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An open-source framework leveraging the Model Context Protocol and simple workflow patterns to streamline the development and orchestration of reliable AI agents.
This project provides a foundational framework for constructing sophisticated AI agents by implementing the Model Context Protocol (MCP) and enabling the definition of agent behaviors through simple, reusable workflow patterns. It aims to simplify the development lifecycle of AI agents, making them more robust and easier to manage.
Building reliable, stateful, and complex AI agents that effectively manage context and orchestrate multiple steps/tools is challenging. Existing approaches often lack structure, standardization, and efficient context handling, leading to brittle and hard-to-maintain agent systems.
Standardized interface for managing conversation history and operational context across agent interactions.
Define agent workflows using simple, declarative patterns for complex task execution and tool chaining.
Easily integrate various large language models (LLMs) and external tools within agent workflows.
Designed for testability and debugging, facilitating the development of robust and predictable agents.
The framework is suitable for any application requiring agents that maintain context, perform multi-step operations, or integrate with external systems.
Develop intelligent customer support agents that can handle multi-turn conversations, retrieve information from knowledge bases, and escalate issues.
Improved customer satisfaction and reduced workload for human agents.
Create agents that can automate business processes, such as data extraction, analysis, and report generation, by chaining together different tools and APIs.
Increased operational efficiency and reduced manual effort in repetitive tasks.
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