semvec/pypi-packages huggingface dataset:
llmbrix 2.0.1
Low abstraction agentic LLM framework.
Requires Python: >=3.11
Keywords: LLM, agent, framework, AI, chatbot, llmbrix, llmbricks

# About

Low abstraction LLM framework focused on simple chat / agentic applications.

Supports only Gemini API.

In early alpha development.

# Install

```bash
pip install llmbrix
```

# Example usage

(see `llmbrix/examples/simple_chatbot/`)

```python
import dotenv

from llmbrix.chat_history import ChatHistory
from llmbrix.gemini_model import GeminiModel
from llmbrix.tool_agent import ToolAgent
from llmbrix.tools import CalculatorTool, DatetimeTool

dotenv.load_dotenv()

model = GeminiModel(model="gemini-2.5-flash-lite")
chat_history = ChatHistory(max_turns=5)

agent = ToolAgent(
    gemini_model=model,
    system_instruction="You are Kevin, super brief to the point chatbot assistant. Speak in corporate words.",
    chat_history=ChatHistory(max_turns=5),
    tools=[CalculatorTool(), DatetimeTool()],
    loop_limit=2,
    tool_timeout=30,
    max_workers=2,
)


def start_chat():
    print("--- Kevin is online! (Type 'exit' or 'quit' to stop) ---")

    while True:
        user_text = input("You: ")
        if user_text.lower() in ["exit", "quit"]:
            response = agent.chat("I'm leaving, see you!")
            print(f"Kevin: {response.text}")
            break
        try:
            for agent_msg in agent.chat_iter(user_text):
                if agent_msg.is_model() and agent_msg.text:
                    print(f"Kevin: {agent_msg.text}")
                elif agent_msg.is_tool():
                    print(f"Kevin used tool: {agent_msg.tool_name} with args: {agent_msg.tool_args}")
        except Exception as e:
            print(f"Error: {e}")


if __name__ == "__main__":
    start_chat()

```
mordu 0.0.26
A python package for the development and assessment of user defined equations of state.
Requires Python: >=3.11

```
======================================================================================
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣤⣔⣂⣤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ 
⠀⠀⠀⠀⠀⠀⠀⠀⣀⣤⢔⠟⠁⠀⠀⠀⠀⠈⠢⣀⣀⡀⠀⠀⠀⠀⠀⠀⠀ 
⠀⠀⠀⠀⠀⢀⣠⠎⠁⢠⠗⠒⠠⠤⠐⠀⠉⠐⡼⠖⠳⣽⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⢠⡞⢁⠀⠰⠘⡞⠑⡲⠊⠀⠀⠀⠀⠈⠒⠠⡘⣷⡀⠀⠀⠀⠀⠀  
⠀⠀⠀⢀⠏⠀⠂⠠⠃⠀⢿⡊⠁⠀⠀⢀⡄⠀⠀⠀⠀⠀⠈⡞⠄⠀⠀⠀⠀  
⠀⠀⠀⡜⠀⢸⠀⠀⠀⠀⠀⢹⠀⠀⣀⡈⠁⢄⣀⠀⠀⠀⠠⠃⠘⡄⠀⠀⠀
⠀⠀⢸⠇⠀⢸⡀⢠⠀⠀⠀⠉⢆⠀⢩⠁⠀⠀⣤⠀⠠⠞⠁⠀⠀⠇⠀⠀⠀
⠀⠀⡿⠀⠀⠀⢧⠀⡇⠀⠀⠀⠀⠑⣶⡀⢠⣄⢸⡑⠁⠀⠀⠀⠀⢸⠀                                      888         
⠀⢸⠇⠀⠀⠀⠘⡷⣷⠀⠀⠀⠀⠀⠀⠱⢤⠮⠋⡀⠀⠀⠀⠀⠀⠈⢆⠀                                     888         
⠀⢸⠀⠀⠀⠀⠀⠀⢻⣇⠀⠀⠀⠀⠀⠀⢀⠐⠊⠰⠀⠀⠀⠀⠀⠀⠈⢇⠀                                    888
⠀⡞⠀⠀⠀⠀⠀⠀⢀⣿⡆⠀⠀⠀⠀⠀⢸⠀⠀⠀⠣⡀⢀⠀⠀⠀⠀⢸⠀ 88888b.d88b.   .d88b.  888d888 .d88888 888  888
⠀⡇⠀⠀⠀⠀⠀⡴⠋⠀⣧⠀⠀⠀⠀⠀⢸⡀⠀⠀⠀⢈⠞⠀⠀⠀⠀⡸⠀ 888 "888 "88b d88""88b 888P"  d88" 888 888  888
⢸⡇⠀⠀⠀⢰⡟⠁⠀⠀⢿⠀⠀⠀⠀⠀⠀⣇⠀⠀ ⣧⠀⠀⠀⢀⠔⠁⠀ 888  888  888 888  888 888    888  888 888  888
⢸⠀⠀⠀⢠⡏⠀⠀⠀⠀⠀⠹⡀⠀⠀⠀⠀⢺⠀⠀⠀ ⠑⠒⠊⠁⠀⠀⠀ 888  888  888 Y88..88P 888    Y88b 888 Y88b 888
⠈⠑⠒⠒⠛⠀⠀⠀⠀⠀⠀⠀⠱⠤⠤⠤⠶⠼⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀  888  888  888  "Y88P"  888     "Y88888  "Y88888 
=====================================================================================
```

# mordu

Modelling Of Real-fluids Defined by Users

The beginnings of a python package which focuses on the creation and usage of equations of state (EOS) for both pure fluids and mixtures.

For now the package is limited to pure fluids, but will be adding mixture EOS in the future.

The package will allow the user to define their own fluids and equations of state to then calculate fluid thermodynamic properties at a certain state.

Future functionality will include vapour liquid equilibria algorithms and P-x diagram creation.
cctx-importer 0.1.0
Import Claude context profiles from configs directory
Requires Python: >=3.13

# cctx-importer | Claude Context Switcher Importer

Import [CCS](https://github.com/kaitranntt/ccs) profiles into [cctx](https://github.com/nwiizo/cctx).

CCS lets you configure Claude Code with different providers and accounts, but it doesn't modify Claude's default settings. This means you must use the `ccs` command and IDE extensions won't work.

cctx solves this by overwriting Claude's settings file, so you can use the `claude` command directly or IDE extensions.

This tool bridges both: it imports your CCS profiles into cctx so you can switch between them and use them with `claude` or IDE extensions.

## Requirements

- [cctx](https://github.com/nwiizo/cctx)

## Installation

The recommended way is to run directly with [uvx](https://docs.astral.sh/uv/guides/tools/) (no installation needed):

```bash
uvx cctx-importer from-ccs
```

Alternatively, install with pip:

```bash
pip install cctx-importer
```

Or with uv:

```bash
uv tool install cctx-importer
```

## Usage

```bash
# Import profiles from ~/.ccs/*.settings.json
cctx-importer from-ccs

# Import profiles from ~/.cctx-importer/*.json
cctx-importer from-configs
```

Both commands merge each profile with `~/.cctx-importer/default.json` if it exists.

## Options

```bash
# Use a custom directory for default.json
cctx-importer from-ccs --configs-dir /path/to/dir
```
langmem0 0.1.0
Add your description here
Requires Python: >=3.13

# LangMem0

![Python Version](https://img.shields.io/badge/python-3.13+-blue.svg)
![Version](https://img.shields.io/badge/version-0.1.0-green.svg)
[![Lint with Ruff](https://github.com/sammyne/langmem0/actions/workflows/lint.yml/badge.svg)](https://github.com/sammyne/langmem0/actions/workflows/lint.yml)

## Environment

- uv >= 0.9

## Quickstart

### 1. Clone the repository

```bash
git clone https://github.com/sammyne/langmem0.git
cd langmem0
```

### 2. Install dependencies

```bash
uv pip install -e .
```

### 3. Set up environment variables

Create a `.env` file in the project root with your OpenAI configuration:

```bash
OPENAI_API_KEY=your_api_key_here
OPENAI_API_BASE_URL=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o-mini
```

### 4. Run examples

LangMem0 provides two main ways to integrate memory into your LangChain applications:

#### Middleware Examples

Use `Mem0Middleware` to enable memory in LangChain agent conversations. This approach provides fine-grained control over memory operations.

```bash
# Synchronous example
uv run examples/middleware/helloworld.py
```

```bash
# Asynchronous example
uv run examples/middleware/helloworld_async.py
```

These examples demonstrate:
- Using `Mem0Middleware` to intercept and augment agent messages with memories
- How agents can remember and retrieve information across conversations
- Different thread contexts for separate conversation memories
- Integration with LangGraph checkpoints

#### Chat Examples

Use `ChatOpenAI` with built-in Mem0 memory capabilities for a simpler integration path.

```bash
# Basic synchronous example
uv run examples/chat/helloworld.py

# Basic asynchronous example
uv run examples/chat/helloworld_async.py

# Advanced synchronous with RunnableConfig:**
uv run examples/chat/runnable_config.py

# Advanced asynchronous with RunnableConfig:**
uv run examples/chat/runnable_config_async.py
```

These examples demonstrate:
- Direct integration with OpenAI-compatible chat models
- Memory-enabled conversations with minimal setup
- Using `RunnableConfig` for thread management
- Both sync and async API patterns

## FAQ
### Why not provide tool-based API like langmem
Tool-based API has a problem: system prompt needs augumenting with memories using a separate middleware as demonstrated
by [LangMem's Hot Path Quickstart](https://langchain-ai.github.io/langmem/hot_path_quickstart/#agent).

## References
- [LangChain Chatbot](https://chat.langchain.com/?threadId=e7011d54-12ce-4c14-8d2a-324996e0d0de)
- [LangChain forum](https://forum.langchain.com/c/oss-product-help-lc-and-lg/langchain/14)
- [Mem0 Open Source](https://docs.mem0.ai/open-source/overview)