Prompt Guard is a tool designed to provide prompt injection defense for any LLM agent, protecting AI agents from manipulation attacks. It works with various LLM-powered systems like Clawdbot, LangChain, AutoGPT, CrewAI, etc. The tool offers features such as protection against injection attacks, secret exfiltration, jailbreak attempts, auto-approve & MCP abuse, browser & Unicode injection, skill weaponization defense, encoded & obfuscated payloads detection, output DLP, enterprise DLP, Canary Tokens, JSONL logging, token smuggling defense, severity scoring, and SHIELD.md compliance. It supports multiple languages and provides an API-enhanced mode for advanced detection. The tool can be used via CLI or integrated into Python scripts for analyzing user input and LLM output for potential threats.

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.

Multi-agent RALPH (Reinforcement Learning with Probabilistic Hierarchies) Loop is a framework for multi-agent reinforcement learning research. It provides a flexible and extensible platform for developing and testing multi-agent reinforcement learning algorithms. The framework supports various environments, including grid-world environments, and allows users to easily define custom environments. Multi-agent RALPH Loop is designed to facilitate research in the field of multi-agent reinforcement learning by providing a set of tools and utilities for experimenting with different algorithms and scenarios.

Roam is a tool that builds a semantic graph of your codebase and allows AI agents to query it with one shell command. It pre-indexes your codebase into a semantic graph stored in a local SQLite DB, providing architecture-level graph queries offline, cross-language, and compact. Roam understands functions, modules, tests coverage, and overall architecture structure. It is best suited for agent-assisted coding, large codebases, architecture governance, safe refactoring, and multi-repo projects. Roam is not suitable for real-time type checking, dynamic/runtime analysis, small scripts, or pure text search. It offers speed, dependency-awareness, LLM-optimized output, fully local operation, and CI readiness.

General Bots is a self-hosted AI automation platform and LLM conversational platform focused on convention over configuration and code-less approaches. It serves as the core API server handling LLM orchestration, business logic, database operations, and multi-channel communication. The platform offers features like multi-vendor LLM API, MCP + LLM Tools Generation, Semantic Caching, Web Automation Engine, Enterprise Data Connectors, and Git-like Version Control. It enforces a ZERO TOLERANCE POLICY for code quality and security, with strict guidelines for error handling, performance optimization, and code patterns. The project structure includes modules for core functionalities like Rhai BASIC interpreter, security, shared types, tasks, auto task system, file operations, learning system, and LLM assistance.

Claude Code Mastery is a comprehensive tool for maximizing Claude Code, offering a production-ready project template with 16 slash commands, deterministic hook enforcement, MongoDB wrapper, live AI monitoring, and three-layer security. It provides a security gatekeeper, project scaffolding blueprint, MCP server integration, workflow automation through custom commands, and emphasizes the importance of single-purpose chats to avoid performance degradation.

Shodh-Memory is a cognitive memory system designed for AI agents to persist memory across sessions, learn from experience, and run entirely offline. It features Hebbian learning, activation decay, and semantic consolidation, packed into a single ~17MB binary. Users can deploy it on cloud, edge devices, or air-gapped systems to enhance the memory capabilities of AI agents.

Control your entire Kubernetes infrastructure through natural language conversations with AI. Talk to your clusters like you talk to a DevOps expert. Debug crashed pods, optimize costs, deploy applications, audit security, manage Helm charts, and visualize dashboards—all through natural language. The tool provides 253 powerful tools, 8 workflow prompts, 8 data resources, and works with all major AI assistants. It offers AI-powered diagnostics, built-in cost optimization, enterprise-ready features, zero learning curve, universal compatibility, visual insights, and production-grade deployment options. From debugging crashed pods to optimizing cluster costs, kubectl-mcp-server is your AI-powered DevOps companion.

Claude Craft is a comprehensive framework for AI-assisted development with Claude Code, providing standardized rules, agents, and commands across multiple technology stacks. It includes autonomous sprint capabilities, documentation accuracy improvements, CI hardening, and test coverage enhancements. With support for 10 technology stacks, 5 languages, 40 AI agents, 157 slash commands, and various project management features like BMAD v6 framework, Ralph Wiggum loop execution, skills, templates, checklists, and hooks system, Claude Craft offers a robust solution for project development and management. The tool also supports workflow methodology, development tracks, document generation, BMAD v6 project management, quality gates, batch processing, backlog migration, and Claude Code hooks integration.

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.

9Router is a free AI router tool designed to help developers maximize their AI subscriptions, auto-route to free and cheap AI models with smart fallback, and avoid hitting limits and wasting money. It offers features like real-time quota tracking, format translation between OpenAI, Claude, and Gemini, multi-account support, auto token refresh, custom model combinations, request logging, cloud sync, usage analytics, and flexible deployment options. The tool supports various providers like Claude Code, Codex, Gemini CLI, GitHub Copilot, GLM, MiniMax, iFlow, Qwen, and Kiro, and allows users to create combos for different scenarios. Users can connect to the tool via CLI tools like Cursor, Claude Code, Codex, OpenClaw, and Cline, and deploy it on VPS, Docker, or Cloudflare Workers.

DeepMCPAgent is a model-agnostic tool that enables the creation of LangChain/LangGraph agents powered by MCP tools over HTTP/SSE. It allows for dynamic discovery of tools, connection to remote MCP servers, and integration with any LangChain chat model instance. The tool provides a deep agent loop for enhanced functionality and supports typed tool arguments for validated calls. DeepMCPAgent emphasizes the importance of MCP-first approach, where agents dynamically discover and call tools rather than hardcoding them.

oh-my-pi is an AI coding agent for the terminal, providing tools for interactive coding, AI-powered git commits, Python code execution, LSP integration, time-traveling streamed rules, interactive code review, task management, interactive questioning, custom TypeScript slash commands, universal config discovery, MCP & plugin system, web search & fetch, SSH tool, Cursor provider integration, multi-credential support, image generation, TUI overhaul, edit fuzzy matching, and more. It offers a modern terminal interface with smart session management, supports multiple AI providers, and includes various tools for coding, task management, code review, and interactive questioning.

KISS AI is a lightweight and powerful multi-agent evolutionary framework that simplifies building AI agents. It uses native function calling for efficiency and accuracy, making building AI agents as straightforward as possible. The framework includes features like multi-agent orchestration, agent evolution and optimization, relentless coding agent for long-running tasks, output formatting, trajectory saving and visualization, GEPA for prompt optimization, KISSEvolve for algorithm discovery, self-evolving multi-agent, Docker integration, multiprocessing support, and support for various models from OpenAI, Anthropic, Gemini, Together AI, and OpenRouter.

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.

Prompt Guard is a tool designed to provide prompt injection defense for any LLM agent, protecting AI agents from manipulation attacks. It works with various LLM-powered systems like Clawdbot, LangChain, AutoGPT, CrewAI, etc. The tool offers features such as protection against injection attacks, secret exfiltration, jailbreak attempts, auto-approve & MCP abuse, browser & Unicode injection, skill weaponization defense, encoded & obfuscated payloads detection, output DLP, enterprise DLP, Canary Tokens, JSONL logging, token smuggling defense, severity scoring, and SHIELD.md compliance. It supports multiple languages and provides an API-enhanced mode for advanced detection. The tool can be used via CLI or integrated into Python scripts for analyzing user input and LLM output for potential threats.

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

MIMIR is a Python package designed for measuring memorization in Large Language Models (LLMs). It provides functionalities for conducting experiments related to membership inference attacks on LLMs. The package includes implementations of various attacks such as Likelihood, Reference-based, Zlib Entropy, Neighborhood, Min-K% Prob, Min-K%++, Gradient Norm, and allows users to extend it by adding their own datasets and attacks.

OpenShield is a firewall designed for AI models to protect against various attacks such as prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency granting, overreliance, and model theft. It provides rate limiting, content filtering, and keyword filtering for AI models. The tool acts as a transparent proxy between AI models and clients, allowing users to set custom rate limits for OpenAI endpoints and perform tokenizer calculations for OpenAI models. OpenShield also supports Python and LLM based rules, with upcoming features including rate limiting per user and model, prompts manager, content filtering, keyword filtering based on LLM/Vector models, OpenMeter integration, and VectorDB integration. The tool requires an OpenAI API key, Postgres, and Redis for operation.

PAIG is an open-source project focused on protecting Generative AI applications by ensuring security, safety, and observability. It offers a versatile framework to address the latest security challenges and integrate point security solutions without rewriting applications. The project aims to provide a secure environment for developing and deploying GenAI applications.

A.I.G (AI-Infra-Guard) is an AI red teaming platform by Tencent Zhuque Lab that integrates capabilities such as AI infra vulnerability scan, MCP Server risk scan, and Jailbreak Evaluation. It aims to provide users with a comprehensive, intelligent, and user-friendly solution for AI security risk self-examination. The platform offers features like AI Infra Scan, AI Tool Protocol Scan, and Jailbreak Evaluation, along with a modern web interface, complete API, multi-language support, cross-platform deployment, and being free and open-source under the MIT license.

Capsule is a secure and durable runtime for AI agents, designed to coordinate tasks in isolated environments. It allows for long-running workflows, large-scale processing, autonomous decision-making, and multi-agent systems. Tasks run in WebAssembly sandboxes with isolated execution, resource limits, automatic retries, and lifecycle tracking. It enables safe execution of untrusted code within AI agent systems.

Prompt Guard is a tool designed to provide prompt injection defense for any LLM agent, protecting AI agents from manipulation attacks. It works with various LLM-powered systems like Clawdbot, LangChain, AutoGPT, CrewAI, etc. The tool offers features such as protection against injection attacks, secret exfiltration, jailbreak attempts, auto-approve & MCP abuse, browser & Unicode injection, skill weaponization defense, encoded & obfuscated payloads detection, output DLP, enterprise DLP, Canary Tokens, JSONL logging, token smuggling defense, severity scoring, and SHIELD.md compliance. It supports multiple languages and provides an API-enhanced mode for advanced detection. The tool can be used via CLI or integrated into Python scripts for analyzing user input and LLM output for potential threats.

This repository contains examples demonstrating how to use the Private AI REST API for identifying and replacing Personally Identifiable Information (PII) in text. The API supports over 50 entity types, such as Credit Card information and Social Security numbers, across 50 languages. Users can access documentation and the API reference on Private AI's website. The examples include common API call scenarios and use cases in both Python and JavaScript, with additional content related to PrivateGPT for secure work with Language Models (LLMs).