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Awesome GitHub RepositoriesAnalysis Result Exporters

Serializes analysis results into machine-readable JSON structures for external integration.

Distinct from Structured Data Exporters: Distinct from Structured Data Exporters: focuses on analysis results rather than general document representations.

Explore 140 awesome GitHub repositories matching data & databases · Analysis Result Exporters. Refine with filters or upvote what's useful.

Awesome Analysis Result Exporters GitHub Repositories

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  • getredash/redashgetredash 的头像

    getredash/redash

    28,653在 GitHub 上查看↗

    Redash is a self-hosted analytics platform and SQL data visualization tool. It provides a web-based SQL query editor for writing, executing, and scheduling database queries, and functions as a business intelligence dashboard for monitoring metrics via visual widgets. The platform distinguishes itself through its data source connectors, which integrate with various SQL, NoSQL, and API-based stores to retrieve information for analysis. It enables self-service analytics by allowing users to run queries with dynamic parameters and supports shared data reporting via public links or embedded dashbo

    Allows downloading query outputs into portable formats such as CSV, TSV, and Excel.

    Pythonanalyticsathenabi
    在 GitHub 上查看↗28,653
  • sharkdp/hyperfinesharkdp 的头像

    sharkdp/hyperfine

    28,316在 GitHub 上查看↗

    Hyperfine is a command-line benchmarking tool used to measure the execution time of shell commands through multiple runs and statistical analysis. It functions as a comparative benchmarking utility and a shell performance analyzer, allowing for the evaluation of multiple commands against a reference baseline to determine relative speed. The tool distinguishes itself by isolating actual command performance through shell overhead correction and the ability to bypass the shell entirely using system calls. It supports parameterized execution, enabling benchmarks to run across a range of varying i

    Saves timing data and statistical analysis into common file formats for use in external reporting tools.

    Rust
    在 GitHub 上查看↗28,316
  • langchain-ai/deepagentslangchain-ai 的头像

    langchain-ai/deepagents

    25,006在 GitHub 上查看↗

    Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai

    Allows downloading experiment results in CSV format for external analysis and sharing.

    Pythonagentsdeepagentslangchain
    在 GitHub 上查看↗25,006
  • mxrch/ghuntmxrch 的头像

    mxrch/GHunt

    19,089在 GitHub 上查看↗

    GHunt is a Google account investigator and open-source intelligence framework designed to retrieve publicly available information and metadata associated with Google accounts. It functions as an OSINT data extractor and offensive security framework used to identify user identities and uncover hidden metadata. The tool extracts public profile data from various Google services and exports the findings into structured JSON formats. This allows for the collection and analysis of digital footprints to support security research and reconnaissance.

    Saves extracted account data into machine-readable JSON structures for external integration and analysis.

    Python
    在 GitHub 上查看↗19,089
  • cayleygraph/cayleycayleygraph 的头像

    cayleygraph/cayley

    15,043在 GitHub 上查看↗

    Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model. It functions as an RDF quad store, managing information through subjects, predicates, objects, and labels. The system features a modular graph store architecture with pluggable backends, allowing it to swap between in-memory storage and various external persistent databases. It includes a GraphQL-inspired API and a dedicated data visualizer for the interactive exploration of nodes and edges. Query capabilities cover bidirectional path traversal and multi-syntax execution usi

    Returns query findings as JavaScript arrays, single values, or custom JSON objects.

    Go
    在 GitHub 上查看↗15,043
  • llmware-ai/llmwarellmware-ai 的头像

    llmware-ai/llmware

    14,838在 GitHub 上查看↗

    llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

    Exports interaction history and model responses into machine-readable JSONL or CSV formats for auditing purposes.

    Python
    在 GitHub 上查看↗14,838
  • dask/daskdask 的头像

    dask/dask

    13,746在 GitHub 上查看↗

    Dask 是一个并行计算框架和分布式任务调度器,旨在将 Python 数据科学工作流从单机扩展到大型集群。它作为一个集群资源管理器,通过将任务及其依赖项表示为有向无环图来编排计算逻辑。这种架构允许系统在管理复杂执行要求的同时,自动将工作负载分配到可用硬件上。 该项目通过一个延迟评估引擎脱颖而出,该引擎将数据操作推迟到明确请求时才执行,从而实现全局图优化和高效的资源分配。它结合了内存感知数据溢出功能,以防止在处理超过可用内存的数据集时系统崩溃,并利用任务图融合将操作序列组合成单个执行步骤,从而最大限度地减少调度开销和节点间通信。 该平台为大规模数据分析提供了全面的功能面,包括对分布式机器学习、高性能计算集成和并行数据处理的支持。它提供了用于集群生命周期管理、性能分析和任务执行实时监控的广泛工具。用户可以在各种基础设施上部署这些环境,包括本地硬件、云提供商、容器化系统和高性能计算集群。

    Writes distributed collections to various formats or computational objects for downstream processing.

    Pythondasknumpypandas
    在 GitHub 上查看↗13,746
  • ydataai/pandas-profilingydataai 的头像

    ydataai/pandas-profiling

    13,610在 GitHub 上查看↗

    This project is an exploratory data analysis framework and profiling tool designed to generate comprehensive statistical reports from Pandas and Spark DataFrames. It functions as a data quality profiler that identifies missing values, duplicates, and high correlations within tabular datasets. The tool distinguishes itself through specialized capabilities for time-series analysis, extracting temporal statistics, seasonality, and auto-correlation plots. It also includes a dataset comparison utility to identify structural or content changes between different versions of a dataset. The analysis

    Saves profiling reports as portable HTML files, JSON structures, or interactive notebook widgets.

    Python
    在 GitHub 上查看↗13,610
  • pandas-profiling/pandas-profilingpandas-profiling 的头像

    pandas-profiling/pandas-profiling

    13,609在 GitHub 上查看↗

    This project is an exploratory data analysis library and profiling tool for Pandas and Spark DataFrames. It automates the initial investigation of datasets by generating comprehensive descriptive analysis reports, statistical summaries, and data quality warnings. The system functions as a data quality profiler to detect missing values, duplicate rows, and type inconsistencies. It includes a dataset comparison tool for identifying structural and content shifts between different versions of the same data, as well as specialized tools for time-series analysis to calculate auto-correlation and se

    Exports profiling results into various formats including HTML files, JSON, and interactive notebook widgets.

    Python
    在 GitHub 上查看↗13,609
  • data-centric-ai-community/fg-data-profilingData-Centric-AI-Community 的头像

    Data-Centric-AI-Community/fg-data-profiling

    13,609在 GitHub 上查看↗

    This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for Pandas and Spark dataframes. It serves as a system for computing descriptive statistics, identifying correlations, and analyzing univariate and multivariate data patterns. The tool provides specialized capabilities for comparing different versions of datasets to identify changes in data quality and distributions. It includes a dedicated profiler for time-dependent data to extract statistical information such as seasonality and auto-correlation. The software covers a broad an

    Serializes profiling reports into HTML, JSON, and interactive widgets for external system integration.

    Python
    在 GitHub 上查看↗13,609
  • projectdiscovery/subfinderprojectdiscovery 的头像

    projectdiscovery/subfinder

    13,105在 GitHub 上查看↗

    Subfinder is a security reconnaissance framework designed for subdomain enumeration and attack surface management. It functions as a discovery engine that identifies and maps internet-exposed infrastructure, cloud-hosted assets, and network ranges to maintain a comprehensive inventory of an organization's digital footprint. The project distinguishes itself through a modular, template-driven scanning engine that executes security checks against discovered assets. It leverages cloud-native asset discovery to query provider APIs and infrastructure metadata, while supporting distributed agent orc

    Formats and exports vulnerability scan results into structured data.

    Gobugbountyhackinghacktoberfest
    在 GitHub 上查看↗13,105
  • millionco/react-doctormillionco 的头像

    millionco/react-doctor

    12,769在 GitHub 上查看↗

    React Doctor is a static analysis tool for React codebases designed to identify security, performance, and architectural issues. It functions as a codebase health diagnostic engine that produces numeric health scores and structured diagnostics to improve maintainability. The tool features an incremental code scanner that evaluates only the files changed between branches or staged in the working tree to provide fast feedback. It is designed to operate as a quality gate within CI pipelines, allowing for the enforcement of codebase health standards by failing builds on critical errors. The anal

    Serializes analysis results into machine-readable JSON structures for integration with external reporting dashboards.

    TypeScriptagentscode-reviewdoctor
    在 GitHub 上查看↗12,769
  • vibrantlabsai/ragasvibrantlabsai 的头像

    vibrantlabsai/ragas

    12,659在 GitHub 上查看↗

    Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin

    Serializes evaluation outcomes and calculated scores into structured files for offline analysis.

    Pythonevaluationllmllmops
    在 GitHub 上查看↗12,659
  • bootandy/dustbootandy 的头像

    bootandy/dust

    11,843在 GitHub 上查看↗

    Dust is a command line disk usage analyzer written in Rust. It functions as a disk space visualization tool and recursive directory size scanner used to identify large files and directories through a formatted list of disk consumption across a filesystem. The tool differentiates itself by providing a recursive tree view to visualize space distribution and the ability to output directory and file size data in machine-readable formats for external processing and analysis. Its broader capabilities include filesystem auditing and storage capacity management. This is achieved through file and dir

    Serializes filesystem analysis results into machine-readable JSON structures for external integration.

    Rust
    在 GitHub 上查看↗11,843
  • perspective-dev/perspectiveperspective-dev 的头像

    perspective-dev/perspective

    10,981在 GitHub 上查看↗

    Perspective is a columnar data analytics engine and high-performance visualization component powered by WebAssembly. It provides a system for analyzing and visualizing large or streaming datasets through interactive data grids and charts, utilizing a compiled binary to achieve near-native performance within the browser. The project distinguishes itself through a WebSocket-based data streaming interface and deep Apache Arrow integration, which minimize memory overhead when synchronizing tables between servers and clients. It acts as a remote query proxy capable of translating visualization con

    Serializes the current state of filtered or aggregated datasets into portable formats like JSON and Apache Arrow.

    C++analyticsbidata-visualization
    在 GitHub 上查看↗10,981
  • gleitz/howdoigleitz 的头像

    gleitz/howdoi

    10,840在 GitHub 上查看↗

    howdoi is a command-line coding answer engine that retrieves programming solutions and code snippets from the web for display directly in the terminal. It functions as a web-based code search tool that uses natural language queries to find technical answers without requiring a web browser. The tool provides a JSON-exportable query system, allowing search results to be output as structured data for integration with other software and text editors. It features terminal-based knowledge retrieval that includes local caching and stashing of answers to reduce network latency and avoid search engine

    Outputs search results as structured JSON for integration with other software and text editors.

    Python
    在 GitHub 上查看↗10,840
  • ryoppippi/ccusageryoppippi 的头像

    ryoppippi/ccusage

    10,826在 GitHub 上查看↗

    This project is a command-line utility designed to monitor and analyze token consumption and financial expenditure for AI coding assistants. By parsing local session logs directly on the user's machine, it provides a privacy-focused way to track development activity without transmitting sensitive data to external servers. The tool distinguishes itself through its ability to aggregate disparate log formats from multiple coding assistants into a unified, schema-agnostic representation. It features a decoupled pricing engine that allows users to apply custom model-specific cost multipliers, over

    Formats analysis results into machine-readable structures for integration with external tools.

    TypeScript
    在 GitHub 上查看↗10,826
  • google/osv-scannergoogle 的头像

    google/osv-scanner

    10,565在 GitHub 上查看↗

    osv-scanner is a software composition analysis tool and vulnerability scanner that checks project dependencies and container images against the Open Source Vulnerabilities database. It functions as a dependency remediation tool and can be integrated into custom Go applications as a programmable security library. The project distinguishes itself through a remediation workflow that includes an interactive terminal user interface and automated scripting for upgrading vulnerable packages in lockfiles and manifests. It employs call-graph reachability analysis to determine if vulnerable code is act

    Writes vulnerability reports to a specified filesystem path instead of printing to standard output.

    Goscannersecurity-auditsecurity-tools
    在 GitHub 上查看↗10,565
  • promptfoo/promptfoopromptfoo 的头像

    promptfoo/promptfoo

    10,529在 GitHub 上查看↗

    Promptfoo is an evaluation framework designed for testing, benchmarking, and red-teaming language models and agentic workflows. It provides a unified environment to run prompts against multiple providers, allowing developers to systematically validate model outputs against objective assertions, semantic similarity metrics, and custom grading rubrics. The platform distinguishes itself through a provider-agnostic execution layer and a stateful orchestrator capable of simulating multi-turn conversations and complex tool-use trajectories. It includes a dedicated adversarial mutation pipeline that

    Exports test results into structured formats to facilitate external reporting and long-term performance tracking.

    TypeScriptcici-cdcicd
    在 GitHub 上查看↗10,529
  • hatoo/ohahatoo 的头像

    hatoo/oha

    10,338在 GitHub 上查看↗

    oha 是一个命令行 HTTP 负载测试和基准测试工具。它作为一个延迟分析工具和性能监视器,向 Web 服务器发送大量请求以测量稳定性并识别性能瓶颈。 该工具具有一个终端用户界面,可在活动测试期间显示实时延迟统计信息和请求动画。它包括一个用于校正协调遗漏(coordinated omission)的机制,确保响应时间百分位数和延迟分布报告准确。 其功能涵盖 HTTP 负载生成、通过随机目标 URL 进行流量模拟以及请求速率控制。该项目通过 TLS 和双向 TLS 支持安全通信,并提供以 JSON、CSV 和 SQLite 格式导出性能指标的选项。 该工具可以在容器化环境中执行,以保持不同主机系统之间的一致性。

    Serializes performance test results into machine-readable formats like JSON, CSV, and SQLite for external analysis.

    Rust
    在 GitHub 上查看↗10,338
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  5. Analysis Result Exporters

探索子标签

  • Classification Result ExportersSaves prediction outputs as formatted JSON, visualized images, or prints them to the terminal for review. **Distinct from Analysis Result Exporters:** Distinct from Analysis Result Exporters: specifically targets classification prediction outputs (category labels, scores), not general analysis results.
  • Forecast Result ExportersSaves prediction outputs to CSV or JSON files, and provides structured attributes for programmatic access to the results. **Distinct from Analysis Result Exporters:** Distinct from Analysis Result Exporters: specifically targets time series forecast outputs (future values, confidence intervals), not general analysis results.
  • Proxy Audit Result Exporters1 个子标签Exports validated proxy data and speed test results to local files or cloud storage for later review. **Distinct from Analysis Result Exporters:** Distinct from Analysis Result Exporters: specifically exports proxy audit and speed test results, not general analysis results.
  • Query Result Analysis1 个子标签Tools for analyzing database result sets through data grids and interactive charting. **Distinct from Query Result Exporters:** Focuses on the visual analysis and exploration of results rather than the act of exporting them to files.
  • Query Result Exporters8 个子标签Utilities for exporting the results of database queries into portable file formats. **Distinct from Analysis Result Exporters:** Focuses on exporting raw query outputs to files like CSV/Excel, rather than serializing analysis results for machine integration.
  • Signal Data ExportersSaving selected time ranges, filtered samples, or demodulated results to a file for further analysis. **Distinct from Analysis Result Exporters:** Distinct from Analysis Result Exporters: focuses on raw signal data export, not structured analysis results.
  • Vulnerability Report Exporters1 个子标签Writes vulnerability analysis findings to specific filesystem paths for persistent storage. **Distinct from Query Result Exporters:** Exports security scan reports to files, unlike query result exporters which handle raw database output