semvec/pypi-packages huggingface dataset:
memorylake 0.1.0
MemoryLake
Requires Python: >=3.9
Keywords: memorylake, memory, ai, llm, agent

# MemoryLake

[![codecov](https://codecov.io/gh/powerdrillai/memorylake-client/branch/main/graph/badge.svg)](https://codecov.io/gh/powerdrillai/memorylake-client)
[![versions](https://img.shields.io/pypi/pyversions/memorylake.svg)](https://github.com/powerdrillai/memorylake-client)
[![license](https://img.shields.io/github/license/powerdrillai/memorylake-client.svg)](https://github.com/powerdrillai/memorylake-client/tree/main/LICENSE)

## Developer Setup

**Prerequisites:** Python 3.9

**Install:**

```bash
pip install -e .[dev]
```

**Development Commands:**

```bash
./cicd/format.sh                 # Format code
./cicd/check-all-locally.sh      # Run all checks  
./cicd/test-all.sh               # Run tests
```
eval-protocol 0.3.20
The official Python SDK for Eval Protocol (EP.) EP is an open protocol that standardizes how developers author evals for large language model (LLM) applications.
Requires Python: >=3.10

# Eval Protocol

[![PyPI - Version](https://img.shields.io/pypi/v/eval-protocol)](https://pypi.org/project/eval-protocol/)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/eval-protocol/python-sdk)

**Eval Protocol (EP) is an open solution for doing reinforcement learning fine-tuning on existing agents — across any language, container, or framework.**

![Eval Protocol overview](https://github.com/eval-protocol/python-sdk/raw/main/docs/intro.png)

Most teams already have complex agents running in production — often across remote services with heavy dependencies, Docker containers, or TypeScript backends deployed on Vercel. When they try to train or fine-tune these agents with reinforcement learning, connecting them to a trainer quickly becomes painful.

Eval Protocol makes this possible in two ways:

1. **Expose your agent through a simple API**
   Wrap your existing agent (Python, TypeScript, Docker, etc.) in a simple HTTP service using EP’s rollout interface. EP handles the rollout orchestration, metadata passing, and trace storage automatically.
2. **Connect with any trainer**
   Once your agent speaks the EP standard, it can be fine-tuned or evaluated with any supported trainer — Fireworks RFT, TRL, Unsloth, or your own — with no environment rewrites.

The result: RL that works out-of-the-box for existing production agents.

## Who This Is For

- **Applied AI teams** adding RL to existing production agents.
- **Research engineers** experimenting with fine-tuning complex, multi-turn or tool-using agents.
- **MLOps teams** building reproducible, language-agnostic rollout pipelines.

## Quickstart

- See the Quickstart repository: [eval-protocol/quickstart](https://github.com/eval-protocol/quickstart/tree/main)

## Resources

- **[Documentation](https://evalprotocol.io)** – Guides and API reference
- **[Discord](https://discord.com/channels/1137072072808472616/1400975572405850155)** – Community
- **[GitHub](https://github.com/eval-protocol/python-sdk)** – Source and examples

## License

[MIT](LICENSE)