Archon is an AI meta-agent designed to autonomously build, refine, and optimize other AI agents. It serves as a practical tool for developers and an educational framework showcasing the evolution of agentic systems. Through iterative development, Archon demonstrates the power of planning, feedback loops, and domain-specific knowledge in creating robust AI agents.

LangManus is a community-driven AI automation framework that combines language models with specialized tools for tasks like web search, crawling, and Python code execution. It implements a hierarchical multi-agent system with agents like Coordinator, Planner, Supervisor, Researcher, Coder, Browser, and Reporter. The framework supports LLM integration, search and retrieval tools, Python integration, workflow management, and visualization. LangManus aims to give back to the open-source community and welcomes contributions in various forms.

Resume Job Matcher is a Python script that automates the process of matching resumes to a job description using AI. It leverages the Anthropic Claude API or OpenAI's GPT API to analyze resumes and provide a match score along with personalized email responses for candidates. The tool offers comprehensive resume processing, advanced AI-powered analysis, in-depth evaluation & scoring, comprehensive analytics & reporting, enhanced candidate profiling, and robust system management. Users can customize font presets, generate PDF versions of unified resumes, adjust logging level, change scoring model, modify AI provider, and adjust AI model. The final score for each resume is calculated based on AI-generated match score and resume quality score, ensuring content relevance and presentation quality are considered. Troubleshooting tips, best practices, contribution guidelines, and required Python packages are provided.

VacAIgent is an AI tool that automates and enhances trip planning by leveraging the CrewAI framework. It integrates a user-friendly Streamlit interface for interactive travel planning. Users can input preferences and receive tailored travel plans with the help of autonomous AI agents. The tool allows for collaborative decision-making on cities and crafting complete itineraries based on specified preferences, all accessible via a streamlined Streamlit user interface. VacAIgent can be customized to use different AI models like GPT-3.5 or local models like Ollama for enhanced privacy and customization.

Auto-Analyst is an AI-driven data analytics agentic system designed to simplify and enhance the data science process. By integrating various specialized AI agents, this tool aims to make complex data analysis tasks more accessible and efficient for data analysts and scientists. Auto-Analyst provides a streamlined approach to data preprocessing, statistical analysis, machine learning, and visualization, all within an interactive Streamlit interface. It offers plug and play Streamlit UI, agents with data science speciality, complete automation, LLM agnostic operation, and is built using lightweight frameworks.

CentralMind Gateway is an AI-first data gateway that securely connects any data source and automatically generates secure, LLM-optimized APIs. It filters out sensitive data, adds traceability, and optimizes for AI workloads. Suitable for companies deploying AI agents for customer support and analytics.

A toolkit for agent autonomy, evolution, and governance enabling agents to learn from experience, collaborate, communicate, and build new tools within governance guardrails. It focuses on autonomous evolution, agent self-discovery, governance firmware, self-building systems, and agent-centric architecture. The toolkit leverages existing frameworks to enable agent autonomy and self-governance, moving towards truly autonomous AI systems.

aiaio (AI-AI-O) is a lightweight, privacy-focused web UI for interacting with AI models. It supports both local and remote LLM deployments through OpenAI-compatible APIs. The tool provides features such as dark/light mode support, local SQLite database for conversation storage, file upload and processing, configurable model parameters through UI, privacy-focused design, responsive design for mobile/desktop, syntax highlighting for code blocks, real-time conversation updates, automatic conversation summarization, customizable system prompts, WebSocket support for real-time updates, Docker support for deployment, multiple API endpoint support, and multiple system prompt support. Users can configure model parameters and API settings through the UI, handle file uploads, manage conversations, and use keyboard shortcuts for efficient interaction. The tool uses SQLite for storage with tables for conversations, messages, attachments, and settings. Contributions to the project are welcome under the Apache License 2.0.

Director is a framework to build video agents that can reason through complex video tasks like search, editing, compilation, generation, etc. It enables users to summarize videos, search for specific moments, create clips instantly, integrate GenAI projects and APIs, add overlays, generate thumbnails, and more. Built on VideoDB's 'video-as-data' infrastructure, Director is perfect for developers, creators, and teams looking to simplify media workflows and unlock new possibilities.

GitDiagram is a tool that turns any GitHub repository into an interactive diagram for visualization in seconds. It offers instant visualization, interactivity, fast generation, customization, and API access. The tool utilizes a tech stack including Next.js, FastAPI, PostgreSQL, Claude 3.5 Sonnet, Vercel, EC2, GitHub Actions, PostHog, and Api-Analytics. Users can self-host the tool for local development and contribute to its development. GitDiagram is inspired by Gitingest and has future plans to use larger context models, allow user API key input, implement RAG with Mermaid.js docs, and include font-awesome icons in diagrams.

Vodalus Expert LLM Forge is a tool designed for crafting datasets and efficiently fine-tuning models using free open-source tools. It includes components for data generation, LLM interaction, RAG engine integration, model training, fine-tuning, and quantization. The tool is suitable for users at all levels and is accompanied by comprehensive documentation. Users can generate synthetic data, interact with LLMs, train models, and optimize performance for local execution. The tool provides detailed guides and instructions for setup, usage, and customization.

This web UI tool is designed to enhance the user experience of the deep-research repository by providing a safe and secure environment for conducting AI research. It offers features such as real-time feedback, search visualization, export as PDF, support for various AI models, and Docker deployment. Users can interact with multiple AI providers and web search services, making research processes more efficient and accessible. The tool also includes recent updates that improve functionality and fix bugs, ensuring a seamless experience for users.

Morphic is an AI-powered answer engine with a generative UI. It utilizes a stack of Next.js, Vercel AI SDK, OpenAI, Tavily AI, shadcn/ui, Radix UI, and Tailwind CSS. To get started, fork and clone the repo, install dependencies, fill out secrets in the .env.local file, and run the app locally using 'bun dev'. You can also deploy your own live version of Morphic with Vercel. Verified models that can be specified to writers include Groq, LLaMA3 8b, and LLaMA3 70b.

EOLE is an open language modeling toolkit based on PyTorch. It aims to provide a research-friendly approach with a comprehensive yet compact and modular codebase for experimenting with various types of language models. The toolkit includes features such as versatile training and inference, dynamic data transforms, comprehensive large language model support, advanced quantization, efficient finetuning, flexible inference, and tensor parallelism. EOLE is a work in progress with ongoing enhancements in configuration management, command line entry points, reproducible recipes, core API simplification, and plans for further simplification, refactoring, inference server development, additional recipes, documentation enhancement, test coverage improvement, logging enhancements, and broader model support.

Cursor Lens is an open-source tool that acts as a proxy between Cursor and various AI providers, logging interactions and providing detailed analytics to help developers optimize their use of AI in their coding workflow. It supports multiple AI providers, captures and logs all requests, provides visual analytics on AI usage, allows users to set up and switch between different AI configurations, offers real-time monitoring of AI interactions, tracks token usage, estimates costs based on token usage and model pricing. Built with Next.js, React, PostgreSQL, Prisma ORM, Vercel AI SDK, Tailwind CSS, and shadcn/ui components.

CodeFuse IDE is an AI-native integrated development environment that leverages AI technologies to enhance productivity and streamline workflows. It supports seamless integration of various models, enabling developers to customize and extend functionality. The platform is compatible with VS Code extensions, providing access to a rich ecosystem of plugins. CodeFuse IDE uses electron-forge for packaging desktop applications and supports development, building, packaging, and auto updates.

OpenAGI is an AI agent creation package designed for researchers and developers to create intelligent agents using advanced machine learning techniques. The package provides tools and resources for building and training AI models, enabling users to develop sophisticated AI applications. With a focus on collaboration and community engagement, OpenAGI aims to facilitate the integration of AI technologies into various domains, fostering innovation and knowledge sharing among experts and enthusiasts.

GPTSwarm is a graph-based framework for LLM-based agents that enables the creation of LLM-based agents from graphs and facilitates the customized and automatic self-organization of agent swarms with self-improvement capabilities. The library includes components for domain-specific operations, graph-related functions, LLM backend selection, memory management, and optimization algorithms to enhance agent performance and swarm efficiency. Users can quickly run predefined swarms or utilize tools like the file analyzer. GPTSwarm supports local LM inference via LM Studio, allowing users to run with a local LLM model. The framework has been accepted by ICML2024 and offers advanced features for experimentation and customization.

AgentForge is a low-code framework tailored for the rapid development, testing, and iteration of AI-powered autonomous agents and Cognitive Architectures. It is compatible with a range of LLM models and offers flexibility to run different models for different agents based on specific needs. The framework is designed for seamless extensibility and database-flexibility, making it an ideal playground for various AI projects. AgentForge is a beta-testing ground and future-proof hub for crafting intelligent, model-agnostic autonomous agents.

Atomic Agents is a modular and extensible framework designed for creating powerful applications. It follows the principles of Atomic Design, emphasizing small and single-purpose components. Leveraging Pydantic for data validation and serialization, the framework offers a set of tools and agents that can be combined to build AI applications. It depends on the Instructor package and supports various APIs like OpenAI, Cohere, Anthropic, and Gemini. Atomic Agents is suitable for developers looking to create AI agents with a focus on modularity and flexibility.

LongRoPE is a method to extend the context window of large language models (LLMs) beyond 2 million tokens. It identifies and exploits non-uniformities in positional embeddings to enable 8x context extension without fine-tuning. The method utilizes a progressive extension strategy with 256k fine-tuning to reach a 2048k context. It adjusts embeddings for shorter contexts to maintain performance within the original window size. LongRoPE has been shown to be effective in maintaining performance across various tasks from 4k to 2048k context lengths.

Ax is a Typescript library that allows users to build intelligent agents inspired by agentic workflows and the Stanford DSP paper. It seamlessly integrates with multiple Large Language Models (LLMs) and VectorDBs to create RAG pipelines or collaborative agents capable of solving complex problems. The library offers advanced features such as streaming validation, multi-modal DSP, and automatic prompt tuning using optimizers. Users can easily convert documents of any format to text, perform smart chunking, embedding, and querying, and ensure output validation while streaming. Ax is production-ready, written in Typescript, and has zero dependencies.

Awesome-AI-Agents is a curated list of projects, frameworks, benchmarks, platforms, and related resources focused on autonomous AI agents powered by Large Language Models (LLMs). The repository showcases a wide range of applications, multi-agent task solver projects, agent society simulations, and advanced components for building and customizing AI agents. It also includes frameworks for orchestrating role-playing, evaluating LLM-as-Agent performance, and connecting LLMs with real-world applications through platforms and APIs. Additionally, the repository features surveys, paper lists, and blogs related to LLM-based autonomous agents, making it a valuable resource for researchers, developers, and enthusiasts in the field of AI.

CodeFuse-muAgent is a Multi-Agent framework designed to streamline Standard Operating Procedure (SOP) orchestration for agents. It integrates toolkits, code libraries, knowledge bases, and sandbox environments for rapid construction of complex Multi-Agent interactive applications. The framework enables efficient execution and handling of multi-layered and multi-dimensional tasks.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.