Search Results for "logging"
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Showing 108 open source projects for "logging"
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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SparkyFitness
Track food, fitness, water, and health
SparkyFitness is a comprehensive self-hosted fitness and wellness tracker designed to help individuals and families monitor nutrition, exercise, hydration, and body measurements in one unified platform. It provides tools for logging daily meals with nutritional breakdowns, tracking workouts with an extensive exercise database, and visualizing long-term progress using interactive charts and reports. The system also supports water intake goals, body metric logging (such as weight and measurements for different muscle groups), and customizable goals to help users stay motivated and accountable. ... -
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BruteForceAI
Advanced LLM-powered brute-force tool combining AI intelligence
...It combines that analysis layer with automated credential testing workflows, framing itself as a more adaptive alternative to older brute-force tooling that depends heavily on manual configuration. The repository emphasizes features such as threaded execution, logging, and notification integrations, which position it as an automation-oriented project for controlled security assessment environments. From a software design perspective, its distinguishing idea is the use of language models as a front-end analysis layer that interprets a target page before the rest of the workflow proceeds. -
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Ralph Orchestrator
An improved implementation of the Ralph Wiggum technique
...Ralph Orchestrator is particularly useful for building agent-based systems, automation pipelines, and AI-driven applications that require coordination across multiple services. It also includes logging and monitoring capabilities to track workflow execution and debug issues effectively. -
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Ollama Grid Search
A multi-platform desktop application to evaluate and compare LLM
...The system integrates directly with local or remote Ollama servers, enabling seamless access to models already deployed in a user’s environment. It also includes experiment logging and A/B testing capabilities, which allow users to compare outputs side by side and track performance metrics such as latency or token usage. -
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Langfuse
Open source LLM engineering platform: LLM Observability, metrics, etc.
Langfuse is a logging and analytics tool for large language model (LLM) applications, providing insights into usage, performance, and troubleshooting. -
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Portkey AI Gateway
A blazing fast AI Gateway with integrated guardrails
...The gateway is multimodal: it can handle text, vision, audio, and image models under a common interface. It also offers features for governance: role-based access, compliance with standards (SOC2, HIPAA, GDPR), secure key management, and logging/analytics of usage, latency, errors, and cost. The system integrates with agent frameworks like LangChain, Autogen, and others, enabling the building of more complex AI applications. It’s lightweight and optimized for low latency with a small footprint. -
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OpenAI Codex CLI
Lightweight coding agent that runs in your terminal
OpenAI Codex CLI is a lightweight, open-source coding assistant that runs directly in your terminal, designed to bring ChatGPT-level reasoning to your code workflows. It allows developers to interactively query, edit, and generate code within their repositories, all while maintaining version control. The CLI can scaffold new files, run code in sandboxed environments, install dependencies, and commit changes automatically, streamlining chat-driven development. It supports various approval... -
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verl
Volcano Engine Reinforcement Learning for LLMs
...Data pipelines treat human feedback, simulated environments, and synthetic preferences as interchangeable sources, which helps with rapid experimentation. VERL is meant for both research and production hardening: logging, checkpointing, and evaluation suites are built in so you can track learning dynamics and regressions over time. -
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Atropos
Language Model Reinforcement Learning Environments frameworks
...This framework facilitates experimentation with RLHF (Reinforcement Learning from Human Feedback), RLAIF, or multi-turn training approaches by abstracting environment logic, scoring, and logging into reusable components. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Kodus
AI code reviews, just like your senior dev would do
...Through configurable pipelines and a focus on modularity, it supports experimentation while maintaining reproducibility and performance. Its tooling is typically designed to handle real-world imperatives like logging, monitoring, versioning, and hooking into operational infrastructure. -
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AgentScript
Build AI agents that think in code
AgentScript is a TypeScript SDK for building reliable AI agents that express their plans as code, enabling stop/start workflows, tool-level state management, and enhanced observability. -
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MCPJam
Postman for MCPs - A tool for testing and debugging MCPs
Inspector by MCPJam is a visual developer tool—akin to Postman—for testing and debugging MCP servers, with capabilities to simulate and trace tool execution via various transports and LLM integrations. -
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MCPHost
A CLI host application that enables Large Language Models (LLMs)
mcphost is a command-line host application that enables Large Language Models (LLMs) to interact with external tools through the Model Context Protocol (MCP). It provides a unified interface for engaging with various AI models and supports integration with multiple MCP servers, streamlining the development of AI-driven applications. -
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OpenClaw Control Center
Turn OpenClaw from a black box into a local control center
...The system is likely built with usability in mind, offering dashboards or visualization tools that make complex agent behaviors easier to understand and manage. It may also include logging, debugging, and orchestration features that support large-scale deployments of autonomous systems. The project emphasizes coordination and observability, ensuring that users maintain control over distributed agent processes. Overall, it functions as the operational backbone for managing advanced AI agent infrastructures. -
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LabClaw
Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)
...LabClaw is designed to integrate with various large language models and external tools, enabling flexible configurations that adapt to different use cases such as automation, research, and product prototyping. It also includes logging and observability features that help developers track agent decisions and debug behavior during execution. -
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TNT
A lightweight library for PyTorch training tools and utilities
...It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. This design helps separate concerns such as model training, evaluation, logging, and checkpointing, making machine learning experiments easier to manage. The framework is particularly useful for large-scale experiments where maintaining clear training workflows becomes increasingly important. Because it is built on top of PyTorch, the framework integrates naturally with existing deep learning models and datasets. -
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Ralph AI Agent
AI agent loop that runs repeatedly until all PRD items are complete
...Designed for extensibility, the framework supports multi-model providers so agents can switch between models or fall back based on task needs. It also aims to be practical for production by including logging, error handling, and state persistence so that agents can resume interrupted workflows. -
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Win-CodexBar
Show usage stats for OpenAI Codex and Claude Code
...It is designed as a lightweight desktop utility that aggregates usage data from various providers and displays it in a compact, always-accessible interface. The app supports dozens of AI services, allowing developers to monitor API usage, quotas, and costs without logging into each platform individually. It includes a dynamic tray icon that visually represents usage levels, along with a detailed panel for deeper insights. The system supports importing credentials and browser cookies to access provider data securely. It also includes a command-line interface for scripting and automation, making it useful in development and CI environments. ... -
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Langroid
Harness LLMs with Multi-Agent Programming
Given the remarkable abilities of recent Large Language Models (LLMs), there is an unprecedented opportunity to build intelligent applications powered by this transformative technology. The top question for any enterprise is: how best to harness the power of LLMs for complex applications? For technical and practical reasons, building LLM-powered applications is not as simple as throwing a task at an LLM system and expecting it to do it. Effectively leveraging LLMs at scale requires a... -
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LangKit
An open-source toolkit for monitoring Language Learning Models (LLMs)
LangKit is an open-source text metrics toolkit for monitoring language models. It offers an array of methods for extracting relevant signals from the input and/or output text, which are compatible with the open-source data logging library whylogs. Productionizing language models, including LLMs, comes with a range of risks due to the infinite amount of input combinations, which can elicit an infinite amount of outputs. The unstructured nature of text poses a challenge in the ML observability space - a challenge worth solving, since the lack of visibility on the model's behavior can have serious consequences. -
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E2B Infra
Infrastructure for AI code interpreting that's powering E2B
E2B Infra is an infrastructure management tool that simplifies the deployment and scaling of applications across cloud environments, focusing on automation and efficiency. -
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AgentRun
The easiest, and fastest way to run AI-generated Python code safely
AgentRun is a framework for building autonomous AI agents capable of executing complex tasks with minimal human intervention. It provides a structured environment for defining agent behaviors, managing workflows, and integrating AI models to achieve specific goals. -
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Arch
Arch is an intelligent prompt gateway. Engineered with (fast) LLMs
Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize LLM applications (agents, assistants, co-pilots) with your APIs. Engineered with purpose-built LLMs, Arch handles the critical but undifferentiated tasks related to the handling and processing of prompts, including detecting and rejecting jailbreak attempts, intelligently calling "backend" APIs to fulfill the user's request represented in a prompt, routing to and offering disaster recovery between upstream... -
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Antigravity Claude Proxy
Proxy that exposes Antigravity provided claude / gemini models
Antigravity Claude Proxy is a purpose-built proxy server that enables developers to interface with Claude models through a standardized RESTful API, allowing tools and workflows that expect generic HTTP APIs to operate on Anthropic’s Claude without native support. The project acts as a translation layer, receiving web requests in common formats (such as OpenAI-style endpoints) and forwarding them to Anthropic’s API in the required structure, while converting responses back into a familiar... -
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mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for both experimentation and production-scale development. It includes built-in support for distributed training strategies such as Fully Sharded Data Parallelism and standard Distributed Data Parallel execution, helping teams scale models without having to assemble as much infrastructure by hand.
