Agent-Go is an open-source Go library for crafting autonomous AI agents that leverage large language models (LLMs), vector memory stores, and custom tools. It offers configurable planners to decompose complex tasks, conversational memory to retain context across interactions, and extensible tool interfaces for external actions. Developers can plug in various LLM providers, define domain-specific tools, and orchestrate multi-step agent workflows, accelerating the creation of intelligent automation and conversational applications.
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Agent-Go
Agent-Go is an open-source Go library for crafting autonomous AI agents that leverage large language models (LLMs), vector memory stores, and custom tools. It offers configurable planners to decompose complex tasks, conversational memory to retain context across interactions, and extensible tool interfaces for external actions. Developers can plug in various LLM providers, define domain-specific tools, and orchestrate multi-step agent workflows, accelerating the creation of intelligent automation and conversational applications.
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What is Agent-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.
Who will use Agent-Go?
- Go developers building AI applications
- Backend engineers integrating intelligent workflows
- AI researchers prototyping autonomous agents
- System architects designing task automation
- Open-source contributors in AI and ML
How to use the Agent-Go?
- Step1: Install the module via go get github.com/aviate-labs/agent-go
- Step2: Import the agent package in your Go application
- Step3: Configure an LLM provider and a vector memory store
- Step4: Define and register custom tools for external actions
- Step5: Initialize the Agent with planner, memory, and tools
- Step6: Call agent.Run(ctx, "your query") and handle the response
Platform
- Linux
- Mac
- Windows
Agent-Go's Core Features & Benefits
The Core Features
- Pluggable LLM integrations (OpenAI, etc.)
- Vector memory store for context retention
- Configurable planning engine
- Custom tool interfaces
- Conversation manager
The Benefits
- Accelerates AI agent development
- Modular and extensible architecture
- Production-ready Go implementation
- Easy integration of external APIs and services
- Maintains long-term conversational context
Agent-Go's Main Use Cases & Applications
- Conversational chatbots with memory
- Automated workflow orchestration
- Document retrieval and summarization
- Task-based virtual assistants
- Domain-specific knowledge agents
FAQs of Agent-Go
Agent-Go Company Information
Agent-Go Reviews
5/5
Agent-Go's Main Competitors and alternatives?
LangChain Go
Autogen for Go
go-llm
go-openai
Haystack (Python)
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