Search Results for "context" - Page 4
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Showing 1844 open source projects for "context"
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DeepSeek Coder V2
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models
...The repository provides updated model weights, evaluation results on benchmarks (e.g. HumanEval, MultiPL-E, APPS), and new inference/serving scripts. Compared to the original, DeepSeek-Coder-V2 likely incorporates improved context management, caching strategies, or enhanced infilling capabilities. The project aims to provide a more performant and reliable open-source alternative to closed-source code models, optimized for practical usage in code completion, infilling, and code understanding across English and Chinese codebases. -
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PyGPT
Open source personal AI Assistant for Linux, Windows and Mac
...PyGPT also adds access to the Internet for GPT via Google Custom Search API and Wikipedia API and includes voice synthesis using Microsoft Azure Text-to-Speech API. Moreover, the application has implemented context memory support, context storage, history of contexts, which can be restored at any time and e.g. continue the conversation from point in history, and also has a convenient and intuitive system of presets that allows you to quickly and pleasantly create and manage your prompts. Plugins support is also available. -
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SonarQube MCP Server
Model Context Protocol (MCP) server for SonarQube
The SonarQube MCP Server is a Rust implementation that integrates SonarQube's code quality analysis with AI assistants through the Model Context Protocol. It provides access to code metrics, issues, quality gate statuses, and project quality analysis. -
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MCP Shell Server
Shell command execution server implementing the Model Context Protocol
A secure shell command execution server implementing the Model Context Protocol (MCP), allowing remote execution of whitelisted shell commands with support for standard input. -
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Continue
Continue is the leading open-source AI code assistant
Continue is the leading open-source AI code assistant. You can connect any models and any context to build custom autocomplete and chat experiences inside VS Code and JetBrains. Continue enables you to use the right model for the job, whether it's open-source or commercial, running local or remote, and used for chat, autocomplete, or embeddings. And we provide numerous points of configuration so that you can customize the extension to fit into your existing workflows. -
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WebMCP
Enabling web apps to get accessed by AI agents
WebMCP is a proposed web standard that enables web applications to expose their functionality as JavaScript-based “tools” accessible to AI agents, browser assistants, and assistive technologies. It allows developers to define structured, natural-language-described functions directly in client-side code, effectively turning web pages into Model Context Protocol (MCP)-like servers running in the browser. Unlike backend integrations, WebMCP executes tools within the live web page, preserving shared context between the user, the agent, and the application UI. This approach supports collaborative, human-in-the-loop workflows where users can delegate tasks to agents while maintaining visibility and control. ... -
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GitHub CLI
GitHub’s official command line tool
...It allows developers to manage repositories, pull requests, issues, and releases without leaving the terminal. With simple commands, users can perform common GitHub tasks such as checking out pull requests or creating issues locally. The CLI helps eliminate context switching by enabling a full GitHub workflow from the terminal environment. Developers can also interact with the GitHub API to automate tasks and create custom scripts or command aliases. Additionally, GitHub CLI supports connections to both GitHub.com and GitHub Enterprise Server for enterprise-ready workflows. -
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Logfire MCP
The Logfire MCP Server is here
The Logfire MCP Server is a Model Context Protocol server that allows AI applications to access OpenTelemetry traces and metrics sent to Logfire. It enables retrieval and analysis of telemetry data, enhancing debugging and observability workflows. -
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Zettelkasten MCP
Implements the Zettelkasten knowledge management methodology
The Zettelkasten MCP Server is a Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology. It allows users to create, link, and manage notes, facilitating a structured and interconnected note-taking system. -
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DeepSeek R1
Open-source, high-performance AI model with advanced reasoning
...DeepSeek R1 offers unrestricted access for both commercial and academic use. The model employs a Mixture of Experts (MoE) architecture, comprising 671 billion total parameters with 37 billion active parameters per token, and supports a context length of up to 128,000 tokens. DeepSeek-R1's training regimen uniquely integrates large-scale reinforcement learning (RL) without relying on supervised fine-tuning, enabling the model to develop advanced reasoning capabilities. This approach has resulted in performance comparable to leading models like OpenAI's o1, while maintaining cost-efficiency. ... -
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Basic Memory
Persistent AI memory using local Markdown knowledge graphs
Basic Memory is an open source knowledge system that turns AI conversations into persistent, structured knowledge you control. Instead of losing context after each chat, it stores information as simple Markdown files on your device, allowing both you and AI to read and write to the same knowledge base. It uses the Model Context Protocol (MCP) so compatible AI tools can access, update, and build on your notes across sessions. Basic Memory creates a semantic knowledge graph by linking related ideas, making it easier to retrieve, expand, and connect information over time. ... -
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MCP Go
A Go implementation of the Model Context Protocol (MCP)
mcp-go is a Go implementation of the Model Context Protocol (MCP), designed to enable seamless integration between Large Language Model (LLM) applications and external data sources and tools. It abstracts the complexities of the protocol and server management, allowing developers to focus on building robust tools. The library is high-level and user-friendly, facilitating the development of MCP servers in Go. -
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Actors MCP Server
Model Context Protocol (MCP) Server for Apify's Actors
The Apify Actors MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to interact with Apify Actors. This integration allows AI models to utilize various web scraping and automation tools provided by Apify, facilitating tasks such as data extraction and web automation. -
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MCP Atlassian
MCP server that integrates Confluence and Jira
The MCP Atlassian server integrates Atlassian products like Confluence and Jira with the Model Context Protocol. It supports both Cloud and Server/Data Center deployments, enabling AI models to interact with these platforms securely. -
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NextChat
Light and Fast AI Assistant
NextChat is a browser extension that injects a floating ChatGPT-style chat sidebar into webpages, enabling instant AI assistance during web browsing. It supports context-aware chatting using page content, quick queries, and code assistance without opening a new tab. Developers appreciate its customization options—like hotkeys, themes, and engine selection—while everyday users enjoy handy tools for writing, summarizing, or brainstorming on the fly. -
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Mistral Vibe CLI
Minimal CLI coding agent by Mistral
Mistral Vibe is an AI-powered “vibe-coding” command-line interface (CLI) and coding-assistant framework built by Mistral AI to let developers write, refactor, search, and manage code through natural language and context-aware automation, rather than manual typing only. It aims to take developers out of repetitive boilerplate and let them stay “in the flow”: you can ask the tool to generate functions, refactor code, search across the codebase, manipulate files, commit changes via Git, or run commands — all from a unified CLI interface. Behind the scenes, it leverages Mistral’s coding-optimized LLM stack (including models tuned for code understanding and generation), with project-wide context awareness: it scans your file structure, Git status, and recent history to inform suggestions so that generated code aligns with existing context. -
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Vectorize MCP Server
Official Vectorize MCP Server
The Vectorize MCP Server is a Model Context Protocol server that integrates with Vectorize, offering advanced vector retrieval and text extraction capabilities. -
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Pi MCP Adapter
Token-efficient MCP adapter for Pi coding agent
Pi MCP Adapter connects Model Context Protocol servers to Pi while minimizing the context consumed by large tool schemas. Instead of loading every MCP tool definition into the conversation, it exposes a compact proxy and discovers individual capabilities only when they are needed. Servers are started lazily, which avoids running integrations that a session never uses. -
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jclasslib bytecode editor
Visualizes all aspects of compiled Java class files
...In addition, it contains a library that enables developers to read and write Java class files and bytecode. All constant pool entries can be edited. Referenced constant pool entries can directly be edited from the context menu that is shown by the "Edit" button of attributes and constant pool entries. Bytecode instructions can be edited from the context menu. -
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CodeMirror MCP
CodeMirror extension to hook up a Model Context Provider (MCP)
The codemirror-mcp project is a CodeMirror extension that integrates the Model Context Protocol (MCP) into the CodeMirror editor. This extension enhances the editor's capabilities by providing features such as autocompletion for resource mentions and prompt commands, as well as visual styling for these elements. It aims to streamline the user experience when working with MCP within the CodeMirror environment. -
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Kanwas
Shared context board for teams and agents
Kanwas is an open-source shared context board built for teams and AI agents working together in the same workspace. It gives people and agents a common canvas where documents, evidence, decisions, notes, tasks, embeds, and outputs can live side by side. Instead of scattering context across chats, documents, and disconnected tools, Kanwas turns messy collaborative work into a shared visual environment that both humans and agents can read and update. -
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MoBA
MoBA: Mixture of Block Attention for Long-Context LLMs
...The architecture adapts ideas from Mixture-of-Experts networks and applies them directly to the attention mechanism of transformer models. Instead of forcing each token to attend to every other token in the sequence, MoBA divides the context into blocks and dynamically routes queries to only the most relevant segments of information. This routing strategy reduces the computational cost associated with traditional attention while preserving performance on reasoning and long-context tasks. The approach allows language models to scale to significantly longer input contexts without the quadratic computational cost normally associated with transformer attention mechanisms. -
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Archon
The knowledge and task management backbone for AI coding assistants
Archon is an open-source “command center” designed to enhance AI coding assistant workflows by giving developers a centralized environment for knowledge management, context engineering, and task coordination across AI agents. It acts as a backend (including an MCP server) that allows different AI coding tools and assistants to share the same structured context, knowledge base, and task lists, improving consistency, productivity, and collaboration across multi-agent interactions. Users can import documentation, project files, and external knowledge so that assistants like Claude Code, Cursor, or other LLM-powered tools work with up-to-date, project-specific context rather than relying on limited prompt memory. ... -
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Telos
Telos is an open-sourced framework for creating Deep Context
Telos is an open-sourced framework designed to help individuals, organisations, or even planetary-scale entities articulate what they are about and how they intend to pursue their purpose. It encourages capturing a “context file” that outlines mission, goals, problems, strategies, KPIs, and other core components of meaningful work. The project emphasises creating deep context about matters that humans care about, making underlying intentions explicit rather than implicit. It is structured so that you can document your purpose, link it to your strategies and metrics, and thereby maintain clarity and alignment over time. ... -
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Letta Code
The memory-first coding agent
Letta Code is a memory-first CLI coding agent built on the Letta platform that offers developers a persistent AI assistant capable of learning and improving over time rather than resetting state each session, giving agents a sense of continuity and context across coding tasks. Unlike traditional session-based coding tools, Letta Code attaches a long-lived agent to a working directory so that the agent accumulates memory about a project’s structure, preferences, and history, effectively acting as a collaborative partner rather than a stateless helper. Users can initialize and connect the agent to various models, including popular large language models, and issue commands, refactor code, or ask context-aware questions directly in the terminal, with memory retained across multiple interactions.
