Showing 3 open source projects for "asynchronous"

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    Context for your AI agents

    Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.

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  • 1
    Anthropic SDK Python

    Anthropic SDK Python

    Provides convenient access to the Anthropic REST API from any Python 3

    ...The SDK supports both synchronous and asynchronous usage (via async/await) depending on context. Importantly, it also supports streaming responses via Server-Sent Events (SSE) so that large outputs can be consumed incrementally rather than waiting for the full response. The client offers helper abstractions for tools (function-style “tools”) and streaming utilities for building interactive agents.
    Downloads: 9 This Week
    Last Update:
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  • 2
    Claude Code SDK Python

    Claude Code SDK Python

    Python SDK for Claude Agent

    claude-code-sdk-python is the Python SDK for Claude Code, Anthropic’s agentic coding system. It provides abstractions to easily query Claude Code (with streaming support) and conduct interactive sessions. The SDK includes core client classes, asynchronous query functions, and support for custom tools and hooks within Claude sessions. It is designed to integrate with local Python workflows and allow developers to embed Claude Code capabilities directly in their applications or scripts. The repo is MIT-licensed and includes documentation and installation instructions (requires Python 3.10+, Node installation of Claude Code). ...
    Downloads: 1 This Week
    Last Update:
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  • 3
    PyTorch-BigGraph

    PyTorch-BigGraph

    Generate embeddings from large-scale graph-structured data

    ...PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions, negative sampling strategies, and typed entities, making it suitable for link prediction and retrieval. Its training loop is built for throughput: asynchronous I/O, memory-mapped tensors, and lock-free updates keep GPUs and CPUs fed even at extreme scale. The toolkit includes evaluation metrics and export tools so learned embeddings can be used in downstream nearest-neighbor search, recommendation, or analytics. In practice, PBG’s design lets practitioners train high-quality graph embeddings.
    Downloads: 0 This Week
    Last Update:
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