Open Source Go Artificial Intelligence Software
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Go Artificial Intelligence Software
View 16733 business solutionsBrowse free open source Go Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Go Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Ollama
Run models like Kimi-K2.5, GLM-5, DeepSeek, gpt-oss, Gemma, Qwen etc.
Ollama is an open-source platform that enables developers to run large language models locally on their own machines. It simplifies working with modern AI models by providing a unified interface to download, manage, and interact with them. Users can run models like Llama, Gemma, Qwen, and others directly from the command line or through APIs. Ollama also integrates with popular developer tools and AI agents, allowing seamless workflows across coding environments and applications. It supports REST APIs, Python, and JavaScript SDKs, making it easy to build AI-powered features into software projects. Overall, Ollama focuses on privacy, local-first AI execution, and developer-friendly tooling for building with open models. -
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MTProto
Full-native go implementation of Telegram API
Full-native implementation of MTProto protocol on Golang. All code, from sending requests to encryption serialization is written in pure Golang. You don't need to fetch any additional dependencies. -
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Slack MCP Server
The most powerful MCP Slack Server with no permission requirements
Slack MCP Server is an open-source server implementation that connects Slack workspaces to AI systems through the Model Context Protocol (MCP). MCP is a standardized protocol that allows large language models and AI agents to securely interact with external tools and data sources such as messaging platforms, databases, or file systems. The slack-mcp-server acts as an intermediary layer that exposes Slack data and messaging functionality to AI clients while enforcing access rules and communication standards. Through this architecture, AI assistants can read message histories, interact with channels, and retrieve contextual information from Slack conversations in order to perform tasks such as automated analysis, collaboration assistance, or contextual code review. The server supports multiple communication transports, including standard input/output streams, HTTP, and Server-Sent Events, allowing flexible integration with different AI client environments. -
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Cog
Package and deploy machine learning models using Docker containers
Cog is an open source tool designed to package machine learning models into standardized, production-ready containers. It simplifies the process of deploying models by automatically generating Docker images based on a simple configuration file, eliminating the need to manually write complex Dockerfiles. Developers can define the runtime environment, dependencies, and Python versions required for their models, allowing Cog to build a consistent container environment that follows best practices. Cog also resolves compatibility issues between frameworks and GPU libraries by automatically selecting compatible combinations of CUDA, cuDNN, and machine learning frameworks such as PyTorch or TensorFlow. Cog automatically generates a RESTful HTTP API for running predictions, enabling models to be accessed programmatically through a built-in prediction server. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Casibase
Open-source enterprise-level AI knowledge base and MCP
Casibase is an open-source AI cloud platform designed to function as an enterprise knowledge base, container management system, and collaboration environment for AI-driven applications. The project combines knowledge management, messaging, and forum features with large language model integration to create an interactive platform for storing and querying domain-specific knowledge. Built with a separated frontend and backend architecture, Casibase provides a web-based administrative interface and supports high concurrency for enterprise environments. The platform integrates embedding techniques and prompt engineering to enable semantic knowledge retrieval and conversational interactions with stored data. It also supports integration with existing systems through database synchronization, allowing organizations to migrate data into the platform without major infrastructure changes. -
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kubectl-ai
AI assistant for managing Kubernetes clusters from the terminal
kubectl-ai is an AI-powered command-line assistant designed to help users manage and interact with Kubernetes clusters through natural language queries. It acts as an intelligent interface that interprets user intent and translates it into appropriate Kubernetes operations and commands. By integrating large language models, it enables users to ask questions or request actions in plain language instead of manually crafting complex Kubernetes commands. kubectl-ai runs directly in the terminal and integrates with the existing kubectl workflow, making it familiar for Kubernetes administrators and developers. It can help perform tasks such as inspecting resources, retrieving logs, troubleshooting issues, and modifying cluster configurations. kubectl-ai supports both cloud-based and locally hosted language models, allowing it to adapt to different infrastructure and privacy requirements. -
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openclaw-kapso-whatsapp
Give your OpenClaw AI agent a WhatsApp number
openclaw-kapso-whatsapp is a plugin repository designed to extend the OpenClaw AI agent by giving it a dedicated WhatsApp phone number using the official Meta Cloud API via Kapso, enabling direct interaction through one of the most widely used messaging platforms. This integration allows the autonomous AI assistant to send and receive messages on WhatsApp, turning the agent into a real-world task performer accessible through text conversations. The plugin is built in Go and handles communication entirely through cloud APIs, avoiding the risk of bans that come with unofficial or reverse-engineered interfaces. Projects like this make it possible for OpenClaw users to automate tasks, interact with personal contacts, or provide AI-driven services without building a custom bot infrastructure from scratch. Because OpenClaw itself runs on the user’s own hardware and can access external services, this WhatsApp extension serves as a bridge between the AI agent and daily messaging workflows. -
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Rill
Fast SQL-based BI tool for real-time dashboards and analytics
Rill is an operational BI tool that turns raw datasets into fast, interactive dashboards using SQL and a code-first approach. It helps data teams move from data lake to insight quickly, without the complexity of traditional BI systems. With an embedded in-memory database powered by DuckDB or ClickHouse, queries run in milliseconds, enabling real-time exploration and analysis. Rill supports local and remote data sources such as CSV, Parquet, S3, and GCS, making it flexible across environments. Its BI-as-code model combines SQL, YAML configuration, Git version control, and CLI tools, allowing teams to build, manage, and deploy analytics workflows efficiently. Automatic data profiling and responsive query updates help users understand datasets instantly. Interactive dashboards come with opinionated defaults, so teams can focus on insights instead of setup, while metrics layers standardize business logic for consistent reporting across dashboards, APIs, and AI systems. -
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Envoy AI Gateway
Manages Unified Access to Generative AI Services
Envoy AI Gateway is an open-source gateway system designed to manage network traffic between applications and generative AI services using the Envoy proxy ecosystem. The project extends Envoy Gateway to support AI-specific workloads, enabling organizations to route, secure, and scale requests to large language models and other generative AI services. In a typical deployment, the architecture uses a two-tier gateway model where an outer gateway handles authentication, routing, and global rate limiting while a second gateway manages traffic to self-hosted model serving clusters. This design allows organizations to centralize control over AI service access while maintaining flexibility in how backend model infrastructure is deployed. The gateway provides policy enforcement, observability, and routing capabilities that are specifically designed for AI inference workloads, including intelligent endpoint selection and request optimization. -
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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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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Sub2API
Sub2API-CRS2 is a one-stop open source transit service
Sub2API is an AI API gateway platform for distributing and managing API quotas from AI product subscriptions. It allows users to create platform-issued API keys while routing requests to upstream AI services behind the scenes. The platform handles authentication, quota allocation, billing logic, load balancing, and request forwarding, making it useful for shared or team-based access management. It is designed to unify access to services such as Claude, OpenAI, Gemini, and related AI tools through a centralized relay layer. Sub2API supports deployment through Docker Compose or binary installation for Linux servers. Its main value is turning multiple subscription-based AI accounts into a managed API access platform with administrative controls. -
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Claude Platform CLI
The CLI for the Claude API
Claude Platform CLI is a command-line tool that lets users interact directly with the Anthropic Claude API from their terminal. It provides access to API resources through structured subcommands, making it useful for both interactive exploration and automated workflows. The CLI simplifies API requests by supporting typed flags, piped YAML or JSON input, and inline file references instead of manually writing complex JSON. Users can format, filter, and transform responses directly in the terminal using built-in output controls and GJSON queries. It also supports authentication through interactive login, API keys, profiles, and workspace switching for flexible development environments. Claude Platform CLI is especially valuable for developers, teams, and automation workflows that need efficient terminal-based access to Claude API resources. -
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ant CLI
ant CLI - Command line tool for Anthropic Claude API
The ant CLI is a command-line tool that lets users interact directly with the Claude API from their terminal. It exposes every API resource as a structured subcommand, making it useful for both hands-on exploration and automated workflows. The ant CLI simplifies API requests by allowing users to build request bodies with typed flags, piped YAML or JSON, stdin input, and inline file references instead of manually writing complex JSON. It also includes built-in output formatting, response filtering, and GJSON-based transformation options so users can inspect and reuse API responses more efficiently. With support for interactive login, API keys, profiles, workspace switching, and authentication status checks, the ant CLI is built for flexible development environments. It is especially useful for developers and teams that want a reliable, scriptable way to manage Claude API resources, run messages, work with agents, debug requests, and integrate Claude workflows into the terminal. -
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vLLM Semantic Router
System Level Intelligent Router for Mixture-of-Models at Cloud
Semantic Router is an open-source system designed to intelligently route requests across multiple large language models based on the semantic meaning and complexity of user queries. Instead of sending every prompt to the same model, the system analyzes the intent and reasoning requirements of the request and dynamically selects the most appropriate model to process it. This approach allows developers to combine multiple models with different strengths, such as lightweight models for simple queries and more advanced reasoning models for complex tasks. The router operates as an intelligent layer between users and model infrastructure, capturing signals from prompts, responses, and contextual data to improve decision-making. It can also integrate safety and monitoring mechanisms that detect issues such as jailbreak attempts, hallucinations, or sensitive information exposure. -
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Vald
Vald. A Highly Scalable Distributed Vector Search Engine
Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search for neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which is made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed. Usually, the graph requires locking during indexing, which causes stop-the-world. But Vald uses distributed index graphs so it continues to work during indexing. Vald implements it's own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery. -
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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. -
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Eino
LLM application development framework for Go with agents and flows
Eino is an LLM application development framework written in Go that helps developers build applications powered by large language models. Eino provides a structured environment for creating AI systems using reusable components such as chat models, retrievers, tools, embeddings, and prompt templates. It draws architectural inspiration from frameworks like LangChain and other modern AI development toolkits while remaining aligned with Go programming conventions. Eino includes an Agent Development Kit that enables developers to create intelligent agents capable of using tools, coordinating with other agents, and managing conversational context. Eino also offers orchestration capabilities that allow components to be connected into chains, graphs, or workflows for complex AI pipelines. These orchestration features handle concerns such as concurrency, streaming responses, and type safety so developers can focus on application logic. -
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LocalAI
The free, Open Source alternative to OpenAI, Claude and others
LocalAI is an open-source platform that allows users to run large language models and other AI systems locally on their own hardware. It acts as a drop-in replacement for APIs such as OpenAI, enabling developers to build AI-powered applications without relying on external cloud services. The platform supports a wide range of model types, including text generation, image creation, speech processing, and embeddings. LocalAI can run on consumer-grade hardware and does not necessarily require a GPU, making it accessible for local development and private deployments. It integrates with multiple backends like llama.cpp, transformers, and diffusers to support different AI workloads. With its self-hosted architecture and OpenAI-compatible API, LocalAI enables developers to build secure, local-first AI applications. -
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MCP Toolbox for Databases
Open source MCP server that exposes database tools for AI agents
GenAI Toolbox, also known as MCP Toolbox for Databases, is an open source server designed to simplify how generative AI applications interact with databases. It provides a central service that exposes database operations as reusable tools that can be consumed by AI agents and developer workflows. It handles common infrastructure concerns such as authentication, connection pooling, and performance optimization so developers do not have to implement them individually in each application. By defining tools and data sources through configuration files, developers can standardize how AI systems access and operate on database resources. GenAI Toolbox is designed to integrate with agent frameworks and development environments so that AI assistants can execute database-related tasks with proper context and security. It also supports observability through built-in metrics and tracing capabilities, allowing developers to monitor how tools are used and debug interactions. -
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PhotoPrism
AI-Powered Photos App for the Decentralized Web 🌈💎✨
PhotoPrism® is an AI-Powered Photos App for the Decentralized Web. It makes use of the latest technologies to tag and find pictures automatically without getting in your way. You can run it at home, on a private server, or in the cloud. Our mission is to provide the most user- and privacy-friendly solution to keep your pictures organized and accessible. That's why PhotoPrism was built from the ground up to run wherever you need it, without compromising freedom, privacy, or functionality. -
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Telegram Drive
Telegram Drive
Telegram Drive is a powerful utility that enables you to organize your telegram files and much more. Teldrive stands out among similar tools, thanks to its implementation in Go, a language known for its efficiency. Its performance surpasses alternatives written in Python and other languages, with the exception of Rust. Teldrive not only excels in speed but also offers an intuitive user interface for efficient file interaction which other tool lacks. Its compatibility with Rclone further enhances file management. -
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csghub-server
csghub-server is the backend server for CSGHub
csghub-server is the backend component of the CSGHub platform, an open-source infrastructure designed to manage and operate large language models, datasets, and AI development workflows within a private deployment environment. The server acts as a centralized management layer that allows teams to store, organize, and operate AI assets such as models, datasets, and machine learning applications in a manner similar to artifact repositories used in software engineering. Built primarily in the Go programming language, the system enables organizations to run model inference, training, and fine-tuning tasks within a unified platform. It integrates capabilities similar to model repositories like Hugging Face while allowing enterprises to host and manage their AI assets internally for security and compliance purposes. -
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kagent
Kubernetes native framework for building AI agents
Kagent is a Kubernetes-native framework for building, deploying, and operating AI agents as first-class cloud-native workloads. It models core agent concepts declaratively using Kubernetes custom resources, so teams can manage agents similarly to other platform components via YAML, controllers, and standard cluster workflows. In kagent’s design, an “Agent” represents a system prompt plus a set of tools and other agents, along with an LLM configuration, making the agent definition portable and repeatable across environments. It supports multiple model providers through a dedicated configuration resource, allowing teams to switch providers or run mixed environments while keeping the agent spec stable. A major focus is tool integration via MCP: agents can connect to MCP servers for tool access, and kagent includes an MCP server with tools for common Kubernetes and platform engineering systems. -
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Telegram File Stream Bot
A telegram bot that will give instant stream links for telegram files
A Telegram bot to generate direct link for your Telegram files. -
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Beta9
Run serverless GPU workloads with fast cold starts on bare-metal
beta9 is a platform that enables running serverless GPU workloads with fast cold starts on bare-metal servers globally. It allows developers to deploy and scale GPU-accelerated applications without managing underlying infrastructure, offering flexibility and efficiency for AI and high-performance computing tasks. beta9 supports various frameworks and provides tools for monitoring and managing deployments effectively.
