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Awesome GitHub RepositoriesAgent-Specific Query Routing

Directing user queries to specific AI agents based on semantic analysis of intent and conversation history.

Distinct from Semantic Query Routing: Routes to AI agent identities rather than database shards or network paths

Explore 7 awesome GitHub repositories matching data & databases · Agent-Specific Query Routing. Refine with filters or upvote what's useful.

Awesome Agent-Specific Query Routing GitHub Repositories

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  • n8n-io/self-hosted-ai-starter-kitn8n-io 的头像

    n8n-io/self-hosted-ai-starter-kit

    14,997在 GitHub 上查看↗

    This project provides a dockerized AI workflow stack and orchestration templates for deploying a self-hosted AI environment. It establishes a localized infrastructure for building autonomous agents and model chains that process private data on-premises without external cloud dependencies. The environment is designed to support autonomous agent development, allowing models to dynamically select tools, execute shell commands, and interact with local file systems. It includes integrated vector database support to enable retrieval augmented generation and private document analysis. The stack cov

    Directs user queries to specific AI agents based on semantic analysis of intent and conversation history.

    aiai-agentslow-code
    在 GitHub 上查看↗14,997
  • basedhardware/omiBasedHardware 的头像

    BasedHardware/omi

    12,869在 GitHub 上查看↗

    Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions. The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio

    Directs user queries to specific processing paths or AI agent personas based on the required context.

    Dartaiappbci
    在 GitHub 上查看↗12,869
  • shaxiu/xianyuautoagentshaxiu 的头像

    shaxiu/XianyuAutoAgent

    8,051在 GitHub 上查看↗

    XianyuAutoAgent is a multi-agent orchestration framework and large language model chatbot designed to automate customer service and sales interactions on a marketplace platform. It functions as a system of specialized AI entities, including context-aware conversational agents and automated price negotiation bots, to manage peer-to-peer conversational commerce. The framework distinguishes itself through an intent-based routing mechanism that dispatches queries to specialized agents and utilizes a multi-expert collaborative decision process to determine response strategies. It features dedicate

    Implements a system to dispatch user queries to specialized AI agents based on semantic intent analysis.

    Pythonagentchatbotllm
    在 GitHub 上查看↗8,051
  • awslabs/agent-squadawslabs 的头像

    awslabs/agent-squad

    7,663在 GitHub 上查看↗

    Agent Squad is a multi-agent system orchestrator and language model agent orchestration framework. It serves as an AI workflow automation engine and tool integration layer designed to coordinate teams of specialized agents to solve complex tasks through routing, parallel execution, and state management. The project is distinguished by its ability to dynamically compose purpose-specific agents on-demand and route requests based on intent, language, or domain expertise. It supports advanced coordination patterns, including parallel subtask distribution, sequential task pipelines, and the abilit

    Analyzes requests and conversation history to direct each query to the most appropriate specialized agent.

    Pythonagentic-aiagentsai-agents
    在 GitHub 上查看↗7,663
  • genkit-ai/genkitgenkit-ai 的头像

    genkit-ai/genkit

    6,141在 GitHub 上查看↗

    Genkit is an LLM application framework and generative AI developer toolkit designed for building production AI applications. It serves as an AI workflow orchestrator that coordinates model calls and agentic tool usage through type-safe execution flows. The project provides a unified model interface and plugin architecture to standardize access to diverse large language models, vector stores, and telemetry backends. It distinguishes itself with a dedicated observability suite for tracing execution steps and a developer toolkit for prompting, debugging, and evaluating AI logic via a local inter

    Provides logic to route complex queries to specialized AI agents based on the subject matter.

    TypeScript
    在 GitHub 上查看↗6,141
  • katanemo/planokatanemo 的头像

    katanemo/plano

    5,120在 GitHub 上查看↗

    Plano is an AI agent orchestrator and LLM gateway proxy that unifies access to multiple AI providers through a single interoperable interface. It functions as a model routing engine that decouples applications from specific vendors using semantic aliases, allowing traffic to be shifted between providers without modifying application code. The system distinguishes itself with intent-based agent routing, which directs prompts to specialized agents based on semantic analysis. It features an interceptor-based filter chain system that acts as guardrail middleware to enforce safety policies, rewrit

    Directs user prompts to specialized AI agents by analyzing semantic intent and conversation history.

    Rustai-gatewayai-gateway-supportenvoy
    在 GitHub 上查看↗5,120
  • 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

    Directs user inputs to specific tools or sub-agents using semantic intent and graph-based routing patterns.

    Kotlinagentframeworkagentic-aiagents
    在 GitHub 上查看↗3,735
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