Showing 58 open source projects for "python-kinterbasdb"

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  • 1
    LitterBox

    LitterBox

    A secure sandbox environment for malware developers and red teamers

    LitterBox is a controlled malware-analysis and payload-testing sandbox aimed at red teams who need to validate evasions and behaviors before deployment. It provides an isolated environment to exercise payloads against modern detection stacks, verify signatures and heuristics, and observe runtime characteristics without leaking binaries to third-party vendors. The README frames typical use cases: testing evasion, validating detections, analyzing behavior, and keeping sensitive tooling...
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  • 2
    ArXiv MCP Server

    ArXiv MCP Server

    A Model Context Protocol server for searching and analyzing arXiv

    arxiv-mcp-server bridges AI assistants and the arXiv repository through a clean MCP interface, enabling search, metadata retrieval, and content access without bespoke scraping. With simple tools like “search” and “fetch,” an agent can find papers, pull abstracts, and download PDFs for downstream summarization or analysis. The project includes packaging and CI to publish to PyPI, plus tests and linting for reliability. Issue threads show feature requests such as extracting embedded LaTeX and...
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  • 3
    ContextForge MCP Gateway

    ContextForge MCP Gateway

    A Model Context Protocol (MCP) Gateway & Registry

    MCP Context Forge is a feature-rich gateway and registry that federates Model Context Protocol (MCP) servers and traditional REST services behind a single, governed endpoint. It exposes an MCP-compliant interface to clients while handling discovery, authentication, rate limiting, retries, and observability on the server side. The gateway scales horizontally, supports multi-cluster deployments on Kubernetes, and uses Redis for federation and caching across instances. Operators can define...
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  • 4
    MaxKB

    MaxKB

    Open-source platform for building enterprise-grade agents

    MaxKB (Max Knowledge Brain) is an open-source platform for building enterprise-grade AI agents with strong knowledge retrieval, RAG pipelines, and workflow orchestration. It focuses on practical deployments such as customer support, internal knowledge bases, research assistants, and education, bundling tools for data ingestion, chunking, embedding, retrieval, and answer synthesis. The system exposes flexible tool-use (including MCP), supports multi-model backends, and provides dashboards for...
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  • 5
    FastAPI-MCP

    FastAPI-MCP

    Expose your FastAPI endpoints as Model Context Protocol (MCP) tools

    fastapi_mcp lets you expose existing FastAPI endpoints as Model Context Protocol (MCP) tools with minimal setup, so AI agents can call your app as first-class tools. Rather than acting as a thin converter, it’s built as a native FastAPI extension that understands dependency injection, so you can reuse Depends() for authentication and authorization across your MCP tools. The server speaks directly to your app over its ASGI interface, avoiding extra HTTP hops between the MCP layer and your...
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  • 6
    AgentQL MCP

    AgentQL MCP

    Model Context Protocol server that integrates AgentQL's data

    The AgentQL MCP Server is a Model Context Protocol (MCP) server that integrates AgentQL's data extraction capabilities, enabling users to extract structured data from web pages using natural language prompts. ​
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  • 7

    nimbletools-core

    Open Source MCP Platform - Build, scale, and deploy MCP servers on k8s

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  • 8
    HttpRunner

    HttpRunner

    Testing framework that began with API and performance testing

    HttpRunner is an open-source testing framework that began with API and performance testing and has evolved into a general, extensible test platform. The current major version is implemented in Go, with the legacy Python edition split to a separate repository; this shift emphasizes a single, fast, cross-platform runtime for modern pipelines. It provides declarative test cases, data-driven parametrization, and plugin mechanisms so teams can compose reusable steps and validations at scale. Beyond HTTP(S) APIs, the ecosystem spans UI automation (via a companion UI extension), load testing, and integrations that turn the framework into a one-stop solution for functional and performance needs. ...
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