Search Results for "context" - Page 13
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Showing 1920 open source projects for "context"
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Shadcn UI v4 MCP Server
A mcp server to allow LLMS gain context about shadcn ui component
Shadcn UI v4 MCP Server is a Model Context Protocol server that enables AI assistants to access, retrieve, and utilize shadcn/ui component libraries within development workflows, effectively bridging UI component systems with AI-driven coding tools. It provides structured access to component source code, demos, metadata, and reusable UI blocks, allowing AI agents to generate accurate and production-ready interface implementations. -
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RAG from Scratch
Demystify RAG by building it from scratch
...Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into language model prompts. Each example is written with detailed explanations so that developers can understand the internal mechanics of semantic search and context-aware language generation. The repository emphasizes learning through direct implementation, allowing users to see how each component of the RAG architecture functions independently. -
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TimesFM
Pretrained time-series foundation model developed by Google Research
...The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to use different backends such as Torch or Flax depending on your environment and performance needs. Newer releases emphasize expanded context handling and more flexible forecasting outputs, including quantile forecasting so users can get uncertainty estimates rather than only point predictions. The repository also documents how model versions evolved, with newer variants focusing on efficiency and longer context windows while maintaining forecasting quality. -
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NagaAgent
A simple yet powerful agent framework for personal assistants
...Rather than being a simple chatbot, NagaAgent emphasizes persistent thought cycles, context retention, and the ability to decompose complex tasks into smaller executable units, earning it a place in research explorations of agent design. Its architecture facilitates extensibility, allowing developers to plug in different reasoning modules or knowledge sources depending on the domain of use. -
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Advanced RAG Techniques
Advanced techniques for RAG systems
Advanced RAG Techniques is a comprehensive collection of tutorials and implementations focused on advanced Retrieval-Augmented Generation (RAG) systems. It is designed to help practitioners move beyond basic RAG setups and explore techniques that improve retrieval quality, context construction, and answer robustness. The repository organizes techniques into categories such as foundational RAG, query enhancement, context enrichment, and advanced retrieval, making it easier to navigate specific areas of interest. It includes hands-on Jupyter notebooks and runnable scripts that show how to implement ideas like optimizing chunk sizes, proposition chunking, HyDE/HyPE query transformations, fusion retrieval, reranking, and ensemble retrieval. ... -
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ArXiv MCP Server
A Model Context Protocol server for searching and analyzing arXiv
arxiv-mcp-server bridges AI assistants and the arXiv repository through a clean MCP interface, enabling search, metadata retrieval, and content access without bespoke scraping. With simple tools like “search” and “fetch,” an agent can find papers, pull abstracts, and download PDFs for downstream summarization or analysis. The project includes packaging and CI to publish to PyPI, plus tests and linting for reliability. Issue threads show feature requests such as extracting embedded LaTeX and... -
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websocat
Command-line client for WebSockets, like netcat (or curl)
Netcat, curl and socat for WebSockets. Command-line client for WebSockets, like netcat (or curl) for ws:// with advanced socat-like functions. Connecting to and serving WebSockets from the command line. Executing external program and making it communicate to WebSocket using stdin/stdout. Text and binary modes, converting between lines (or null-terminated records) and messages. Inetd mode, UNIX sockets (including abstract namespaced on Linux). Integration with Nginx using TCP or UNIX sockets.... -
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Hexis
Git-backed control plane for AI-agent skills, tools, context, identity
...Everything is stored in a Git repository controlled by the organization, giving teams branches, history, change review, and rollback. Role-based access determines which skills, tools, and context each employee and agent can use. A built-in remote MCP server lets compatible clients such as Claude, ChatGPT, Cursor, and Cline connect to the same governed workspace. Tool manuals can include encrypted secrets without exposing credentials directly to agents. Proposed skill changes can move through owner approval before they become available to the wider team. ... -
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GSD Pi
Development system that enables agents to work for long periods
...It is built for developers who want an AI-assisted workflow that can handle structured tasks rather than only single chat prompts. The project emphasizes spec-driven development, context engineering, and longer autonomous work sessions. It helps users break ideas into plans, manage execution steps, verify results, and keep track of progress across a project. Because it runs as a command-line tool, it fits naturally into developer environments and repository-based workflows. Its main value is turning AI coding assistance into a more organized project system with planning, task management, and implementation support. -
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Docker Agent
AI Agent Builder and Runtime by Docker Engineering
...It is provider-agnostic, meaning it can integrate with multiple AI model providers such as OpenAI, Anthropic, and local inference engines, helping avoid vendor lock-in. cagent also supports the Model Context Protocol, enabling seamless integration with external tools, APIs, and services. -
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TarpC
An RPC framework for Rust with a focus on ease of use
...The framework is transport-agnostic: it commonly uses Tokio with serde-based codecs, but you can plug in your own framing and serialization. It bakes in RPC concerns such as deadlines, cancellation, and context propagation so production behavior is predictable under load. The programming model feels native—call methods on a client stub and await results—while the server side exposes clean concurrency primitives for handling many requests. Because the interface is just Rust code, refactoring and IDE tooling work naturally without an external IDL. -
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TeXworks
A simple interface for working with TeX documents
TeXworks is a free and simple working environment for authoring TeX (LaTeX, ConTeXt and XeTeX) documents. Inspired by Dick Koch's award-winning TeXShop program for Mac OS X, it makes entry into the TeX world easier for those using desktop operating systems other than OS X. It provides an integrated, easy-to-use environment for users on other platforms particularly GNU/Linux and Windows and features a clean, simple interface accessible to casual and non-technical users. -
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mirrord
Connect your local process and your cloud environment
mirrord lets developers run local processes in the context of their Kubernetes environment. It’s meant to provide the benefits of running your service on a cloud environment (e.g. staging) without actually going through the hassle of deploying it there, and without disrupting the environment by deploying untested code. It comes as a Visual Studio Code extension, an IntelliJ plugin, and a CLI tool. -
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Codex plugin for Claude Code
Use Codex from Claude Code to review code or delegate tasks
...The system emphasizes interoperability, allowing developers to plug Codex capabilities into broader agent ecosystems without rewriting core logic. It may also include mechanisms for managing execution context, permissions, and tool access, ensuring that generated code can be safely applied. This makes it particularly useful for complex development pipelines where multiple agents or tools need to collaborate. -
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HumanLayer
Open source IDE for orchestrating AI coding agents in large codebases
...HumanLayer integrates with modern AI models and coding assistants to automate tasks such as code research, planning, and implementation while maintaining a structured development process. HumanLayer introduces advanced context management techniques that help AI agents understand large repositories and operate effectively across multiple tasks. It also supports collaborative workflows where developers and AI agents can work together with human oversight and control. -
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WeKnora
LLM framework for document understanding and semantic retrieval
...It focuses on analyzing complex and heterogeneous documents by combining multiple processing stages such as multimodal document parsing, vector indexing, and intelligent retrieval. It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. This approach enables the system to provide more reliable answers by grounding model reasoning in the content of uploaded documents. WeKnora is designed with a modular architecture that separates components for document processing, search strategies, and model inference, allowing developers to customize or extend different parts of the pipeline. ... -
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Serena
Agent toolkit providing semantic retrieval and editing capabilities
Serena is a coding-focused agent toolkit that turns an LLM into a practical software-engineering agent with semantic retrieval and editing over real repositories. It operates as an MCP server (and other integrations), exposing IDE-like tools so agents can locate symbols, reason about code structure, make targeted edits, and validate changes. The toolkit is LLM-agnostic and framework-agnostic, positioning itself as a drop-in capability for different chat UIs, orchestrators, or custom agent... -
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Tencent-Hunyuan-Large
Open-source large language model family from Tencent Hunyuan
Tencent-Hunyuan-Large is the flagship open-source large language model family from Tencent Hunyuan, offering both pre-trained and instruct (fine-tuned) variants. It is designed with long-context capabilities, quantization support, and high performance on benchmarks across general reasoning, mathematics, language understanding, and Chinese / multilingual tasks. It aims to provide competitive capability with efficient deployment and inference. FP8 quantization support to reduce memory usage (~50%) while maintaining precision. ... -
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Kubescape
Kubescape is an open-source Kubernetes security platform for your IDE
...You can run scans via the CLI, or add the Kubescape Helm chart, which gives an in-depth view of what is going on in the cluster. Kubescape includes misconfiguration and vulnerability scanning as well as risk analysis and security compliance indicators. All results are presented in context and users get many cues on what to do based on scan results. Targeted at the DevSecOps practitioner or platform engineer, it offers an easy-to-use CLI interface, flexible output formats, and automated scanning capabilities. -
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kube-ps1
Kubernetes prompt info for bash and zsh
A script that lets you add the current Kubernetes context and namespace configured on kubectl to your Bash/Zsh prompt strings (i.e. the $PS1). The default prompt assumes you have the kubectl command-line utility installed. Official installation instructions and binaries are available. Blue was used for the default symbol to match the Kubernetes color as closely as possible. Red was chosen as the context name to stand out, and cyan for the namespace. 256 colors are available by specifying the numerical value as the variable argument. ... -
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MCP Server Simulator iOS IDB
Enables LLMs to interact with iOS simulators
The MCP Server Simulator for iOS IDB provides a bridge between Large Language Models (LLMs) and iOS simulators, offering comprehensive control through natural language commands. It enables operations such as launching apps, interacting with the UI, and retrieving device information, enhancing the capabilities of AI-driven development tools. -
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Gateway MCP
Universal MCP-Server for your Databases optimized for LLMs
Gateway is an MCP server that connects to structured databases like PostgreSQL, automatically analyzing schemas and data samples to generate optimized API structures. It leverages large language models (LLMs) during the discovery stage to produce API configurations, ensuring secure and efficient interactions between AI agents and databases. -
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PearAI
The Open Source AI-Powered Code Editor. A fork of VSCode and Continue
Ask questions or generate code with the context of your codebase for accurate results. This also works with choosing specific folders, online docs, terminal content, files, and more. PearAI can directly code in your files, and allow you to see diffs. Try CMD+I (CTRL+I on Windows). Here, we ask PearAI to help us add error handling and comments. Without writing a single line of code, we were able to make a new feature in an unfamiliar codebase: adding a documentation page to the PearAI landing page. ... -
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InfiAgent
Build your own Cowork, AI Scientist and other SoTA Agents
infiAgent is an open-source AI agent framework for building powerful, long-running autonomous agents capable of tackling complex tasks without collapsing under growing context or tool invocation histories. Designed as a “Multi-Level Agent” (MLA) system, it externalizes persistent state to the file system so that agents can operate over unlimited runtime without the need for token-intensive context compression, enabling workflows such as research paper drafting, experiments, coding, and document generation to run reliably. ... -
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Anyhow
Flexible concrete Error type built on std::error::Error
...Its primary goal is to make error handling in applications easy: instead of defining lots of custom error types, you can use anyhow::Error (or the alias anyhow::Result<T>) for fallible functions. The crate supports attaching context to errors, so you can convert a low-level error (like “file not found”) into one with richer diagnostics (“Failed to read instructions from path X”) using .context() or .with_context(). It supports downcasting (so you can inspect the underlying error type), and for recent versions of Rust, it will capture backtraces by default when the underlying error type doesn’t already. ...
