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Awesome GitHub RepositoriesExecution Interception Hooks

Mechanisms to intercept and modify the flow of AI agent execution, such as tool calls and outputs.

Distinct from Cross-Session Behavioral Preference Models: Focuses on the runtime interception of agent behaviors rather than session preferences or DB hooks

Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Execution Interception Hooks. Refine with filters or upvote what's useful.

Awesome Execution Interception Hooks GitHub Repositories

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  • github/copilot-sdkgithub 的头像

    github/copilot-sdk

    7,233在 GitHub 上查看↗

    This project is a software development kit and framework for building AI agent orchestration, session management, and tool integration systems. It provides a backend infrastructure for hosting remote AI sessions and coordinating multi-agent workflows using large language models. The SDK enables the definition of specialized agents and the orchestration of complex tasks through parallel workstreams. It distinguishes itself by offering a multi-tenant backend capable of horizontal scaling and a headless server runtime that separates session execution from the client interface. The system covers

    Provides hooks to control tool usage and transform output results during the session lifecycle.

    TypeScript
    在 GitHub 上查看↗7,233
  • jetbrains/koogJetBrains 的头像

    JetBrains/koog

    3,735在 GitHub 上查看↗

    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

    Implements hooks to observe or modify behavior during model calls and agent startup.

    Kotlinagentframeworkagentic-aiagents
    在 GitHub 上查看↗3,735
  • i-am-bee/beeai-frameworki-am-bee 的头像

    i-am-bee/beeai-framework

    3,304在 GitHub 上查看↗

    The BeeAI Framework is an LLM agent framework and multi-agent orchestration engine used to build autonomous agents that coordinate reasoning, tool execution, and complex workflows. It functions as a structured AI output controller and RAG integration library, providing a unified interface to manage multiple language model providers. The framework is distinguished by its implementation of the Model Context Protocol, allowing agents, tools, and models to be shared between different AI platforms and hosted as agentic tooling servers. It enables the design of collaborative agent teams through dec

    Provides hooks to intercept and modify the flow of agent execution, enabling safety checks and logging for tool calls.

    Pythonagentsaiai-agent
    在 GitHub 上查看↗3,304
  • ibm/mcp-context-forgeIBM 的头像

    IBM/mcp-context-forge

    3,310在 GitHub 上查看↗

    mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a single consistent interface for discovery and execution. It acts as a centralized proxy that aggregates multiple servers and APIs, allowing AI agents to access and invoke a unified set of tools, prompts, and resources. The project distinguishes itself through a multi-protocol translation bridge that converts communication between standard I/O, SSE, gRPC, and REST to enable interoperability between disparate tool servers. It includes a comprehensive LLM evaluation framework for

    Modifies prompts and tool calls via execution hooks to implement safety and validation guardrails.

    Pythonagentsaiapi-gateway
    在 GitHub 上查看↗3,310
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