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Comprehensive 状態保持メモリ Tools for Every Need

Get access to 状態保持メモリ solutions that address multiple requirements. One-stop resources for streamlined workflows.

状態保持メモリ

  • Langroid

    An open-source Python framework for building and customizing multimodal AI agents with integrated memory, tools, and LLM support.
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    What is Langroid?
    Langroid provides a comprehensive agent framework that empowers developers to build sophisticated AI-driven applications with minimal overhead. It features a modular design allowing custom agent personas, stateful memory for context retention, and seamless integration with large language models (LLMs) such as OpenAI, Hugging Face, and private endpoints. Langroid’s toolkits enable agents to execute code, fetch data from databases, call external APIs, and process multimodal inputs like text, images, and audio. Its orchestration engine manages asynchronous workflows and tool invocations, while the plugin system facilitates extending agent capabilities. By abstracting complex LLM interactions and memory management, Langroid accelerates the development of chatbots, virtual assistants, and task automation solutions for diverse industry needs.
    Langroid Core Features
    • Modular agent architecture
    • Stateful memory management
    • LLM integrations (OpenAI, Hugging Face)
    • Tool and plugin system
    • Multimodal input processing
    • Orchestration engine for workflows
    • Asynchronous task handling
    • Extensible API for custom integrations
    Langroid Pro & Cons

    The Cons

    No explicit pricing information available publicly.
    No direct links to GitHub or open source repository found.
    Lacks mention of end-user applications or marketplaces, more framework focused.
    Potentially steep learning curve for non-expert developers.

    The Pros

    Focus on multi-agent programming, enabling complex LLM orchestration.
    Modular design with reusable agent and task abstractions.
    Supports a variety of LLMs, vector-stores, and caching mechanisms.
    Detailed observability and lineage tracking of agent interactions.
    Developer-friendly tooling with Pydantic-based function calling and tools/plugins.

  • Playbooks AI

    Playbooks AI is an open-source low-code framework to design, deploy, and manage custom AI agents with modular workflows.
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    What is Playbooks AI?
    Playbooks AI is a developer framework for building AI agents through a declarative playbook DSL. It enables integration with various LLMs, custom tools, and memory stores. With a CLI and web UI, users can define agent behavior, orchestrate multi-step workflows, and monitor execution. Features include tool routing, stateful memory, version control, analytics, and multi-agent collaboration, making it easy to prototype and deploy production-ready AI assistants.

  • OCO-Agent

    An open-source Python AI agent framework enabling autonomous LLM-driven task execution with customizable tools and memory.
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    What is OCO-Agent?
    OCO-Agent leverages OpenAI-compatible language models to transform plain-language prompts into actionable workflows. It provides a flexible plugin system for integrating external APIs, shell commands, and data-processing routines. The framework maintains conversation history and context in memory, enabling long-running, multi-step tasks. With a CLI interface and Docker support, OCO-Agent accelerates prototyping and deployment of intelligent assistants for operations, analytics, and developer productivity.
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