6 repository-uri
Middleware hooks specifically designed to intercept and modify the steps of an AI agent's reasoning loop.
Distinct from Workflow Extensibility: Focuses on agentic reasoning steps and tool error handling rather than general IDE or dev-tool plugins.
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Open-SWE is an asynchronous software engineering agent and orchestrator designed to automate end-to-end coding tasks and pull request reviews. It functions as a middleware framework that coordinates long-running AI operations across multiple subagents, utilizing state persistence and human-in-the-loop oversight to manage complex workflows. The system is distinguished by its use of isolated remote Linux sandboxes for secure code execution and shell command processing. It features a webhook-driven integration platform that triggers automated engineering tasks via mentions and events in GitHub,
Runs deterministic middleware hooks to inject messages, handle tool errors, or notify users of step limits.
Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che
Injects custom logic at lifecycle stages to modify instructions, state, or tool invocations.
This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu
Provides middleware hooks to run custom logic via callbacks after each task finishes for dynamic flow control.
This project is a Java-based framework integration that provides an AI agent runtime, a graph-based AI workflow engine, and an LLM orchestration framework for Spring applications. It enables the development of stateful autonomous agents and the implementation of retrieval-augmented generation systems using document processing and vector databases. The framework distinguishes itself through a graph-based workflow runtime for designing complex AI pipelines with conditional routing and persistent state. It supports multi-agent orchestration via service-discovery coordination and provides human-i
Provides middleware hooks to intercept and modify the steps of an AI agent's reasoning loop.
Claude Agent SDK is a Python library from Anthropic for building AI agents that use Claude’s tool‑calling, streaming, and session‑management capabilities. It provides a structured framework for intercepting and logging every tool call an agent makes, managing conversation sessions across multiple turns, and controlling which tools the agent is allowed to invoke through configurable permission rules. The SDK distinguishes itself with middleware‑driven tool interception that lets developers block, modify, or require approval for tool calls before or after execution. A permission policy engine e
Middleware hooks intercept and modify AI agent tool calls before or after execution.
Koog is an LLM agent framework used to build autonomous entities that execute tool-based workflows. It utilizes a graph-based workflow engine to define agent behaviors and decision paths as a directed graph of nodes and edges. The framework distinguishes itself through a model provider orchestrator that enables dynamic switching, load balancing, and automatic fallbacks between different AI backends. It implements the Model Context Protocol to connect agents to remote tool servers and features a RAG memory system using vector embeddings to maintain long-term conversation context. The project
Provides middleware hooks to intercept and modify the steps of an agent's reasoning loop.