Showing 4 open source projects for "logging"

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  • 1
    LabClaw

    LabClaw

    Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)

    ...LabClaw is designed to integrate with various large language models and external tools, enabling flexible configurations that adapt to different use cases such as automation, research, and product prototyping. It also includes logging and observability features that help developers track agent decisions and debug behavior during execution.
    Downloads: 0 This Week
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  • 2
    Antigravity Claude Proxy

    Antigravity Claude Proxy

    Proxy that exposes Antigravity provided claude / gemini models

    Antigravity Claude Proxy is a purpose-built proxy server that enables developers to interface with Claude models through a standardized RESTful API, allowing tools and workflows that expect generic HTTP APIs to operate on Anthropic’s Claude without native support. The project acts as a translation layer, receiving web requests in common formats (such as OpenAI-style endpoints) and forwarding them to Anthropic’s API in the required structure, while converting responses back into a familiar...
    Downloads: 13 This Week
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  • 3
    OpenClaw Opik Observability Plugin

    OpenClaw Opik Observability Plugin

    Official plugin for OpenClaw that exports agent traces to Opik

    OpenClaw Opik Observability Plugin is an open-source plugin designed to add observability and monitoring capabilities to OpenClaw autonomous AI agents by exporting operational traces to the Opik observability platform. The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a...
    Downloads: 7 This Week
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  • 4
    mcp-use

    mcp-use

    A solution to build and deploy MCP agents and applications

    ...Developers can create custom agents (via MCPAgent) that dynamically select the most appropriate server for each task using configurable pipelines or a built-in server manager. It simplifies authentication, access control, audit logging, observability, sandboxed runtime environments, and deployment workflows, whether self-hosted or managed, making MCP development production-ready. With integrations for popular frameworks like LangChain (Python) and LangChain.js (TypeScript), mcp-use accelerates the creation of tool-enabled AI agents.
    Downloads: 2 This Week
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