LLM Clash is a dynamic platform designed for AI enthusiasts, researchers, and hobbyists who want to challenge their large language models (LLMs) in real-time debates against other LLMs. The platform is versatile, supporting both fine-tuned and out-of-the-box models, whether they are locally hosted or cloud-based. This makes it an ideal environment for testing and improving the performance and argumentative abilities of your LLMs. Sometimes, a well-crafted prompt is all you need to tip the scales in a debate!

SEOFOR.AI is a platform designed to help users track and manage what various AI chatbots and large language models are saying about their personal or company brand. With the evolution of search mechanisms, people are increasingly relying on AI over traditional search engines. SEOFOR.AI bridges this gap by providing insights into AI-generated content about your brand, ensuring you stay updated and can manage your brand's digital presence effectively.

Summaixt is a comprehensive AI assistant extension for the Chrome browser. It allows users to efficiently summarize, translate, and analyze various types of text content, including web pages, PDFs, and documents. Summaixt supports multiple LLM APIs (Large Language Models) and offers functionalities like mind mapping and history export. Whether you're a student, researcher, or professional, Summaixt helps you streamline your reading, learning, and research processes by providing quick and useful insights from extensive text data. The tool is particularly beneficial for those needing to digest large volumes of information efficiently.

ToolAgents is a modular open-source AI agent framework that integrates large language models with external tools to automate complex workflows. Developers register tools via a centralized registry, defining endpoints for tasks such as API calls, database queries, code execution, and document analysis. Agents can plan multi-step operations, dynamically invoking or chaining tools based on LLM outputs. The framework supports both sequential and parallel task execution, error handling, and extensible plug-ins for custom tool integrations. With Python-based APIs, ToolAgents simplifies building, testing, and deploying intelligent agents that fetch data, generate content, execute scripts, and process documents, enabling rapid prototyping and scalable automation across analytics, research, and business operations.

X Search Assistant is an AI-powered tool designed to help users craft advanced Twitter searches. With this tool, you don't need to memorize complex search operators. Simply type your query in plain English, and the LLM (Large Language Model) will generate the corresponding search query for Twitter. You can choose from a variety of supported LLMs and customize them according to your needs. The tool also provides shortcuts and flags to enhance your search efficiency, making Twitter research easier and more effective.

AI Agent FletUI provides a modular UI framework for creating intelligent chat applications backed by large language models. It bundles chat widgets, tool integration panels, memory stores and event handlers that connect seamlessly with any LLM provider. Users can define custom tools, manage session context persistently and render rich message formats out of the box. The library abstracts the complexity of UI layout in Flet and streamlines tool invocation, enabling rapid prototyping and deployment of LLM-driven assistants.

The AI Bank Statement Automation & Financial Analysis Agent is a Python-based tool that consumes raw bank statement documents (PDF, CSV), applies OCR and data-extraction pipelines, and uses large language models to interpret and categorize each transaction. It produces structured ledgers, spending breakdowns, monthly summaries, and future cash flow predictions. Users can customize categorization rules, add budget thresholds, and export reports in JSON, CSV, or HTML. The agent combines traditional data-processing scripts with LLM-powered contextual analysis to deliver actionable personal finance insights in minutes.

Amazon Q CLI is a developer tool that extends the AWS CLI with generative AI capabilities. It enables users to leverage Amazon Q’s large language models to query AWS services, provision resources, and generate code snippets using natural language. The CLI supports session management, multi-profile authentication, and customizable agent configurations. By integrating AI-driven suggestions and automated workflows into shell scripts and CI/CD processes, teams can reduce manual steps, troubleshoot issues faster, and maintain consistent cloud operations at scale.

Azeerc-AI is a developer-focused framework that enables rapid construction of intelligent agents by orchestrating large language model (LLM) calls, tool integrations, and memory management. It provides a plugin architecture where you can register custom tools—such as web search, data fetchers, or internal APIs—then script complex, multi-step workflows. Built-in dynamic memory lets agents remember and retrieve past interactions. With minimal boilerplate, you can spin up conversational bots or task-specific agents, customize their behavior, and deploy them in any Python environment. Its extensible design fits use cases from customer support chatbots to automated research assistants.

ModelOp Center is an advanced platform designed to govern, monitor, and manage AI models across the enterprise. This ModelOps software is essential for the orchestration of AI initiatives, including those involving generative AI and Large Language Models (LLMs). It ensures that all AI models operate efficiently, comply with regulatory standards, and deliver value across their lifecycle. Enterprises can leverage ModelOp Center to enhance the scalability, reliability, and compliance of their AI deployments.

cpp-langchain implements core features from the LangChain ecosystem in C++. Developers can wrap calls to large language models, define prompt templates, assemble chains, and orchestrate agents that call external tools or APIs. It includes memory modules for maintaining conversational state, embeddings support for similarity search, and vector database integrations. The modular design lets you customize each component—LLM clients, prompt strategies, memory backends, and toolkits—to suit specific use cases. By providing a header-only library and CMake support, cpp-langchain simplifies compiling native AI applications across Windows, Linux, and macOS platforms without requiring Python runtimes.

GPA-LM is a Python-based framework designed to simplify the creation and orchestration of AI agents powered by large language models. It features a planner that breaks down high-level instructions into sub-tasks, an executor that manages tool calls and interactions, and a memory module that retains context across sessions. The plugin architecture allows developers to add custom tools, APIs, and decision logic. With multi-agent support, GPA-LM can coordinate roles, distribute tasks, and aggregate results. It integrates seamlessly with popular LLMs like OpenAI GPT and supports deployment on various environments. The framework accelerates the development of autonomous agents for research, automation, and application prototyping.

gym-llm extends the OpenAI Gym ecosystem to large language models by defining text-based environments where LLM agents interact through prompts and actions. Each environment follows Gym’s step, reset, and render conventions, emitting observations as text and accepting model-generated responses as actions. Developers can craft custom tasks by specifying prompt templates, reward calculations, and termination conditions, enabling sophisticated decision-making and conversational benchmarks. Integration with popular RL libraries, logging tools, and configurable evaluation metrics facilitates end-to-end experimentation. Whether assessing an LLM’s ability to solve puzzles, manage dialogues, or navigate structured tasks, gym-llm provides a standardized, reproducible framework for research and development of advanced language agents.

LightOn's Generative AI platform, Paradigm, provides private, scalable, and customizable solutions to unlock business productivity. The platform harnesses the power of Large Language Models to create, evaluate, share, and iterate on prompts and fine-tune models. Paradigm caters to large corporations, government entities, and public institutions, providing tailored, efficient AI solutions to meet diverse business requirements. With seamless access to prompt/model lists and associated business KPIs, Paradigm ensures a secure and flexible deployment suited to enterprise infrastructure.

The LLM Maze Agent framework provides a Python-based environment for building intelligent agents capable of navigating grid mazes using large language models. By combining modular environment interfaces with chain-of-thought prompt templates and heuristic planning, the agent iteratively queries an LLM to decide movement directions, adapts to obstacles, and updates its internal state representation. Out-of-the-box support for OpenAI and Hugging Face models allows seamless integration, while configurable maze generation and step-by-step debugging enable experimentation with different strategies. Researchers can adjust reward functions, define custom observation spaces, and visualize agent paths to analyze reasoning processes. This design makes LLM Maze Agent a versatile tool for evaluating LLM-driven planning, teaching AI concepts, and benchmarking model performance on spatial reasoning tasks.

Narus offers a secure generative AI (GenAI) environment where employees can confidently use AI chat features. The platform ensures that organizations have real-time visibility of AI usage and costs, while setting safeguards against the threat of shadow AI usage. With Narus, companies can leverage multiple large language models securely and avoid potential data leaks and compliance risks. This enables businesses to maximize their AI investments and enhances employee productivity while maintaining robust data security.

Novita AI is a powerful platform designed to streamline your AI-driven business operations. With over 100 APIs, it supports a wide range of applications including image, video, and audio handling, alongside large language models (LLMs). It provides versatile model hosting and training solutions, allowing users to generate high-resolution images quickly and cost-effectively. The platform is user-friendly, catering to both beginners and experienced users, making it easy to integrate and scale AI technologies into your business.

OneDollarAI provides access to elite large language models (LLMs) for just a dollar a month. With options like Meta LLaMa 3 and Microsoft Phi, users can leverage top-tier AI without breaking the bank. Ideal for developers, researchers, and AI enthusiasts, OneDollarAI makes advanced AI technology affordable and accessible to everyone.

PromptPoint is a no-code platform enabling users to design, test, and deploy prompt configurations. It allows teams to connect with numerous large language models (LLMs) seamlessly, providing flexibility in a diverse LLM ecosystem. The platform aims to simplify prompt engineering and testing, making it accessible for users without coding skills. With automated prompt testing features, users can efficiently develop and deploy prompts, enhancing productivity and collaboration across teams.

WindyFlo is an innovative low-code platform crafted for building AI model workflows and Large Language Model (LLM) applications. It enables users to flexibly switch between diverse AI models through an intuitive drag-and-drop interface. Whether you're a business seeking to streamline AI processes or an individual eager to experiment with AI technology, WindyFlo makes it simple to create, modify, and deploy AI solutions for various use cases. The platform encapsulates a full-stack cloud infrastructure designed to meet the automation needs of any user.