Open Source LLM Projects
Discover 260 open source LLM repositories from GitHub, each with an analysis of what it does, key features, use cases and alternatives. LLM projects here are most often combined with Python, AI and AI Agents. Last updated October 3, 2026.
260 repositories · updated October 3, 2026
QwenPaw: Run a Self-Hosted Personal AI Assistant
QwenPaw is a personal AI assistant that runs locally or in the cloud and connects to chat apps, models, and extensible tools. It suits people who want a customizable agent with persistent memory and security controls under their own deployment.
Memori: Give AI Agents Persistent Memory
Memori adds persistent, structured memory to LLM applications and agents, capturing conversations and execution context for later recall. It suits teams building stateful agents who want a memory layer that works across models and existing infrastructure.
awesome-agentic-ai: Explore Agentic AI Tools and Learning Resources
A curated guide to agentic AI frameworks, tools, papers, and learning materials, with example projects and recommendations for choosing technologies. Useful for developers learning agent systems or evaluating options for a project.
colibri: Run Large MoE Models on Local Hardware
colibri is a C inference engine that streams Mixture-of-Experts model weights from disk, so large models can run without fitting entirely in RAM or VRAM. It supports CPU-only use and optional GPU backends, with speed depending on hardware and storage.
rag-zero-to-hero-guide: Learn Retrieval-Augmented Generation
A learning guide to retrieval-augmented generation, from core concepts to evaluation and advanced approaches. It combines explanations, Jupyter notebook implementations, tool references, and survey papers for learners building RAG knowledge.
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.
TencentDB-Agent-Memory: Share Reusable Memory Across AI Agents
TencentDB Agent Memory is a team memory hub that turns conversations, documents, and code into reusable assets for AI agents. It suits teams that want agents to share curated context and skills across sessions and frameworks.
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.
zero: Run a Local Terminal Coding Agent
Zero is a Go-based terminal coding agent that can inspect and edit repositories, run commands, and work with many hosted or local models. It suits developers who want scriptable coding assistance with local sessions and explicit controls over side effects.
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.