Search Results for "high level programming"
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Showing 32 open source projects for "high level programming"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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tt-metal
TT-NN operator library, and TT-Metalium low level kernel programming
tt-metal, also referred to in its documentation as TT-Metalium, is Tenstorrent’s low-level software development kit for programming applications on Tenstorrent AI accelerators. The project is designed for developers who need direct access to the company’s Tensix processor architecture, exposing a programming model that is closer to hardware control than high-level inference frameworks. Instead of following a traditional GPU model centered on massive thread parallelism, the platform is built around a grid of specialized compute nodes called Tensix cores, each with local SRAM, dedicated compute units, and multiple RISC-V control processors. ... -
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wllama
WebAssembly binding for llama.cpp - Enabling on-browser LLM inference
...By running models locally on the user’s device, wllama enables privacy-preserving AI applications that do not require sending data to remote servers. The framework provides both high-level APIs for common tasks such as text generation and embeddings, as well as low-level APIs that expose tokenization, sampling controls, and model state management. -
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uzu
A high-performance inference engine for AI models
...By utilizing Apple’s unified memory architecture, uzu reduces memory copying overhead and improves inference throughput for local AI workloads. The system includes a simple high-level API that enables developers to run models, create inference sessions, and generate outputs with minimal configuration. -
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AICGSecEval
A.S.E (AICGSecEval) is a repository-level AI-generated code security
AICGSecEval is an open-source benchmark framework designed to evaluate the security of code generated by artificial intelligence systems. The project was developed to address concerns that AI-assisted programming tools may produce insecure code containing vulnerabilities such as injection flaws or unsafe logic. The framework constructs evaluation tasks based on real-world software repositories and known vulnerability cases derived from CVE records. By simulating realistic development... -
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tiny-llm
A course of learning LLM inference serving on Apple Silicon
...The project is structured as a guided course that walks developers through the process of implementing the core components required to run a modern language model, including attention mechanisms, token generation, and optimization techniques. Rather than relying on high-level machine learning frameworks, the codebase uses mostly low-level array and matrix manipulation APIs so that developers can understand exactly how model inference works internally. The project demonstrates how to load and run models such as Qwen-style architectures while progressively implementing performance improvements like KV caching, request batching, and optimized attention mechanisms. ... -
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RAPTOR
The official implementation of RAPTOR
...Traditional RAG systems typically retrieve small text chunks independently, which can limit a model’s ability to understand broader document context. RAPTOR addresses this limitation by recursively embedding, clustering, and summarizing documents to create a tree-structured hierarchy of information. Each level of the tree represents summaries at different levels of abstraction, allowing retrieval to operate at both detailed and high-level conceptual layers. During inference, the system can navigate this hierarchical representation to retrieve information that best matches the user’s query while preserving broader contextual understanding. ... -
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TokenSpeed
TokenSpeed is a speed-of-light LLM inference engine
TokenSpeed is an LLM inference engine designed for high-performance production agent workloads. It aims to combine TensorRT-LLM-level speed with vLLM-level usability, making it relevant for teams that need fast generation without sacrificing developer ergonomics. The project is focused on the specific needs of agentic systems, where latency, throughput, and efficient scheduling matter across many short or tool-heavy requests. -
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BruteForceAI
Advanced LLM-powered brute-force tool combining AI intelligence
BruteForceAI is an open-source security testing tool that applies large language models to the analysis of login forms and authentication flows in web applications. At a high level, the project uses AI to inspect HTML content, identify the relevant form elements, and automate selector discovery so that a tester does not need to hand-map every field before evaluation. It combines that analysis layer with automated credential testing workflows, framing itself as a more adaptive alternative to older brute-force tooling that depends heavily on manual configuration. ... -
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Chitu
High-performance inference framework for large language models
Chitu is a high-performance inference engine designed to deploy and run large language models efficiently in production environments. The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations.... -
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UFO³
Weaving the Digital Agent Galaxy
...This enables the agent to navigate complex software environments and perform tasks that normally require manual interaction. UFO integrates mechanisms for task decomposition, planning, and execution so that high-level user requests can be broken down into smaller steps performed by specialized agents. The framework can operate across multiple applications simultaneously, allowing workflows that span several programs to be automated seamlessly. -
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LOTUS
AI-Powered Data Processing: Use LOTUS to process all of your datasets
LOTUS is an open-source framework and query engine designed to enable efficient processing of structured and unstructured datasets using large language models. The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. The core concept of the framework is the use of semantic operators, which extend traditional relational database operations to support reasoning over text and other unstructured data. ... -
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Mooncake
Mooncake is the serving platform for Kimi
Mooncake is an open-source infrastructure platform designed to optimize large language model serving by focusing on efficient management and transfer of model data and KV cache. The platform was originally developed as part of the serving infrastructure for the Kimi large language model system. Its architecture centers on a high-performance transfer engine that provides unified data transfer across different storage and networking technologies. This engine enables efficient movement of... -
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Qwen3 Embedding
Designed for text embedding and ranking tasks
Qwen3-Embedding is a model series from the Qwen family designed specifically for text embedding and ranking tasks. It builds upon the Qwen3 base/dense models and offers several sizes (0.6B, 4B, 8B parameters), for both embedding and reranking, with high multilingual capability, long‐context understanding, and reasoning. It achieves state-of-the-art performance on benchmarks like MTEB (Multilingual Text Embedding Benchmark) and supports instruction-aware embedding (i.e. embedding task... -
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how-to-optim-algorithm-in-cuda
How to optimize some algorithm in cuda
...These examples show how different optimization techniques influence performance on modern GPU hardware and allow readers to experiment with real implementations. The repository also contains extensive learning notes that summarize CUDA programming concepts, GPU architecture details, and performance engineering strategies. -
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Guidance
A guidance language for controlling large language models
Guidance is an efficient programming paradigm for steering language models. With Guidance, you can control how output is structured and get high-quality output for your use case—while reducing latency and cost vs. conventional prompting or fine-tuning. It allows users to constrain generation (e.g. with regex and CFGs) as well as to interleave control (conditionals, loops, tool use) and generation seamlessly. -
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GLM-4.7
Advanced language and coding AI model
GLM-4.7 is an advanced agent-oriented large language model designed as a high-performance coding and reasoning partner. It delivers significant gains over GLM-4.6 in multilingual agentic coding, terminal-based workflows, and real-world developer benchmarks such as SWE-bench and Terminal Bench 2.0. The model introduces stronger “thinking before acting” behavior, improving stability and accuracy in complex agent frameworks like Claude Code, Cline, and Roo Code. GLM-4.7 also advances “vibe... -
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How to Train Your GPT
Build a modern LLM from scratch. Every line commented
...It includes chapters and topic explainers on tokenizers, embeddings, attention, RoPE, RMSNorm, SwiGLU, KV cache, AdamW, mixed precision, training loops, and inference. The guide emphasizes writing every important component manually rather than only calling high-level APIs. Its purpose is to make the internals of language models understandable through runnable code and step-by-step explanations. -
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Hephaestus
Semi-Structured Agentic Framework. Workflows build themselves
Hephaestus is an open-source semi-structured agentic framework designed to orchestrate multiple AI agents working together on complex tasks. Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression, allowing workflows to evolve as new discoveries are made. For example, if an agent detects a bug or optimization opportunity, it can automatically create a new task and integrate it into the workflow. ... -
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Casibase
Open-source enterprise-level AI knowledge base and MCP
Casibase is an open-source AI cloud platform designed to function as an enterprise knowledge base, container management system, and collaboration environment for AI-driven applications. The project combines knowledge management, messaging, and forum features with large language model integration to create an interactive platform for storing and querying domain-specific knowledge. Built with a separated frontend and backend architecture, Casibase provides a web-based administrative interface... -
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llmware
Unified framework for building enterprise RAG pipelines
llmware is an open source framework designed to simplify the creation of enterprise-grade applications powered by large language models. The platform focuses on building secure and private AI workflows that can run locally on laptops, edge devices, or self-hosted servers without relying exclusively on cloud APIs. It provides a unified interface for constructing retrieval-augmented generation pipelines, agent workflows, and document intelligence applications. One of the framework’s defining... -
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NeMo Curator
Scalable data pre processing and curation toolkit for LLMs
NeMo Curator is a Python library specifically designed for fast and scalable dataset preparation and curation for large language model (LLM) use-cases such as foundation model pretraining, domain-adaptive pretraining (DAPT), supervised fine-tuning (SFT) and paramter-efficient fine-tuning (PEFT). It greatly accelerates data curation by leveraging GPUs with Dask and RAPIDS, resulting in significant time savings. The library provides a customizable and modular interface, simplifying pipeline... -
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csghub-server
csghub-server is the backend server for CSGHub
csghub-server is the backend component of the CSGHub platform, an open-source infrastructure designed to manage and operate large language models, datasets, and AI development workflows within a private deployment environment. The server acts as a centralized management layer that allows teams to store, organize, and operate AI assets such as models, datasets, and machine learning applications in a manner similar to artifact repositories used in software engineering. Built primarily in the... -
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Eidos
An extensible framework for Personal Data Management
...Its interface is inspired by tools like Notion, allowing users to create documents, databases, and custom views to organize personal information. Unlike cloud-based knowledge tools, Eidos runs entirely on the user’s machine, ensuring privacy and high performance through local storage. The platform integrates large language models to enable AI-assisted features such as summarizing documents, translating content, and interacting with stored data conversationally. It also includes an extension system that allows developers to create custom tools, scripts, and workflows using programming languages such as TypeScript or Python. -
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MiniMax-M2.5
State of the art LLM and coding model
MiniMax-M2.5 is a state-of-the-art foundation model extensively trained with reinforcement learning across hundreds of thousands of real-world environments. It delivers leading performance in coding, agentic tool use, search, and complex office workflows, achieving top benchmark scores such as 80.2% on SWE-Bench Verified and 76.3% on BrowseComp. Designed to reason efficiently and decompose tasks like an experienced architect, M2.5 plans features, structure, and system design before... -
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Controllable-RAG-Agent
This repository provides an advanced RAG
...The pipeline ingests PDFs, splits them into chapters, cleans and preprocesses text, then constructs vector stores for fine-grained chunks, chapter summaries, and book quotes to support nuanced queries. At query time, it anonymizes entities, creates a high-level plan, de-anonymizes and expands that plan into concrete retrieval or reasoning tasks, and executes them in sequence while continuously revising the plan. A key focus is hallucination control: each answer is verified against retrieved context, and responses are reworked when they are not sufficiently grounded in the source documents.