Comprehensive ビジネスロジック自動化 Tools for Every Need

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ビジネスロジック自動化

  • AgentGateway connects autonomous AI agents to your internal data sources and services for real-time document retrieval and workflow automation.
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    What is AgentGateway?
    AgentGateway provides a developer-focused environment for creating multi-agent AI applications. It supports distributed agent orchestration, plugin integration, and secure access control. With built-in connectors for vector databases, REST/gRPC APIs, and common services like Slack and Notion, agents can query documents, execute business logic, and generate responses autonomously. The platform includes monitoring, logging, and role-based access controls, making it easy to deploy scalable, auditable AI solutions across enterprises.
    AgentGateway Core Features
    • Multi-agent orchestration
    • Plugin and API integration
    • Vector database support
    • OpenAI function calling
    • REST and gRPC endpoints
    • Authentication and RBAC
    • Logging and monitoring
    AgentGateway Pro & Cons

    The Cons

    No direct product pricing details available, possibly requiring custom pricing or enterprise support
    May require technical expertise to set up and integrate effectively
    Primarily focused on agent connectivity; may not suit standalone AI tool needs

    The Pros

    Open source with community support
    Supports leading AI protocols for agent and tool communication
    Unified security, observability, and governance for agent communications
    Federated MCP endpoint to manage multiple tool integrations efficiently
    Developer portal for easy agent and tool configuration and debugging
    Automatically converts existing REST APIs into MCP-native tools
    AgentGateway Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://agentgateway.dev
  • A lightweight Python library enabling developers to define, register, and automatically invoke functions through LLM outputs.
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    What is LLM Functions?
    LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.
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