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2 Repos

Awesome GitHub RepositoriesClient-Side Tool Execution Bridges

Interop layers that allow remote agents to trigger local functions on the user's device.

Distinct from Agent-to-Server Bridges: Distinct from Agent-to-Server Bridges: focuses on the remote-to-local execution flow for hardware or private data access.

Explore 2 awesome GitHub repositories matching development tools & productivity · Client-Side Tool Execution Bridges. Refine with filters or upvote what's useful.

Awesome Client-Side Tool Execution Bridges GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • ag-ui-protocol/ag-uiAvatar von ag-ui-protocol

    ag-ui-protocol/ag-ui

    14,395Auf GitHub ansehen↗

    ag-ui is an agent-frontend interoperability layer and communication protocol designed to connect AI agent backends with web and mobile user interfaces. It provides a standardized event-driven framework for exchanging messages, session state, and tool calls, utilizing a generative UI framework to render dynamic interface components and structured content triggered by an agent. The project distinguishes itself through an SSE-based event streamer that delivers real-time incremental model responses and reasoning telemetry. It enables bi-directional state synchronization and allows remote agents t

    Synchronizes application state and executes local client-side tools in response to remote agent requests.

    Pythonag-ui-protocolagent-frontendagent-ui
    Auf GitHub ansehen↗14,395
  • atmosphere/atmosphereAvatar von Atmosphere

    Atmosphere/atmosphere

    3,780Auf GitHub ansehen↗

    Atmosphere is a Java-based framework for building and coordinating AI agents. It provides a real-time transport layer for streaming data via WebSockets, SSE, gRPC, and WebTransport, alongside a multi-agent orchestration framework for managing agent fleets through sequential, parallel, and graph-based execution workflows. The project features a durable workflow engine that persists agent state as snapshots, allowing long-running tasks to survive system restarts and incorporate human-in-the-loop approvals. It also implements Model Context Protocol servers to expose tools, resources, and prompt

    Provides a correlation-tracked bridge to invoke tools registered on connected clients and receive asynchronous results.

    Javaacpagentic-aiembabel
    Auf GitHub ansehen↗3,780
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