AI Flow is an advanced agentic AI framework that transforms static, rule-based bots into dynamic AI agents capable of meaningful engagement. It focuses on adaptivity, human-like interaction, and deep environmental integration. AI Flow agents embody rich personalities, opinions, and emotions, remember past conversations, evolve autonomously, create content independently, collaborate with other agents, and anticipate user needs without explicit prompts.

OPEN SOULS offers developers clean, simple, and extensible abstractions for directing the cognitive processes of large language models (LLMs), streamlining the creation of more effective and engaging AI souls. This repo is the public, monorepo hosting our open source core, our command line tool, and code for interacting with the hosted Soul Engine. AI Souls are agentic and embodied digital beings, one day comprising thousands of mental processes (managed by the Soul Engine). Unlike traditional chatbots, this code will give digital souls personality, drive, ego, and will.

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.

The Codebase Context Specification (CCS) project aims to standardize embedding contextual information within codebases to enhance understanding for both AI and human developers. It introduces a convention similar to `.env` and `.editorconfig` files but focused on documenting code for both AI and humans. By providing structured contextual metadata, collaborative documentation guidelines, and standardized context files, developers can improve code comprehension, collaboration, and development efficiency. The project includes a linter for validating context files and provides guidelines for using the specification with AI assistants. Tooling recommendations suggest creating memory systems, IDE plugins, AI model integrations, and agents for context creation and utilization. Future directions include integration with existing documentation systems, dynamic context generation, and support for explicit context overriding.

PydanticAI is a Python agent framework designed to make it less painful to build production grade applications with Generative AI. It is built by the Pydantic Team and supports various AI models like OpenAI, Anthropic, Gemini, Ollama, Groq, and Mistral. PydanticAI seamlessly integrates with Pydantic Logfire for real-time debugging, performance monitoring, and behavior tracking of LLM-powered applications. It is type-safe, Python-centric, and offers structured responses, dependency injection system, and streamed responses. PydanticAI is in early beta, offering a Python-centric design to apply standard Python best practices in AI-driven projects.

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.

Nagato-AI is an intuitive AI Agent library that supports multiple LLMs including OpenAI's GPT, Anthropic's Claude, Google's Gemini, and Groq LLMs. Users can create agents from these models and combine them to build an effective AI Agent system. The library is named after the powerful ninja Nagato from the anime Naruto, who can control multiple bodies with different abilities. Nagato-AI acts as a linchpin to summon and coordinate AI Agents for specific missions. It provides flexibility in programming and supports tools like Coordinator, Researcher, Critic agents, and HumanConfirmInputTool.

Arch is an intelligent Layer 7 gateway designed to protect, observe, and personalize LLM applications with APIs. It handles tasks like detecting and rejecting jailbreak attempts, calling backend APIs, disaster recovery, and observability. Built on Envoy Proxy, it offers features like function calling, prompt guardrails, traffic management, and standards-based observability. Arch aims to improve the speed, security, and personalization of generative AI applications.

GenAI Factory is a collection of end-to-end blueprints to deploy generative AI infrastructures in Google Cloud Platform (GCP), following security best practices. It embraces Infrastructure as Code (IaC) best practices, implements infrastructure in Terraform, and follows the least-privilege principle. The tool is compatible with Cloud Foundation Fabric FAST project-factory and application templates, allowing users to deploy various AI applications and systems on GCP.

GhostOS is an AI Agent framework designed to replace JSON Schema with a Turing-complete code interaction interface (Moss Protocol). It aims to create intelligent entities capable of continuous learning and growth through code generation and project management. The framework supports various capabilities such as turning Python files into web agents, real-time voice conversation, body movements control, and emotion expression. GhostOS is still in early experimental development and focuses on out-of-the-box capabilities for AI agents.

The 'talking-avatar-with-ai' project is a digital human system that utilizes OpenAI's GPT-3 for generating responses, Whisper for audio transcription, Eleven Labs for voice generation, and Rhubarb Lip Sync for lip synchronization. The system allows users to interact with a digital avatar that responds with text, facial expressions, and animations, creating a realistic conversational experience. The project includes setup for environment variables, chat prompt templates, chat model configuration, and structured output parsing to enhance the interaction with the digital human.

Nucleoid is a declarative (logic) runtime environment that manages both data and logic under the same runtime. It uses a declarative programming paradigm, which allows developers to focus on the business logic of the application, while the runtime manages the technical details. This allows for faster development and reduces the amount of code that needs to be written. Additionally, the sharding feature can help to distribute the load across multiple instances, which can further improve the performance of the system.

Flows AI is a lightweight, type-safe AI workflow orchestrator inspired by Anthropic's agent patterns and built on top of Vercel AI SDK. It provides a simple and deterministic way to build AI workflows by connecting different input/outputs together, either explicitly defining workflows or dynamically breaking down complex tasks using an orchestrator agent. The library is designed without classes or state, focusing on flexible input/output contracts for nodes.

Writer Framework is an open-source framework for creating AI applications. It allows users to build user interfaces using a visual editor and write the backend code in Python. The framework is fast, flexible, and provides separation of concerns between UI and business logic. It is reactive and state-driven, highly customizable without requiring CSS, fast in event handling, developer-friendly with easy installation and quick start options, and contains full documentation for using its AI module and deployment options.

Sublayer is a model-agnostic Ruby AI Agent framework that provides base classes for building Generators, Actions, Tasks, and Agents to create AI-powered applications in Ruby. It supports various AI models and providers, such as OpenAI, Gemini, and Claude. Generators generate specific outputs, Actions perform operations, Agents are autonomous entities for tasks or monitoring, and Triggers decide when Agents are activated. The framework offers sample Generators and usage examples for building AI applications.

Mastra is an opinionated Typescript framework designed to help users quickly build AI applications and features. It provides primitives such as workflows, agents, RAG, integrations, syncs, and evals. Users can run Mastra locally or deploy it to a serverless cloud. The framework supports various LLM providers, offers tools for building language models, workflows, and accessing knowledge bases. It includes features like durable graph-based state machines, retrieval-augmented generation, integrations, syncs, and automated tests for evaluating LLM outputs.

AI Flow is an advanced agentic AI framework that transforms static, rule-based bots into dynamic AI agents capable of meaningful engagement. It focuses on adaptivity, human-like interaction, and deep environmental integration. AI Flow agents embody rich personalities, opinions, and emotions, remember past conversations, evolve autonomously, create content independently, collaborate with other agents, and anticipate user needs without explicit prompts.

Composio is a production-ready toolset for AI agents that enables users to integrate AI agents with various agentic tools effortlessly. It provides support for over 100 tools across different categories, including popular softwares like GitHub, Notion, Linear, Gmail, Slack, and more. Composio ensures managed authorization with support for six different authentication protocols, offering better agentic accuracy and ease of use. Users can easily extend Composio with additional tools, frameworks, and authorization protocols. The toolset is designed to be embeddable and pluggable, allowing for seamless integration and consistent user experience.

Alan AI SDK for iOS is a powerful tool that allows developers to quickly create AI agents for their iOS apps. With Alan AI Platform, users can easily design, embed, and host conversational experiences in their applications. The platform offers a web-based IDE called Alan AI Studio for creating dialog scenarios, lightweight SDKs for embedding AI agents, and a backend powered by top-notch speech recognition and natural language understanding technologies. Alan AI enables human-like conversations and actions through voice commands, with features like on-the-fly updates, dialog flow testing, and analytics.

MAIAR is a composable, plugin-based AI agent framework designed to abstract data ingestion, decision-making, and action execution into modular plugins. It enables developers to define triggers and actions as standalone plugins, while the core runtime handles decision-making dynamically. This framework offers extensibility, composability, and model-driven behavior, allowing seamless addition of new functionality. MAIAR's architecture is influenced by Unix pipes, ensuring highly composable plugins, dynamic execution pipelines, and transparent debugging. It remains declarative and extensible, allowing developers to build complex AI workflows without rigid architectures.

ALog is an open-source project designed to facilitate the deployment of server-side code to Cloudflare. It provides a step-by-step guide on creating a Cloudflare worker, configuring environment variables, and updating API base URL. The project aims to simplify the process of deploying server-side code and interacting with OpenAI API. ALog is distributed under the GNU General Public License v2.0, allowing users to modify and distribute the app while adhering to App Store Review Guidelines.

Farfalle is an open-source AI-powered search engine that allows users to run their own local LLM or utilize the cloud. It provides a tech stack including Next.js for frontend, FastAPI for backend, Tavily for search API, Logfire for logging, and Redis for rate limiting. Users can get started by setting up prerequisites like Docker and Ollama, and obtaining API keys for Tavily, OpenAI, and Groq. The tool supports models like llama3, mistral, and gemma. Users can clone the repository, set environment variables, run containers using Docker Compose, and deploy the backend and frontend using services like Render and Vercel.

GURU-BOT 2.O is a simple multi-device WhatsApp bot that allows users to set up the bot by forking the repository, obtaining session IDs, deploying to various platforms like Toystack, Heroku, and Koyeb, and setting environment variables. The bot provides features like reading messages, viewing status, forwarding deleted messages, and more. It is important to note that misusing the bot may result in a ban on the user's WhatsApp account. The repository includes credits to the developers and contributors, along with a reminder and notice about the terms of use and licensing.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.