semvec/pypi-packages huggingface dataset:
iflow-mcp_nyanta012-think-server 0.1.0
Add your description here
Requires Python: >=3.11

# Think Server

## セットアップ方法

### MCPサーバーの設定

Claude Desktopでこのサーバーを使用するには、以下の設定を`claude_desktop_config.json`に追加してください：

```json
{
    "mcpServers": {
        "think": {
            "command": "uv",
            "args": [
                "--directory",
                "C:\\Users\\{user}\\mcp_server\\think_server",
                "run",
                "server.py"
            ]
        }
    }
}
```

注意：
- `{user}`は実際のユーザー名に置き換えてください
- パスは実際のサーバーのインストール場所に合わせて調整してください
pi-agent 0.1.0
Python tools for building AI agents and managing LLM deployments
Requires Python: >=3.11
Keywords: ai, agents, llm, tool-calling, sdk

# pi-agent

Python tools for building AI agents and managing LLM deployments.

This repository is an open-source Python reimplementation of core ideas from Pi (JS/TS), starting with a provider-agnostic agent runtime that is composable and SDK-friendly.

## Status

Phase 1 and an initial Phase 2 slice are implemented:

- Core typed domain model for messages, content blocks, tools, usage, model config
- Async event stream primitive with terminal-result handling
- Agent loop runtime with turn/tool orchestration and steering/follow-up queues
- Stateful `Agent` wrapper with prompt/continue/abort/wait APIs
- Provider abstraction (`pi_ai`) with registry + runtime adapter
- Providers:
  - mock provider
  - OpenAI Responses provider (with streaming deltas)
  - OpenAI Completions-compatible provider (with streaming deltas)
- Strict checks and tests (`ruff`, `mypy`, `pytest`)

Roadmap details are tracked in [`PLAN.md`](./PLAN.md).

## Requirements

- Python `>=3.11`
- [`uv`](https://docs.astral.sh/uv/)

## Install

```bash
pip install pi-agent
```

```python
from pi_agent.agent_core import Agent, Model
```

## Development

```bash
uv sync
uv run ruff check .
uv run mypy src tests
uv run pytest -q
```

## Package layout

```text
src/pi_agent/agent_core/
  types.py         # domain model + event types + runtime protocols
  event_stream.py  # generic async stream + assistant stream specialization
  agent_loop.py    # turn execution + tool execution loop
  agent.py         # high-level agent state wrapper

src/pi_agent/pi_ai/
  types.py         # provider request + provider protocol
  registry.py      # provider registry + defaults
  runtime.py       # stream/complete APIs + Agent adapter
  providers/mock.py
  providers/openai.py
  providers/openai_completions.py
```

## End-to-end example

Run:

```bash
uv sync --extra openai
export OPENAI_API_KEY=your_key_here
uv run python examples/agent_e2e.py
```

This example uses OpenAI (`gpt-5-mini`), routes calls through `pi_ai`, invokes a tool, and prints the final assistant response.

## Streaming example (OpenAI)

Run:

```bash
uv sync --extra openai
export OPENAI_API_KEY=your_key_here
uv run python examples/openai_streaming.py
```

This example prints streaming delta events (`text_delta`, and tool-call events when present) from the OpenAI provider.

## Build and publish

```bash
uv build
uvx twine check dist/*
uv publish
```

## Releases

Tag-based releases use `.github/workflows/release.yml` and expect:

- `pyproject.toml` version matches the tag (`vX.Y.Z`).
- A matching `CHANGELOG.md` section exists (`## [X.Y.Z] - YYYY-MM-DD`).
langchain-b12 0.1.13
A reusable collection of tools and implementations for Langchain
Requires Python: >=3.11

# Langchain B12

This repo hosts a collection of custom LangChain components.

## Installation

To install this package, run
```bash
pip install langchain-b12
```

Some components rely on additional packages that may be installed as extras.
For example, to use the Google chatmodel `ChatGenAI`, you can run
```bash
pip install langchain-b12[google]
```

## Components

The repo contains these components:

- `ChatGenAI`: An implementation of `BaseChatModel` that uses the `google-genai` package. Note that `langchain-google-genai` and `langchain-google-vertexai` exist, but neither uses the latest and recommended `google-genai` package.
- `GenAIEmbeddings`: An implementation of `Embeddings` that uses the `google-genai` package.

## Comments

This repo exists for easy reuse and extension of custom LangChain components.
When appropriate, we will create PRs to merge these components directly into LangChain.
article-introduction-generator 0.1.4
Program to generate a prose-style introductory text from a structured JSON file.
Requires Python: >=3.8
Keywords: writing, article

# article-introduction-generator

Program to generate a prose-style introductory text from a structured JSON file.

![logo](https://raw.githubusercontent.com/trucomanx/ArticleIntroductionGenerator/main/screenshot.png)

## 1. Installing

To install the package from [PyPI](https://pypi.org/project/article_introduction_generator/), follow the instructions below:


```bash
pip install --upgrade article_introduction_generator
```

Execute `which article-introduction-generator` to see where it was installed, probably in `/home/USERNAME/.local/bin/article-introduction-generator`.

### Using

To start, use the command below:

```bash
article-introduction-generator
```
## 2. More information

If you want more information go to [doc](https://github.com/trucomanx/ArticleIntroductionGenerator/blob/main/doc) directory.

## 3. Buy me a coffee

If you find this tool useful and would like to support its development, you can buy me a coffee!  
Your donations help keep the project running and improve future updates.  

[☕ Buy me a coffee](https://ko-fi.com/trucomanx) 

## 4. License

This project is licensed under the GPL license. See the `LICENSE` file for more details.
snorkelai-sdk 26.1.1
Python SDK for the Snorkel AI Data Development Platform

# Snorkel Python SDK

The Snorkel Python SDK provides tools and APIs for building, managing, and deploying data-centric AI workflows. It enables users to efficiently label, transform, and analyze data for machine learning applications.

[SDK Documentation](https://docs.snorkel.ai/docs/user-guide/sdk/overview)