ruvnet/claude-flow
Claude-Flow is an enterprise-grade AI orchestration platform that enables deployment and coordination of intelligent multi-agent swarms and autonomous workflows for building advanced conversational AI systems.
Awesome AI Agents › AI Agent Orchestration
AgentEra/Agently
Agently is a GenAI application development framework designed to enable developers to build AI agent-native applications quickly and easily. It provides a simple and intuitive way to interact with AI agents in code using structured data and chained-calls syntax, allowing developers to focus on business logic rather than the complexities of language model integration. The framework supports managing complex GenAI workflows through its Agently Workflow feature and allows seamless switching between different AI models without rewriting application code. The framework is designed to simplify the development of AI agent applications by treating AI agents as callable functions within the code. This approach replaces traditional programming logic with natural language or natural-language-like expressions, making AI agents integral to solving problems within applications. Agently offers a variety of example applications, including SQL query generation from natural language based on database metadata, survey agents that collect customer feedback, and educational agents with search capabilities. Agently provides comprehensive documentation and tutorials, including a step-by-step guide to developing complex LLM applications, and offers resources such as Colab notebooks and code examples to help developers get started. The framework supports easy installation via pip and includes features for human review integration and function calling within AI agent workflows. Overall, Agently aims to accelerate the development of AI-powered applications by providing a robust, flexible, and easy-to-use framework that abstracts the complexities of working with large language models and AI agents, enabling developers to build sophisticated AI-native applications efficiently.
https://github.com/AgentEra/Agently
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