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9 个仓库

Awesome GitHub RepositoriesAgent Connectivity Interfaces

Tools and protocols for establishing communication between local agents and remote agent systems.

Distinguishing note: Focuses on the connectivity layer for distributed agent systems rather than general networking.

Explore 9 awesome GitHub repositories matching artificial intelligence & ml · Agent Connectivity Interfaces. Refine with filters or upvote what's useful.

Awesome Agent Connectivity Interfaces GitHub Repositories

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  • agno-agi/agnoagno-agi 的头像

    agno-agi/agno

    40,717在 GitHub 上查看↗

    Agno is an agent operating system designed to manage the lifecycle, tool execution, and persistent state of autonomous agents across distributed infrastructure. It provides a unified runtime environment that wraps diverse agent frameworks into a consistent, interoperable protocol, allowing developers to build and deploy complex multi-agent systems that coordinate tasks and delegate sub-processes. The platform distinguishes itself through a robust governance and orchestration layer that includes human-in-the-loop approval gates, role-based access control, and a centralized API gateway. It feat

    AgentOS provides a simple asynchronous client interface to connect to remote agent systems, retrieve configuration, list agents, and execute tasks.

    Pythonagentsaiai-agents
    在 GitHub 上查看↗40,717
  • zeroclaw-labs/zeroclawzeroclaw-labs 的头像

    zeroclaw-labs/zeroclaw

    31,920在 GitHub 上查看↗

    Zeroclaw is a modular framework for building and deploying autonomous agents that integrate AI models, messaging platforms, and hardware interfaces. It functions as a multi-agent orchestrator and embedded systems controller, providing a unified runtime for managing agent lifecycles, memory, and security policies across diverse environments. The system distinguishes itself through its focus on secure, verifiable hardware and software orchestration. It enforces strict security boundaries, including command allowlisting, resource throttling, and interactive human-in-the-loop approval for sensiti

    Exposes agent capabilities to external clients and development environments through standardized HTTP, WebSocket, and JSON-RPC interfaces.

    Rust
    在 GitHub 上查看↗31,920
  • letta-ai/lettaletta-ai 的头像

    letta-ai/letta

    21,168在 GitHub 上查看↗

    Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

    Facilitates interaction with agents deployed on remote infrastructure through centralized interfaces.

    Pythonaiai-agentsllm
    在 GitHub 上查看↗21,168
  • aiming-lab/autoresearchclawaiming-lab 的头像

    aiming-lab/AutoResearchClaw

    13,453在 GitHub 上查看↗

    AutoResearchClaw is an agentic system designed to automate the scientific research process. It functions as an autonomous research agent and workflow automator that manages the entire lifecycle of a project, from initial hypothesis generation and literature review to experimental execution and the production of LaTeX-formatted academic papers. The system distinguishes itself through a multi-agent research pipeline that utilizes structured debates for hypothesis refinement and peer review. It employs a branch-and-merge architecture to explore parallel research directions and integrates human-i

    Links command line agents and messaging platforms to coordinate research execution and team communication.

    Python
    在 GitHub 上查看↗13,453
  • pipecat-ai/pipecatpipecat-ai 的头像

    pipecat-ai/pipecat

    12,846在 GitHub 上查看↗

    Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag

    Implements protocols for establishing real-time communication between local clients and remote conversational agent systems.

    Pythonaichatbot-frameworkchatbots
    在 GitHub 上查看↗12,846
  • nearai/ironclawnearai 的头像

    nearai/ironclaw

    12,456在 GitHub 上查看↗

    Ironclaw is an LLM orchestration framework and AI agent gateway designed to connect large language models with external tools, messaging interfaces, and persistent memory systems. It functions as a communication layer that routes interactions between users and AI models via HTTP webhooks and various messaging channels. The system focuses on secure tool execution through a WebAssembly sandbox and isolated containers, which allows the framework to run untrusted code and dynamically generate new tools from natural language descriptions. Security middleware provides prompt injection defense and s

    Implements connectivity interfaces that bridge AI assistants to users via CLIs, web gateways, and messaging channels.

    Rust
    在 GitHub 上查看↗12,456
  • microsoft/agent-frameworkmicrosoft 的头像

    microsoft/agent-framework

    7,277在 GitHub 上查看↗

    The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit

    Implements protocols for resolving agent capabilities via metadata and establishing communication across remote endpoints.

    Pythonagent-frameworkagentic-aiagents
    在 GitHub 上查看↗7,277
  • getstream/vision-agentsGetStream 的头像

    GetStream/Vision-Agents

    6,029在 GitHub 上查看↗

    Emits events when speech-to-text connections are established or closed with disconnection reasons.

    Pythonagentic-aiagentsai
    在 GitHub 上查看↗6,029
  • azure/data-api-builderAzure 的头像

    Azure/data-api-builder

    1,447在 GitHub 上查看↗

    Data API builder is a service that automatically generates REST and GraphQL endpoints directly from database schemas and stored procedures. By interpreting database metadata, it provides immediate programmatic access to information without requiring the development of custom backend routes or manual query logic. The engine distinguishes itself through a metadata-driven translation layer that maps incoming HTTP requests to native database queries while enforcing granular, role-based access control and row-level security policies. It includes a dedicated bridge for AI agents, allowing these sys

    Connects intelligent agents to database schemas using standardized protocols for natural language data interaction.

    C#apiazuredatabase
    在 GitHub 上查看↗1,447
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  2. Artificial Intelligence & ML
  3. Agent Connectivity Interfaces

探索子标签

  • STT Connection EmittersEmits events when a speech-to-text connection is established or closed, including disconnection reasons. **Distinct from Agent Connectivity Interfaces:** Distinct from Agent Connectivity Interfaces: focuses on emitting lifecycle events for STT connections specifically rather than general agent connectivity.
  • TTS Connection EmittersEmits events when a text-to-speech connection is established or closed, including disconnection reasons. **Distinct from STT Connection Emitters:** Distinct from STT Connection Emitters: focuses on TTS connection lifecycle events rather than STT.