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arcade ai

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Basic Information

Arcade AI Platform is a developer-focused platform and library collection for building, deploying, and managing tools for AI agents. The repository hosts modular packages that split core capabilities into focused libraries: arcade-core for platform functionality and schemas, arcade-tdk as a Tool Development Kit that exposes an @tool decorator for creating tools, arcade-serve which provides serving infrastructure for workers and MCP servers, arcade-evals which supplies an evaluation framework for testing tool performance, and arcade-cli which offers a command-line interface to interact with the platform. The README documents installation options for development and production, a Makefile-driven development workflow, and references client libraries for Python, JavaScript, and Go. The project is intended for developers creating agent tooling, running worker processes, and integrating Arcade components into broader AI systems.

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603
App URL
https://github.com/ArcadeAI/arcade-ai
Github Repository
https://github.com/ArcadeAI/arcade-ai/blob/main/README.md

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App Details

Features
Modular library layout that separates responsibilities into distinct packages so developers can install only what they need. A Tool Development Kit (arcade-tdk) that simplifies authoring tools via an @tool decorator. Serving infrastructure (arcade-serve) designed for workers and MCP servers to host and scale agent tooling. An evaluation framework (arcade-evals) for measuring and testing tool performance. A command-line interface (arcade-cli) for managing platform operations. Client libraries for multiple languages (Python, JavaScript, Go) to ease integration. Development tooling including uv workspace sync, a Makefile with test, lint, type check and build commands, and package extras for installing combinations like evals or full platform components.
Use Cases
Arcade helps developer teams shorten the time to build and operate AI agent tools by providing reusable, composable libraries and tooling. The TDK accelerates tool creation with a structured decorator-based API. Serving components let teams run workers and MCP servers to host tools in production environments. The evals package enables automated testing and benchmarking of tool behavior to improve reliability. The CLI and language clients provide straightforward integration and operational controls. Modular package installs and documented development workflows reduce friction for both development and production use. Community channels and documentation are referenced for support and onboarding, making it easier for teams to adopt and extend the platform.

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