This is an open-source framework for building stateful, durable AI agents that run on Cloudflare Workers. It provides a runtime for long-lived agents that maintain a persistent identity, local SQL storage, and real-time connections, utilizing a lifecycle where agents hibernate when idle and wake on demand.
Die Hauptfunktionen von cloudflare/agents sind: Agentic AI Frameworks, Stateful Agent Runtimes, Stateful Durable Objects, Agent State Persistence, Durable Execution Runtimes, Model Context Protocol, AI Agent State Coordination, Chat History APIs.
Open-Source-Alternativen zu cloudflare/agents sind unter anderem: langchain-ai/langchain-mcp-adapters — This project provides a translation layer and set of adapters designed to bridge AI agents with the Model Context… i-am-bee/beeai-framework — The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents… voltagent/voltagent. opensquilla/opensquilla — OpenSquilla is an LLM agent orchestration framework designed to coordinate multi-step AI workflows and tool execution… inngest/inngest — Inngest is a durable execution framework and event-driven automation engine designed to orchestrate background… langchain-ai/deepagents — Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing…
This project provides a translation layer and set of adapters designed to bridge AI agents with the Model Context Protocol. It functions as an integration layer that allows agents to operate as protocol-compliant servers and enables the conversion of protocol-based tools into formats compatible with agent frameworks and logic graphs. The adapters facilitate tool interoperability by wrapping external protocol tools for use within agent workflows and exposing internal agent capabilities to any client implementing the Model Context Protocol. This creates a communication bridge that supports inte
The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec
OpenSquilla is an LLM agent orchestration framework designed to coordinate multi-step AI workflows and tool execution using directed acyclic graphs. It functions as a centralized system for managing specialized skill packages and executing complex reasoning sequences. The project distinguishes itself through a routing gateway that directs tasks to different AI providers based on complexity, cost, and performance. It utilizes a multi-tier AI memory system that organizes working, episodic, and semantic knowledge using local embeddings and SQLite, alongside a secure execution sandbox that isolat