Best AI tools for< Expose Agents Via Api >

DataLang is a tool that allows you to chat with your databases, expose a specific set of data (using SQL) to train GPT, and then chat with it in natural language. You can also use DataLang to automatically make your SQL views available via API, share it with your privately users, or make it public.

iCAD is an AI-powered application designed for cancer detection, specifically focusing on breast cancer. The platform offers a suite of solutions including Detection, Density Assessment, and Risk Evaluation, all backed by science, clinical evidence, and proven patient outcomes. iCAD's AI-powered solutions aim to expose the hiding place of cancer, providing certainty and peace of mind, ultimately improving patient outcomes and saving more lives.

OpenBuckets is a web application designed to help users find and secure open buckets in cloud storage systems. It provides a user-friendly interface for scanning and identifying publicly accessible buckets, allowing users to take necessary actions to secure their data. With OpenBuckets, users can easily detect misconfigured buckets and prevent potential data breaches. The application offers a simple yet effective solution for enhancing cloud security and protecting sensitive information stored in cloud storage platforms.

CensysGPT Beta is a tool that simplifies building queries and empowers users to conduct efficient and effective reconnaissance operations. It enables users to quickly and easily gain insights into hosts on the internet, streamlining the process and allowing for more proactive threat hunting and exposure management.

Junbi.ai is an AI-powered insights platform designed for YouTube advertisers. It offers AI-powered creative insights for YouTube ads, allowing users to benchmark their ads, predict performance, and test quickly and easily with fully AI-powered technology. The platform also includes expoze.io API for attention prediction on images or videos, with scientifically valid results and developer-friendly features for easy integration into software applications.

Sacred is a tool to configure, organize, log and reproduce computational experiments. It is designed to introduce only minimal overhead, while encouraging modularity and configurability of experiments. The ability to conveniently make experiments configurable is at the heart of Sacred. If the parameters of an experiment are exposed in this way, it will help you to: keep track of all the parameters of your experiment easily run your experiment for different settings save configurations for individual runs in files or a database reproduce your results In Sacred we achieve this through the following main mechanisms: Config Scopes are functions with a @ex.config decorator, that turn all local variables into configuration entries. This helps to set up your configuration really easily. Those entries can then be used in captured functions via dependency injection. That way the system takes care of passing parameters around for you, which makes using your config values really easy. The command-line interface can be used to change the parameters, which makes it really easy to run your experiment with modified parameters. Observers log every information about your experiment and the configuration you used, and saves them for example to a Database. This helps to keep track of all your experiments. Automatic seeding helps controlling the randomness in your experiments, such that they stay reproducible.

Schwarzthal Tech's Wunderschild is an AI-driven platform for financial crime intelligence that revolutionizes compliance and investigation techniques. It provides intelligence solutions based on network assessment, data linkage, flow aggregation, and machine learning. The platform offers expertise in identifying risks related to Politically Exposed Persons, Serious Organised Crime, Terrorism Financing, and more. Wunderschild's capabilities include compliance enhancement through semi-supervised learning techniques, deep-dive investigations into transnational crime cases, and global companies' beneficiaries network analysis.

Sparkle is a tool that streamlines the process of building AI-driven features in applications using Large Language Models (LLMs). It guides users through creating and managing agents, defining tools, and interacting with LLM providers like OpenAI. Sparkle allows customization of LLM provider settings, model configurations, and provides a seamless integration with Sparkle Server for exposing agents via an OpenAI-compatible chat API endpoint.

VulcanSQL is an Analytical Data API Framework for AI agents and data apps. It aims to help data professionals deliver RESTful APIs from databases, data warehouses or data lakes much easier and secure. It turns your SQL into APIs in no time!

Council is an open-source platform designed for the rapid development and deployment of customized generative AI applications using teams of agents. It extends the LLM tool ecosystem by providing advanced control flow and scalable oversight for AI agents. Users can create sophisticated agents with predictable behavior by leveraging Council's powerful approach to control flow using Controllers, Filters, Evaluators, and Budgets. The framework allows for automated routing between agents, comparing, evaluating, and selecting the best results for a task. Council aims to facilitate packaging and deploying agents at scale on multiple platforms while enabling enterprise-grade monitoring and quality control.

Ollama Operator is a Kubernetes operator designed to facilitate running large language models on Kubernetes clusters. It simplifies the process of deploying and managing multiple models on the same cluster, providing an easy-to-use interface for users. With support for various Kubernetes environments and seamless integration with Ollama models, APIs, and CLI, Ollama Operator streamlines the deployment and management of language models. By leveraging the capabilities of lama.cpp, Ollama Operator eliminates the need to worry about Python environments and CUDA drivers, making it a reliable tool for running large language models on Kubernetes.

LangServe helps developers deploy `LangChain` runnables and chains as a REST API. This library is integrated with FastAPI and uses pydantic for data validation. In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.

FastAgency is a powerful tool that leverages the AutoGen framework to quickly build applications with multi-agent workflows. It supports various interfaces like ConsoleUI and MesopUI, allowing users to create interactive applications. The tool enables defining workflows between agents, such as students and teachers, and summarizing conversations. FastAgency aims to expand its capabilities by integrating with additional agentic frameworks like CrewAI, providing more options for workflow definition and AI tool integration.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost. Zep persists and recalls chat histories, and automatically generates summaries and other artifacts from these chat histories. It also embeds messages and summaries, enabling you to search Zep for relevant context from past conversations. Zep does all of this asyncronously, ensuring these operations don't impact your user's chat experience. Data is persisted to database, allowing you to scale out when growth demands. Zep also provides a simple, easy to use abstraction for document vector search called Document Collections. This is designed to complement Zep's core memory features, but is not designed to be a general purpose vector database. Zep allows you to be more intentional about constructing your prompt: 1. automatically adding a few recent messages, with the number customized for your app; 2. a summary of recent conversations prior to the messages above; 3. and/or contextually relevant summaries or messages surfaced from the entire chat session. 4. and/or relevant Business data from Zep Document Collections.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

The Bard API is a Python package that returns responses from Google Bard through the value of a cookie. It is an unofficial API that operates through reverse-engineering, utilizing cookie values to interact with Google Bard for users struggling with frequent authentication problems or unable to authenticate via Google Authentication. The Bard API is not a free service, but rather a tool provided to assist developers with testing certain functionalities due to the delayed development and release of Google Bard's API. It has been designed with a lightweight structure that can easily adapt to the emergence of an official API. Therefore, using it for any other purposes is strongly discouraged. If you have access to a reliable official PaLM-2 API or Google Generative AI API, replace the provided response with the corresponding official code. Check out https://github.com/dsdanielpark/Bard-API/issues/262.

Home LLM is a project that provides the necessary components to control your Home Assistant installation with a completely local Large Language Model acting as a personal assistant. The goal is to provide a drop-in solution to be used as a "conversation agent" component by Home Assistant. The 2 main pieces of this solution are Home LLM and Llama Conversation. Home LLM is a fine-tuning of the Phi model series from Microsoft and the StableLM model series from StabilityAI. The model is able to control devices in the user's house as well as perform basic question and answering. The fine-tuning dataset is a custom synthetic dataset designed to teach the model function calling based on the device information in the context. Llama Conversation is a custom component that exposes the locally running LLM as a "conversation agent" in Home Assistant. This component can be interacted with in a few ways: using a chat interface, integrating with Speech-to-Text and Text-to-Speech addons, or running the oobabooga/text-generation-webui project to provide access to the LLM via an API interface.

This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

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