Goated Agents simplifies building sophisticated AI-driven autonomous systems in Go. By embedding chain-of-thought processing directly in the language runtime, developers can implement multi-step reasoning with transparent intermediate reasoning logs. The library offers a tool definition API, allowing agents to call external services, databases, or custom code modules. Memory management support enables persistent context across interactions. Plugin architecture facilitates extending core capabilities such as tool wrappers, logging, and monitoring. Goated Agents leverages Go’s performance and static typing to deliver efficient, reliable agent execution. Whether constructing chatbots, automation pipelines, or research prototypes, Goated Agents provides the building blocks to orchestrate complex reasoning flows and integrate LLM-driven intelligence seamlessly into Go applications.

GoLC provides developers with a comprehensive toolkit for constructing language model chains and agents in Go. At its core, it includes chain management, customizable prompt templates, and seamless integration with major LLM providers. Through document loaders and vector stores, GoLC enables embedding-based retrieval, powering RAG workflows. The framework supports stateful memory modules for conversational contexts and a lightweight agent architecture to orchestrate multi-step reasoning and tool invocations. Its modular design allows plugging in custom tools, data sources, and output handlers. With Go-native performance and minimal dependencies, GoLC streamlines AI pipeline development, making it ideal for building chatbots, knowledge assistants, automated reasoning agents, and production-grade backend AI services in Go.

Agent-Go provides a modular framework for building autonomous AI agents in Go. It integrates LLM providers (such as OpenAI), vector-based memory stores for long-term context retention, and a flexible planning engine that breaks down user requests into executable steps. Developers define and register custom tools (APIs, databases, or shell commands) that agents can invoke. A conversation manager tracks dialog history, while a configurable planner orchestrates tool calls and LLM interactions. This allows teams to rapidly prototype AI-driven assistants, automated workflows, and task-oriented bots in a production-ready Go environment.

Go-Pilot leverages advanced language models to understand your Go project context after you connect your repository. It provides real-time code completions, creates new functions or types from high-level descriptions, auto-generates tests, and performs code reviews with style and performance recommendations. Its refactoring suggestions help maintain code quality, while its explanation mode demystifies complex code blocks. Go-Pilot integrates seamlessly into your workflow, minimizing context switches and boosting productivity.

Serving as a robust Go implementation of the popular LlamaIndex framework, Llama-Index-Go offers end-to-end capabilities for constructing and querying vector-based indexes from textual data. Users can load documents via built-in or custom loaders, generate embeddings using OpenAI or other providers, and store vectors in memory or external vector databases. The library exposes a QueryEngine API that supports keyword and semantic search, boolean filters, and retrieval-augmented generation with LLMs. Developers can extend parsers for markdown, JSON, or HTML, and plug in alternative embedding models. Designed with modular components and clear interfaces, it provides high performance, easy debugging, and flexible integration in microservices, CLI tools, or web applications, enabling rapid prototyping of AI-powered search and chat solutions.

multiagent-golang provides a structured approach to building multi-agent systems in Go. It introduces an Agent abstraction where each agent can be equipped with various sensors to perceive its environment and actuators to take actions. Agents run concurrently using Go routines and communicate through dedicated messaging channels. The framework also includes an environment simulation layer to handle events, manage the agent lifecycle, and track state changes. Developers can easily extend or customize agent behaviors, configure simulation parameters, and integrate additional modules for logging or analytics. It streamlines the creation of scalable, concurrent simulations for research and prototyping.