Taiat is an open-source TypeScript AI agent framework that empowers developers to build autonomous agents powered by large language models. It provides seamless integration with OpenAI APIs, tool definitions, customizable memory implementations, and action management. With taiat, developers can configure conversational workflows, define tasks, and orchestrate multi-step reasoning. It abstracts API calls, memory storage, and decision loops into simple interfaces.
Taiat is an open-source TypeScript AI agent framework that empowers developers to build autonomous agents powered by large language models. It provides seamless integration with OpenAI APIs, tool definitions, customizable memory implementations, and action management. With taiat, developers can configure conversational workflows, define tasks, and orchestrate multi-step reasoning. It abstracts API calls, memory storage, and decision loops into simple interfaces.
Taiat (TypeScript AI Agent Toolkit) is a lightweight, extensible framework for building autonomous AI agents in Node.js and browser environments. It enables developers to define agent behaviors, integrate with large language model APIs such as OpenAI and Hugging Face, and orchestrate multi-step tool execution workflows. The framework supports customizable memory backends for stateful conversations, tool registration for web searches, file operations, and external API calls, as well as pluggable decision strategies. With taiat, you can rapidly prototype agents that plan, reason, and execute tasks autonomously, from data retrieval and summarization to automated code generation and conversational assistants.
Who will use Taiat?
Node.js developers
AI researchers
Software engineers building chatbots
Tech companies creating custom AI agents
AI hobbyists and enthusiasts
How to use the Taiat?
Step1: Install Taiat via npm with `npm install taiat`.
Step2: Import Taiat and configure your LLM API key.
Step3: Define and register custom tools and a memory backend.
Step4: Create an agent instance with prompts and toolset.
Step5: Call `agent.run()` to execute the agent workflow.
Step6: Process and handle the agent’s output and extend behavior.
Platform
web
mac
windows
linux
Taiat's Core Features & Benefits
The Core Features
LLM integration
Tool registration
Customizable memory backends
Multi-step reasoning workflows
Pluggable decision strategies
Action management
The Benefits
Rapid prototyping of AI agents
Abstracts LLM and API complexity
Extensible and modular architecture
Open-source and community-driven
Lightweight and easy to integrate
Taiat's Main Use Cases & Applications
Conversational AI chatbots
Automated data retrieval and analysis
Intelligent summarization agents
Automated code generation bots
Task automation via external tools
FAQs of Taiat
What is Taiat?
Which programming languages and platforms does Taiat support?
How do I install Taiat?
Which LLM providers are supported by Taiat?
How do I define custom tools in Taiat?
Does Taiat support conversational memory?
Can I extend Taiat with my own decision strategies?