Headroom is a tool designed to optimize the context layer for Large Language Models (LLMs) applications by compressing redundant boilerplate outputs. It intercepts context from tool outputs, logs, search results, and intermediate agent steps, stabilizes dynamic content like timestamps and UUIDs, removes low-signal content, and preserves original data for retrieval only when needed by the LLM. It ensures provider caching works efficiently by aligning prompts for cache hits. The tool works as a transparent proxy with zero code changes, offering significant savings in token count and enabling reversible compression for various types of content like code, logs, JSON, and images. Headroom integrates seamlessly with frameworks like LangChain, Agno, and MCP, supporting features like memory, retrievers, agents, and more.

Pipelock is an all-in-one security harness designed for AI agents, offering control over network egress, detection of credential exfiltration, scanning for prompt injection, and monitoring workspace integrity. It utilizes capability separation to restrict the agent process with secrets and employs a separate fetch proxy for web browsing. The tool runs a 7-layer scanner pipeline on every request to ensure security. Pipelock is suitable for users running AI agents like Claude Code, OpenHands, or any AI agent with shell access and API keys.

LlamaFarm is a comprehensive AI framework that empowers users to build powerful AI applications locally, with full control over costs and deployment options. It provides modular components for RAG systems, vector databases, model management, prompt engineering, and fine-tuning. Users can create differentiated AI products without needing extensive ML expertise, using simple CLI commands and YAML configs. The framework supports local-first development, production-ready components, strategy-based configuration, and deployment anywhere from laptops to the cloud.

RubyLLM::Agents is a production-ready Rails engine for building, managing, and monitoring LLM-powered AI agents. It seamlessly integrates with Rails apps, providing features like automatic execution tracking, cost analytics, budget controls, and a real-time dashboard. Users can build intelligent AI agents in Ruby using a clean DSL and support various LLM providers like OpenAI GPT-4, Anthropic Claude, and Google Gemini. The engine offers features such as agent DSL configuration, execution tracking, cost analytics, reliability with retries and fallbacks, budget controls, multi-tenancy support, async execution with Ruby fibers, real-time dashboard, streaming, conversation history, image operations, alerts, and more.

Tokscale is a high-performance CLI tool and visualization dashboard for tracking token usage and costs across multiple AI coding agents. It helps monitor and analyze token consumption from various AI coding tools, providing real-time pricing calculations using LiteLLM's pricing data. Inspired by the Kardashev scale, Tokscale measures token consumption as users scale the ranks of AI-augmented development. It offers interactive TUI mode, multi-platform support, real-time pricing, detailed breakdowns, web visualization, flexible filtering, and social platform features.

Augustus is a Go-based LLM vulnerability scanner designed for security professionals to test large language models against a wide range of adversarial attacks. It integrates with 28 LLM providers, covers 210+ adversarial attacks including prompt injection, jailbreaks, encoding exploits, and data extraction, and produces actionable vulnerability reports. The tool is built for production security testing with features like concurrent scanning, rate limiting, retry logic, and timeout handling out of the box.

The @ai-coders/context repository provides the Ultimate MCP for AI Agent Orchestration, Context Engineering, and Spec-Driven Development. It simplifies context engineering for AI by offering a universal process called PREVC, which consists of Planning, Review, Execution, Validation, and Confirmation steps. The tool aims to address the problem of context fragmentation by introducing a single `.context/` directory that works universally across different tools. It enables users to create structured documentation, generate agent playbooks, manage workflows, provide on-demand expertise, and sync across various AI tools. The tool follows a structured, spec-driven development approach to improve AI output quality and ensure reproducible results across projects.

GPT-Load is a high-performance, enterprise-grade AI API transparent proxy service designed for enterprises and developers needing to integrate multiple AI services. Built with Go, it features intelligent key management, load balancing, and comprehensive monitoring capabilities for high-concurrency production environments. The tool serves as a transparent proxy service, preserving native API formats of various AI service providers like OpenAI, Google Gemini, and Anthropic Claude. It supports dynamic configuration, distributed leader-follower deployment, and a Vue 3-based web management interface. GPT-Load is production-ready with features like dual authentication, graceful shutdown, and error recovery.

Neogenesis System is an advanced AI decision-making framework that enables agents to 'think about how to think'. It implements a metacognitive approach with real-time learning, tool integration, and multi-LLM support, allowing AI to make expert-level decisions in complex environments. Key features include metacognitive intelligence, tool-enhanced decisions, real-time learning, aha-moment breakthroughs, experience accumulation, and multi-LLM support.

Dexto is a lightweight runtime for creating and running AI agents that turn natural language into real-world actions. It serves as the missing intelligence layer for building AI applications, standalone chatbots, or as the reasoning engine inside larger products. Dexto features a powerful CLI and Web UI for running AI agents, supports multiple interfaces, allows hot-swapping of LLMs from various providers, connects to remote tool servers via the Model Context Protocol, is config-driven with version-controlled YAML, offers production-ready core features, extensibility for custom services, and enables multi-agent collaboration via MCP and A2A.

UMbreLLa is a tool designed for deploying Large Language Models (LLMs) for personal agents. It combines offloading, speculative decoding, and quantization to optimize single-user LLM deployment scenarios. With UMbreLLa, 70B-level models can achieve performance comparable to human reading speed on an RTX 4070Ti, delivering exceptional efficiency and responsiveness, especially for coding tasks. The tool supports deploying models on various GPUs and offers features like code completion and CLI/Gradio chatbots. Users can configure the LLM engine for optimal performance based on their hardware setup.

ExStruct is an Excel structured extraction engine that reads Excel workbooks and outputs structured data as JSON, including cells, table candidates, shapes, charts, smartart, merged cell ranges, print areas/views, auto page-break areas, and hyperlinks. It offers different output modes, formula map extraction, table detection tuning, CLI rendering options, and graceful fallback in case Excel COM is unavailable. The tool is designed to fit LLM/RAG pipelines and provides benchmark reports for accuracy and utility. It supports various formats like JSON, YAML, and TOON, with optional extras for rendering and full extraction targeting Windows + Excel environments.

Alphora is a full-stack framework for building production AI agents, providing agent orchestration, prompt engineering, tool execution, memory management, streaming, and deployment with an async-first, OpenAI-compatible design. It offers features like agent derivation, reasoning-action loop, async streaming, visual debugger, OpenAI compatibility, multimodal support, tool system with zero-config tools and type safety, prompt engine with dynamic prompts, memory and storage management, sandbox for secure execution, deployment as API, and more. Alphora allows users to build sophisticated AI agents easily and efficiently.

The 'Everything Claude Code' repository is a comprehensive collection of production-ready agents, skills, hooks, commands, rules, and MCP configurations developed over 10+ months. It includes guides for setup, foundations, and philosophy, as well as detailed explanations of various topics such as token optimization, memory persistence, continuous learning, verification loops, parallelization, and subagent orchestration. The repository also provides updates on bug fixes, multi-language rules, installation wizard, PM2 support, OpenCode plugin integration, unified commands and skills, and cross-platform support. It offers a quick start guide for installation, ecosystem tools like Skill Creator and Continuous Learning v2, requirements for CLI version compatibility, key concepts like agents, skills, hooks, and rules, running tests, contributing guidelines, OpenCode support, background information, important notes on context window management and customization, star history chart, and relevant links.

LLM Checker is an AI-powered CLI tool that analyzes your hardware to recommend optimal LLM models. It features deterministic scoring across 35+ curated models with hardware-calibrated memory estimation. The tool helps users understand memory bandwidth, VRAM limits, and performance characteristics to choose the right LLM for their hardware. It provides actionable recommendations in seconds by scoring compatible models across four dimensions: Quality, Speed, Fit, and Context. LLM Checker is designed to work on any Node.js 16+ system, with optional SQLite search features for advanced functionality.

NeuroLink is an Enterprise AI SDK for Production Applications that serves as a universal AI integration platform unifying 13 major AI providers and 100+ models under one consistent API. It offers production-ready tooling, including a TypeScript SDK and a professional CLI, for teams to quickly build, operate, and iterate on AI features. NeuroLink enables switching providers with a single parameter change, provides 64+ built-in tools and MCP servers, supports enterprise features like Redis memory and multi-provider failover, and optimizes costs automatically with intelligent routing. It is designed for the future of AI with edge-first execution and continuous streaming architectures.

Headroom is a tool designed to optimize the context layer for Large Language Models (LLMs) applications by compressing redundant boilerplate outputs. It intercepts context from tool outputs, logs, search results, and intermediate agent steps, stabilizes dynamic content like timestamps and UUIDs, removes low-signal content, and preserves original data for retrieval only when needed by the LLM. It ensures provider caching works efficiently by aligning prompts for cache hits. The tool works as a transparent proxy with zero code changes, offering significant savings in token count and enabling reversible compression for various types of content like code, logs, JSON, and images. Headroom integrates seamlessly with frameworks like LangChain, Agno, and MCP, supporting features like memory, retrievers, agents, and more.

Klavis AI is a production-ready solution for managing Multiple Communication Protocol (MCP) servers. It offers self-hosted solutions and a hosted service with enterprise OAuth support. With Klavis AI, users can easily deploy and manage over 50 MCP servers for various services like GitHub, Gmail, Google Sheets, YouTube, Slack, and more. The tool provides instant access to MCP servers, seamless authentication, and integration with AI frameworks, making it ideal for individuals and businesses looking to streamline their communication and data management workflows.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.