Search Results for "logging"
Sort By:
Showing 97 open source projects for "logging"
View related business solutions-
$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.
-
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.
-
1
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. ... -
2
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. -
3
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. -
4
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. -
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.
-
5
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... -
6
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. -
7
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. -
8
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. -
9
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. -
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.
-
10
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. -
11
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. -
12
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. -
13
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. -
14
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. -
15
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. -
16
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. -
17
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... -
18
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. -
19
camofox-browser
Headless browser automation server for AI agents to visit sites
camofox-browser is a headless browser automation server built specifically for AI agents that need to interact with websites that often block standard automation stacks. It wraps Camoufox, a Firefox fork that performs fingerprint spoofing at the C++ level, which means many browser characteristics are altered before page scripts can inspect them, rather than relying on JavaScript-layer stealth patches. The project is designed around a REST API, making it easier for agents and external tools... -
20
Agentex
Open source codebase for Scale Agentex
AgentEX is an open framework from Scale for building, running, and evaluating agentic workflows, with an emphasis on reproducibility and measurable outcomes rather than ad-hoc demos. It treats an “agent” as a composition of a policy (the LLM), tools, memory, and an execution runtime so you can test the whole loop, not just prompting. The repo focuses on structured experiments: standardized tasks, canonical tool interfaces, and logs that make it possible to compare models, prompts, and tool... -
21
Kong
The Cloud-Native API Gateway
...When building for the web, mobile, or Internet of Things, you’ll need a common functionality to run your software, and Kong is that solution. Kong acts as a gateway, connecting microservices requests and APIs natively while also providing load balancing, logging, monitoring, authentication, rate-limiting, and so much more through plugins. Kong is highly extensible as well as platform agnostic, connecting APIs across different environments, platforms and patterns. Achieve architectural freedom with Kong today. -
22
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... -
23
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. -
24
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. -
25
OpenAI Forward
An efficient forwarding service designed for LLMs
...Its main purpose is to make model access more manageable and efficient by adding operational controls such as request rate limiting, token rate limiting, caching, logging, routing, and key management around existing LLM endpoints. The project can proxy both local and cloud-hosted language model services, which makes it useful for teams that want a single control layer regardless of whether they are using something like LocalAI or a hosted provider compatible with OpenAI-style APIs. A major emphasis of the repository is asynchronous performance, using tools such as uvicorn, aiohttp, and asyncio to support high-throughput forwarding workloads.
