🤖 Agenite

Created By
subeshb1a year ago
🤖 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.
Overview

What is Agenite?

Agenite is a powerful TypeScript framework designed for building sophisticated AI agents. It provides a modular, type-safe, and flexible architecture that makes it easy to create, compose, and control AI agents with advanced capabilities.

How to use Agenite?

To use Agenite, install the core packages and at least one provider using npm. You can then create agents and tools in TypeScript to perform various tasks, such as mathematical calculations or data retrieval.

Key features of Agenite?

  • Type safety and developer experience with robust type checking and excellent IDE support.
  • Tool integration with first-class support for function calling and structured error handling.
  • Provider agnostic, supporting multiple AI providers like OpenAI and AWS Bedrock.
  • Advanced architecture with bidirectional flow and built-in state management.
  • Model context protocol (MCP) for connecting LLMs to data sources.

Use cases of Agenite?

  1. Building AI assistants for various domains, such as math or weather.
  2. Creating multi-agent systems for complex problem-solving.
  3. Integrating with different AI models and tools seamlessly.

FAQ from Agenite?

  • Can Agenite be used with any AI provider?

Yes! Agenite supports multiple providers including OpenAI, Anthropic, and AWS Bedrock.

  • Is Agenite suitable for beginners?

Yes! While it is powerful, it is designed to be user-friendly for developers of all levels.

  • What programming language is used?

Agenite is built using TypeScript, which provides type safety and better developer experience.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
subeshb1
Star
54
Language
MDX
License
MIT license

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago