Open Source Go Artificial Intelligence Software for Mac - Page 3
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Go Artificial Intelligence Software for Mac
View 1754 business solutionsBrowse free open source Go Artificial Intelligence Software for Mac and projects below. Use the toggles on the left to filter open source Go Artificial Intelligence Software for Mac by OS, license, language, programming language, and project status.
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Feishu ChatGPT
Voice dialogue, role-playing, multi-topic discussion, picture creation
Feishu × (GPT-3.5 + DALL·E + Whisper) = flying-like work experience. Voice dialogue, role-playing, multi-topic discussion, picture creation, table analysis, document export. Golang language, it goes without saying! Master the gin framework proficiently, developing the backend is as natural as breathing! Familiar with the SDKs of DingTalk, Feishu, Qiwei and other platforms, and be able to develop and integrate a series of amazing functions! Proficient in platform-based detail thinking, let the efficient server-side hot update script you develop amaze the audience! Easily control Docker containerization technology and deploy code as you like! With some experience in payment function development, it really makes money fly! Understand some Linux scripting and socket programming. -
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KOM
Kubernetes Operations Manager
A Kubernetes Operations Manager (kom) that serves as an SDK-level tool, encapsulating functionalities of kubectl and client-go, providing a comprehensive suite of features for managing Kubernetes resources efficiently. -
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Last9 MCP Server
Last9 MCP Server
The Last9 MCP Server is a Model Context Protocol server implementation for Last9, enabling AI agents to seamlessly bring real-time production context—logs, metrics, and traces—into local environments to auto-fix code faster. -
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MCP Filesystem Server
Go server implementing Model Context Protocol (MCP) for filesystem
Filesystem MCP Server is a Go-based server implementing the Model Context Protocol (MCP) for filesystem operations. It allows for various file and directory manipulations, including reading, writing, moving, and searching files, as well as retrieving file metadata. -
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MCPJungle
Self-hosted MCP Gateway and Registry for AI agents
MCPJungle is a self-hosted gateway and registry for the Model Context Protocol (MCP), aimed at managing tool/integration servers for AI agents within organizations. It offers a “single source of truth” registry where developers can register MCP servers and the tools they provide, and MCP clients (such as AI agents) discover and consume those tools through one gateway endpoint. This greatly simplifies the architecture when you have many MCP servers; agents only need to connect to one gateway rather than multiple endpoints. The platform supports enterprise-grade workflows; centralized tool management, access control, self-hosting so that internal servers and tools remain under your organization’s control, and registry metadata to track what tools exist and who can use them. For organizations building internal AI automation systems, MCPJungle helps enforce governance, tool discovery, and integration scalability. -
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Memobase
Fast backend for long-term AI user memory via structured profiles
Memobase is an open source backend system that enables long-term user memory functionality for AI applications by capturing and structuring information about users across interactions. Its design centers on creating user profiles and recording event timelines, allowing AI systems to remember, understand, and evolve in their behaviour toward individual users over time. Instead of relying purely on traditional embedding-based retrieval or RAG systems, Memobase uses profile and timeline structures to deliver memory that reflects user context efficiently and meaningfully. The system focuses on three principal performance metrics: high search performance, reduced large language model (LLM) costs through batch processing techniques, and low latency with minimal SQL operations. Memobase supports integration with existing LLM workflows via APIs and SDKs (including Python, Node, and Go), making it easy to adopt within diverse application stacks. -
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Quint Code
Structured reasoning framework for Claude Code, Gemini, and Cursor
Quint Code is a structured reasoning and decision-support framework aimed at making AI-assisted software engineering and decision workflows more rigorous and auditable. It implements the First Principles Framework (FPF) to guide users and AI tools through hypothesis generation, logical verification, evidence gathering, and documented decision making, reducing reliance on ad hoc or “vibe” coding. Instead of accepting the first plausible answer generated by an AI assistant, Quint Code encourages generating multiple competing hypotheses, verifying them, and validating them against real evidence stored in a structured “knowledge base” within your project. It supports a cycle of abduction, deduction, and induction backed by CLI commands (like /q1-hypothesize, /q2-verify, /q3-validate, etc.) that create a persisting audit trail in a .quint/ directory. -
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aqueduct LLM
Aqueduct allows you to run LLM and ML workloads on any infrastructure
Aqueduct is an MLOps framework that allows you to define and deploy machine learning and LLM workloads on any cloud infrastructure. Aqueduct is an open-source MLOps framework that allows you to write code in vanilla Python, run that code on any cloud infrastructure you'd like to use, and gain visibility into the execution and performance of your models and predictions. Aqueduct's Python native API allows you to define ML tasks in regular Python code. You can connect Aqueduct to your existing cloud infrastructure (docs), and Aqueduct will seamlessly move your code from your laptop to the cloud or between different cloud infrastructure layers. Aqueduct provides a single interface to running machine learning tasks on your existing cloud infrastructure — Kubernetes, Spark, Lambda, etc. From the same Python API, you can run code across any or all of these systems seamlessly and gain visibility into how your code is performing. -
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cortex
Production infrastructure for machine learning at scale
Cortex is an open-source platform designed for building, deploying, and managing machine learning applications in production environments. The framework provides infrastructure tools that allow developers to transform trained machine learning models into scalable web services. Cortex handles many operational challenges associated with deploying AI systems, such as managing dependencies, orchestrating data pipelines, and scaling services under load. Developers can define machine learning pipelines as code using declarative configuration files, which simplifies the process of managing complex ML workflows. The platform supports integration with cloud environments and container orchestration systems so that applications can scale dynamically based on demand. It is designed to help teams focus on building machine learning logic rather than managing infrastructure details. -
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10
flowerss bot
A telegram bot for RSS reader
A telegram bot for RSS readers. A Telegram RSS Bot that supports reading in the application. -
11
td
Telegram client, in Go. (MTProto API)
Telegram MTProto API client in Go for users and bots. -
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Qwen2.5-Coder
Qwen2.5-Coder is the code version of Qwen2.5, the large language model
Qwen2.5-Coder, developed by QwenLM, is an advanced open-source code generation model designed for developers seeking powerful and diverse coding capabilities. It includes multiple model sizes—ranging from 0.5B to 32B parameters—providing solutions for a wide array of coding needs. The model supports over 92 programming languages and offers exceptional performance in generating code, debugging, and mathematical problem-solving. Qwen2.5-Coder, with its long context length of 128K tokens, is ideal for a variety of use cases, from simple code assistants to complex programming scenarios, matching the capabilities of models like GPT-4o. -
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AxonHub
Use any SDK to call 100+ LLMs
AxonHub is an open-source AI gateway platform designed to simplify the process of integrating and switching between different large language model providers. The system acts as a compatibility layer that allows developers to use the same SDK interface while routing requests to various AI services behind the scenes. Instead of rewriting code when switching providers such as OpenAI or Anthropic, developers can simply change configuration settings within the gateway. AxonHub translates requests from one provider’s API format into another, enabling seamless interoperability across different AI platforms. The system also provides infrastructure features such as request routing, failover mechanisms, load balancing, and cost management for AI applications. This architecture makes it easier to experiment with multiple models and manage production deployments that rely on several providers simultaneously. -
14
Caire
Content aware image resize library
Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper. An energy map (edge detection) is generated from the provided image. The algorithm tries to find the least important parts of the image taking into account the lowest energy values. Using a dynamic programming approach the algorithm will generate individual seams across the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will allocate for each seam a custom value, the least important pixels having the lowest energy cost and the most important ones having the highest cost. We traverse the image from the second row to the last row and compute the cumulative minimum energy for all possible connected seams for each entry. The minimum energy level is calculated by summing up the current pixel value with the lowest value of the neighboring pixels obtained from the previous row. -
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E5SubBot
Telebot for E5 Renewal
A simple Telegram bot for E5 renewal. -
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GitHub MCP Server
GitHub's official MCP Server
The GitHub MCP Server exposes GitHub as a Model Context Protocol server so AI assistants can safely act on repos, issues, pull requests, gists, and more through a consistent tool interface. It’s designed to run locally or remotely and then be attached to MCP-capable clients (for example, Copilot Chat) so an LLM can search code, open files, create branches, draft PRs, label or triage issues, and query metadata without hard-coding GitHub APIs. The server defines tools and resources with fine-grained scopes, leaning on GitHub’s auth to enforce least privilege and auditable access. It supports both stdio and HTTP transports, enabling IDE and headless integrations, and adopts common MCP behaviors like prompts, schemas, and tool definitions to keep agent calls predictable. Documentation covers setup, tokens, and client configuration, highlighting native editor integrations. Its design goal is to give AI agents first-class, governed access to GitHub workflows. -
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Go OpenAI
OpenAI ChatGPT, GPT-3, GPT-4, DALL·E, Whisper API wrapper for Go
This library provides Go clients for OpenAI API. OpenAI ChatGPT, GPT-3, GPT-4, DALL·E, Whisper API wrapper for Go. -
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KubeAI
Private Open AI on Kubernetes
Get inferencing running on Kubernetes: LLMs, Embeddings, Speech-to-Text. KubeAI serves an OpenAI compatible HTTP API. Admins can configure ML models by using the Model Kubernetes Custom Resources. KubeAI can be thought of as a Model Operator (See Operator Pattern) that manages vLLM and Ollama servers. -
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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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MCP Grafana
MCP server for Grafana
The Grafana MCP Server is a Model Context Protocol (MCP) server designed to provide access to Grafana instances and their surrounding ecosystems. It enables seamless integration with Grafana's visualization and monitoring capabilities. -
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MCP K8s Eye
MCP Server for kubernetes management and analyze workload status
A tool designed to manage Kubernetes clusters and analyze workload statuses, providing insights and operational capabilities to enhance cluster performance and reliability. -
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Obot MCP Gateway
Hosting, Registry, Gateway, and Chat Client
Obot is an open-source platform built to help organizations adopt and operate Model Context Protocol (MCP) capabilities in a centralized, production-friendly way. It combines multiple MCP building blocks into one system, including hosting for MCP servers, a registry for discovery, a gateway layer to route access, and a standards-compliant chat client experience. The project is aimed at solving common enterprise rollout problems such as reliably hosting servers for internal and external users, curating “approved” MCP servers for employees to find, and enforcing authentication, access control, and auditable activity. It also supports building richer agents and chatbots that can leverage MCP servers while keeping operations manageable for IT and platform teams. The platform is designed to work with a variety of workflows and clients, so MCP servers managed inside Obot can be used by automation/agent frameworks as well as popular chat clients that speak MCP. -
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Telegram MCP
MCP server to work with Telegram through MTProto
An MCP server that bridges the Telegram API and AI assistants, enabling seamless interaction between AI applications and Telegram through MTProto. -
24
Terminal GPT
AI Chatbots in terminal without needing API keys
tgpt is a cross-platform command-line interface (CLI) tool that allows you to use AI chatbot in your Terminal without requiring API keys. -
25
Vearch
A distributed system for embedding-based vector retrieval
Vearch is the vector search infrastructure for deep learning and AI applications. Vearch is a distributed vector storage and retrieval system which can be easily extended to billions scale. Vearch implements a high-performance, lockless real-time vector indexing subsystem that utilizes various optimization techniques to support millisecond vector update and retrieval. End-to-end one-click deployment. Through the module of the plugin, a complete default visual search system can be deployed just with one click. Otherwise, you can easily customize your own image, video, or text feature extraction algorithm plugin. This GIF provides a clear demonstration of the project vearch usage and its internal structure. The use of vearch is mainly divided into three steps. Firstly, create DB and Space, then import your data, and finally, you can search on your own dataset.
