Search Results for "git:/git.code.sf.net/p/docfetcher/code" - Page 11

Showing 1508 open source projects for "git:/git.code.sf.net/p/docfetcher/code"

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  • 1
    Amazon Q Developer CLI

    Amazon Q Developer CLI

    Chat experience in your terminal

    ...The CLI targets macOS and Linux and is designed to coexist with standard tools rather than replace them, acting as a smart layer on top. Team-focused features center on repeatability and transparency so generated changes can be reviewed, amended, and committed like any other code.
    Downloads: 4 This Week
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  • 2
    Gemini CLI

    Gemini CLI

    Open source AI agent CLI tool to bring Gemini into your terminal

    Gemini CLI is an open‑source AI agent that brings the capabilities of Google’s Gemini 2.5 Pro large‑language model directly into your terminal, enabling tasks ranging from coding and debugging to content creation and research via natural‑language prompts, with support for multimodal outputs like image and video generation. Gemini CLI integrates with external tools and MCP servers, enabling media generation and enhanced workflow automation. It also includes a built-in Google Search tool to...
    Downloads: 8 This Week
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  • 3
    Fragments by E2B

    Fragments by E2B

    Open source template for AI-powered code generation apps w/ sandboxes

    Fragments is an open source template designed for building applications where artificial intelligence generates and executes code directly from natural language prompts. It provides a ready-to-use web interface where users can chat with AI models to create code snippets, applications, or scripts in various programming environments. Generated code can be executed safely in isolated sandbox environments using the E2B SDK, which helps protect the host system while running AI-produced programs. ...
    Downloads: 0 This Week
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  • 4
    Serena

    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 stacks. It emphasizes symbol-level understanding rather than naive file-wide diffs, enabling more precise refactors and additions. ...
    Downloads: 10 This Week
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  • 5
    Nexent

    Nexent

    Zero-code platform for building AI agents from natural language input

    Nexent is an open source platform designed to enable users to create intelligent agents using natural language instead of traditional programming or visual orchestration tools. It focuses on a zero-code approach, allowing users to define workflows and agent behavior purely through language prompts, significantly lowering the barrier to entry for AI development. Built on the MCP ecosystem, Nexent integrates a wide range of tools, models, and data sources into a unified environment for agent creation and execution. Nexent supports multi-agent collaboration, enabling multiple intelligent agents to interact and coordinate tasks within complex workflows. ...
    Downloads: 3 This Week
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  • 6
    DocETL

    DocETL

    A system for agentic LLM-powered data processing and ETL

    ...Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation.
    Downloads: 3 This Week
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  • 7
    Self-hosted AI Package

    Self-hosted AI Package

    Run all your local AI together in one package

    Self-hosted AI Package is an open-source Docker Compose-based starter kit that makes it easy to bootstrap a full local AI and low-code development environment with commonly used open tools, empowering developers to run LLMs and AI workflows entirely on their infrastructure. The stack typically includes Ollama for running local large language models, n8n as a low-code workflow automation platform, Supabase for database and vector storage, Open WebUI for interacting with models, Flowise for agent building, and additional services like SearXNG, Neo4j, and Langfuse for search, knowledge graphs, and observability. ...
    Downloads: 3 This Week
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  • 8
    UpTrain

    UpTrain

    Your open-source LLM evaluation toolkit

    ...By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users. Unleash unparalleled power with a single line of code and tailor every detail as per as your use-case.
    Downloads: 3 This Week
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  • 9
    Inkeep

    Inkeep

    Create AI Agents in a No-Code Visual Builder or TypeScript SDK

    Inkeep is an open-source framework for building and deploying AI agent workflows and interactive assistants that operate autonomously across applications, enterprise environments, and customer engagement use cases. It lets developers and non-technical users create, manage, and orchestrate multi-agent systems using both a no-code visual builder and a full TypeScript SDK, giving two ways to define agent behaviors that stay in sync with each other. Agents built with this framework can act as real-time conversational assistants — for example, handling help desk inquiries, providing internal support to teams, or driving in-app experiences — and they can be extended to automate multi-step tasks that interact with external systems like CRMs, knowledge bases, or ticketing systems. ...
    Downloads: 1 This Week
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  • 10
    CVPR 2025

    CVPR 2025

    Collection of CVPR 2025 papers and open source projects

    CVPR 2025 curates accepted CVPR 2025 papers and pairs them with their corresponding code implementations when available, giving researchers and practitioners a fast way to move from reading to reproducing. It organizes entries by topic areas such as detection, segmentation, generative models, 3D vision, multi-modal learning, and efficiency, so you can navigate the year’s output efficiently. Each paper entry typically includes a title, author list, and links to the paper PDF and official or third-party code repositories. ...
    Downloads: 1 This Week
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  • 11
    Codex MCP Server

    Codex MCP Server

    MCP server wrapper for OpenAI Codex CLI

    Codex MCP Server is an open-source integration tool that allows AI development environments to access the capabilities of the OpenAI Codex command-line interface through the Model Context Protocol. The project acts as a bridge between AI assistants such as Claude Code and the Codex CLI, enabling those assistants to perform advanced coding operations using Codex as a backend engine. Through this architecture, developers can request tasks such as code explanation, refactoring, or analysis directly from their AI assistant while the server forwards the request to Codex. The system manages communication between the assistant and the Codex CLI, handling sessions, command execution, and structured responses. ...
    Downloads: 0 This Week
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  • 12
    Claude Cognitive

    Claude Cognitive

    Persistent context and multi-instance coordination

    Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code.
    Downloads: 0 This Week
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  • 13
    DSPy

    DSPy

    DSPy: The framework for programming—not prompting—language models

    Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs.
    Downloads: 5 This Week
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  • 14
    DeepSeek V2

    DeepSeek V2

    Strong, Economical, and Efficient Mixture-of-Experts Language Model

    ...The V2 model is expected to support more advanced features like better context window handling, more efficient inference, better performance on challenging tasks, and stronger alignment with human feedback. Because DeepSeek is pushing open-weight competition, this V2 iteration is meant to solidify its position in benchmark rankings and in developer adoption. The code in the repository may include description files, support for tool use or plug-in architectures, and artifacts showing fine-tuning or prompt templates.
    Downloads: 9 This Week
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  • 15
    Lepton AI

    Lepton AI

    A Pythonic framework to simplify AI service building

    A Pythonic framework to simplify AI service building. Cutting-edge AI inference and training, unmatched cloud-native experience, and top-tier GPU infrastructure. Ensure 99.9% uptime with comprehensive health checks and automatic repairs.
    Downloads: 1 This Week
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  • 16
    Factory AI

    Factory AI

    Agent-Native Software Development

    ...At its core, the system introduces the concept of “Droids,” specialized AI agents that operate across development environments such as the command line, web interfaces, project management tools, and communication platforms, enabling seamless interaction with engineering workflows. Rather than acting as a simple coding assistant, Factory is positioned as a full command center for development, where AI agents can autonomously write code, review pull requests, debug issues, and even coordinate tasks across tools like GitHub, Jira, or Slack. The platform integrates deeply with existing repositories and infrastructure, allowing it to analyze source code, monitor logs, and use contextual data from observability systems to make informed decisions.
    Downloads: 2 This Week
    Last Update:
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  • 17
    Beelzebub

    Beelzebub

    A secure low code honeypot framework

    ...By using AI models to simulate realistic system behavior, the honeypot becomes harder for attackers to identify, increasing the likelihood that malicious activity can be observed and analyzed. The framework is designed with a low-code configuration approach so security teams can easily deploy honeypots for multiple services and ports.
    Downloads: 2 This Week
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  • 18
    Spark TTS

    Spark TTS

    Spark-TTS Inference Code

    ...The project supports zero-shot voice cloning, meaning it can imitate a new speaker’s voice without dedicated training for that specific voice, and works across languages, including English and Chinese, even in cross-lingual code-switching scenarios. Spark-TTS allows users to control speech characteristics like gender, pitch, and speaking rate to customize synthesized output and support virtual speaker creation.
    Downloads: 2 This Week
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  • 19
    AutoCoder

    AutoCoder

    A long-running autonomous coding agent powered by the Claude Agent

    ...The core idea is to accelerate software production while preserving correctness and readability, minimizing the cognitive overhead that comes from switching between concept and implementation. Its architecture typically integrates language models with static analysis and template logic so that generated code is not only syntactically valid but also idiomatic and testable.
    Downloads: 2 This Week
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  • 20
    Label Sleuth

    Label Sleuth

    Open source no-code system for text annotation and building of text

    An open-source no-code system for text annotation and building text classifiers. No AI knowledge needed. From task definition to working model in just a few hours! While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next.
    Downloads: 2 This Week
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  • 21
    AI-Codereview-Gitlab

    AI-Codereview-Gitlab

    GitLab automatic code review tool based on large models

    ...When code changes occur, the system can automatically generate review comments and feedback that are posted directly into GitLab merge requests, allowing developers to see suggestions alongside human reviewer comments. In addition to code analysis, the tool can produce daily development summaries and notifications that help teams track progress and review activity across projects.
    Downloads: 0 This Week
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  • 22
    CodeCompanion

    CodeCompanion

    AI-powered coding, seamlessly in Neovim. Supports Anthropic, etc.

    Currently supports Anthropic, Copilot, Gemini, Ollama and OpenAI adapters.
    Downloads: 2 This Week
    Last Update:
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  • 23
    emgucv

    emgucv

    Cross platform .Net wrapper to the OpenCV image processing library

    Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio and Unity, it can run on Windows, Linux, Mac OS, iOS and Android.
    Downloads: 13 This Week
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  • 24
    Gp.nvim

    Gp.nvim

    Gp.nvim (GPT prompt) Neovim AI plugin

    Gp.nvim (GPT prompt) Neovim AI plugin: ChatGPT sessions & Instructable text/code operations & Speech to text [OpenAI, Ollama, Anthropic, ..] The goal is to extend Neovim with the power of GPT models in a simple unobtrusive extensible way. Trying to keep things as native as possible - reusing and integrating well with the natural features of (Neo)vim.
    Downloads: 2 This Week
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  • 25
    magentic

    magentic

    Seamlessly integrate LLMs as Python functions

    Easily integrate Large Language Models into your Python code. Simply use the @prompt and @chatprompt decorators to create functions that return structured output from the LLM. Mix LLM queries and function calling with regular Python code to create complex logic.
    Downloads: 4 This Week
    Last Update:
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