Search Results for "shared memory allocator"
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Showing 14 open source projects for "shared memory allocator"
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Cipher
Cipher is an opensource memory layer designed for coding agents
Cipher is an open‑source memory layer designed for AI coding agents. It records both high‑level conceptual memory (design decisions, business logic) and agent reasoning steps so AI assistants can maintain context seamlessly across IDEs or sessions. Built to integrate with MCP-compatible environments (e.g. Cursor, Windsurf, Gemini CLI, Claude Desktop, AWS Kiro), it enables team-aware workspace memory sharing and real-time collaboration. -
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Vellum
A personal AI assistant that evolves with you
Vellum is an open-source personal AI assistant platform designed to function as a persistent, autonomous digital companion across desktop and messaging environments. Unlike traditional chatbot interfaces, the project focuses on long-term memory, identity, proactive behavior, and real-world tool usage, enabling assistants to evolve alongside the user over time. The system integrates with macOS, Telegram, Slack, SMS, and additional communication channels while maintaining shared memory and context across platforms. Its architecture combines local-first storage, tool orchestration, sandboxed execution, and extensible workflow automation to allow assistants to read files, manage schedules, send messages, browse the web, and control applications. ... -
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FramePack
Lets make video diffusion practical
FramePack explores compact representations for sequences of image frames, targeting tasks where many near-duplicate frames carry redundant information. The idea is to “pack” frames by detecting shared structure and storing differences efficiently, which can accelerate training or inference on video-like data. By reducing I/O and memory bandwidth, datasets become lighter to load while models still see the essential temporal variation. The repository demonstrates both packing and unpacking steps, making it straightforward to integrate into preprocessing pipelines. ... -
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CogVideo
Text and image to video generation: CogVideoX and CogVideo
CogVideo is an open-source family of advanced video generation models that can create videos from text, images, or existing video inputs. Built on large-scale Transformer and diffusion architectures, it enables multimodal generation across text-to-video, image-to-video, and video continuation tasks. The latest CogVideoX models offer higher resolution outputs, longer video durations, and improved controllability through prompt engineering. The project includes tools for inference,... -
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Oh My codeX (OMX)
Your codex is not alone. Add hooks, agent teams, HUDs
Oh My codeX (OMX) is a multi-agent orchestration layer designed to extend the capabilities of OpenAI Codex CLI by introducing structured teamwork, automation, and advanced workflow management. It addresses limitations in the base Codex environment, such as the lack of hooks, agent coordination, and persistent execution, by layering a shell-based system that enables richer interaction patterns. The project transforms a single AI coding assistant into a coordinated system of specialized agents... -
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Qwen
The official repo of Qwen chat & pretrained large language model
Qwen is a series of large language models developed by Alibaba Cloud, consisting of various pretrained versions like Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B. These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a... -
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IronClaw
IronClaw is OpenClaw inspired but focused on privacy & security
...With dynamic tool building, persistent memory, and background automation, IronClaw is built to securely expand and adapt to your personal and professional workflows. -
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Context Hub
Makes coding agents get smarter with every task
Context Hub is a curated documentation system built to help coding agents write more accurate code. It gives agents versioned, language-specific reference material instead of forcing them to rely on noisy web searches or stale model memory. The project includes a CLI called chub that agents can use to search for available docs, fetch specific API guidance, and request only the files they need. It also supports local annotations, allowing an agent to remember project-specific notes, pitfalls, or workarounds across future sessions. Feedback can be sent back to maintainers so shared documentation improves over time. ... -
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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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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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Tiny CUDA Neural Networks
Lightning fast C++/CUDA neural network framework
...We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared memory in its default configuration. It will likely only work on an RTX 3090, an RTX 2080 Ti, or high-end enterprise GPUs. Lower-end cards must reduce the n_neurons parameter or use the CutlassMLP (better compatibility but slower) instead. tiny-cuda-nn comes with a PyTorch extension that allows using the fast MLPs and input encodings from within a Python context. ... -
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Punica
Serving multiple LoRA finetuned LLM as one
Punica is a system designed to efficiently serve multiple LoRA-fine-tuned large language models within a shared GPU environment. LoRA is a parameter-efficient fine-tuning method that allows developers to adapt large pretrained models to specific tasks by adding lightweight adapter layers rather than retraining the entire model. Punica introduces a serving architecture that allows multiple LoRA adapters to share the same base model during inference, significantly reducing memory consumption and computational overhead. ... -
13
popt4jlib
Parallel Optimization Library for Java
...A fast parallel implementation of the network simplex method, and some full-fledged parallel/distributed MIP solvers will be added in the next version. In general, emphasis is given in improving the efficiency of the algorithms in shared-memory models via java threads, since multi-core machines are so wide-spread today. -
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Blackboard implements a lightweight, portable tuple space suitable for multi-agent system and distributed component design. Supports implicit invocation via content-filtered asynchronous events, blocking call semantics, and shared memory messaging.
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