3 repositorios
Mechanisms for maintaining a global state accessible by all processing nodes in a graph.
Distinct from Reactive Data Sharing: Distinct from Reactive Data Sharing: focuses on global state access for workflow nodes rather than reactive object references.
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This project is a comprehensive suite of AI tools and frameworks, featuring an LLM multi-agent orchestrator, an autonomous agent runtime, and a stateful application framework. It provides the infrastructure to build and manage specialized AI agents capable of coordinating complex tasks through graph-based workflows and shared state. The system is distinguished by its implementation of the Model Context Protocol, allowing for standardized resource discovery and communication between AI clients and servers. It further includes an AI-powered documentation generator designed to analyze source cod
Maintains a shared global state across graph nodes to track conversation history and task progress for agents.
PocketFlow is a graph-based framework for designing and executing large language model operations and reasoning patterns. It serves as an orchestrator for building goal-oriented autonomous agents, multi-agent systems, and retrieval-augmented generation pipelines. The system is distinguished by its ability to coordinate autonomous AI agents that use shared memory and tools to solve complex goals, supported by a structured output engine that enforces schema-consistent responses. It utilizes graph-based workflow orchestration to manage sequences of model operations and supports supervisor-based
Maintains a global data structure that allows all nodes to read and write to decouple schema from logic.
Shares session state across multiple servers via a shared key-value store for distributed agent management.