Open Source Go Agentic AI Tools
Sort By:
Go Agentic AI Tools
View 104 business solutionsBrowse free open source Go Agentic AI Tools and projects below. Use the toggles on the left to filter open source Go Agentic AI Tools by OS, license, language, programming language, and project status.
-
Veeam Data Platform v13.1Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
$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.
-
1
magpie
One place. Codex on DeepSeek, Claude Code on Kimi, from the menu bar
Magpie is a cross-platform utility for managing which AI models local coding agents use from one interface. It detects supported tools such as Claude Code, Codex, Gemini CLI, OpenCode, Cursor CLI, Copilot CLI, and many others. Users can switch providers, models, reasoning settings, and related fields without manually editing each agent's configuration file. A local gateway translates OpenAI and Anthropic-style APIs so different agents can share one model catalog and provider setup. Existing subscriptions and configured providers can be reused across compatible agents without copying credentials between tools. Profiles capture complete agent configurations so multiple settings can be restored together. Magpie is available as a desktop menu-bar app, regular window, terminal interface, and CLI on macOS, Windows, and Linux. -
2
SuperBased
Local-first control plane for AI coding agents
SuperBased is a local-first control plane for tracking, launching, and analyzing AI coding agents across many tools. It normalizes local session data from more than 40 adapters, including Claude Code, Codex, Cursor, Gemini CLI, Copilot, and others. Its optional reverse proxy captures provider-billed token details for more accurate cost attribution. A local dashboard, CLI, MCP server, and VS Code extension expose sessions, files, tokens, costs, and runtime state. Twenty-seven supported tools can also be launched as managed terminal sessions. Data stays on the user's machine by default, with no telemetry or remote reporting unless optional cloud features are explicitly enabled. SuperBased also supports backfills, resumable sessions, Tailscale-based remote viewing, and configurable compression profiles. -
3
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. -
4
Defang
Defang CLI and sample projects
Defang is a developer-centric platform that simplifies the process of developing, deploying, and debugging cloud applications. By leveraging AI-assisted tooling, Defang enables developers to swiftly transition from an idea to a deployed application on their preferred cloud provider. The platform supports multiple programming languages, including Go, JavaScript, and Python, allowing developers to start with sample projects or generate project outlines using natural language prompts. With a single command, Defang builds and deploys applications, handling configurations for computing, storage, load balancing, networking, logging, and security. The Defang Command Line Interface (CLI) facilitates interactions with the platform, offering installation options via shell scripts, Homebrew, Winget, Nix, or direct download. Developers can define services using compose.yaml files, which Defang utilizes to deploy applications to the cloud. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
-
5
Caprock
Free desktop app for Claude Code, Codex and OpenCode
Caprock is a free, open-source desktop app for AI coding agents, for macOS, Windows and Linux. It puts Claude Code, Codex and OpenCode sessions — including the ones you start in your own terminal — and the Gemini CLI sessions it starts into one window: terminals and projects in tabs and split panes, with activity, cost, plan limits and loop alerts for every session. Cost is per repository and per model, priced at API list prices from the tokens the agents already record, next to the 5-hour and weekly plan-limit windows. Everything the agent wrote is searchable across sessions and kept after Claude Code's 30-day transcript cleanup. A phone can carry on the work. It works with the subscription you already have. A daemon on loopback records sessions into a local SQLite database: no account, no hosted service, no telemetry. Free under Apache-2.0; an optional Premium licence adds paid features on top. -
6
GitHub Agentic Workflows
GitHub Agentic Workflows
GitHub Agentic Workflows is an experimental CLI extension and framework for the gh GitHub CLI that lets developers author automation driven by natural language specifications instead of hand-written code, compiling those descriptions into GitHub Actions workflows that run AI agents (like Copilot, Claude Code, or Codex) on schedule or in response to repository events. By writing intent in markdown files, a developer can quickly generate .yml Actions workflows that perform tasks such as summarizing issues, automating triage, generating reports, or maintaining documentation, all without manually crafting YAML logic from scratch. The system emphasizes safety and guardrails, running agents in sandboxed environments with minimal permissions by default, and using “safe outputs” to constrain what the workflow can write back into the repository. It includes tooling for compiling, testing, and iterating on agentic workflows locally and integrates with GitHub’s existing Actions ecosystem. -
7
CLI Printing Press
Reads official API docs, studies CLI and MCP servers
CLI Printing Press is a Go-based tool that generates agent-ready command-line interfaces and MCP servers from APIs, websites, OpenAPI specs, or browser-captured HAR files. Instead of only wrapping endpoints, it studies the API, competing tools, useful workflows, authentication behavior, and hidden data opportunities before producing a more opinionated CLI. The generated tools are designed for AI agents first, with SQLite sync, offline search, structured output, compact modes, typed exit codes, and compound insight commands. It supports multiple entry paths, including direct specs, URLs without documentation, and HAR imports from browser developer tools. The project includes validation stages such as scorecards, dogfooding, proof-of-behavior checks, and optional live read-only smoke tests. Overall, CLI Printing Press aims to turn any useful web service into a practical automation surface for both humans and AI agents. -
8
PentAGI
Perform penetration testing tasks
PentAGI is a fully autonomous AI agent system designed to perform complex penetration testing tasks by orchestrating multiple intelligent components into a coordinated offensive security workflow. The platform aims to automate significant portions of the penetration testing lifecycle, including reconnaissance, vulnerability discovery, and exploitation planning, reducing the amount of manual effort required from security professionals. It leverages agent-based architecture and AI reasoning to chain together tools and strategies in a way that mimics experienced human testers. The project is built to be modular and extensible so researchers and red teams can customize behavior or integrate additional tools as needed. By focusing on autonomous decision-making in cybersecurity contexts, PentAGI represents part of the broader trend toward AI-assisted offensive security automation. -
9
GoClaw
GoClaw is OpenClaw rebuilt in Go
GoClaw is an enterprise AI agent platform that rebuilds OpenClaw in Go for stronger concurrency, simpler deployment, and production-oriented multi-agent orchestration. It acts as a gateway that connects language models to tools, channels, memory, and organizational data through a single binary. The platform is built for teams that need multiple agents to collaborate, delegate tasks, and operate across different providers without losing tenant isolation. It supports more than 20 LLM providers and several communication channels, making it flexible for internal automation, customer-facing assistants, and agent operations. GoClaw emphasizes five-layer security, PostgreSQL-backed multi-tenancy, observability, and controlled execution. It is best suited for teams that want to deploy AI agent teams at scale while keeping governance, isolation, and operational visibility built into the system. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
10
Plandex
AI driven development in your terminal
Plandex is an AI-powered project planning and scheduling tool that optimizes resource allocation and workflow efficiency using predictive algorithms. -
11
Beads
A memory upgrade for your coding agent
Beads is an open-source project providing a distributed, structured memory system for AI coding agents, replacing ad-hoc text plans with a git-backed graph that represents tasks, dependencies, and progress in a persistent, queryable format. Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. This approach helps coding agents — and human collaborators — track which tasks depend on others, what has been done, and where workflows branch or reunify without losing important data. By leveraging Git as the storage backbone, the project ensures that memory is persistent, diffable, and sharable, with the ability to roll back, branch, or merge memory states just like source code. -
12
ZeroBot-Plugin
OneBot plugin based on ZeroBot
ZeroBot-Plugin is a Go-based plugin collection for the ZeroBot ecosystem and OneBot-compatible bot frameworks. It packages many bot capabilities into a ready-to-run project that can be used through release binaries, direct local execution, or custom compilation. The repository is aimed mainly at QQ and OneBot-style chatbot deployments, with plugins that can be enabled, modified, or extended by developers. It is designed for users who want a richer bot feature set without writing every plugin from scratch. The project supports a community-oriented workflow where plugins can evolve and new ideas can be tested in related playground repositories. It is most useful for bot operators and Go developers building modular chat automation around the ZeroBot framework. -
13
PicoClaw
Ultra-Efficient AI Assistant in Go
PicoClaw is an ultra-lightweight, open-source personal AI assistant written in Go, architected from the ground up to operate with extremely low memory usage (under 10 MB) and fast boot times, making it suitable for inexpensive hardware platforms and embedded devices. Inspired by earlier AI assistant projects like “nanobot,” it was refactored to emphasize resource efficiency while still supporting meaningful AI-driven interactions such as conversational workflows, planning tasks, and automation. PicoClaw can run on hardware costing as little as $10 and on resource-constrained environments like RISC-V or ARM boards, with cross-architecture portability achieved through a single self-contained binary. The project’s goals include broad platform support (including Linux, macOS, and multiple CPU architectures), rapid startup times that make the assistant feel responsive, and integration with popular messaging platforms via gateways or bots. -
14
Codai
Codai is an AI code assistant that helps developers
Codai is an AI code assistant designed to help developers efficiently manage their daily tasks through a session-based CLI, such as adding new features, refactoring, and performing detailed code reviews. What makes codai stand out is its deep understanding of the entire context of your project, enabling it to analyze your code base and suggest improvements or new code based on your context. This AI-powered tool supports multiple LLM providers, such as OpenAI, Azure OpenAI, Ollama, Anthropic, and OpenRouter. -
15
Calabrass
Calabrass Open-source desktop personal AI agent for local workflows
Calabrass: Open Source Personal AI Agent Calabrass is a free, open-source personal AI assistant built to run locally on your Windows desktop. Chat with AI models, organize files in your local folders, and connect external tools via the Model Context Protocol (MCP) 🔑 Key Features 🔒 100% Private & Local: Runs entirely on your PC. No cloud account required; your data never leaves your device. 📂 Local Workspace Access: Let your agent inspect, read, and manage files directly inside your local folders. 🔌 MCP Integration: Connect external tools, custom functions, and automated workflows using MCP. 🤖 Multi-Model Support: Modern UI designed to interact with flexible AI models for coding and research. ⚡ Easy Setup: Lightweight 64-bit Windows installer with no admin rights needed. 📄 License: MIT 🌐 Official Site: https://calabrass.vercel.app/ 💻 GitHub: https://github.com/CodewithMubasher/Calabrass -
16
Ai-Assistant
Open-source novel writing & AI coding assistant aggregating top models
This is an open‑source, powerful novel‑writing and AI programming assistant with the following core strengths: Model Aggregation: Natively supports the latest DeepSeek and seamlessly integrates with top‑tier models such as Gemini, Claude, GPT, Tongyi Qianwen, Kimi, and others—both domestic and international—delivering a one‑stop intelligent experience. Multimodal Capability: Accurately interprets images and PDF content, and supports invoking advanced models for high‑quality text‑to‑image generation. Security & Management: Conversation records are encrypted and stored locally (SQLite), with convenient history search, bookmarking, and categorization. Precise Context Control: Individual dialogue entries can be freely edited or deleted, allowing precise mastery of context to elicit optimal AI performance. Native, Fluid Experience: Built on a proprietary lightweight GUI framework designed specifically for AI interaction, it offers smooth, typewriter‑style streaming output. -
17
PC-Gui
Lightweight PC-Gui framework for AI, typewriter stream Gemini-like
PC-GUI: A lightweight desktop GUI framework for AI, natively supporting live typewriter-style streaming output like Gemini! 🎉 💡 Core philosophy: Rapid development · Minimal footprint · Native performance. We empower you to build premium desktop tools that users are willing to pay for. PC-GUI helps you meet strong market demands by building compact, powerful, commercial-grade applications with a simple and stable tech stack. We adopt a "backend-first approach" to desktop development: a stable Go backend (net/http) powers a standard web frontend (HTML/CSS/JS), coupled with encrypted SQLite storage for an extremely lightweight and high-performance design. Key Advantages: ✅ Zero runtime dependencies—a single Go binary, no WebView2/Python/Node.js installations required. ✅ Modern UI via HTML—fast templating, AI-friendly styling. ✅ Simple async streaming for AI output vs. complex callbacks elsewhere. Rapidly build compact, commercial-grade desktop applications. -
18
1Panel
1Panel provides an intuitive web interface and MCP Server
1Panel is a comprehensive Linux server management dashboard and MCP server built in Go. It offers UI control over websites, containers, databases, file systems, LLMs, backups, and monitoring, streamlining typical admin workflows via web. -
19
Agent Executor (AX)
Google's open source distributed agent runtime
Agent Executor (AX) is a Google research project for learning discrete choice models through differentiable maximum likelihood estimation. It is designed for situations where a model needs to predict choices from a finite set of alternatives, such as ranking, recommendation, preference modeling, or decision behavior analysis. The project provides JAX-based tools for defining and training choice models with automatic differentiation. It focuses on flexible model construction rather than a single fixed estimator, making it useful for researchers who want to experiment with different utility functions and optimization setups. ax is especially relevant for machine learning and econometrics workflows that need scalable, differentiable approaches to choice modeling. Its main value is giving researchers a modern, accelerator-friendly framework for estimating and analyzing discrete choice behavior. -
20
Agent Substrate
Agent Substrate: the core system
Agent Substrate is an open-source, zero-trust runtime designed to securely execute millions of autonomous AI agents and stateful workloads on Kubernetes. It isolates agents using gVisor sandboxes or Cloud Hypervisor microVMs, protecting compute, networking, and storage resources. Its architecture maps many agents onto a smaller pool of workers to improve infrastructure efficiency. Idle agents can be suspended and restored with their memory and filesystem states intact. The platform provides cryptographic identities, default-deny networking, and external credential injection to limit security exposure. It supports standard OCI images without requiring changes to existing agent frameworks. Compatible environments include Claude Code, Codex, LangChain, and MCP servers. The project remains in pre-1.0 development. -
21
AgentHub
GitHub is for humans. AgentHub is for agents
AgentHub is an experimental platform designed to enable collaboration between autonomous AI agents working on shared codebases. The project functions as a lightweight infrastructure layer that combines a Git repository with a message-board-style communication system, allowing multiple agents to coordinate development tasks within the same workspace. Rather than focusing solely on human collaboration, the system is designed around an “agent-first” paradigm where AI agents can contribute code, discuss changes, and coordinate problem solving. The platform treats code repositories not just as storage for files but as active environments where agents can propose modifications, review changes, and exchange structured messages. This approach reflects emerging research in multi-agent software development where groups of AI systems collaborate on complex engineering problems. -
22
Coze Loop
Next-generation AI Agent Optimization Platform
Coze Loop is a developer-oriented platform that provides full lifecycle management for AI agents, covering everything from prompt engineering to production monitoring. The project aims to simplify the increasingly complex workflow of building reliable AI agents by offering integrated tools for debugging, evaluation, observability, and optimization. Through its visual playground, developers can test prompts interactively and compare outputs across different language models. The platform also includes automated evaluation capabilities that assess agent performance across multiple quality dimensions such as accuracy and compliance. Its observability layer captures detailed execution traces, enabling teams to understand how inputs, prompts, and tools interact during runtime. Designed as an extensible open-source framework, Coze Loop helps teams move beyond ad-hoc prompt experiments toward structured, production-ready AI agent operations. -
23
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. -
24
GoGogot
Lightweight self-hosted AI agent
GoGogot is an experimental automation and agent-oriented project that appears to focus on simplifying task execution and orchestration through lightweight scripting and structured workflows. The system is likely designed to enable rapid execution of commands and processes, acting as a bridge between manual scripting and more advanced agent frameworks. It emphasizes simplicity and speed, allowing developers to define and run tasks without heavy configuration or overhead. The architecture suggests a modular approach where tasks can be composed and reused across different contexts. It may also incorporate elements of automation pipelines, enabling sequential or conditional execution of operations. The project is particularly suited for developers who want to experiment with automation concepts without adopting complex infrastructure. Overall, GoGogot serves as a lightweight entry point into agent-driven or automated task execution systems. -
25
HiClaw
An open source collaborative multi-agent OS
HiClaw is an AI-powered legal assistant framework developed within the AgentScope ecosystem to support intelligent legal reasoning, document analysis, and workflow automation for legal research and compliance tasks. The project combines large language models with agent orchestration systems to process legal documents, interpret regulations, summarize contracts, and assist with legal knowledge retrieval. It is designed to provide structured, explainable workflows that help legal professionals interact with AI systems more transparently and efficiently. hiclaw emphasizes modularity and integration with external legal databases, retrieval systems, and enterprise tooling, enabling customization for different legal domains and operational requirements. The platform explores how agentic AI systems can coordinate reasoning, retrieval, and procedural logic within legal workflows while maintaining contextual awareness.
