Search Results for "llm software"
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Showing 30 open source projects for "llm software"
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BricksLLM
Enterprise-grade API gateway that helps you monitor and impose cost
BricksLLM is an open-source framework for building and managing production-ready LLM (Large Language Model) applications. It provides tooling for prompt engineering, memory management, observability, and chaining, all in one unified developer experience. BricksLLM is designed to reduce boilerplate and increase the maintainability of LLM-based workflows. -
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LlamaIndexTS
Data framework for your LLM applications
LlamaIndexTS is a data framework designed for Large Language Model (LLM) applications, focusing on server-side solutions to manage and process data efficiently. -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference... -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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Output
TypeScript framework for building AI workflows and agents
Output is an open-source TypeScript framework designed to build, orchestrate, and manage AI workflows and agents within a single unified system. It consolidates multiple aspects of AI development, including prompt management, evaluation, tracing, cost tracking, and orchestration, into a file-based architecture that lives entirely within the codebase. The framework is built specifically to work with AI coding agents, enabling them to read, modify, and execute workflows directly from... -
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TradingAgents
Chinese Financial Trading Framework Based on Multi-Agent LLM
TradingAgents-CN is a Chinese-enhanced, multi-agent LLM framework aimed at building financial analysis and trading-oriented workflows, with an emphasis on collaboration between specialized agents rather than a single monolithic prompt. It organizes market-related tasks into roles and stages so different agents can contribute research, reasoning, aggregation, and decision support in a structured pipeline. The project is oriented toward practical usage, including a stack that can be run in a... -
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Markdown Site
An open-source publishing framework built for AI agents and developers
Markdown Site is an open-source publishing framework built to help developers and AI agents quickly ship content-driven websites, blogs, or documentation directly from Markdown files with a seamless sync workflow. It is built on modern web technologies such as React, Convex, and Vite, and integrates real-time syncing so that changes to Markdown content locally instantly propagate to live views without the need to rebuild or redeploy. The framework supports an efficient authoring experience... -
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Fabric
Fabric is an open-source framework for augmenting humans using AI
Fabric is a command-line framework that turns a curated library of prompt “patterns” into reusable, automatable workflows for large language models. Instead of repeatedly crafting ad-hoc prompts, you pick a pattern (for research, summarization, brainstorming, code review, and more) and feed it inputs or files. The tool standardizes execution with configuration files and environment variables, enabling reproducible runs across different models and providers. Patterns can be customized with... -
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MNN
MNN is a blazing fast, lightweight deep learning framework
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity... -
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Superduper
Superduper: Integrate AI models and machine learning workflows
Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This... -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase... -
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Unsloth-MLX
Bringing the Unsloth experience to Mac users via Apple's MLX framework
Unsloth-MLX offers developers the power of Unsloth’s efficient large language model fine-tuning experience on Apple Silicon Macs by wrapping Apple’s native MLX framework with an API fully compatible with Unsloth workflows. This project removes traditional barriers that prevent Mac users from prototyping and experimenting with LLM training locally by allowing the same code used in cloud GPU environments to run on M-series hardware, improving workflow continuity and reducing iteration costs.... -
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
The de facto standard open-source platform for rapidly deploying machine learning models on Kubernetes. Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and... -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best... -
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TensorZero
TensorZero is an open-source stack for industrial-grade LLM apps
tensorzero is a lightweight C++ library designed for tensor operations and numerical computing. It offers a minimal and readable implementation of core tensor functionality, making it ideal for educational purposes, lightweight applications, or those wanting to understand how tensor libraries work under the hood. With no external dependencies, tensorzero is easy to integrate into C++ projects needing basic multi-dimensional array support. -
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Backtrack Sampler
An easy-to-understand framework for LLM samplers
Backtrack Sampler is a framework designed for experimenting with custom sampling strategies for language models (LLMs), enabling the ability to rewind and revise generated tokens. It allows developers to create and test their own token generation strategies by providing a base structure for manipulating logits and probabilities, making it a flexible tool for those interested in fine-tuning the behavior of LLMs. -
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OWL
Optimized Workforce Learning for General Multi-Agent Assistance
OWL (Optimized Workforce Learning) is a sophisticated open-source framework built on the CAMEL-AI ecosystem for orchestrating teams of AI agents to collaboratively solve complex, real-world tasks with dynamic planning and automation capabilities. Unlike single-agent systems, it treats task completion as a collaborative workforce where agents take on specialized roles (planning, execution, analysis) and coordinate via a modular multi-agent architecture that supports flexible teamwork across... -
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any-agent
A single interface to use and evaluate different agent frameworks
any-agent is a Python library that provides a unified interface for working with multiple agent frameworks, allowing developers to create, run, and evaluate AI agents without being locked into a single ecosystem. It abstracts the differences between frameworks such as LangChain, LlamaIndex, OpenAI Agents, and others, enabling users to switch between them with minimal configuration changes. The library is designed to simplify experimentation and benchmarking, making it easier to compare agent... -
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FastMCP Framework
A TypeScript framework for building MCP servers
FastMCP is a TypeScript framework aimed at building servers compliant with the Model Context Protocol (MCP), enabling LLMs (large language models) or other clients to access tools, resources, and context through a defined protocol. It allows developers to define “tools” (basically operations or services) and “resources” that can be fetched or interacted with, and supports multiple transport mechanisms (HTTP streaming, SSE, etc) for client-server communication. Because it’s built in... -
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PydanticAI
Agent Framework / shim to use Pydantic with LLMs
When I first found FastAPI, I got it immediately. I was excited to find something so innovative and ergonomic built on Pydantic. Virtually every Agent Framework and LLM library in Python uses Pydantic, but when we began to use LLMs in Pydantic Logfire, I couldn't find anything that gave me the same feeling. PydanticAI is a Python Agent Framework designed to make it less painful to build production-grade applications with Generative AI. Built by the team behind Pydantic (the validation layer... -
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OpenAI Agents JS
A lightweight, powerful framework for multi-agent workflows
openai-agents-js is the JavaScript/TypeScript version of the OpenAI Agents SDK, designed to give developers a lightweight but powerful framework for building agentic workflows, voice agents, and tool-augmented LLM systems in JS/TS environments. The SDK is provider-agnostic, meaning while it’s deeply compatible with OpenAI’s APIs, you can also adapt it to use other backends or extension layers. At its core, it introduces primitives like Agents, Tools, Guardrails, and Handoffs, letting you... -
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Astro Stardrive Boilerplate
The Astro boilerplate to create super stable websites even faster
Stardrive is a batteries-included Astro boilerplate built for the AI age. It skips the first 36 steps of creating a high-class website, giving you a fast, secure, accessible, and SEO-optimized foundation without reinventing the wheel. What makes it special: it is LLM-friendly by design, with dedicated AI guidance files so coding assistants immediately understand the project. It also prepares your site for WebMCP, auto-populates an llms.txt, and generates Schema.org structured data... -
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MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR... -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN... -
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towhee
Framework that is dedicated to making neural data processing
Towhee is an open-source machine-learning pipeline that helps you encode your unstructured data into embeddings. You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to...
