Search Results for "jack-graph" - Page 8
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Showing 1665 open source projects for "jack-graph"
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grepai
Semantic Search & Call Graphs for AI Agents
...It builds a semantic index of a project using vector embeddings, enabling natural language queries like “authentication logic” to return contextually relevant functions and modules even when naming differs dramatically, making code exploration far more intuitive. In addition to semantic search, grepai offers call graph tracing so developers can understand which functions call or are called by others, aiding impact analysis and confident refactoring. Because it runs 100 % locally, your codebase never leaves your machine, preserving privacy and security while supporting AI agents and custom integrations. -
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pprof
pprof is a tool for visualization and analysis of profiling data
pprof is a profiling visualization and analysis tool that ingests profiles in the profile.proto format and generates human-readable and graph-based reports. It supports multiple profile types (CPU, heap, allocations, contention, etc.) and can present data as text tables, call graphs (via Graphviz/dot), flame graphs, and interactive web UIs. The tool helps developers find hot paths, quantify resource usage, and compare profiles across runs to validate performance changes. -
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Radar
The missing open source Kubernetes UI
Radar is a modern, local-first Kubernetes visibility and management tool that provides a unified interface for exploring, debugging, and operating cluster resources in real time. It runs as a single lightweight binary with no required cloud account, connecting directly to clusters using existing kubeconfig credentials. The platform combines multiple observability features such as topology graphs, event timelines, and traffic visualization into one cohesive UI. It allows users to inspect... -
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Foam
A personal knowledge management and sharing system for VSCode
Foam is a personal knowledge management and sharing system inspired by Roam Research, built on Visual Studio Code and GitHub. You can use Foam for organizing your research, keeping re-discoverable notes, writing long-form content and, optionally, publishing it to the web. Foam is free, open source, and extremely extensible to suit your personal workflow. You own the information you create with Foam, and you're free to share it and collaborate on it with anyone you want. -
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Raven
Self-evolving multi-agent ecosystem for all-domain collaboration
Raven is a pre-alpha multi-agent orchestration system that coordinates specialized agents through one persistent workspace. It ships with built-in agents for research, coding, visual design, and supervision of long-running jobs. Raven can also orchestrate third-party agents such as Claude Code, Codex, OpenCode, GitHub Copilot, Pi, and others. Its runtime is powered by EverOS, allowing user context, agent experience, and reusable knowledge to persist across sessions. The system can generate... -
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Rat
Compose shell commands to build interactive terminal applications
...Rather than reimplementing features, it delegates work to existing tools: a shell command generates output, a pager displays it, and “annotators” attach actions to recognized patterns. For example, a mode might run git log --graph and annotate commit hashes so hitting a key can open a diff, check out a branch, or run a follow-up command. Another mode could list files or search results and let you act on selected lines, chaining shell operations together. The design encourages small, composable behaviors that feel like browsing within structured terminal output, while retaining the power of the underlying CLI. ... -
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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. -
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Git Extensions
UI tool for managing git repositories
Git Extensions is a standalone UI tool for managing Git repositories. It also integrates with Windows Explorer and Microsoft Visual Studio (2015/2017/2019). The full commit history can be browsed. Branches are shown using a graph which highlights commits that are included in the current revision. Explore the history of single files. Renamed and moved files are matched and shown in a single history. You do not need to mark files as renamed/moved. The system detects renamed files automatically. Find the last person that edited a specific part of a file. Double click on the line shows the commit and allows you to drill-down to other files. ... -
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Lossless Claw
LCM (Lossless Context Management) plugin for OpenClaw
...Instead of relying on traditional sliding-window truncation or lossy summarization, it introduces a lossless architecture that preserves all historical messages while maintaining usable context within token limits. The system stores every interaction in a persistent database and incrementally summarizes older content into a hierarchical directed acyclic graph, allowing efficient compression without discarding information. This structure enables agents to dynamically reconstruct detailed context by expanding summaries when needed, effectively simulating perfect long-term memory. -
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conky
Light-weight system monitor for X
...A plethora of OS stats (uname, uptime, CPU usage, mem usage, disk usage, "top" like process stats, and network monitoring, just to name a few). Conky can display information either as text, or using simple progress bars and graph widgets, with different fonts and colours. -
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Koila
Prevent PyTorch's `CUDA error: out of memory` in just 1 line of code
...The library introduces a lazy evaluation mechanism that delays computation until it is actually required, allowing the framework to better estimate the memory requirements of a model before execution. By building a computational graph first and executing operations only when necessary, koila reduces the risk of running out of GPU memory during the forward pass of neural network training. This approach enables developers to experiment with larger batch sizes and more complex architectures while maintaining stable training behavior. The system acts as a thin wrapper around PyTorch tensors and operations, meaning that it integrates easily into existing PyTorch code without requiring major changes to model implementations. ... -
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openbench
Provider-agnostic, open-source evaluation infrastructure
openbench is an open-source, provider-agnostic evaluation infrastructure designed to run standardized, reproducible benchmarks on large language models (LLMs), enabling fair comparison across different model providers. It bundles dozens of evaluation suites — covering knowledge, reasoning, math, code, science, reading comprehension, long-context recall, graph reasoning, and more — so users don’t need to assemble disparate datasets themselves. With a simple CLI interface (e.g. bench eval <benchmark> --model <model-id>), you can quickly evaluate any model supported by Groq or other providers (OpenAI, Anthropic, HuggingFace, local models, etc.). openbench also supports private/local evaluations: you can integrate your own custom benchmarks or data (e.g. internal test suites, domain-specific tasks) to evaluate models in a privacy-preserving way. -
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Greenmask
PostgreSQL database anonymization and synthetic data generation tool
Greenmask is a powerful open-source utility that is designed for logical database backup dumping, obfuscation, and restoration. It offers extensive functionality for backup, anonymization, and data masking. Greenmask is written in pure Go and includes ported PostgreSQL libraries that allows for platform independence. This tool is stateless and does not require any changes to your database schema. It is designed to be highly customizable and backward-compatible with existing PostgreSQL... -
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gitoxide
An idiomatic, lean, fast & safe pure Rust implementation of Git
An idiomatic, lean, fast & safe pure Rust implementation of Git. gix is a command-line interface (CLI) to access git repositories. It's written to optimize the user experience and perform as well or better than the canonical implementation. Furthermore, it provides an easy and safe to use API in the form of various small crates for implementing your own tools in a breeze. Please see 'Development Status' for a listing of all crates and their capabilities. Please note that all functionality... -
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DGL
Python package built to ease deep learning on graph
...We want to make it easy to implement graph neural networks model family. We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks. -
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Logseq
A privacy-first, open-source platform for knowledge management
...No data lock-in, no proprietary formats, you can edit the same Markdown/Org-mode file with any tools at the same time. Connect your [[ideas]] and [[thoughts]] with Logseq. Your knowledge graph grows just as your brain generates and connects neurons from new knowledge and ideas. -
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JavaScript Algo and Data Structures
Algorithms and data structures implemented in JavaScript
...Each algorithm includes explanations, complexity analysis, and references for further reading, making it both a coding resource and a study guide. The repository covers topics such as sorting, searching, graph algorithms, cryptography, and data structures like linked lists, stacks, and queues. It is designed to help developers understand algorithm fundamentals and practice problem-solving with JavaScript. Multilingual README files make the resource accessible to a global audience. With strong community engagement, it is one of the most popular algorithm repositories on GitHub. -
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Bevy
A refreshingly simple data-driven game engine built in Rust
A refreshingly simple data-driven game engine built in Rust. All engine and game logic uses Bevy ECS, a custom Entity Component System. Massively Parallel and Cache-Friendly. The fastest ECS according to some benchmarks. Components are Rust structs, Systems are Rust functions. Queries, Global Resources, Local Resources, Change Detection, Lock-Free Parallel Scheduler. Bevy is still in the very early stages of development. APIs can and will change (now is the time to make suggestions!).... -
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Webgrind
Xdebug Profiling Web Frontend in PHP
Webgrind is an Xdebug profiling web frontend for PHP, offering a lightweight and easy-to-use interface to visualize profiling data. It helps developers identify performance bottlenecks in their applications by displaying function call statistics, memory usage, and execution time. Webgrind is ideal for performance analysis and optimization of PHP code, providing insights into code efficiency. -
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Aden Hive
Outcome driven agent development framework that evolves
Hive is an open-source agent development framework that helps developers build autonomous, reliable, self-improving AI agents by letting them describe goals in ordinary natural language instead of hand-coding detailed workflows. Rather than manually defining execution graphs, Hive’s coding agent generates the agent graph, connection code, and test cases based on your high-level objectives, enabling outcome-driven agent creation that fits real business processes. Once deployed, agents can capture failure data, evolve automatically to meet their success criteria, and redeploy without constant manual intervention, delivering continual improvement over time. ... -
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Mastra
The TypeScript AI agent framework
...Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. When explicit control is needed, Mastra’s workflow engine uses a graph-style API (.then(), .branch(), .parallel()) to orchestrate multi-step processes. -
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Controllable-RAG-Agent
This repository provides an advanced RAG
Controllable-RAG-Agent is an advanced Retrieval-Augmented Generation (RAG) system designed specifically for complex, multi-step question answering over your own documents. Instead of relying solely on simple semantic search, it builds a deterministic control graph that acts as the “brain” of the agent, orchestrating planning, retrieval, reasoning, and verification across many steps. 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. ... -
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ONNX.jl
Read ONNX graphs in Julia
ONNX.jl is in the process of a total reconstruction and currently supports saving & loading graphs as a Umlaut.Tape. When possible, functions from NNlib or the standard library are used, but no conversion to Flux is implemented yet. See resnet18.jl for a practical example of graph loading. -
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Concourse
Concourse is a container-based continuous thing-doer written in Go
...The visualization provides a "gut check" feedback loop: if it looks wrong, it probably is wrong. Jobs can depend on other jobs by configuring passed constraints. The resulting chain of jobs and resources is a dependency graph that continuously pushes your project forward, from source code to production. -
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AI Maestro
Give AI Agents superpowers: memory search, code graph queries
AI Maestro is a framework designed to orchestrate AI workflows and coordinate multiple components into cohesive systems. It focuses on enabling structured interaction between different AI modules, allowing them to collaborate on complex tasks. The system emphasizes modular design, enabling developers to build workflows by combining independent components. It supports automation, allowing tasks to be executed with minimal manual intervention. The framework is designed to be flexible,...
