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Comprehensive API呼び出し Tools for Every Need

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API呼び出し

  • LangGraph-Swift

    LangGraph-Swift enables composing modular AI agent pipelines in Swift with LLMs, memory, tools, and graph-based execution.
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    What is LangGraph-Swift?
    LangGraph-Swift provides a graph-based DSL for constructing AI workflows by chaining nodes representing actions such as LLM queries, retrieval operations, tool calls, and memory management. Each node is type-safe and can be connected to define execution order. The framework supports adapters for popular LLM services like OpenAI, Azure, and Anthropic, as well as custom tool integrations for calling APIs or functions. It includes built-in memory modules to retain context across sessions, debugging and visualization tools, and cross-platform support for iOS, macOS, and Linux. Developers can extend nodes with custom logic, enabling rapid prototyping of chatbots, document processors, and autonomous agents within native Swift.
    LangGraph-Swift Core Features
    • Graph-based composable pipelines
    • LLM integration via adapters
    • Memory modules for context
    • Tool and API integrations
    • Type-safe Swift DSL
    • Debugging and visualization utilities
    LangGraph-Swift Pro & Cons

    The Cons

    Limited to Swift language environment.
    Documentation and community support appear minimal.
    No explicit information about open-source status or active maintenance.

    The Pros

    Enables advanced visualization of language model constructs.
    Facilitates complex language data representations.
    Designed specifically for Swift developers, integrating well in Apple ecosystem.
    Supports natural language processing tasks through graph-based representation.
    LangGraph-Swift 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://bsorrentino.github.io/LangGraph-Swift/documentation/langgraph/

  • LLM Functions

    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.

  • Agent Starter Pack

    An open-source Google Cloud framework offering templates and samples to build conversational AI agents with memory, planning, and API integrations.
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    What is Agent Starter Pack?
    Agent Starter Pack is a developer toolkit that scaffolds intelligent, interactive agents on Google Cloud. It offers templates in Node.js and Python to manage conversation flows, maintain long-term memory, and perform tool and API invocations. Built on Vertex AI and Cloud Functions or Cloud Run, it supports multi-step planning, dynamic routing, observability, and logging. Developers can extend connectors to custom services, build domain-specific assistants, and deploy scalable agents in minutes.
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