Search Results for "jack-graph" - Page 7
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Showing 1665 open source projects for "jack-graph"
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CGraph
A general, three-party dependency-free, cross-platform
CGraph is a high-performance, cross-platform Directed Acyclic Graph (DAG) framework implemented in pure C++ with no third-party dependencies, designed for building complex task pipelines and parallel execution workflows. It allows developers to model computational processes as graph structures, where nodes represent tasks and edges define dependencies, enabling efficient scheduling and execution. -
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Apache Hamilton
Helps data scientists define testable self-documenting dataflows
Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph representing the pipeline, allowing the system to execute transformations in the correct order. This approach encourages modular, testable, and maintainable data pipelines because each transformation is isolated and easily unit tested. ... -
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Kodezi Chronos
Kodezi Chronos is a debugging-first language model
...Unlike general-purpose language models that focus primarily on code generation, Chronos is built to diagnose and repair bugs by analyzing complex relationships across files within a codebase. The project introduces architectural techniques such as Adaptive Graph-Guided Retrieval, which allows the system to navigate large repositories and retrieve relevant debugging information from multiple sources. Another component, Persistent Debug Memory, allows the system to learn patterns from past debugging sessions and apply that knowledge to future problems. The repository mainly contains research documentation, evaluation benchmarks, and experimental frameworks rather than the full proprietary model implementation. -
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Koito
Koito is a modern, themeable scrobbler
...It combines concepts from wikis, Zettelkasten systems, and modern note apps by letting you create interconnected notes, bidirectional links, and structured collections that mirror how knowledge naturally grows. The interface supports markdown editing with live preview, tag management, and graph-style browsing so you can explore relationships between pages instead of treating documents as isolated files. Because Koito is self-hosted, you retain ownership of your data and can integrate authentication systems for team access control. It also includes collaboration features like real-time editing or change history, making it suitable for team documentation, research projects, and personal knowledge management alike. -
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5
Beads
A memory upgrade for your coding agent
Beads is an open-source project providing a distributed, structured memory system for AI coding agents, replacing ad-hoc text plans with a git-backed graph that represents tasks, dependencies, and progress in a persistent, queryable format. Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. ... -
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ArcticDB
ArcticDB is a high performance, serverless DataFrame database
Built for the modern Python Data Science ecosystem, ArcticDB transforms your ability to handle complex real-world data with an Incredibly fast proven Petabyte scale. ArcticDB is designed with quant users in mind. It allows you to self-manage your data leveraging your preferred infrastructure. Giving you the keys to protect your most valuable asset. Supports large concurrent writes to many tables ensuring datasets can be onboarded fast and in the most convenient format. Scale-out architecture... -
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AtomicServer
An open source headless CMS / real-time database
Create, share, fetch, and model Atomic Data! AtomicServer is a lightweight, yet powerful CMS / Graph Database. Atomic Data is a modular specification for sharing, modifying, and modeling graph data. It combines the ease of use of JSON, the connectivity of RDF (linked data), and the reliability of type-safety. Atomic Data uses links to connect pieces of data, and therefore makes it easier to connect datasets to each other - even when these datasets exist on separate machines. -
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HASH
The best way to use and work with blocks
...HASH is a platform for decision-making, which helps you integrate, understand and use data in a variety of different ways. HASH does this by combining various different powerful tools together into one simple interface. These range from data pipelines and a graph database, through to an all-in-one workspace, no-code tool builder, and agent-based simulation engine. These exist at varying stages of maturity, and while some are polished, not all are ready for real-world production use. You can read more about our big-picture vision at hash.dev -
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Easy Random
The simple, stupid random Java beans/records generator
Easy Random is a library that generates random Java objects. You can think of it as an ObjectMother for the JVM. The java.util.Random API provides 7 methods to generate random data: nextInt(), nextLong(), nextDouble(), nextFloat(), nextBytes(), nextBoolean() and nextGaussian(). What if you need to generate a random String? Or say a random instance of your domain object? Easy Random provides the EasyRandom API that extends java.util.Random with a method called nextObject(Class type). This... -
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Graphene-Django
Integrate GraphQL into your Django project
...Our primary focus in this tutorial is to give a good understanding of how to connect models from Django ORM to Graphene object types. GraphQL presents your objects to the world as a graph structure rather than a more hierarchical structure to which you may be accustomed. In order to create this representation, Graphene needs to know about each type of object which will appear in the graph. -
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Wire
Compile-time dependency injection for Go
...Dependency injection tools like Wire aim to simplify the management of initialization code. You describe your services and their dependencies, either as code or as configuration, then Wire processes the resulting graph to figure out ordering and how to pass each service what it needs. -
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QjackCtl
JACK Audio Connection Kit Qt GUI Interface
JACK Audio Connection Kit - Qt GUI Interface: A simple Qt application to control the JACK server daemon. Written in C++ around the Qt framework for X11, most exclusively using Qt Designer. Provides a simple GUI dialog for setting several JACK server p -
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PipesHub
Workplace AI platform for enterprise search and workflow automation
PipesHub AI is an open-source, enterprise-grade workplace AI platform designed to unify search, knowledge management, and workflow automation across distributed organizational systems. It connects to a wide range of enterprise tools such as Google Workspace, Slack, Jira, and Confluence, aggregating data into a centralized knowledge layer that can be queried using natural language. The platform uses knowledge graphs and ranking algorithms to provide context-rich answers along with traceable... -
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Vald
Vald. A Highly Scalable Distributed Vector Search Engine
...Vald has automatic vector indexing and index backup, and horizontal scaling which is made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed. Usually, the graph requires locking during indexing, which causes stop-the-world. But Vald uses distributed index graphs so it continues to work during indexing. Vald implements it's own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery. -
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Terraform
Safely and predictably create, change, and improve infrastructure
Terraform is an open source tool that allows you to use infrastructure as code to provision and manage any cloud, infrastructure or service. It codifies APIs into declarative configuration files, which can then be shared among team members, reviewed, applied, edited and versioned. -
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Awesome-FL
Comprehensive and timely academic information on federated learning
A “awesome” curated list of federated learning (FL) academic resources: research papers, tools, frameworks, datasets, tutorials, and workshops. A hub for FL knowledge maintained by the academic community. -
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css-doodle
A web component for drawing patterns with CSS
...The component will generate a grid of divs by the rules (plain CSS) inside it. You can easily manipulate those cells using CSS to come up with a graphic pattern or an animated graph. The limit is the limit of CSS itself. Download the latest version or include it directly from a CDN. You can also install it from npm and import the module in JavaScript. The syntax of CSS-doodle is based on CSS and provides several extra utility functions and shorthand properties. The number of rows and columns in the grid is defined by the grid attribute on the element, ranging from 1 to 64. ... -
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agentmemory
#1 Persistent memory for AI coding agents
...The system automatically records what agents do, compresses that activity into searchable memory, and injects relevant context when a new coding session begins. It combines keyword search, vector search, confidence scoring, lifecycle management, and knowledge graph querying to retrieve useful memories without overloading the context window. The project also includes hooks, an API, import and export support, audit trails, and team sharing workflows. Overall, agentmemory is designed to make AI coding assistants more continuous, context-aware, and useful across long-running software projects. -
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trustfall
A query engine for any combination of data sources
trustfall is a flexible and extensible query engine designed to retrieve and analyze data from a wide variety of heterogeneous sources using a unified query language. It allows developers to query APIs, databases, files, and even structured outputs from AI systems as if they were part of a single, cohesive graph-based dataset. The system is inspired by GraphQL-like paradigms, enabling expressive and composable queries that can traverse relationships across different data domains seamlessly. Trustfall operates by parsing, compiling, and executing queries through adapters that connect to specific data sources, making it highly modular and adaptable to new environments. ... -
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OpenSage
An agent framework that enables AI to create their own agent
...The framework is built around the concept of an Agent Development Kit (ADK), providing structured components for memory, reasoning, and task decomposition while allowing agents to iteratively improve their own design. A key innovation is its hierarchical and graph-based memory system, which enables agents to store, retrieve, and organize information across complex workflows with improved efficiency and contextual awareness. -
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OpenMemory
Local long-term memory engine for AI apps with persistent storage
...It enables developers to give otherwise stateless models a structured memory layer that can store, retrieve, and manage contextual information over time. OpenMemory is built around a hierarchical memory architecture that organizes data into semantic sectors and connects them through a graph-based structure for efficient retrieval. It supports multiple embedding strategies, including synthetic and semantic embeddings, allowing developers to balance speed and accuracy depending on their use case. OpenMemory integrates with various AI tools and environments, offering SDKs and APIs that simplify adding memory capabilities to applications. ... -
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Agent Framework
Framework for building, orchestrating, and deploying AI agents
...It includes tools and abstractions for constructing simple conversational agents as well as complex workflows where multiple agents collaborate to complete tasks. Microsoft Agent Framework supports graph-based orchestration that enables developers to connect agents, functions, and tools into structured workflows capable of handling multi-step processes. It also includes components such as agent sessions for managing state, context providers for maintaining memory, and middleware for intercepting and extending agent behavior. Developers can integrate external tools and services so that agents can execute actions beyond text generation. -
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LangGraph.js
Framework to build resilient language agents as graphs
LangGraphJS is a JavaScript framework designed to build stateful AI applications and autonomous agents using graph-based execution models. Developed as part of the LangChain ecosystem, the framework allows developers to represent complex AI workflows as graphs where nodes represent tasks and edges define the flow of execution. This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. -
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OmAgent
Build multimodal language agents for fast prototype and production
...Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker coordination, and node optimization behind the scenes. Its architecture uses a graph-based workflow engine where tasks are represented as nodes in a directed workflow, enabling modular composition of complex reasoning pipelines. The framework also includes support for various reasoning strategies commonly used in language agents, such as chain-of-thought prompting, self-consistency reasoning, and ReAct-style decision loops. -
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Nano-vLLM
A lightweight vLLM implementation built from scratch
...The project recreates the core functionality of vLLM in a simplified architecture written in approximately a thousand lines of Python, making it easier for developers and researchers to understand how modern LLM inference systems work. Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. The engine is intended primarily for educational use, experimentation, and lightweight deployments where a full production-grade inference stack may be unnecessary. Its API closely mirrors that of the original vLLM framework, allowing developers familiar with vLLM to adopt the tool with minimal changes.
