Goated Agents is an open-source Go library for constructing AI agents with in-process chain-of-thought reasoning. It provides abstractions to define tools, manage memory, and orchestrate agent workflows. Developers can integrate custom plugins, handle tool invocation, and perform multi-step reasoning within Go applications. The framework is lightweight, type-safe, and designed for seamless integration into existing Go-based systems to automate tasks and build intelligent agents.
AI Platforms & Frameworks
Tool Libraries
#AI Agents
#Autonomous Systems
#Go programming
#chain-of-thought
#multi-step reasoning
#memory management
#plugin architecture
#LLM Integration
#Developer Tools
#Chatbot Development
#process automation
#context persistence
#efficient coding
#transparent logging
#agent orchestration
#high performance
#reliable execution
#Customizable APIs
#software development
#intelligent applications
...
Promote this Tool
Update this Tool
Goated Agents
Goated Agents is an open-source Go library for constructing AI agents with in-process chain-of-thought reasoning. It provides abstractions to define tools, manage memory, and orchestrate agent workflows. Developers can integrate custom plugins, handle tool invocation, and perform multi-step reasoning within Go applications. The framework is lightweight, type-safe, and designed for seamless integration into existing Go-based systems to automate tasks and build intelligent agents.
AI Platforms & Frameworks
Tool Libraries
#AI Agents
#Autonomous Systems
#Go programming
#chain-of-thought
#multi-step reasoning
#memory management
#plugin architecture
#LLM Integration
#Developer Tools
#Chatbot Development
#process automation
#context persistence
#efficient coding
#transparent logging
#agent orchestration
#high performance
#reliable execution
#Customizable APIs
#software development
#intelligent applications
...
Featured
What is Goated Agents?
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.
Who will use Goated Agents?
- Go Developers
- AI Researchers
- Software Architects
- Automation Engineers
- Tech Enthusiasts
How to use the Goated Agents?
- Step1: Install the Goated Agents library via `go get github.com/bcanfield/goated-agents`.
- Step2: Import the package in your Go source: `import "github.com/bcanfield/goated-agents"`.
- Step3: Define and register custom tools implementing the `Tool` interface.
- Step4: Configure agent options including LLM client, memory storage, and tools.
- Step5: Create a new agent instance with `agents.New(...)` and provide configuration.
- Step6: Invoke `agent.Run` or `agent.Step` methods with initial input prompts.
- Step7: Process the agent response and handle tool outputs.
- Step8: Extend functionality using plugins or custom middleware.
Platform
- mac
- windows
- linux
Goated Agents's Core Features & Benefits
The Core Features
- In-process chain-of-thought reasoning
- Customizable tool definition API
- Memory management for context persistence
- Plugin architecture for extensions
- Multi-step reasoning with transparent logs
- LLM integration support
- Type-safe Go implementation
- Agent orchestration and execution
The Benefits
- Seamless Go integration
- High performance and reliability
- Transparent reasoning logs
- Extensible plugin support
- Persistent conversational context
- Modular and maintainable codebase
Goated Agents's Main Use Cases & Applications
- Building Go-based chatbots
- Automating backend workflows
- Developing research prototypes
- Creating intelligent automation pipelines
- Implementing LLM-powered services
FAQs of Goated Agents
Goated Agents Company Information
Goated Agents Reviews
5/5
Goated Agents's Main Competitors and alternatives?
- LangChain
- Microsoft Semantic Kernel
- LlamaIndex
- ReACT Framework
You may also like:
Gobii lets teams create 24/7 autonomous digital workers to automate web research and routine tasks.
Salesloft is an AI-driven platform enhancing sales engagement and workflow automation.
Autogpt is a Rust library for building autonomous AI agents that interact with the OpenAI API to complete multi-step tasks
Angular is a web development framework for building modern, scalable applications.
An AI-driven RAG pipeline builder that ingests documents, generates embeddings, and provides real-time Q&A through customizable chat interfaces.
BrandCrowd offers customizable logos, business cards, and social media designs with thousands of templates.
Refly.AI empowers non-technical creators to automate workflows using natural language and a visual canvas.
Skywork AI is an innovative tool to enhance productivity using AI.
Five9 AI Agents enhance customer interactions with intelligent automation.
Mosaic AI Agent Framework enhances AI capabilities with data retrieval and advanced generation techniques.
Windsurf AI Agent helps optimize windsurfing conditions and gear recommendations.
Glean is an AI assistant platform for enterprise search and knowledge discovery.
NVIDIA Cosmos empowers AI developers with advanced tools for data processing and model training.
AI-driven customer service platform offering efficient communication solutions.
A framework that dynamically routes requests across multiple LLMs and uses GraphQL to handle composite prompts efficiently.
Flowith is a canvas-based agentic workspace which offers free 🍌Nano Banana Pro and other effective models...
AI Library is a developer platform for building and deploying customizable AI agents using modular chains and tools.
A Python-based framework implementing flocking algorithms for multi-agent simulation, enabling AI agents to coordinate and navigate dynamically.
An open-source framework enabling autonomous LLM agents with retrieval-augmented generation, vector database support, tool integration, and customizable workflows.
An AI agent template showing automated task planning, memory management, and tool execution via OpenAI API.
Pipe Pilot is a Python framework that orchestrates LLM-driven agent pipelines, enabling complex multi-step AI workflows with ease.
Open-source repository providing practical code recipes to build AI agents leveraging Google Gemini's reasoning and tool usage capabilities.
RModel is an open-source AI agent framework orchestrating LLMs, tool integration, and memory for advanced conversational and task-driven applications.
An open-source framework implementing cooperative multi-agent reinforcement learning for autonomous driving coordination in simulation.
Python library with Flet-based interactive chat UI for building LLM agents, featuring tool execution and memory support.
Agentic Workflow is a Python framework to design, orchestrate, and manage multi-agent AI workflows for complex automated tasks.
Clone, Design, and Create Expressive AI Voices in Seconds, with Perfect Sound Effects and Music.
A GitHub demo showcasing SmolAgents, a lightweight Python framework for orchestrating LLM-powered multi-agent workflows with tool integration.
A Python framework for easily defining and executing AI agent workflows declaratively using YAML-like specifications.
OpenSpiel provides a library of environments and algorithms for research in reinforcement learning and game theoretic planning.
A Pythonic framework implementing the Model Context Protocol to build and run AI agent servers with custom tools.
pyafai is a Python modular framework to build, train, and run autonomous AI agents with plug-in memory and tool support.
LangGraph enables Python developers to construct and orchestrate custom AI agent workflows using modular graph-based pipelines.
A Python wrapper enabling seamless Anthropic Claude API calls through existing OpenAI Python SDK interfaces.
Agent Adapters provides pluggable middleware to integrate LLM-based agents with various external frameworks and tools seamlessly.
Java-Action-Storage is a LightJason module that logs, stores, and retrieves agent actions for distributed multi-agent applications.
LinkAgent orchestrates multiple language models, retrieval systems, and external tools to automate complex AI-driven workflows.
All-in-one AI video creation studio that turns any text and images into full videos up to 30 minutes.