Open Source Python Projects
Discover 606 open source Python repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. Python projects here are most often combined with Library, Developer Tools and LLM. Last updated October 4, 2026.
606 repositories · updated October 4, 2026
MacOS-MCP: Automate macOS with AI Agents
MacOS-MCP is a Python MCP server that lets AI agents interact with macOS apps and interfaces through accessibility controls and system tools. It suits developers building desktop workflows, with macOS permissions and careful security review required.
axolotl: Fine-Tune Large Language Models
Axolotl is a Python framework for fine-tuning and post-training language models through configurable workflows. It supports methods from LoRA and QLoRA to preference tuning and reinforcement learning, with options for multimodal and distributed training.
mergoo: Combine Fine-Tuned LLM Experts into Routed Models
Mergoo combines fine-tuned language models or LoRA adapters into routed expert models, then supports training the resulting model. It is aimed at teams that want one model to draw on specialized experts rather than use them separately.
ludwig: Configure and Train AI Models with YAML
Ludwig is a Python framework for configuring, training, evaluating, and deploying AI models through declarative YAML rather than custom training loops. It suits teams that want one workflow for LLM fine-tuning, tabular prediction, and multimodal modeling.
lamini: Access the Lamini API from Python
Lamini is a Python client and SDK for working with Lamini's generative AI API. It suits developers who want to connect Python applications to Lamini without building API requests from scratch.
xTuring: Fine-Tune and Run Personalized LLMs
xTuring is a Python library for preparing data, fine-tuning, evaluating, and running open-source language models locally or in a private cloud. It is aimed at developers who want model customization through a high-level API and methods such as LoRA and quantization.
RL4LMs: Fine-Tune Language Models with Reinforcement Learning
RL4LMs is a Python library for training language models against custom reward functions using on-policy reinforcement learning. It suits NLP researchers and developers who need configurable training components for text-generation tasks.
torchtune: Fine-Tune and Post-Train Large Language Models
torchtune is a PyTorch library for configuring and running LLM post-training workflows, from supervised fine-tuning to preference optimization. It is aimed at developers who want editable recipes and model-specific configs, but is no longer actively maintained.
griptape: Build AI Agents and Workflows in Python
Griptape is a modular Python framework for building generative AI applications with tasks, agents, pipelines, and workflows. It suits developers who want to combine language models with tools, memory, and retrieval components through swappable integrations.
RouteLLM: Route LLM Requests to Balance Cost and Quality
RouteLLM is a Python framework for routing prompts between stronger, costlier models and cheaper alternatives. It helps teams manage the cost-quality tradeoff, serve requests through an OpenAI-compatible interface, and evaluate routing strategies.
memoripy: Build Evidence-Based Memory for AI Agents
Memoripy is a local memory runtime for AI agents that keeps versioned, sourced records and explains why they appear in recall. It suits developers who need scoped, auditable memory rather than an opaque store of accumulated text.
simpleaichat: Build Python ChatGPT Apps with Minimal Code
simpleaichat is a Python library for building chat applications and workflows with OpenAI chat models. It offers conversation management, streaming, asynchronous calls, structured data, and custom tools through a compact interface.