Showing 345 open source projects for "testing"

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  • 1
    ASSERT

    ASSERT

    Requirement-driven evaluation harness for AI agents and LLM

    ...ASSERT is designed to close the gap between what a system is supposed to do and what evaluation actually measures. It is useful for responsible AI teams, product teams, and developers who need traceable, spec-aligned testing.
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  • 2
    LLMFarm

    LLMFarm

    llama and other large language models on iOS and MacOS offline

    ...It emphasizes modularity, allowing users to integrate different models, backends, or tools depending on their needs and hardware capabilities. LLMFarm is particularly useful for developers and researchers experimenting with local AI systems, as it lowers the barrier to entry for running and testing models without extensive setup. It also supports optimization techniques to improve performance on limited hardware, making it viable for smaller-scale deployments.
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  • 3
    Get Physics Done (GPD)

    Get Physics Done (GPD)

    The first open-source agentic AI physicist

    Get Physics Done (GPD) is an open-source project designed to accelerate scientific research in physics by leveraging modern computational tools and automation techniques. It aims to simplify the process of performing simulations, calculations, and experimental analysis by providing structured workflows that integrate computational physics methods with reproducible research practices. The project focuses on reducing the friction involved in setting up experiments, running simulations, and...
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  • 4
    Apache Hamilton

    Apache Hamilton

    Helps data scientists define testable self-documenting dataflows

    Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes. Hamilton automatically analyzes these functions and constructs a directed acyclic graph...
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    Build Agents and Models on One Platform

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  • 5
    Hephaestus

    Hephaestus

    Semi-Structured Agentic Framework. Workflows build themselves

    ...Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression, allowing workflows to evolve as new discoveries are made. For example, if an agent detects a bug or optimization opportunity, it can automatically create a new task and integrate it into the workflow. ...
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  • 6
    Deta Surf

    Deta Surf

    Personal AI Notebooks. Organize files & webpages and generate notes

    Surf is an open-source AI-driven development tool designed to simplify the process of building and experimenting with artificial intelligence applications. The platform provides a streamlined development environment where developers can test models, run experiments, and deploy small AI services with minimal infrastructure overhead. It focuses on simplicity and speed, allowing developers to prototype ideas quickly without managing complex cloud configurations. Surf integrates modern AI...
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  • 7
    WeClone

    WeClone

    One-stop solution for creating your digital avatar from chat history

    ...By processing large volumes of conversation data, WeClone can build a profile of an individual’s writing tone, vocabulary preferences, and conversational tendencies. Developers can use the resulting model to create chatbots that simulate a specific user’s communication patterns for testing or research purposes. Overall, WeClone explores the idea of digital identity replication through machine learning and conversational modeling.
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  • 8
    Neovim 99

    Neovim 99

    Neovim AI agent done right

    Neovim 99 is an experimental GitHub repository created by well-known developer and educator ThePrimeagen that explores what he describes as the “ideal AI workflow” for developers who want a streamlined, high-quality integration of AI tooling into real coding environments — particularly focused on tools like Neovim and agent-centric workflows. Rather than a polished end-product, this repo serves as a playground for testing, iterating, and documenting workflows that integrate AI agents directly into everyday coding tools, emphasizing rapid feedback loops, automation, and minimal friction. The project often includes configuration files, scripts, and examples that show how to coerce modern AI assistants into productive roles within editors, plugins, and terminal workflows, with a focus on “no excuses” productivity. ...
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  • 9
    Loki Mode

    Loki Mode

    Multi-agent autonomous startup system for Claude Code

    Loki Mode is a multi-agent autonomous execution system designed to take structured product requirements or specifications and autonomously drive the creation, testing, deployment, and scaling of complex software projects using a large team of specialized AI agents. It orchestrates dozens of agent types across swarms that handle designated roles — such as architecture, coding, QA, deployment, and business workflows — running in parallel to cover both engineering and operational tasks without continuous human intervention. ...
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  • 10
    SERA CLI

    SERA CLI

    A tool to use the Ai2 Open Coding Agents Soft-Verified Agents

    SERA CLI is a command-line tool created by AllenAI to enable developers to interact with the SERA (Soft-Verified Efficient Repository Agents) model family using Claude Code as the execution front end. It provides a convenient interface for deploying, testing, and using SERA models without needing to write scaffold code from scratch, acting as both a proxy and utility wrapper to simplify workflows that involve large agent models. Through sera-cli, users can connect to local or cloud-hosted SERA deployments, including via Modal for quick GPU provisioning and model caching, which helps accelerate experiments. ...
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  • 11
    Vibium

    Vibium

    Browser automation for AI agents and humans

    ...This design makes it ideal for AI agents that need to interact with the web, perform tasks, or simulate human interactions in a browser environment, and it also works well for traditional testing and automation workflows. Vibium strikes a balance between AI-native capabilities and conventional developer usability by offering language bindings and client APIs for JavaScript and Python.
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  • 12
    RamaLama

    RamaLama

    Simplifies the local serving of AI models from any source

    ...RamaLama supports multiple model registries and offers a REST API or chatbot interface for interacting with running models, making it flexible for local development, testing, or integration into larger systems.
    Downloads: 0 This Week
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  • 13
    Sapiens

    Sapiens

    High-resolution models for human tasks

    ...The project emphasizes long-horizon reasoning and cross-modal grounding—connecting language, perception, and action into a single agentic model capable of following abstract goals. It includes simulation environments, datasets, and benchmarks for testing grounded understanding, imitation learning, and decision-making. The system’s modular pipeline supports both imitation-based and reinforcement-based training strategies, allowing flexible experimentation with different embodiments and tasks.
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  • 14
    Mosec

    Mosec

    A high-performance ML model serving framework, offers dynamic batching

    Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API.
    Downloads: 0 This Week
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  • 15
    RecBole

    RecBole

    A unified, comprehensive and efficient recommendation library

    ...We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. It can be installed from pip, conda and source, and is easy to use. We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation and sub-packages.
    Downloads: 0 This Week
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  • 16
    Cangjie Skill

    Cangjie Skill

    Distill high-value content like books, long videos, podcasts, and more

    cangjie-skill is a workflow for converting books and other long-form knowledge into executable AI skill packs. Its goal is structured reuse rather than producing another summary or set of reading notes. The seven-stage RIA-TV++ pipeline analyzes the full source, extracts candidate frameworks, verifies them, constructs skill modules, links related ideas, pressure-tests behavior, and prepares delivery files. Each accepted skill records supporting material, a reconstructed explanation,...
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  • 17
    Gemini MCP Tool

    Gemini MCP Tool

    MCP server that enables AI assistants to interact with Google Gemini

    ...It supports workflows where users can reference files or directories using simple syntax, allowing the AI to process entire projects or documents in a single request. The tool also includes sandbox execution features, which allow safe testing of code or commands in an isolated environment without affecting the host system.
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  • 18
    Browserbase MCP Server

    Browserbase MCP Server

    Allow LLMs to control a browser with Browserbase and Stagehand

    ...It leverages Browserbase infrastructure along with Stagehand to deliver high-performance browser automation with improved speed and efficiency through caching and optimized execution pipelines. The system supports multiple AI models and integrates seamlessly into agent workflows, making it suitable for applications such as web scraping, testing, and intelligent automation. It also includes advanced capabilities such as screenshot capture, DOM analysis, and session persistence, enabling complex interactions across multiple browsing sessions.
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  • 19
    Sandbox Agent

    Sandbox Agent

    Run Coding Agents in Sandboxes

    Sandbox Agent by Rivet is an experimental framework for running AI agents in controlled, isolated environments where they can safely execute code, interact with tools, and perform autonomous tasks without risking system integrity. It is designed to provide a secure sandbox that allows agents to test actions, manipulate files, and run commands while enforcing strict boundaries and monitoring capabilities. The project focuses on enabling more reliable and auditable agent behavior by separating...
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  • 20
    Agent Starter Pack

    Agent Starter Pack

    Ship AI Agents to Google Cloud in minutes, not months

    Agent Starter Pack is a production-focused framework that provides pre-built templates and infrastructure for rapidly developing and deploying generative AI agents on Google Cloud. It is designed to eliminate the complexity of moving from prototype to production by bundling essential components such as deployment pipelines, monitoring, security, and evaluation tools into a single package. Developers can create fully functional agent projects with a single command, generating both backend and...
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  • 21
    Agent Behavior Monitoring

    Agent Behavior Monitoring

    The open source post-building layer for agents

    Agent Behavior Monitoring is an open-source framework designed to monitor, evaluate, and improve the behavior of AI agents operating in real or simulated environments. The system focuses on agent behavior monitoring by collecting interaction data and analyzing how agents perform across different scenarios and tasks. Developers can use the framework to observe agent actions in both online production environments and offline evaluation settings, making it useful for debugging and performance...
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  • 22
    Prometheus-Eval

    Prometheus-Eval

    Evaluate your LLM's response with Prometheus and GPT4

    Prometheus-Eval is an open-source framework designed to evaluate the outputs of large language models using specialized evaluator models known as Prometheus. The project provides tools, datasets, and scripts that allow developers and researchers to measure the quality of LLM responses through automated scoring rather than relying solely on human evaluators. It implements an “LLM-as-a-judge” approach in which a dedicated language model analyzes instruction–response pairs and assigns scores or...
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  • 23
    AICGSecEval

    AICGSecEval

    A.S.E (AICGSecEval) is a repository-level AI-generated code security

    AICGSecEval is an open-source benchmark framework designed to evaluate the security of code generated by artificial intelligence systems. The project was developed to address concerns that AI-assisted programming tools may produce insecure code containing vulnerabilities such as injection flaws or unsafe logic. The framework constructs evaluation tasks based on real-world software repositories and known vulnerability cases derived from CVE records. By simulating realistic development...
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  • 24
    E2B Desktop Sandbox

    E2B Desktop Sandbox

    E2B Desktop Sandbox for LLMs. E2B Sandbox

    ...Within a sandbox, developers can launch applications like browsers, editors, or other software that an AI agent may need to interact with. This approach is particularly useful for building AI agents capable of interacting with graphical environments or performing tasks such as browsing, testing software, or automating workflows.
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  • 25
    Agent Development Kit (ADK) for Java

    Agent Development Kit (ADK) for Java

    An open-source, code-first Java toolkit

    Google’s Agent Development Kit for Java is an open-source toolkit that helps developers design, evaluate, and deploy advanced AI agents using the Java programming language. The framework follows a code-first approach that treats agent development as a structured software engineering task rather than a collection of prompt scripts. It provides abstractions and tools that allow developers to create agents capable of executing complex workflows, calling tools, and interacting with external...
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