Alternatives to neatlogs

Kayba empowers AI agents to enhance their performance through experiential learning. By analyzing execution traces, it identifies and rectifies failures while assessing the effectiveness of these corrections. Rather than depending on generic evaluations that fail to clarify the reasons behind an agent's shortcomings, Kayba utilizes the agent's unique traces to identify failure modes and create tailored benchmarks relevant to the user's specific context, enabling teams to gauge improvements against authentic production failure patterns. With a simple one-line setup, Kayba integrates tracing into the agent, continuously monitors its performance, and promptly alerts users when any step ceases to be recorded. Since even effective tracing can degrade as teams implement changes, Kayba actively reviews existing tracing, highlights any broken elements, identifies the specific file requiring attention, and relays the issue to a coding agent via MCP. This coding agent then addresses the problem, after which Kayba confirms that the trace is fully functional again, ensuring ongoing reliability and performance enhancement. Ultimately, this process allows teams to maintain high standards of operational continuity while fostering continual improvement in their AI systems.

Scout Monitoring is Application Performance Monitoring that shows you what charts cannot. Scout APM is an application performance monitoring tool that helps developers identify and fix performance problems before customers even see them. Scout APM's real-time alerting system, developer-centric interface, and tracing logic, which ties bottlenecks to source code directly, helps you spend less time on debugging, and more time creating great products. With an agent that instrument the dependencies needed at a fraction the overhead, you can quickly identify, prioritize and resolve performance issues - memory bloats, N+1 queries and slow database queries. Scout APM monitors Ruby, PHP and Python applications.

Atla serves as a comprehensive observability and evaluation platform tailored for AI agents, focusing on diagnosing and resolving failures effectively. It enables real-time insights into every decision, tool utilization, and interaction, allowing users to track each agent's execution, comprehend errors at each step, and pinpoint the underlying causes of failures. By intelligently identifying recurring issues across a vast array of traces, Atla eliminates the need for tedious manual log reviews and offers concrete, actionable recommendations for enhancements based on observed error trends. Users can concurrently test different models and prompts to assess their performance, apply suggested improvements, and evaluate the impact of modifications on success rates. Each individual trace is distilled into clear, concise narratives for detailed examination, while aggregated data reveals overarching patterns that highlight systemic challenges rather than mere isolated incidents. Additionally, Atla is designed for seamless integration with existing tools such as OpenAI, LangChain, Autogen AI, Pydantic AI, and several others, ensuring a smooth user experience. This platform not only enhances the efficiency of AI agents but also empowers users with the insights needed to drive continuous improvement and innovation.

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

Inspector is a powerful tool designed for monitoring code execution, assisting software developers in the automatic detection of errors and performance bottlenecks. It efficiently traces applications regardless of their scale, allowing developers to address issues proactively before they impact customers, all while maintaining predictable and low debugging costs. By using Inspector, teams can enhance their development process and ensure a smoother user experience.

Sentry is a comprehensive application monitoring solution that empowers development teams to maintain reliable, high-performing software throughout the development lifecycle. The platform automatically captures errors, performance bottlenecks, logs, traces, user session data, and infrastructure insights, providing complete context for troubleshooting and optimization. With built-in AI capabilities such as Seer, Sentry helps engineers understand why failures occur, generate code fixes, and identify potential issues during code review. Integrations with popular development tools including GitHub, Slack, Jira, and Linear ensure that teams can monitor, investigate, and resolve issues without disrupting existing workflows.

Deleaker is a powerful tool that detects memory leaks in C++,.NET and Delphi. It integrates seamlessly with Visual Studio and RAD Studio. It also works as a stand-alone app. Deleaker can identify and fix leaks such as memory leaks and GDI leaks. It also handles leaks in real-time. Debugging is easier with an intuitive interface, detailed reports and precise stack traces. Deleaker is a great tool for any project, big or small. It ensures that your software is efficient and leak-free.

Unexpected outcomes are a common occurrence in software development. With complete insight into the entire sequence of calls, developers can pinpoint the origins of errors and unexpected results in real time with remarkable accuracy. The discipline of software engineering heavily depends on unit testing to create efficient and production-ready software solutions. LangSmith offers similar capabilities tailored specifically for LLM applications. You can quickly generate test datasets, execute your applications on them, and analyze the results without leaving the LangSmith platform. This tool provides essential observability for mission-critical applications with minimal coding effort. LangSmith is crafted to empower developers in navigating the complexities and leveraging the potential of LLMs. We aim to do more than just create tools; we are dedicated to establishing reliable best practices for developers. You can confidently build and deploy LLM applications, backed by comprehensive application usage statistics. This includes gathering feedback, filtering traces, measuring costs and performance, curating datasets, comparing chain efficiencies, utilizing AI-assisted evaluations, and embracing industry-leading practices to enhance your development process. This holistic approach ensures that developers are well-equipped to handle the challenges of LLM integrations.

Manufact serves as a comprehensive platform designed for the creation and deployment of MCP applications and servers, providing teams with expedited access to the ChatGPT Apps Store, Claude Connectors, and various user-agent interaction points. The mcp-use SDK functions as a complete MCP framework, facilitating the development of MCP applications for both ChatGPT and Claude, along with MCP servers tailored for AI agents. With Manufact, every phase of the MCP lifecycle is streamlined without the need for additional tools: developers can create using an SDK, a skill, or a vibe; initiate deployment with a single command; publish by following marketplace guidelines and utilizing auto-generated submission resources; refine their products through Cloud Inspector; and oversee performance with features like analytics, session replays, trace logs, error metrics, and notifications. Teams benefit from the flexibility to scaffold with the MCP-use SDK, integrate a skill into a coding agent, outline an app and observe the scaffolding process, or seamlessly incorporate an existing MCP server without modifications. Moreover, Manufact Cloud establishes a connection to a repository just once, ensuring that every push leads to automatic deployment, while providing preview URLs for pull requests, as well as managing custom domain setups and SSL certificates. This all-in-one solution enables teams to focus more on innovation rather than the complexities of infrastructure management.

Luciq is an advanced mobile observability platform powered by AI, tailored for app developers and enterprises, enabling them to effectively monitor, diagnose, and enhance mobile applications with ease. This comprehensive solution integrates bug reporting, crash analytics, session replay, and performance monitoring within a single SDK that accommodates Android, iOS, web, and hybrid applications. Users can collect extensive device logs, network traces, annotated screenshots, videos, and user feedback, while machine learning automatically correlates events and errors to prioritize issues based on their impact. By offering developers insights into user sessions where problems occurred, they can replicate defects through replay and expedite issue resolution via integrations with tools like JIRA, Slack, Zapier, and Zendesk. Luciq's “Agentic Mobile Observability” methodology not only highlights the most pressing issues but also identifies potential root causes and suggests remediation strategies, empowering teams to boost their efficiency, enhance application stability, and improve the overall user experience. Ultimately, this platform transforms the way teams approach mobile app development and maintenance, ensuring they stay ahead of potential challenges.

Agnost AI serves as a comprehensive analytics tool for conversational agents, enabling teams to identify silent failures that may drive users away before they even realize it. By analyzing each conversation and its associated data, the platform highlights areas where the agent underperforms, user frustration occurs, inquiries are made, and points of potential churn emerge. It organizes thousands of interactions into identifiable issues and objectives, prioritizing them by their impact and connecting them to the specific conversations and data that reveal these trends. The system is adept at identifying hallucinations, unmet promises, quality concerns, policy breaches, compliance issues, increasing user friction, and instances where technical data suggests success despite a lack of satisfactory user outcomes. Rather than merely providing dashboards, Agnost AI focuses on pinpointing the most significant fixes, offering supporting evidence, suggesting necessary modifications, and outlining evaluations required to implement improvements effectively. Additionally, it can produce self-improvement recommendations and initiate pull requests for modifications to system prompts, agent interfaces, and more, ensuring continuous enhancement of conversational experiences. Through these capabilities, Agnost AI empowers teams to transform insights into actionable strategies that enhance user satisfaction and retention.

Mistral AI Studio serves as a comprehensive platform for organizations and development teams to create, tailor, deploy, and oversee sophisticated AI agents, models, and workflows, guiding them from initial concepts to full-scale production. This platform includes a variety of reusable components such as agents, tools, connectors, guardrails, datasets, workflows, and evaluation mechanisms, all enhanced by observability and telemetry features that allow users to monitor agent performance, identify root causes, and ensure transparency in AI operations. With capabilities like Agent Runtime for facilitating the repetition and sharing of multi-step AI behaviors, AI Registry for organizing and managing model assets, and Data & Tool Connections that ensure smooth integration with existing enterprise systems, Mistral AI Studio accommodates a wide range of tasks, from refining open-source models to integrating them seamlessly into infrastructure and deploying robust AI solutions at an enterprise level. Furthermore, the platform's modular design promotes flexibility, enabling teams to adapt and scale their AI initiatives as needed.

Introducing a premier developer platform designed for the testing and debugging of AI agents, we provide the essential tools so you can focus on innovation. With our system, you can visually monitor events like LLM calls, tool usage, and the interactions of multiple agents. Additionally, our rewind and replay feature allows for precise review of agent executions at specific moments. Maintain a comprehensive log of data, encompassing logs, errors, and prompt injection attempts throughout the development cycle from prototype to production. Our platform seamlessly integrates with leading agent frameworks, enabling you to track, save, and oversee every token your agent processes. You can also manage and visualize your agent's expenditures with real-time price updates. Furthermore, our service enables you to fine-tune specialized LLMs at a fraction of the cost, making it up to 25 times more affordable on saved completions. Create your next agent with the benefits of evaluations, observability, and replays at your disposal. With just two simple lines of code, you can liberate yourself from terminal constraints and instead visualize your agents' actions through your AgentOps dashboard. Once AgentOps is configured, every execution of your program is documented as a session, ensuring that all relevant data is captured automatically, allowing for enhanced analysis and optimization. This not only streamlines your workflow but also empowers you to make data-driven decisions to improve your AI agents continuously.

Respan is an AI observability and evaluation platform designed to help teams monitor, test, and optimize AI agents at scale. It provides deep execution tracing across conversations, tool invocations, routing logic, memory states, and final outputs. Rather than stopping at basic logging, Respan creates a closed-loop system that links monitoring, evaluation, and iteration into one workflow. Teams can define stable, metric-driven evaluation frameworks focused on performance indicators like reliability, safety, cost efficiency, and accuracy. Built-in capability and regression testing protects existing behaviors while enabling controlled experimentation and improvement. A dedicated evaluation agent uses AI to analyze failed trials, localize root causes, and suggest what to test next. Multi-trial evaluation accounts for non-deterministic outputs common in modern AI systems. Respan integrates with major AI providers and frameworks including OpenAI, Anthropic, LangChain, and Google Vertex AI. Designed for high-scale environments handling trillions of tokens, it supports enterprise-grade reliability. Backed by ISO 27001, SOC 2, GDPR, and HIPAA compliance, Respan delivers secure observability for production AI systems.

Trace is an innovative automated platform for KPI analysis that empowers teams to understand the reasons behind shifts in business metrics without the hassle of navigating dashboards or enduring long wait times for insights. By utilizing metric trees, it links KPIs to their fundamental drivers within a communal and computable performance model, enabling analyses that are not only repeatable and clear but also conducive to dependable AI-driven insights. Trace meticulously dissects performance, identifies and ranks significant drivers of change, evaluates actual outcomes against projections, uncovers anomalies, and examines various segments, thus allowing teams to transition from inquiries to definitive explanations in mere minutes. In contrast to conventional dashboards that merely illustrate past events or AI assistants that require user guidance for investigative processes, Trace equips its AI agent with embedded analytical capabilities to autonomously execute the analyst's workflow. Furthermore, teams can initiate their journey with one or two essential KPIs, customize templates to their liking, and seamlessly integrate the platform with their data warehouse without the need for extensive data migration or restructuring, ensuring a smooth onboarding experience. This functionality makes Trace an invaluable tool for organizations looking to enhance their analytical efficiency and deepen their understanding of performance drivers.

LiveDebugger is an innovative tool designed to simplify the debugging process for Phoenix LiveView, allowing developers to monitor their applications in real time. By providing a transparent view of the LiveView and LiveComponent architecture, it enhances comprehension of how various components are interrelated and contribute to the overall interface. Developers can readily navigate the component tree, pinpointing the sources of issues with ease, and traverse the hierarchy of a LiveView application without uncertainty regarding which UI element is causing a glitch. Additionally, LiveDebugger empowers users to monitor assigns for any LiveView or LiveComponent in real time, enabling them to track state changes, grasp data flow, and observe value modifications during interactions with the application. Its callback tracing capabilities allow developers to effectively follow lifecycle callbacks, evaluate execution times, assess performance metrics, and filter events to concentrate on the most relevant information. Ultimately, LiveDebugger streamlines the debugging experience, making it an invaluable resource for developers aiming to enhance their workflow efficiency.

Kanvo offers a seamless JavaScript SDK that enhances LangGraph-based AI agents with integrated memory, observability, and resilience, all without the need for any infrastructure setup. The SDK allows developers to integrate just a few lines of code to activate features such as persistent memory for storing facts, preferences, and goals, as well as threaded conversations for multi-user engagement and real-time monitoring of agent activities, which records every interaction, tool usage, and LLM output. Its innovative time-travel debugging capabilities enable users to checkpoint, rewind, and restore any agent's run state with ease, ensuring that workflows are easily reproducible and errors can be swiftly identified. Built with an emphasis on efficiency and user-friendliness, Convo's streamlined interface paired with its MIT-licensed SDK provides developers with production-ready, easily debuggable agents straight from installation, while also ensuring that data control remains entirely with the users. This combination of features positions Kanvo as a powerful tool for developers looking to create sophisticated AI applications without the typical complexities associated with data management.

Vivgrid serves as a comprehensive development platform tailored for AI agents, focusing on critical aspects such as observability, debugging, safety, and a robust global deployment framework. It provides complete transparency into agent activities by logging prompts, memory retrievals, tool interactions, and reasoning processes, allowing developers to identify and address any points of failure or unexpected behavior. Furthermore, it enables the testing and enforcement of safety protocols, including refusal rules and filters, while facilitating human-in-the-loop oversight prior to deployment. Vivgrid also manages the orchestration of multi-agent systems equipped with stateful memory, dynamically assigning tasks across various agent workflows. On the deployment front, it utilizes a globally distributed inference network to guarantee low-latency execution, achieving response times under 50 milliseconds, and offers real-time metrics on latency, costs, and usage. By integrating debugging, evaluation, safety, and deployment into a single coherent framework, Vivgrid aims to streamline the process of delivering resilient AI systems without the need for disparate components in observability, infrastructure, and orchestration, ultimately enhancing efficiency for developers. This holistic approach empowers teams to focus on innovation rather than the complexities of system integration.

With Bugsee, you can access videos, network activity, and logs that pinpoint the source of bugs and crashes in your live applications, eliminating the hassle of trying to reproduce sporadic issues. This tool ensures that all essential data is readily available, providing vital traces from your app to identify the root causes of unexpected behavior. You can view recordings of user interactions, backend communications, and system states that contributed to the issues. Additionally, Bugsee offers statistics on recurring crashes while also allowing you to analyze trends categorized by device type, operating system version, and time. You'll receive detailed information, including the specific filename, method, and line number associated with the crash. Furthermore, you can examine the states of all other threads active during the incident and review all HTTP and HTTPS requests and responses—complete with headers and body content—between your app and the server. The platform even allows you to replay all console logs from your app, perfectly synchronized with the video and network data, giving you a comprehensive view of the situation. This functionality ultimately empowers developers to enhance their applications with greater efficiency and precision.

Laminar is a comprehensive open-source platform designed to facilitate the creation of top-tier LLM products. The quality of your LLM application is heavily dependent on the data you manage. With Laminar, you can efficiently gather, analyze, and leverage this data. By tracing your LLM application, you gain insight into each execution phase while simultaneously gathering critical information. This data can be utilized to enhance evaluations through the use of dynamic few-shot examples and for the purpose of fine-tuning your models. Tracing occurs seamlessly in the background via gRPC, ensuring minimal impact on performance. Currently, both text and image models can be traced, with audio model tracing expected to be available soon. You have the option to implement LLM-as-a-judge or Python script evaluators that operate on each data span received. These evaluators provide labeling for spans, offering a more scalable solution than relying solely on human labeling, which is particularly beneficial for smaller teams. Laminar empowers users to go beyond the constraints of a single prompt, allowing for the creation and hosting of intricate chains that may include various agents or self-reflective LLM pipelines, thus enhancing overall functionality and versatility. This capability opens up new avenues for experimentation and innovation in LLM development.

AgentHub serves as a dedicated staging platform designed to emulate, trace, and assess AI agents within a secure and private sandbox, allowing for deployment with assurance, agility, and accuracy. Its straightforward setup enables users to onboard agents in mere minutes, complemented by a strong evaluation framework that offers detailed multi-step trace logging, LLM graders, and customizable assessment options. Users can engage in realistic simulations with adjustable personas to replicate varied behaviors and stress-test scenarios, while dataset enhancement techniques artificially increase test set size for thorough evaluation. The system also supports prompt experimentation, facilitating large-scale dynamic testing across multiple prompts, and includes side-by-side trace analysis for comparing decisions, tool usage, and results from different runs. Additionally, an integrated AI Copilot is available to scrutinize traces, interpret outcomes, and respond to inquiries based on the user's specific code and data, transforming agent executions into clear and actionable insights. Furthermore, the platform offers a combination of human-in-the-loop and automated feedback mechanisms, alongside tailored onboarding and expert guidance to ensure best practices are followed throughout the process. This comprehensive approach empowers users to optimize agent performance effectively.

Enhance both your production and staging environments by integrating logs, metrics, and traces in real-time and on-demand directly from your IDE or command line interface. With Lightrun, you can significantly improve productivity and achieve complete code-level visibility. You can add logs and metrics instantly while services are operational, making it easier to debug complex architectures like monoliths, microservices, Kubernetes, Docker Swarm, ECS, and serverless applications. Quickly insert any missing log lines, instrument necessary metrics, or establish snapshots as needed without the hassle of recreating the production setup or redeploying. When you invoke instrumentation, the resulting data gets sent to your log analysis platform, IDE, or preferred APM tool. This allows for thorough analysis of code behavior to identify bottlenecks and errors without interrupting the running application. You can seamlessly incorporate extensive logs, snapshots, counters, timers, function durations, and much more without risking system stability. This streamlined approach lets you focus on coding rather than getting bogged down in debugging, eliminating the need for constant restarts or redeployments when troubleshooting. Ultimately, this results in a more efficient development workflow, allowing you to maintain momentum on your projects.

Elevate your log management with Honeycomb, a platform designed specifically for contemporary development teams aiming to gain insights into application performance while enhancing log management capabilities. With Honeycomb’s rapid query functionality, you can uncover hidden issues across your system’s logs, metrics, and traces, utilizing interactive charts that provide an in-depth analysis of raw data that boasts high cardinality. You can set up Service Level Objectives (SLOs) that reflect user priorities, which helps in reducing unnecessary alerts and allows you to focus on what truly matters. By minimizing on-call responsibilities and speeding up code deployment, you can ensure customer satisfaction remains high. Identify the root causes of performance issues, optimize your code efficiently, and view your production environment in high resolution. Our SLOs will alert you when customers experience difficulties, enabling you to swiftly investigate the underlying problems—all from a single interface. Additionally, the Query Builder empowers you to dissect your data effortlessly, allowing you to visualize behavioral trends for both individual users and services, organized by various dimensions for enhanced analytical insights. This comprehensive approach ensures that your team can respond proactively to performance challenges while refining the overall user experience.

Stetho serves as an advanced debugging bridge tailored for Android applications. By activating it, developers gain access to the Chrome Developer Tools, a feature that is inherently part of the Chrome desktop browser. Additionally, users have the option to enable the dumpapp tool, which provides a robust command-line interface to delve into app internals. Stetho can be conveniently integrated from Maven Central using either Gradle or Maven. While only the primary Stetho dependency is essential, developers may also find it beneficial to incorporate one of the network helpers. The integration with the Chrome DevTools frontend relies on a client/server protocol that Stetho establishes for the application. The platform allows for comprehensive network inspection utilizing the full range of Chrome Developer Tools features, which include image previews, JSON response aids, and the capability to export traces in the HAR format. Furthermore, Stetho enables users to visualize SQLite databases, allowing for interactive exploration with complete read and write functionalities. This makes it an invaluable tool for any developer seeking to enhance their application's debugging process.

Spark offers a three-dimensional perspective of your application's interface along with the capability to adjust view settings dynamically during runtime, enabling you to design exceptional applications. If your app relies on notifications, Spark's notification monitor tracks each NSNotification as it is dispatched, providing a comprehensive stack trace, a detailed list of recipients, the methods invoked, and additional relevant information. This feature allows for a quick understanding of your app's architecture while enhancing debugging efficiency. By connecting your application to the Spark Inspector, you place your app's interface in the spotlight, with real-time updates reflecting your interactions. We keep track of every alteration within your app's view hierarchy, ensuring you remain informed about ongoing changes. The visual representation of your app in Spark is not only aesthetically pleasing but also fully customizable. You have the ability to alter nearly every aspect of your views, from class-level properties to CALayer transformations, and upon making any changes, Spark triggers a method within your app to directly implement that adjustment. This seamless integration fosters a more intuitive development experience, allowing for rapid iteration and refinement.

Discover a seamless approach to creating serverless autonomous AI agents equipped with memory capabilities. Begin by developing local-first, agentic pipelines, tools, and memory systems, and deploy them effortlessly with a single command. Base AI empowers developers to craft high-quality AI agents with memory (RAG) using TypeScript, which can then be deployed as a highly scalable API via Langbase, the creators behind Base AI. This web-first platform offers TypeScript support and a user-friendly RESTful API, allowing for straightforward integration of AI into your web stack, similar to the process of adding a React component or API route, regardless of whether you are utilizing Next.js, Vue, or standard Node.js. With many AI applications available on the web, Base AI accelerates the delivery of AI features, enabling you to develop locally without incurring cloud expenses. Moreover, Git support is integrated by default, facilitating the branching and merging of AI models as if they were code. Comprehensive observability logs provide the ability to debug AI-related JavaScript, offering insights into decisions, data points, and outputs. Essentially, this tool functions like Chrome DevTools tailored for your AI projects, transforming the way you develop and manage AI functionalities in your applications. By utilizing Base AI, developers can significantly enhance productivity while maintaining full control over their AI implementations.

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

Autoblocks offers AI teams the tools to streamline the process of testing, validating, and launching reliable AI agents. The platform eliminates traditional manual testing by automating the generation of test cases based on real user inputs and continuously integrating SME feedback into the model evaluation. Autoblocks ensures the stability and predictability of AI agents, even in industries with sensitive data, by providing tools for edge case detection, red-teaming, and simulation to catch potential risks before deployment. This solution enables faster, safer deployment without sacrificing quality or compliance.

Xdebug is a powerful PHP extension that enhances the development workflow by offering various tools and functionalities. It allows developers to step through code in their integrated development environment as scripts run, making debugging much easier. The extension provides an enhanced version of the var_dump() function and delivers stack traces for notices, warnings, errors, and exceptions, clearly indicating the path leading to issues. Additionally, it logs all function calls, including arguments and their locations, to the disk, and can be configured to also record every variable assignment and return value for each function. This feature set enables developers, with the aid of visualization tools, to thoroughly examine the performance of their PHP applications and identify any bottlenecks. Moreover, Xdebug reveals the sections of code that are executed during unit testing with PHPUnit, aiding in better test coverage. For convenience, installing Xdebug via a package manager is typically the quickest method; simply replace the PHP version with the version you are currently using. You can also install Xdebug using PECL on both Linux and macOS, utilizing Homebrew for a streamlined setup process. Overall, Xdebug significantly enhances PHP development by providing essential debugging tools and performance insights.

Start developing in the cloud and deploying on your own server using retrieval-augmented generation, agents, and more. We offer a straightforward pricing model with a fixed fee for each request. Requests can be categorized into two main types: document indexation and generation. Document indexation involves incorporating a document into your knowledge base, while generation utilizes that knowledge base to produce LLM-generated content through RAG. You can establish a RAG workflow by implementing pre-existing components and crafting a prototype tailored to your specific needs. Additionally, we provide various supporting features, such as the ability to trace outputs back to their original documents and support for multiple file formats during ingestion. By utilizing Agents, you can empower the LLM to access additional tools. An Agent-based architecture can determine the necessary data and conduct searches accordingly. Our agent implementation simplifies the hosting of execution layers and offers pre-built agents suited for numerous applications, making your development process even more efficient. With these resources at your disposal, you can create a robust system that meets your demands.

Traced MTD delivers a groundbreaking MTD (mobile threats defense) for businesses that gives you instant visibility and analysis of mobile threat. Every mobile device, whether it belongs to a business or a person, is a gateway for business data. It also provides a way to connect to your network. Traced Protected MTD and Traced App protects mobile devices, while Traced Trusted MTD provides your IT and Security teams with the tools needed to protect your business. Traced protects employees' devices from application-, network- and devicebased threats, including malware, phishing and rogue WiFi. It also protects against unauthorized screen, microphone and camera access, permission abuse, and device exploits. The Traced Deep Learning Engine detects malicious apps, both known and unknown. It provides you with vital information for analysis, investigation and response. WiFi protection alerts the device as well as Traced Trustd when a user connects a compromised or unsecure WiFi network.

Hyground serves as an AI-enhanced co-pilot for DevOps and Site Reliability Engineering (SRE), functioning as a comprehensive operational intelligence platform that integrates seamlessly within the client's Kubernetes environment without any data leaving the premises. This sophisticated agent interfaces with over 21 enterprise systems to analyze incidents through various sources such as logs, metrics, traces, and Kubernetes events. Engineers can pose questions in everyday language and receive insights tailored to their specific datasets, eliminating the need to master new query languages. The AutoRCA feature transforms alert webhooks into self-sufficient root-cause analyses, providing updates directly to platforms like Slack or Teams. The investigation process initiates immediately upon alert, rather than waiting for an engineer to respond, leading customers to experience reductions in mean time to resolution (MTTR) of up to 85%. Leveraging Google's Agent Development Kit, Hyground employs a multi-agent framework that evolves by learning from the customer's infrastructure over time. Each resolved incident enhances the knowledge base, ensuring that operational runbooks remain up to date and relevant for future challenges. By facilitating real-time insights and continuous learning, Hyground empowers teams to operate more efficiently and effectively.

If you’ve ever taken a screenshot or recorded your screen, you’re already familiar with how to use Jam. Jam magically generates comprehensive bug reports containing all the essential information engineers require. It enhances bug reporting in any tool you prefer to utilize. You can easily share links to your bug reports or send them directly to your chosen issue tracker. Your teammates, regardless of whether they have signed up or not, can benefit from this tool. Jam gathers everything necessary for engineers to effectively troubleshoot issues. We have eliminated the need for those tedious follow-up calls to provide additional context, as all the required information is readily accessible. With Jam, you get perfectly crafted bug reports every time, minimizing unnecessary back and forth. It integrates seamlessly with your preferred issue trackers and tools, making it as straightforward as taking a screenshot. Trusted by countless teams across various companies, you can report bugs in mere seconds without interrupting your workflow. Currently, Jam assists developers during the code review and testing phases by capturing crucial bug diagnostic data and instantly replaying the issue, making it easier to pinpoint what needs fixing. This innovative approach not only saves time but also enhances collaboration among team members.

Flowise is an open-source agentic development platform designed to help teams build AI agents and LLM-powered applications using a visual workflow interface. The platform allows users to design intelligent workflows through modular components that can be combined to create chatbots, automation systems, and autonomous AI agents. Developers can build both single-agent chat assistants and multi-agent systems that collaborate to complete complex tasks. Flowise integrates with more than 100 large language models, embedding models, and vector databases, providing flexibility in selecting AI technologies. The platform also supports retrieval-augmented generation (RAG), enabling applications to retrieve knowledge from documents and data sources. Built-in features such as human-in-the-loop workflows allow users to review and validate agent actions before execution. Observability tools provide detailed execution traces and compatibility with monitoring systems like Prometheus and OpenTelemetry. Developers can integrate Flowise with existing applications using APIs, SDKs, or embedded chat widgets. The platform supports both cloud and on-premises deployment environments for enterprise scalability. By providing visual tools and flexible integrations, Flowise accelerates the development and deployment of advanced AI-driven applications.

Z/XDC offers developers an interactive and source-level debugging experience for mainframe programs within any z/OS environment, enabling them to navigate through live code, monitor variables in real-time, and address issues more swiftly than conventional techniques allow. By eliminating the guesswork inherent in traditional debugging, it grants comprehensive, real-time insight into program execution and introduces advanced debugging features to mainframe development. With Z/XDC, developers can significantly cut down on debugging durations, transforming a process that typically takes days into one that can be accomplished in hours, allowing them to test hypotheses instantly without the need for lengthy recompilation, make spontaneous modifications, run “what if” scenarios, correct problems, and validate solutions efficiently. Among its capabilities, Z/XDC allows for precise line-by-line stepping through code, instant code alterations, flag adjustments, modifications of data fields and registers, control over the Program Status Word (PSW), and the ability to load or delete modules, as well as manage memory with GETMAINs and FREEMAINs, and post Event Control Blocks. Additionally, it ensures source-level visibility, allowing developers to view the code as initially written, automatically load source image maps from ADATA, and access a variety of other helpful features, making it an indispensable tool for modern mainframe programming. This integration of advanced debugging functionalities enhances overall productivity and streamlines the development workflow for programmers working in mainframe environments.

FlowLens is an innovative debugging and session-recording tool driven by AI, designed to capture all essential elements required for accurate and context-sensitive bug diagnosis, while enabling AI coding agents to autonomously resolve issues. Through an easy-to-use browser extension and an optional MCP server, FlowLens documents comprehensive user sessions, recording video of the user interface, network requests, console logs, user actions (such as clicks and inputs), storage states (including cookies and local/session storage), and system information, all meticulously aligned on a cohesive timeline. After a bug is replicated, FlowLens compiles this complete context into a single "flow" that can be easily shared through a link. Compatible AI coding agents that work with MCP, including those from leading providers, can then access the flow to review network activities, error logs, UI states, and user inputs, facilitating automatic root cause analysis and code fix suggestions or generation. This streamlined process eliminates the tedious tasks of manual replays, the hassle of copying and pasting logs, and the need for lengthy bug descriptions, ultimately enhancing productivity and efficiency. Additionally, FlowLens empowers teams to focus on more complex problems, as the platform simplifies the debugging workflow, allowing developers to leverage AI effectively.

Introducing a comprehensive managed PaaS that features a low-code visual builder, VectorDB, along with integrations for various applications and models, designed for the creation, testing, and deployment of high-performance AI applications on the edge. This solution eliminates inefficient and error-prone tasks, allowing users to simply drag and drop models, applications, data, and agents to discover the most effective combinations. You can deploy solutions in less than 60 seconds while significantly reducing latency. The platform supports seamless observation, testing, and iteration processes, ensuring that you maintain visibility and utilize tools that guarantee precision and dependability. Make informed, data-driven decisions with detailed reports on requests, LLM interactions, and usage analytics, while also accessing real-time traces by node. The experimentation feature simplifies the optimization of various elements, including embeddings, prompts, and models, ensuring continuous enhancement. This platform provides everything necessary to launch and iterate at scale, backed by a vibrant community of innovative builders who share valuable insights and experiences. The collective effort distills the most effective tips and techniques for developing AI applications, resulting in an elegant solution that enables the creation of agentic systems with the efficiency of a large team. Furthermore, its intuitive and user-friendly interface fosters seamless collaboration and management of AI applications, making it accessible for everyone involved.

Trace is an innovative PCB design platform tailored for hardware teams, guiding them from initial concept to a fully manufactured board. It integrates advanced schematic capture and PCB layout tools with smart agents that comprehend the context of hardware design. Users can articulate their project requirements in natural language, enabling Trace to conduct thorough research, produce complex multi-sheet schematics, interpret datasheets, recommend components, verify their availability in real time, arrange parts, route multi-layer boards, optimize matched-length signals, and perform essential checks like ERC and DRC along with design reviews. With a comprehensive library boasting over 30,000 symbols and footprints, the platform sources data from leading electronic distributors. Additionally, Plan Mode delves into intricate tasks, poses clarifying questions, devises a detailed action plan, and initiates execution only after receiving user approval. Moreover, TraceRules ensures that preferences regarding components, layout restrictions, and manufacturing objectives are consistently maintained throughout user interactions, facilitating a smoother design process. This comprehensive approach not only enhances efficiency but also streamlines collaboration within hardware teams.

LayerLens serves as an autonomous platform dedicated to evaluating AI models, providing insights into their performance through verified benchmarks, prompt-specific outcomes, agentic comparisons, and audit-ready assessments across different vendors. This platform enables teams to conduct side-by-side comparisons of over 200 AI models, utilizing transparent benchmarks and consistent evaluation techniques focused on accuracy, latency, behavior, and practical application in real-world scenarios. Designed for comprehensive model analysis, LayerLens features Spaces that allow teams to organize benchmarks and evaluations, identify strengths in tasks, and monitor performance trends in relevant contexts. The platform also facilitates ongoing evaluations by continuously assessing model updates, prompt modifications, judge changes, and live traces, thereby empowering teams to identify issues like quality regressions, drift, silent failures, contamination, and policy concerns before they impact production. By prioritizing transparency and collaboration, LayerLens ensures that teams can make informed decisions about their AI model choices.

StackPilot is a next-generation incident response solution designed to reduce engineering toil and accelerate bug resolution. Acting as an AI-powered copilot, it plugs into your monitoring and logging ecosystem to immediately act on alerts. When issues occur, StackPilot cross-references code commits, stack traces, and system data to identify root causes with precision. It then auto-generates a pull request containing a recommended fix, saving engineers countless hours of manual debugging. Beyond incident resolution, the platform builds real-time incident timelines and turns troubleshooting steps into standardized runbooks for future use. Setup takes just minutes, requiring only a GitHub and monitoring tool connection. The platform is built with privacy-first principles—your data never leaves your environment and is not used for AI training. Teams using StackPilot benefit from reduced mean time to resolution (MTTR), stronger reliability, and higher developer productivity.

Trace.Space is a platform built on AI principles that streamlines requirements management and traceability, enhancing efficiency in the complex landscape of large-scale product development. It allows teams to seamlessly import requirements, tests, and change logs from various formats and tools, including PDFs, documents, Jira, Git, and APIs, consolidating them into a unified system. By leveraging AI capabilities, it creates trace links, identifies gaps in coverage, and points out inconsistencies among requirements, design artifacts, and testing layers, effectively transforming disparate data into an interconnected, dynamic graph. This trace graph undergoes continuous analysis to unearth potential risks, broken links, and the ramifications of changes, ensuring that teams can proactively address issues before they lead to project delays. Furthermore, Trace.Space fosters real-time collaboration, enabling team members to review, comment on, and approve modifications while preserving comprehensive traceability of decisions and their effects across hardware, software, and systems engineering. This collaborative approach not only improves communication but also enhances the overall quality and reliability of the development process.

Shoreline is the only cloud reliability platform that allows DevOps engineers to build automations in a matter of minutes and fix problems forever. Shoreline’s modern “Operations at the Edge” architecture runs efficient agents in the background of all monitored hosts. Agents run as a DaemonSet on Kubernetes or an installed package on VMs (apt, yum). The Shoreline backend is hosted by Shoreline in AWS, or deployed in your AWS virtual private cloud. Debugging and repairing issues is easy with advanced tooling for your best SREs, Jupyter style notebooks for the broader team, and a platform that makes building automations 30X faster by allowing operators to manage their entire fleet as if it were a single box. Shoreline does the heavy lifting, setting up monitors and building repair scripts, so that customers only need to configure them for their environment.

TierZero Production Agents actively monitor incidents, manage alerts, and autonomously resolve production issues, enabling your engineering teams to release updates more swiftly. When an incident occurs, TierZero immediately engages, conducting a thorough investigation that spans your entire stack, including logs, traces, metrics, deployments, code alterations, and historical incidents. Unlike conventional AI SRE tools that merely handle triage, Production Agents encompass the entire post-merge process, which includes investigation, remediation, support Q&A, and proactive discovery. The Context Engine from TierZero integrates signals from code, infrastructure, discussions, and documentation into a dynamic knowledge graph that evolves and improves with each resolved issue. Installation within your environment can be accomplished in less than an hour, and every AI-driven investigation is fully auditable. This solution is specifically designed for highly regulated industries, such as fintech, healthcare, and cryptocurrency, where maintaining security is imperative. Furthermore, with its continuous learning capabilities, TierZero not only addresses current incidents but also anticipates potential future challenges.

Agenta provides a complete open-source LLMOps solution that brings prompt engineering, evaluation, and observability together in one platform. Instead of storing prompts across scattered documents and communication channels, teams get a single source of truth for managing and versioning all prompt iterations. The platform includes a unified playground where users can compare prompts, models, and parameters side-by-side, making experimentation faster and more organized. Agenta supports automated evaluation pipelines that leverage LLM-as-a-judge, human reviewers, and custom evaluators to ensure changes actually improve performance. Its observability stack traces every request and highlights failure points, helping teams debug issues and convert problematic interactions into reusable test cases. Product managers, developers, and domain experts can collaborate through shared test sets, annotations, and interactive evaluations directly from the UI. Agenta integrates seamlessly with LangChain, LlamaIndex, OpenAI APIs, and any model provider, avoiding vendor lock-in. By consolidating collaboration, experimentation, testing, and monitoring, Agenta enables AI teams to move from chaotic workflows to streamlined, reliable LLM development.

LocatePLUS stands out as the premier source for precise and comprehensive investigative and skip-tracing information in the country. Its extensive database encompasses billions of historical and current records, both public and private, all meticulously cross-referenced. By leveraging cutting-edge technology and detailed reports, LocatePLUS helps clients save valuable time and resources in their investigations. The case management system offered by LocatePLUS simplifies workflows, making tasks more efficient than ever before. Users can conveniently access vast amounts of data through proprietary online investigative databases, enabling them to retrieve billions of public and non-public skip-tracing records using any web browser. The advanced web-based search tools and skip trace software are designed to seamlessly match and link data based on the specific parameters entered by the user. Additionally, the skip tracing and law enforcement software provided by LocatePLUS delivers a wealth of cross-referenced information drawn from hundreds of databases. With a strong commitment to reliability and regulatory compliance, the integrity of LocatePLUS is further underscored by its SSAE 16 certification for data centers. This dedication ensures that users can trust the accuracy and security of the data provided, reinforcing LocatePLUS's position as a leader in the industry.