Search Results for "detect" - Page 2
Sort By:
Showing 108 open source projects for "detect"
View related business solutions-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
1
Monkey Code
Enterprise-grade AI programming assistant designed for R&D collab
...The system includes a comprehensive management panel that allows teams to audit, monitor, and control how AI participates in coding workflows, ensuring accountability and governance at scale. MonkeyCode also integrates automated code security scanning to detect vulnerabilities in both human-written and AI-generated code, reinforcing secure development practices. -
2
Rewriting Project Claw Code
Ensure consistency and alignment between different codebases
...The project provides mechanisms to compare, validate, and synchronize code or behavior, helping teams avoid discrepancies that can lead to bugs or inconsistencies. It may include automation tools that detect differences and enforce standards across repositories. The tool is useful in scenarios such as maintaining parity between frontend and backend logic, ensuring API consistency, or synchronizing multiple deployments. Its design promotes reliability and reduces the risk of divergence in complex systems. Overall, Claw Code Parity helps teams maintain cohesion across their codebases while improving development efficiency and quality assurance. -
3
Rogue
AI Agent Evaluator & Red Team Platform
...The platform automatically interacts with an AI agent by generating dynamic scenarios and multi-turn conversations that simulate real-world interactions. Instead of relying solely on static test scripts, Rogue uses an agent-as-a-judge architecture where one agent probes another agent to detect failures or unexpected behaviors. The system allows developers to define specific scenarios, expected outcomes, and business rules so that the framework can verify whether an agent behaves according to required policies. During testing, Rogue records conversations and produces detailed reports that explain whether the agent passed or failed each scenario. ... -
4
Agent Behavior Monitoring
The open source post-building layer for agents
...Judgeval transforms agent interaction trajectories into structured evaluation datasets that can be used for reinforcement learning, supervised fine-tuning, or other forms of post-training improvement. The framework includes tools that analyze agent behavior patterns and group interaction trajectories by behavior type or topic, allowing researchers to detect weaknesses or unexpected behaviors. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
5
Superagent
Superagent protects your AI applications
...Superagent provides guardrails that block jailbreaks, prompt manipulation, and sensitive data exfiltration. It includes redaction tools to remove PII, PHI, and secrets automatically from text. The platform also scans code repositories to detect AI-specific attack vectors like repo poisoning. Superagent is designed for low-latency production environments and works with any major LLM provider. It enables teams to prove compliance with modern AI security and regulatory standards. -
6
OpenAI Symphony
Symphony turns work into isolated, autonomous implementation runs
...Instead of directly supervising AI agents, engineers can oversee higher-level workflows and project outcomes. Symphony integrates with project management tools to detect new tasks and initiate isolated environments where agents implement solutions. Each run generates proof of work such as CI results, pull requests, code reviews, and analysis to validate the completed task. By automating execution and verification, Symphony helps engineering teams scale development workflows with minimal manual oversight. -
7
hCaptcha Challenger
Gracefully face hCaptcha challenge with multimodal llms
...Instead of relying on third-party captcha-solving services or browser scripts, the system operates independently by using pretrained neural networks that can classify images, detect objects, and interpret spatial relationships. The framework includes support for multiple types of captcha challenges such as object selection, drag-and-drop puzzles, and image labeling tasks. It implements an agent-style workflow where the system interprets the challenge prompt, selects the appropriate vision model, and generates the required interaction automatically. -
8
Local File Organizer
An AI-powered file management tool that ensures privacy
...The system scans directories, extracts relevant information from files, and restructures folder hierarchies to make content easier to locate and manage. Through AI-driven analysis, the software can detect themes, topics, and metadata in files, allowing it to organize information in ways that traditional rule-based file managers cannot achieve. The tool supports multiple sorting strategies that allow users to categorize files by content, date, or type depending on their workflow preferences. -
9
OpenFace Face Recognition
Face recognition with deep neural networks
OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google,... -
Atera - an All-in-one platform for IT managementYour IT essentials, integrated & elevated. Take your IT management from automated to autonomous, download Atera's agent to start your free trial!
-
10
Future AGI
Open-source platform for evaluating, observing, and improving LLM
...It is built for teams that need more than basic tracing, combining evaluations, simulations, datasets, guardrails, gateway routing, and optimization in one feedback loop. The platform helps developers detect hallucinations, measure agent quality, monitor production behavior, and use evaluation results to improve prompts or workflows over time. It supports both cloud and self-hosted deployment models, making it useful for teams with different privacy, infrastructure, and compliance needs. Future AGI is especially relevant for agent-heavy products where reliability, regression testing, and safety checks matter before and after release. ... -
11
Superglue
Builds integrations and tools from natural language
...One of its defining features is its ability to handle authentication, schema mapping, and data transformation automatically, reducing the need for manual configuration when connecting systems. The platform also includes self-healing capabilities, meaning it can detect and repair failures in workflows when upstream APIs change or break. Superglue supports multiple interfaces, including a web application, SDK, and MCP server, allowing it to be used in both development and production environments. -
12
RF-DETR
RF-DETR is a real-time object detection and segmentation
...Developed by Roboflow, the project builds upon modern vision transformer backbones such as DINOv2 to achieve strong accuracy while maintaining efficient inference speeds suitable for real-time applications. The model is designed to detect objects and segment them within images or video streams using a unified detection pipeline. RF-DETR emphasizes strong performance across both accuracy and latency benchmarks, allowing developers to deploy high-quality detection models in applications that require immediate processing such as robotics, autonomous systems, and industrial inspection. ... -
13
OpenAI Privacy Filter
Bidirectional token-classification model for identifiable info
OpenAI Privacy Filter is an open-weight machine learning model designed to detect and mask personally identifiable information in text with high efficiency and contextual awareness. It operates as a bidirectional token classification system that labels sensitive data in a single forward pass rather than generating text sequentially, enabling fast processing for large datasets. The model supports long-context inputs, allowing it to analyze extensive documents without chunking, which improves consistency in redaction tasks. ... -
14
Autoskills
One command. Your entire AI skill stack. Installed
The Autoskills project is a developer tool that automates the installation of AI agent skills based on a project’s technology stack. It operates through a simple command-line interface that scans configuration files such as package.json and build scripts to detect the frameworks, languages, and tools used in a project. Once the stack is identified, it automatically installs a curated set of AI skills tailored to those technologies, significantly reducing setup time for AI-assisted development environments. The system is designed to work across a wide range of ecosystems, including frontend, backend, mobile, cloud, and AI tooling stacks. ... -
15
Claw Hunter
MDM-ready scripts for detecting and monitoring OpenClaw
Claw Hunter is an open-source security tool designed to detect, analyze, and mitigate risks associated with autonomous AI agents, specifically those built on platforms like OpenClaw. As agentic AI systems gain popularity, they introduce a new class of security challenges because they can execute commands, access files, and interact with external systems with minimal human oversight. Claw-Hunter addresses this emerging threat landscape by providing visibility into these agents, helping organizations identify instances running within their environments. ... -
16
Gonzo
Real-time terminal log analyzer with AI insights and dashboards
...Users can explore logs through a k9s-inspired layout, combining visualizations like heatmaps, severity distributions, and timelines. Advanced filtering with regex and attribute search helps isolate issues quickly. Gonzo also integrates AI capabilities to detect patterns, highlight anomalies, and suggest root causes, making it easier to understand complex system behavior. With customizable themes, keyboard and mouse navigation, and support for local or external AI models, it provides a fast, developer-friendly way to turn raw logs into actionable insights without leaving the terminal. -
17
Qodo Cover
AI tool that generates tests to improve code coverage quickly
...Internally, Qodo Cover uses a modular architecture that includes components for prompt generation, AI interaction, coverage analysis, and test validation. It supports scanning entire repositories to automatically detect test files, gather relevant context, and extend test suites accordingly. -
18
Guardrails
Framework for validating and controlling LLM outputs in AI apps
...Guardrails works by applying configurable guards that intercept and evaluate interactions with the model before results are returned to the end user. These guards can detect and mitigate specific issues by applying validators that analyze content, enforce rules, or ensure structured output formats. Guardrails also supports generating structured data from language models, allowing developers to enforce schemas or type constraints on responses. A companion ecosystem known as a hub provides reusable validators that can be combined into input and output guards to address different reliability and safety concerns. -
19
Ralph for Claude Code
Autonomous development loop that iteratively improves projects
...It automates the process of running AI-assisted development tasks, allowing the model to progressively refine a project without constant manual intervention. Ralph introduces mechanisms to detect completion signals and determine when the development loop should stop, preventing endless execution cycles. It also includes built-in safeguards such as rate limiting and circuit breaker protections to avoid excessive API usage or runaway processes. Ralph for Claude Code is designed to be installed globally so it can operate as a command-line utility available in any directory, enabling developers to apply autonomous development workflows across multiple projects. -
20
Advanced AI explainability for PyTorch
Advanced AI Explainability for computer vision
...It also provides metrics and evaluation tools that help measure the reliability and quality of the generated explanations. By integrating easily with PyTorch models, the library allows developers to diagnose model errors, detect biases in datasets, and improve model transparency. -
21
uqlm
Uncertainty Quantification for Language Models, is a Python package
UQLM is a Python library developed to detect hallucinations and quantify uncertainty in the outputs of large language models. The system implements a variety of uncertainty quantification techniques that assign confidence scores to model responses. These scores help developers determine how likely a generated answer is to contain errors or fabricated information. The library includes both black-box and white-box approaches to uncertainty estimation. -
22
vLLM Semantic Router
System Level Intelligent Router for Mixture-of-Models at Cloud
...The router operates as an intelligent layer between users and model infrastructure, capturing signals from prompts, responses, and contextual data to improve decision-making. It can also integrate safety and monitoring mechanisms that detect issues such as jailbreak attempts, hallucinations, or sensitive information exposure. -
23
4M
4M: Massively Multimodal Masked Modeling
4M is a training framework for “any-to-any” vision foundation models that uses tokenization and masking to scale across many modalities and tasks. The same model family can classify, segment, detect, caption, and even generate images, with a single interface for both discriminative and generative use. The repository releases code and models for multiple variants (e.g., 4M-7 and 4M-21), emphasizing transfer to unseen tasks and modalities. Training/inference configs and issues discuss things like depth tokenizers, input masks for generation, and CUDA build questions, signaling active research iteration. ... -
24
DeepAudit
AI multi-agent platform for automated code security auditing system
...Instead of relying solely on traditional static analysis, it simulates the reasoning process of security experts through coordinated agents responsible for orchestration, reconnaissance, analysis, and verification. DeepAudit performs deep semantic understanding of code, enabling it to detect complex vulnerabilities that span multiple files and business logic layers. It also includes automated proof-of-concept validation using a sandboxed environment, allowing detected issues to be tested for real exploitability. DeepAudit integrates retrieval-augmented generation techniques to enhance contextual understanding and reduce false positives during analysis. ... -
25
Mesh R-CNN
code for Mesh R-CNN, ICCV 2019
Mesh R-CNN is a 3D reconstruction and object understanding framework developed by Facebook Research that extends Mask R-CNN into the 3D domain. Built on top of Detectron2 and PyTorch3D, Mesh R-CNN enables end-to-end 3D mesh prediction directly from single RGB images. The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. Unlike voxel-based or point-based approaches, Mesh R-CNN uses a differentiable mesh representation, allowing it to efficiently refine surface geometry while maintaining high spatial detail. ...
