Search Results for "graph" - Page 6
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Showing 1441 open source projects for "graph"
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Clerk
Moldable Live Programming for Clojure
...Clerk notebooks are either regular Clojure namespaces (interspersed with markdown comments) or regular markdown files (interspersed with Clojure code fences). This also means Clerk notebooks are meant to be stored in source control. Clerk notebooks always evaluate from top to bottom. Clerk builds a dependency graph of Clojure vars and only recomputes the needed changes to keep the feedback loop fast. -
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Angel
A Flexible and Powerful Parameter Server for large-scale ML
Angel is a high-performance distributed machine learning and graph computing platform based on the philosophy of Parameter Server. It is tuned for performance with big data from Tencent and has a wide range of applicability and stability, demonstrating an increasing advantage in handling higher-dimension models. Angel is jointly developed by Tencent and Peking University, taking account of both high availability in industry and innovation in academia. -
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Graphiti
Stylish Graph APIs
Graphiti makes RESTful Resources a first-class concept. This enables reading and writing a graph of data in a single request, a schema with a backward-compatible guarantee, end-to-end integration test patterns, seamless microservices and much more. If you just want to get a 5-minute feel for Graphiti code and all the functionality that comes out-of-the-box, head to Quickstart. If you want a birds-eye view of Graphiti’s moving pieces, check out our Intro to Graphiti video or The Lifecycle of a Graphiti Request. ... -
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Trial
A modular Common Lisp game engine
Trial is a real-time, 3D game engine written in Common Lisp, offering a modern and modular approach to game development using the expressive power of Lisp. Built on top of the cepl graphics framework, Trial combines live-coding capabilities with powerful abstractions for building simulations, scenes, and interactive applications. It emphasizes flexibility, clarity, and developer control, making it an appealing choice for experimental games, visualizations, and research projects that benefit... -
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pgvector
Open-source vector similarity search for Postgres
...You can add an index to use approximate nearest neighbor search, which trades some recall for speed. Unlike typical indexes, you will see different results for queries after adding an approximate index. An HNSW index creates a multilayer graph. It has better query performance than IVFFlat (in terms of speed-recall tradeoff), but has slower build times and uses more memory. Also, an index can be created without any data in the table since there isn’t a training step like IVFFlat. -
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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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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. -
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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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React Native Audio API
High-performance audio engine for react-native
...It is designed for apps that need real-time control over audio playback, recording, routing, effects, and signal processing. Developers can build consistent audio behavior across iOS, Android, and the web without rewriting separate logic for each platform. The library supports audio graph concepts, making it useful for music tools, voice apps, sound effects, games, audio editors, and interactive media. It also includes recording-to-file capabilities and playback or recording notifications. Its main value is giving React Native developers a flexible audio engine for advanced sound experiences. -
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LLM Tornado
The .NET library to build AI agents with 30+ built-in connectors
...It provides a unified interface that connects to more than 30 AI providers and vector databases, allowing developers to switch between models and services without rewriting application logic. The framework introduces a powerful orchestration system based on graph-like structures, where agents, tasks, and transitions can be defined and executed in parallel or sequential flows. It supports multimodal inputs and outputs, including text, images, audio, and documents, making it suitable for a wide range of AI applications. LLMTornado also integrates advanced protocols such as Model Context Protocol and agent-to-agent communication, enabling complex interactions between systems. ... -
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SynaBun
Persistent vector memory for AI assistants
...The system integrates tightly with developer workflows by running alongside tools like Claude Code, enabling automatic memory capture, retrieval, and contextual augmentation through lifecycle hooks and commands. One of its defining characteristics is its Neural Interface, a browser-based 3D visualization that represents stored memories as nodes in an interactive graph, allowing users to explore relationships, edit entries, and manage knowledge visually. -
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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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Autonomous Agents
Autonomous Agents (LLMs) research papers. Updated Daily
...One of the central concepts explored in the repository is the integration of different sensory modalities using advanced machine learning techniques such as Feature-wise Linear Modulation and graph-based attention mechanisms. These methods allow agents to combine visual and geometric information while maintaining awareness of the spatial relationships between agents and objects. -
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Aix-DB
Based on the LangChain/LangGraph framework
...Its architecture includes multiple layers such as a web interface, API gateway, AI service layer, and data storage layer that support relational databases, vector stores, graph databases, and file systems. -
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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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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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AdminJS
AdminJS is an admin panel for apps written in node.js
Our system leverages AI to effortlessly generate text. Simplify your product database management with just one click. The out-of-the-box version of AdminJS is pretty powerful, but its in-depth customizability is where it really shines. With a basic knowledge of React and Node.js, you can change nearly every behavior of your admin panel. With AdminJS you can Create, Read, Update, and Delete all of your resources, no matter where they come from. Thanks to the tight integration with your... -
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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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ONNX Runtime
ONNX Runtime: cross-platform, high performance ML inferencing
...ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Support for a variety of frameworks, operating systems and hardware platforms. Built-in optimizations that deliver up to 17X faster inferencing and up to 1.4X faster training.
