Search Results for "memory" - Page 32
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Showing 3192 open source projects for "memory"
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lldap
Light LDAP implementation
...It focuses on delivering essential LDAP functionality while avoiding the heavy operational overhead commonly associated with enterprise-grade directory servers. Written in Rust, lldap benefits from strong memory safety guarantees and performance efficiency, making it suitable for self-hosted environments and small-to-medium-scale deployments. The project emphasizes usability, offering a more approachable configuration model and integration capabilities for authentication and identity management systems. lldap is particularly useful for developers and organizations that need LDAP-compatible authentication without the complexity of legacy systems like OpenLDAP. ... -
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TensorRT LLM
TensorRT LLM provides users with an easy-to-use Python API
...It provides a Python-based API built on top of PyTorch that allows developers to define, customize, and deploy LLMs efficiently across a variety of hardware configurations, from single GPUs to large multi-node clusters. The library focuses on maximizing throughput and minimizing latency through advanced techniques such as quantization, custom attention kernels, and optimized memory management strategies. It includes support for cutting-edge inference methods like speculative decoding and inflight batching, enabling real-time and large-scale AI applications. TensorRT-LLM integrates seamlessly with NVIDIA’s broader inference ecosystem, including Triton Inference Server and distributed deployment frameworks, making it suitable for production environments. -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
PySpur is a visual development environment designed to help AI engineers build, test, and iterate on agent-based workflows more efficiently. It provides a structured playground where users can define test cases, construct agents either through Python code or a graphical interface, and continuously refine their behavior. It addresses common challenges in AI agent development such as prompt tuning difficulties and lack of visibility into workflow execution. By offering a visual representation... -
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Swarms
Enterprise multi-agent orchestration framework for scalable AI apps
...It supports integration with multiple model providers and existing ecosystems, allowing developers to combine different AI tools and frameworks within a unified system. Swarms also includes mechanisms for agent lifecycle management, memory handling, and dynamic composition, making it adaptable to evolving workloads. Additionally, it focuses on developer productivity through APIs, CLI tools, and templates that simplify building and deploying agent-based applications. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
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AI Agents Papers
A collection of AI Agents papers
AI-Agent-Papers is a curated open-source repository that collects research papers related to artificial intelligence agents and agentic systems. The project organizes a large body of academic work covering topics such as planning, reasoning, tool use, self-correction, memory systems, and safety mechanisms for AI agents. The repository categorizes papers into structured themes including agent capabilities, agent architectures, and practical applications across different domains. It also includes categories for survey papers, benchmarks, and tutorials, helping researchers understand both foundational theory and emerging developments in the field. ... -
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Extractous
Fast and efficient unstructured data extraction
...Its purpose is to extract text and metadata efficiently from formats such as PDF, Word, HTML, email archives, images, and more, without depending on external APIs or separate parsing servers. The project emphasizes performance and low memory usage, and its maintainers describe it as a local-first alternative to heavier extraction stacks. For broader format support, the system combines its Rust core with ahead-of-time compiled Apache Tika shared libraries, which allows it to extend parsing coverage while still avoiding traditional server-based overhead. It also supports OCR for images and scanned documents through Tesseract, making it useful for document ingestion pipelines that include image-based or scanned inputs. -
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how-to-optim-algorithm-in-cuda
How to optimize some algorithm in cuda
...The project combines technical notes, code examples, and practical experiments that demonstrate how common computational kernels can be optimized to improve speed and memory efficiency. Instead of presenting only theoretical explanations, the repository includes hand-written CUDA implementations of fundamental operations such as reductions, element-wise computations, softmax, and attention mechanisms. These examples show how different optimization techniques influence performance on modern GPU hardware and allow readers to experiment with real implementations. ... -
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WGCLOUD
Visibility into servers, applications, and infrastructure
...Built on a Spring Boot microservices foundation with an agent-server model, the system emphasizes rapid deployment, minimal configuration overhead, and automated operation for large-scale environments. It collects extensive host metrics such as CPU usage, temperature, memory utilization, disk performance, network throughput, and hardware health while also supporting monitoring of processes, containers, ports, and databases. The platform includes advanced operational capabilities such as web-based SSH access, batch command execution, and visual topology generation, making it more than a passive monitoring tool. wgcloud is intended for private, self-hosted deployments where organizations want full control over monitoring data without relying on external cloud services. -
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OpenViking
Context database designed specifically for AI Agents
...It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. The project is implemented with performance in mind, often leveraging optimized data structures that balance fast reads and writes with minimal resource consumption. Developers can integrate OpenViking into modern AI stacks to unify context storage across services, enabling consistent session history, personalized responses, and richer search experiences. -
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Refly
The first open-source agent skills builder
...With a focus on making automation accessible, it provides a visual canvas and low-code components that feel similar to drag-and-drop builders but backed by powerful AI orchestration, memory handling, and integrations with external services. Refly’s approach bridges the gap between workflow ideas and stable, deterministic infrastructure: skills become governed capabilities that can be versioned, shared, and monetized, not just temporary scripts. -
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Mantic.sh
A structural code search engine for Al agents
Mantic.sh is a context-aware, structural code search engine designed specifically for use with AI coding agents and developers who need deep, semantically relevant search across large codebases. Unlike traditional text-based search tools that mainly match keywords, Mantic.sh understands code structure and meaning by combining syntactic heuristics with neural semantic reranking to produce results that reflect conceptual relevance, which helps find functions, definitions, and patterns that... -
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Go Katas
A collection of daily coding challenges
...It mirrors the kata practice tradition from martial arts—repetitive, thoughtful practice where each exercise reinforces technique, discipline, and problem-solving approach. Each kata prompt focuses on a precise aspect of Go, such as concurrency patterns, memory management, interfaces, error handling, or performance optimization, giving learners structured practice opportunities that go beyond syntax. Implementations can be tested locally, graded automatically with included test suites, and iterated on so that learners get rapid feedback and measurable progress. Because Go is widely used for backend services, cloud tooling, and systems programming, this repository helps participants build confidence in writing reliable, idiomatic, and maintainable code in real environments. -
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Engram
A New Axis of Sparsity for Large Language Models
...It provides utilities to generate embeddings from text or other structured data, index them using efficient approximate nearest neighbor algorithms, and perform real-time similarity queries even on large corpora. Engineered with speed and memory efficiency in mind, Engram supports batched indexing, incremental updates, and custom distance metrics so developers can tailor search behaviors to their domain’s needs. In addition to raw similarity search, the project includes tools for clustering, ranking, and filtering results, enabling richer user experiences like “related content”, semantic auto-completion, and contextual filtering. -
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Z80-μLM
Z80-μLM is a 2-bit quantized language model
...The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. It also functions as an educational reference for how to reduce inference to operations that fit an old-school instruction set and runtime environment. -
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Archon
The knowledge and task management backbone for AI coding assistants
...Users can import documentation, project files, and external knowledge so that assistants like Claude Code, Cursor, or other LLM-powered tools work with up-to-date, project-specific context rather than relying on limited prompt memory. Archon’s UI and APIs are intended to streamline how developers interact with their agents, whether for exploratory coding, automated task execution, or integrated RAG workflows, helping reduce friction between manual coding tasks and AI-generated suggestions. -
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rust-by-practice
Challenging examples, exercises and projects
...Rather than simply listing Rust syntax or language features, it structures its content around progressively complex problems, each designed to illustrate a core Rust concept such as ownership, borrowing, lifetimes, traits, concurrency, zero-cost abstractions, and safe systems programming idioms. The repository aggregates explanations, example code, and interactive practice so that learners build both conceptual understanding and muscle memory writing idiomatic Rust. It’s especially valuable for developers transitioning from other languages who want to truly grok Rust’s unique safety model and performance mindset, because the exercises force you to confront common pitfalls and solutions firsthand. -
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Swift Guide
Swift Featured Projects in brain Mapping
SwiftGuide is a comprehensive, community-maintained guide to the Swift programming language, designed to serve as both a learning resource and a handy reference. It covers all major language aspects: syntax, control flow, functions, closures, generics, protocols, extensions, memory management, concurrency, and the standard library. Each topic typically includes clear explanations, annotated code snippets, and tips for best practices, helping readers understand both how features work and how to use them idiomatically. Over time, the guide has evolved alongside Swift itself, with updates to reflect new language releases, deprecations, and shifting patterns. ... -
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JMH Gradle Plugin
Integrates the JMH benchmarking framework with Gradle
...This reduces the manual effort of setting up JMH, making performance testing a natural part of the development cycle. The plugin is especially useful in projects where regression in execution speed or memory use must be carefully monitored. -
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Barter
Open-source Rust framework for building event-driven systems
Barter is an open-source, Rust-based ecosystem of libraries for building high-performance, event-driven algorithmic trading systems—covering live trading, paper trading, and backtesting. It is designed for safety, speed, and flexibility in quantitative finance workflows. Use mock MarketStream or Execution components to enable back-testing on a near-identical trading system as live-trading. Centralised cache-friendly state management system with O(1) constant lookups using indexed data... -
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ChatGLM.cpp
C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)
ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware. -
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Monitorix
Monitorix is a free, open source, lightweight system monitoring tool
Monitorix is a free, open source, lightweight system monitoring tool designed to monitor as many services and system resources as possible. It has been created to be used under production Linux/UNIX servers, but due to its simplicity and small size can be used on embedded devices as well. It consists mainly of two programs: a collector, called monitorix, which is a Perl daemon that is started automatically like any other system service, and a CGI script called monitorix.cgi. Monitorix... -
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cognee
Deterministic LLMs Outputs for AI Applications and AI Agents
Cognee implements scalable, modular data pipelines that allow for creating the LLM-enriched data layer using graph and vector stores. Cognee acts a semantic memory layer, unveiling hidden connections within your data and infusing it with your company's language and principles. This self-optimizing process ensures ultra-relevant, personalized, and contextually aware LLM retrievals. Any kind of data works; unstructured text or raw media files, PDFs, tables, presentations, JSON files, and so many more. ... -
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whisper-timestamped
Multilingual Automatic Speech Recognition with word-level timestamps
Multilingual Automatic Speech Recognition with word-level timestamps and confidence. Whisper is a set of multi-lingual, robust speech recognition models trained by OpenAI that achieve state-of-the-art results in many languages. Whisper models were trained to predict approximate timestamps on speech segments (most of the time with 1-second accuracy), but they cannot originally predict word timestamps. This repository proposes an implementation to predict word timestamps and provide a more... -
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Spice.ai OSS
A self-hostable CDN for databases
...Spice makes it easy and fast to query data from one or more sources using SQL. You can co-locate a managed dataset with your application or machine learning model, and accelerate it with Arrow in-memory, SQLite/DuckDB, or with attached PostgreSQL for fast, high-concurrency, low-latency queries. Accelerated engines give you flexibility and control over query cost and performance. -
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Core ML Tools
Core ML tools contain supporting tools for Core ML model conversion
...Your app uses Core ML APIs and user data to make predictions, and to fine-tune models, all on the user’s device. Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption. Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive.