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LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing
Shimmy is a local large language model inference engine and server that loads and serves GGUF formatted weights. It is distributed as a single binary runtime written in Rust, providing a standalone environment for running models without external runtime dependencies. The project utilizes WebGPU for hardware acceleration, allowing model compute kernels to execute across diverse graphics hardware through a standardized interface. It features a local server that implements an OpenAI-compatible API layer, enabling applications to interface with local models via standardized REST endpoints. Memor
LiteLLM is a unified gateway and proxy server designed to centralize access to over one hundred language model providers. It provides a standardized API interface that abstracts vendor-specific schemas, allowing developers to interact with diverse models through a single, consistent format. By acting as a central traffic management layer, it enables organizations to route, secure, and govern model interactions across multiple deployments. The platform distinguishes itself through its policy-driven architecture, which uses configuration-based routing to manage traffic distribution, load balanc
Enchanted is a privacy-focused, cross-platform chat frontend for interacting with self-hosted large language models on iOS and macOS. It serves as a native client for communicating with private model servers, specifically providing integration for the Ollama API. The application supports multimodal interactions, allowing users to combine text, image attachments, and voice prompts. It provides tools for local AI model management, including the ability to define persistent system prompts and switch between different models for specific tasks. The interface includes capabilities for rendering m
Chat with your favourite LLaMA models in a native macOS app
The main features of alexrozanski/llamachat are: Inference and Serving, Inference Engines, LLM Training and Optimization, Natural Language Processing.
Projects with overlapping indexed features include: berriai/litellm — LiteLLM is a unified gateway and proxy server designed to centralize access to over one hundred language model… michael-a-kuykendall/shimmy — Shimmy is a local large language model inference engine and server that loads and serves GGUF formatted weights. It is… langchain-ai/langchain — LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large… bentoml/openllm — OpenLLM is a framework for deploying, managing, and scaling open-source large language models. artidoro/qlora — This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation… aethercortex/llama-x — Open Academic Research on Improving LLaMA to SOTA LLM.