Open Source Go Artificial Intelligence Software - Page 2
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Go Artificial Intelligence Software
View 13749 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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NOFX
Open source AI trading OS for autonomous multi-model trading systems
NOFX is an open source AI-powered trading operating system designed to automate financial trading workflows using autonomous AI agents. It acts as an infrastructure layer that transforms market data into AI-driven trade decisions and execution. Instead of requiring users to manually configure machine learning models, data sources, and API integrations, the system allows AI components to perceive market conditions, select models, and perform trading actions automatically. It supports running multiple AI models simultaneously and allows them to compete or collaborate when making trading decisions. NOFX integrates trading infrastructure such as exchange connectivity, strategy management, and performance monitoring into a single environment. It also includes components for strategy development, backtesting, and real-time monitoring so traders and researchers can evaluate algorithmic trading approaches. -
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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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ChatGPT Proxy
Simple Cloudflare bypass for ChatGPT
ChatGPTProxy is an open-source project that creates a lightweight proxy server to intermediate between client applications and the ChatGPT web endpoints, allowing developers to integrate ChatGPT-style functionality into their software without using an official API or embedding web UI code directly. This tool works by accepting requests in a defined format, forwarding them through the proxy to ChatGPT’s backend services, and returning responses to the caller, abstracting away direct browser automation or scraping concerns from the application layer. By consolidating the traffic through a proxy, developers can centralize logging, throttling, authentication, and caching in one place, making it easier to build consistent and controlled AI workflows. The proxy can also be customized to enforce usage policies, attach additional metadata, or translate request/response formats for compatibility with other tools. -
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Microsandbox
Secure local-first microVM sandbox for running untrusted code fast
Microsandbox is an open source platform designed to securely execute untrusted code in isolated environments using lightweight virtualization techniques. It focuses on combining strong security guarantees with fast startup times by leveraging hardware-level microVM isolation instead of relying solely on traditional containers or full virtual machines. It aims to solve the common tradeoffs between speed, isolation, and control that developers encounter when running untrusted workloads. It provides a local-first and self-hosted approach, allowing users to maintain full ownership of their execution environment without depending on external cloud services. Microsandbox is particularly geared toward AI agent workflows, offering integrations that enable automated systems to safely run generated code and commands. It also supports standard container images, making it compatible with existing development ecosystems and tooling. -
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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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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Beelzebub
A secure low code honeypot framework
Beelzebub is an open-source cybersecurity framework designed to create intelligent honeypot environments for detecting and studying cyber attacks. Honeypots are systems intentionally exposed to attackers in order to capture malicious behavior, and Beelzebub enhances this concept by incorporating artificial intelligence and virtualization techniques. The platform allows organizations and researchers to deploy decoy services that mimic real infrastructure while recording attacker interactions. 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. -
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CyberStrikeAI
CyberStrikeAI is an AI-native security testing platform built in Go
CyberStrikeAI is an AI-native security testing platform built in Go that brings autonomous penetration testing, vulnerability discovery, and attack chain analysis into a unified interface. The platform integrates over 100 security tools out of the box and pairs them with an intelligent orchestration engine that can be directed via natural language or policy definitions, allowing users to automate reconnaissance, scanning, exploitation, and reporting without manual sequencing of tools. It supports role-based testing, letting teams define security roles with tailored tool access and prompts, and includes a skills system that encapsulates specialized testing strategies that the AI can incorporate into its planning. Through comprehensive lifecycle management, results are tracked, aggregated, and visualized, with support for versioned persistence, search, and risk severity scoring. -
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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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HumanLayer
Open source IDE for orchestrating AI coding agents in large codebases
HumanLayer is an open source development environment designed to help developers orchestrate and manage AI coding agents working within complex software projects. It provides a framework and tooling that allow AI agents to research, plan, and implement changes in large codebases while maintaining structured workflows. It focuses on enabling AI-assisted development through coordinated agent workflows rather than isolated code generation tasks. 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. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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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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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients. -
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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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tgState
Using Telegram as a stored file chain system
A file chain system with Telegram as a storage. No limit to file size and format. It can be used as a telegram drawing bed or as a telegram net. Support web upload files and telegram upload directly. -
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Astron Agent
Enterprise platform for building and orchestrating AI agent workflows
Astron Agent is an enterprise-grade platform designed for building and managing intelligent AI agent workflows in production environments. It provides a development environment that combines workflow orchestration, model management, and integration with various AI tools and services. Astron Agent enables organizations to design complex agent-driven processes that coordinate models, automation tools, and enterprise systems. It also integrates robotic process automation capabilities so agents can execute tasks across digital systems instead of only generating responses. Astron Agent supports scalable and high-availability deployments, allowing teams to run reliable AI agent infrastructure in distributed environments. It includes collaboration features that allow teams to develop, manage, and operate AI applications together. With its extensible architecture and enterprise-focused design, it aims to help organizations build production-ready intelligent agent solutions. -
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Jira MCP
A Go-based MCP (Model Control Protocol) connector for Jira
The Jira MCP is a Go-based MCP connector that enables AI assistants to interact with Atlassian Jira. It provides a seamless interface for performing common Jira operations, including issue management, sprint planning, and workflow transitions. -
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Scriberr
Self-hosted AI audio transcription
Scriberr is a self-hosted AI-powered transcription platform designed to convert audio and video into highly accurate text while prioritizing privacy and local processing. Unlike cloud-based transcription services, Scriberr runs entirely on the user’s machine, ensuring that sensitive recordings are never sent to third-party servers and remain fully under user control. It leverages modern speech recognition models such as Whisper and other advanced architectures to deliver precise transcripts with word-level timing and speaker identification. The application includes a polished user interface that simplifies the management of recordings, transcripts, and annotations, making it suitable for both casual users and professionals handling large volumes of audio. Beyond transcription, Scriberr also integrates features such as summarization, tagging, and interaction with language models, allowing users to extract insights from conversations or meetings efficiently. -
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Zep
Zep: A long-term memory store for LLM / Chatbot applications
Easily add relevant documents, chat history memory & rich user data to your LLM app's prompts. Understands chat messages, roles, and user metadata, not just texts and embeddings. Zep Memory and VectorStore implementations are shipped with your favorite frameworks: LangChain, LangChain.js, LlamaIndex, and more. Automatically embed texts and messages using state-of-the-art opeb source models, OpenAI, or bring your own vectors. Zep’s local embedding models and async enrichment ensure a snappy user experience. -
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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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AgentField
Build and run AI agents like microservices
AgentField is an open-source control plane designed to run AI agents as production-grade backend services, applying cloud-native principles similar to Kubernetes to the world of autonomous software. Instead of treating agents as isolated scripts or prototypes, the system elevates them to first-class infrastructure components that can be deployed, orchestrated, and managed at scale across distributed environments. Developers define agents as typed functions, and the platform automatically handles orchestration, communication, identity, and execution, allowing agents to behave like APIs within a broader system architecture. The framework includes built-in support for asynchronous execution, long-running processes, and multi-agent coordination, enabling complex workflows that go far beyond simple prompt-response interactions. It also introduces strong identity and governance mechanisms, such as cryptographic identities and policy enforcement, ensuring that agents can operate securely. -
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ChatGPT-bot
Run your own GPTChat Telegram bot, with a single command
Go CLI to fuels a Telegram bot that lets you interact with ChatGPT, a large language model trained by OpenAI. -
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Docker Agent
AI Agent Builder and Runtime by Docker Engineering
Docker Agent is an open-source multi-agent runtime developed by Docker that enables developers to define, run, and orchestrate AI agents using simple declarative configuration files instead of traditional code-heavy approaches. It introduces a YAML-based configuration model where users describe agent behavior, tools, models, and interaction logic in a single file, significantly reducing complexity in building AI systems. The runtime supports multi-agent collaboration, allowing specialized agents to delegate tasks to each other and operate as coordinated systems rather than isolated units. 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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DownloadBot
A distributed cross-platform Telegram Bot
A distributed cross-platform Telegram Bot that can control your Aria2 server, control server files and also upload to OneDrive / Google Drive. This project is mainly to use a small hard disk server for offline downloading, for large BitTorrent files to be downloaded in sections according to the size of the hard disk, each time downloading a part, then uploading the network disk, deleting and then downloading the other parts, until all the files are downloaded. At the same time, communication via the bot protocol facilitates use on machines that cannot intranet penetration and simplifies the usual use of download programs for added convenience. For links, sending a message directly to the Bot will directly identify and download them. It can actually delete files from the download folder, which is not possible with web panels such as AriaNG, and is very convenient as a tool for managing downloads and notifying timely completion of downloads. -
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E2B Infra
Infrastructure for AI code interpreting that's powering E2B
E2B Infra is an infrastructure management tool that simplifies the deployment and scaling of applications across cloud environments, focusing on automation and efficiency. -
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ENScan Go
ENScan_GO is an enterprise information reconnaissance tool
ENScan_GO is an enterprise information reconnaissance tool focused on Chinese corporate data sources. It aggregates official and third-party APIs to pull records like ICP filings, affiliated/holding companies, apps, mini-programs, and WeChat official accounts, then exports merged results for analysis. The tool targets analysts who need one-click collection and normalized output to reduce manual lookups across registries and platforms. Recent releases added a reworked task model with queueing, resumable searches via cached progress, export format options, and a public API surface for custom keyword strategies. Documentation and issues discuss operational concerns such as rate limits, verification challenges, and use of proxies to reduce bans. The project is maintained under Apache-2.0 and is positioned for both single-shot queries and batch investigations. -
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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.
