Comprehensive 多後端支持 Tools for Every Need

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多後端支持

  • ChainStream enables streaming submodel chaining inference for large language models on mobile and desktop devices with cross-platform support.
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    What is ChainStream?
    ChainStream is a cross-platform mobile and desktop inference framework that streams partial outputs from large language models in real time. It breaks LLM inference into submodel chains, enabling incremental token delivery and reducing perceived latency. Developers can integrate ChainStream into their apps using a simple C++ API, select preferred backends like ONNX Runtime or TFLite, and customize pipeline stages. It runs on Android, iOS, Windows, Linux, and macOS, allowing for truly on-device AI-driven chat, translation, and assistant features without server dependencies.
    ChainStream Core Features
    • Real-time token streaming inference
    • Submodel chain execution
    • Cross-platform C++ SDK
    • Multi-backend support (ONNX, MNN, TFLite)
    • Low-latency on-device LLM
    ChainStream Pro & Cons

    The Cons

    Project is still a work in progress with evolving documentation
    May require advanced knowledge to fully utilize framework capabilities
    No direct pricing or commercial product details available yet

    The Pros

    Supports continuous context sensing and sharing for enhanced agent interaction
    Open-source with active community engagement and contributor participation
    Provides comprehensive documentation for multiple user roles
    Developed by a reputable AI research institute
    Demonstrated in academic and industry workshops and conferences
  • AI memory system enabling agents to capture, summarize, embed, and retrieve contextual conversation memories across sessions.
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    What is Memonto?
    Memonto functions as a middleware library for AI agents, orchestrating the complete memory lifecycle. During each conversation turn, it records user and AI messages, distills salient details, and generates concise summaries. These summaries are converted into embeddings and stored in vector databases or file-based stores. When constructing new prompts, Memonto performs semantic searches to retrieve the most relevant historical memories, enabling agents to maintain context, recall user preferences, and provide personalized responses. It supports multiple storage backends (SQLite, FAISS, Redis) and offers configurable pipelines for embedding, summarization, and retrieval. Developers can seamlessly integrate Memonto into existing agent frameworks, boosting coherence and long-term engagement.
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