The Expected Parrot Domain-Specific Language (EDSL) package enables users to conduct computational social science and market research with AI. It facilitates designing surveys and experiments, simulating responses using large language models, and performing data labeling and other research tasks. EDSL includes built-in methods for analyzing, visualizing, and sharing research results. It is compatible with Python 3.9 - 3.11 and requires API keys for LLMs stored in a `.env` file.

CrewAI is a cutting-edge framework designed to orchestrate role-playing autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. It enables AI agents to assume roles, share goals, and operate in a cohesive unit, much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI provides the backbone for sophisticated multi-agent interactions. With features like role-based agent design, autonomous inter-agent delegation, flexible task management, and support for various LLMs, CrewAI offers a dynamic and adaptable solution for both development and production workflows.

LazyLLM is a low-code development tool for building complex AI applications with multiple agents. It assists developers in building AI applications at a low cost and continuously optimizing their performance. The tool provides a convenient workflow for application development and offers standard processes and tools for various stages of application development. Users can quickly prototype applications with LazyLLM, analyze bad cases with scenario task data, and iteratively optimize key components to enhance the overall application performance. LazyLLM aims to simplify the AI application development process and provide flexibility for both beginners and experts to create high-quality applications.

LOTUS (LLMs Over Tables of Unstructured and Structured Data) is a query engine that provides a declarative programming model and an optimized query engine for reasoning-based query pipelines over structured and unstructured data. It offers a simple and intuitive Pandas-like API with semantic operators for fast and easy LLM-powered data processing. The tool implements a semantic operator programming model, allowing users to write AI-based pipelines with high-level logic and leaving the rest of the work to the query engine. LOTUS supports various semantic operators like sem_map, sem_filter, sem_extract, sem_agg, sem_topk, sem_join, sem_sim_join, and sem_search, enabling users to perform tasks like mapping records, filtering data, aggregating records, and more. The tool also supports different model classes such as LM, RM, and Reranker for language modeling, retrieval, and reranking tasks respectively.

ControlFlow is a Python framework designed for building agentic AI workflows. It provides a structured approach for defining tasks, assigning specialized AI agents, and orchestrating complex behaviors. By balancing AI autonomy with precise oversight, users can create sophisticated AI-powered applications with confidence. ControlFlow offers a task-centric architecture, structured results with type-safe outputs, specialized agents for efficient problem-solving, ecosystem integration with LangChain models, flexible control over workflows, multi-agent orchestration, and native observability and debugging capabilities.

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.

Chainlit is an open-source async Python framework which allows developers to build scalable Conversational AI or agentic applications. It enables users to create ChatGPT-like applications, embedded chatbots, custom frontends, and API endpoints. The framework provides features such as multi-modal chats, chain of thought visualization, data persistence, human feedback, and an in-context prompt playground. Chainlit is compatible with various Python programs and libraries, including LangChain, Llama Index, Autogen, OpenAI Assistant, and Haystack. It offers a range of examples and a cookbook to showcase its capabilities and inspire users. Chainlit welcomes contributions and is licensed under the Apache 2.0 license.

The Atomic Agents framework is a modular and extensible tool designed for creating powerful applications. It leverages Pydantic for data validation and serialization. The framework follows the principles of Atomic Design, providing small and single-purpose components that can be combined. It integrates with Instructor for AI agent architecture and supports various APIs like Cohere, Anthropic, and Gemini. The tool includes documentation, examples, and testing features to ensure smooth development and usage.

Dataformer is a robust framework for creating high-quality synthetic datasets for AI, offering speed, reliability, and scalability. It empowers engineers to rapidly generate diverse datasets grounded in proven research methodologies, enabling them to prioritize data excellence and achieve superior standards for AI models. Dataformer integrates with multiple LLM providers using one unified API, allowing parallel async API calls and caching responses to minimize redundant calls and reduce operational expenses. Leveraging state-of-the-art research papers, Dataformer enables users to generate synthetic data with adaptability, scalability, and resilience, shifting their focus from infrastructure concerns to refining data and enhancing models.

Embedchain is an Open Source Framework for personalizing LLM responses. It simplifies the creation and deployment of personalized AI applications by efficiently managing unstructured data, generating relevant embeddings, and storing them in a vector database. With diverse APIs, users can extract contextual information, find precise answers, and engage in interactive chat conversations tailored to their data. The framework follows the design principle of being 'Conventional but Configurable' to cater to both software engineers and machine learning engineers.

Tiger is a community-driven project developing a reusable and integrated tool ecosystem for LLM Agent Revolution. It utilizes Upsonic for isolated tool storage, profiling, and automatic document generation. With Tiger, you can create a customized environment for your agents or leverage the robust and publicly maintained Tiger curated by the community itself.

AI-Driven Research Assistant is an advanced AI-powered system utilizing specialized agents for data analysis, visualization, and report generation. It integrates LangChain, OpenAI's GPT models, and LangGraph for complex research processes. Key features include hypothesis generation, data processing, web search, code generation, and report writing. The system's unique Note Taker agent maintains project state, reducing overhead and improving context retention. System requirements include Python 3.10+ and Jupyter Notebook environment. Installation involves cloning the repository, setting up a Conda virtual environment, installing dependencies, and configuring environment variables. Usage instructions include setting data, running Jupyter Notebook, customizing research tasks, and viewing results. Main components include agents for hypothesis generation, process supervision, visualization, code writing, search, report writing, quality review, and note-taking. Workflow involves hypothesis generation, processing, quality review, and revision. Customization is possible by modifying agent creation and workflow definition. Current issues include OpenAI errors, NoteTaker efficiency, runtime optimization, and refiner improvement. Contributions via pull requests are welcome under the MIT License.

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.

AgentKit is a framework for creating and orchestrating AI Agents, enabling developers to build, test, and deploy reliable AI applications at scale. It allows for creating networked agents with separate tasks and instructions to solve specific tasks, as well as simple agents for tasks like writing content. The framework requires the Inngest TypeScript SDK as a dependency and provides documentation on agents, tools, network, state, and routing. Example projects showcase AgentKit in action, such as the Test Writing Network demo using Workflow Kit, Supabase, and OpenAI.

Craftium is an open-source platform based on the Minetest voxel game engine and the Gymnasium and PettingZoo APIs, designed for creating fast, rich, and diverse single and multi-agent environments. It allows for connecting to Craftium's Python process, executing actions as keyboard and mouse controls, extending the Lua API for creating RL environments and tasks, and supporting client/server synchronization for slow agents. Craftium is fully extensible, extensively documented, modern RL API compatible, fully open source, and eliminates the need for Java. It offers a variety of environments for research and development in reinforcement learning.

Woodpecker is a tool designed to correct hallucinations in Multimodal Large Language Models (MLLMs) by introducing a training-free method that picks out and corrects inconsistencies between generated text and image content. It consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Woodpecker can be easily integrated with different MLLMs and provides interpretable results by accessing intermediate outputs of the stages. The tool has shown significant improvements in accuracy over baseline models like MiniGPT-4 and mPLUG-Owl.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

The Expected Parrot Domain-Specific Language (EDSL) package enables users to conduct computational social science and market research with AI. It facilitates designing surveys and experiments, simulating responses using large language models, and performing data labeling and other research tasks. EDSL includes built-in methods for analyzing, visualizing, and sharing research results. It is compatible with Python 3.9 - 3.11 and requires API keys for LLMs stored in a `.env` file.

Fast-stable-diffusion is a project that offers notebooks for RunPod, Paperspace, and Colab Pro adaptations with AUTOMATIC1111 Webui and Dreambooth. It provides tools for running and implementing Dreambooth, a stable diffusion project. The project includes implementations by XavierXiao and is sponsored by Runpod, Paperspace, and Colab Pro.

This repository contains code for the paper 'Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models'. It focuses on fine-tuning CLIP in an unsupervised manner to enhance its robustness against visual adversarial attacks. By replacing the vision encoder of large vision-language models with the fine-tuned CLIP models, it achieves state-of-the-art adversarial robustness on various vision-language tasks. The repository provides adversarially fine-tuned ViT-L/14 CLIP models and offers insights into zero-shot classification settings and clean accuracy improvements.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

This repository contains code for reproducing experiments on lie detection in black-box LLMs by asking unrelated questions. It includes Q/A datasets, prompts, and fine-tuning datasets for generating lies with language models. The lie detectors rely on asking binary 'elicitation questions' to diagnose whether the model has lied. The code covers generating lies from language models, training and testing lie detectors, and generalization experiments. It requires access to GPUs and OpenAI API calls for running experiments with open-source models. Results are stored in the repository for reproducibility.

BigCodeBench is an easy-to-use benchmark for code generation with practical and challenging programming tasks. It aims to evaluate the true programming capabilities of large language models (LLMs) in a more realistic setting. The benchmark is designed for HumanEval-like function-level code generation tasks, but with much more complex instructions and diverse function calls. BigCodeBench focuses on the evaluation of LLM4Code with diverse function calls and complex instructions, providing precise evaluation & ranking and pre-generated samples to accelerate code intelligence research. It inherits the design of the EvalPlus framework but differs in terms of execution environment and test evaluation.

RAG with txtai is a Retrieval Augmented Generation (RAG) Streamlit application that helps generate factually correct content by limiting the context in which a Large Language Model (LLM) can generate answers. It supports two categories of RAG: Vector RAG, where context is supplied via a vector search query, and Graph RAG, where context is supplied via a graph path traversal query. The application allows users to run queries, add data to the index, and configure various parameters to control its behavior.

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.