_documents(documents)
```

This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).