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Basic Information

This repository is a curated, community-maintained hub that collects and organizes hundreds to thousands of AI agent resources in one place. It is designed as a discovery index for people interested in autonomous and agentic systems, aggregating frameworks, agent projects, benchmarks, datasets, LLM models, prompt engineering material, tools, deployment examples, courses, ethics and security references, testing and workflow integrations. The README groups content into clear sections for using, learning, building and deploying agents and highlights practical examples, popular projects and how to contribute. The collection is updated frequently and aims to help researchers, engineers and enthusiasts find working agents, libraries, tutorials and infrastructure components needed to evaluate, construct and run agent-driven applications.

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Features
Organized, browsable categories covering applications, learning resources, benchmarks, datasets, frameworks, LLM models, prompt engineering, deployment, security, testing, tools and workflows. Extensive curated lists of open-source projects and examples that include multi-agent frameworks, IDEs, memory and observability tools, RAG tooling, and end-to-end platforms. Table of contents and annotated entries make it easy to find repositories, example apps and courses. Community contribution guidelines, daily update cadence, a newsletter for updates, and an Apache 2.0 license for reuse. The README provides immediate use cases, recommended starter projects, and pointers to popular frameworks and toolchains to accelerate discovery and evaluation.
Use Cases
This collection helps developers, researchers and practitioners quickly locate the right components to experiment with or deploy AI agents. Users can find frameworks to orchestrate multi-agent behavior, benchmarks and datasets to evaluate agent performance, model recommendations for tool use, and prompt engineering and security guidance for safer deployments. Educators and learners get curated courses and cookbooks. Product teams and hobbyists can identify deployment examples, orchestration tools, and observability or memory modules. The repo lowers onboarding time by aggregating examples, integration patterns, and community projects while inviting contributions so the index stays current and practical under an open-source license.

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