What LLM providers are supported?
The framework supports OpenAI, Anthropic (Claude), AWS Bedrock (Claude and Titan), and local models via Ollama.
Agenite MCP Server
@subeshb1
基本情報
Agenite MCP Server について
🤖 Build powerful AI agents with TypeScript. Agenite makes it easy to create, compose, and control AI agents with first-class support for tools, streaming, and multi-agent architectures. Switch seamlessly between providers like OpenAI, Anthropic, AWS Bedrock, and Ollama.
Agenite MCP Server の設定
Agenite MCP Server のツール
ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Agenite?
Agenite is a TypeScript framework for building sophisticated AI agents. It offers a modular, type-safe, and flexible architecture for creating, composing, and controlling AI agents with advanced capabilities like tool integration, multi-agent coordination, and step-based execution.
How to use Agenite?
Install core packages (@agenite/agent, @agenite/tool, @agenite/llm) and at least one provider (e.g., @agenite/openai). Then create an Agent instance with a provider, tools, and instructions, and execute it using agent.execute() or iterate with agent.iterate() for streaming control.
Key features of Agenite
- TypeScript-first design with robust type checking
- Provider agnostic: supports OpenAI, Anthropic, Bedrock, Ollama
- First-class tool integration with JSON Schema validation
- Step-based execution using JavaScript generators
- Built-in state management with reducers and middleware system
- Model Context Protocol (MCP) client for standardized data access
Use cases of Agenite
- Building single-purpose assistants with custom tool sets
- Creating multi-agent workflows where specialist agents are coordinated
- Streaming real-time responses with fine-grained control over LLM calls and tool results
- Connecting LLMs to external data sources via MCP servers
- Debugging agent execution with middleware like pretty-logger
よくある質問
How do I define tools for an agent?
Tools are defined using the `Tool` class with a name, description, input schema, and an async `execute` function. Inputs are validated against the schema.
What is the Model Context Protocol (MCP)?
MCP is a standardized protocol for connecting LLMs to data sources (web content, filesystem, databases). Agenite provides an MCP client implementation.
Can I create multi-agent systems?
Yes – agents can be nested. A coordinator agent can delegate tasks to specialist agents, each with their own tools and instructions.
What are the runtime requirements?
Agenite requires Node.js (for npm packages) and TypeScript. Provider packages may need API keys or environment configuration (e.g., AWS credentials for Bedrock).
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