EvoAgentX

EvoAgentX is an open-source framework for building, evaluating, and evolving LLM-based agents or agentic workflows in an automated, modular, and goal-driven manner. It enables developers and researchers to move beyond static prompt chaining or manual workflow orchestration by introducing a self-evolving agent ecosystem. The framework includes features such as agent workflow autoconstruction, built-in evaluation, self-evolution engine, plug-and-play compatibility, comprehensive built-in tools, memory module support, and human-in-the-loop interactions.

BeeAI Framework is a versatile tool for building production-ready multi-agent systems. It offers flexibility in orchestrating agents, seamless integration with various models and tools, and production-grade controls for scaling. The framework supports Python and TypeScript libraries, enabling users to implement simple to complex multi-agent patterns, connect with AI services, and optimize token usage and resource management.

HolmesGPT is an AI agent designed for troubleshooting and investigating issues in cloud environments. It utilizes AI models to analyze data from various sources, identify root causes, and provide remediation suggestions. The tool offers integrations with popular cloud providers, observability tools, and on-call systems, enabling users to streamline the troubleshooting process. HolmesGPT can automate the investigation of alerts and tickets from external systems, providing insights back to the source or communication platforms like Slack. It supports end-to-end automation and offers a CLI for interacting with the AI agent. Users can customize HolmesGPT by adding custom data sources and runbooks to enhance investigation capabilities. The tool prioritizes data privacy, ensuring read-only access and respecting RBAC permissions. HolmesGPT is a CNCF Sandbox Project and is distributed under the Apache 2.0 License.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

GraphBit is an industry-grade agentic AI framework built for developers and AI teams that demand stability, scalability, and low resource usage. It is written in Rust for maximum performance and safety, delivering significantly lower CPU usage and memory footprint compared to leading alternatives. The framework is designed to run multi-agent workflows in parallel, persist memory across steps, recover from failures, and ensure 100% task success under load. With lightweight architecture, observability, and concurrency support, GraphBit is suitable for deployment in high-scale enterprise environments and low-resource edge scenarios.

Generative AI models have shown tremendous usefulness in increasing accessibility and automation of a wide range of tasks. This repository contains the `biochatter` Python package, a generic backend library for the connection of biomedical applications to conversational AI. It aims to provide a common framework for deploying, testing, and evaluating diverse models and auxiliary technologies in the biomedical domain. BioChatter is part of the BioCypher ecosystem, connecting natively to BioCypher knowledge graphs.

Flow-Like is an enterprise-grade workflow operating system built upon Rust for uncompromising performance, efficiency, and code safety. It offers a modular frontend for apps, a rich set of events, a node catalog, a powerful no-code workflow IDE, and tools to manage teams, templates, and projects within organizations. With typed workflows, users can create complex, large-scale workflows with clear data origins, transformations, and contracts. Flow-Like is designed to automate any process through seamless integration of LLM, ML-based, and deterministic decision-making instances.

spaCy is an industrial-strength Natural Language Processing (NLP) library in Python and Cython. It incorporates the latest research and is designed for real-world applications. The library offers pretrained pipelines supporting 70+ languages, with advanced neural network models for tasks such as tagging, parsing, named entity recognition, and text classification. It also facilitates multi-task learning with pretrained transformers like BERT, along with a production-ready training system and streamlined model packaging, deployment, and workflow management. spaCy is commercial open-source software released under the MIT license.

Agents Towards Production is an open-source playbook for building production-ready GenAI agents that scale from prototype to enterprise. Tutorials cover stateful workflows, vector memory, real-time web search APIs, Docker deployment, FastAPI endpoints, security guardrails, GPU scaling, browser automation, fine-tuning, multi-agent coordination, observability, evaluation, and UI development.

Open Interface is a self-driving software that automates computer tasks by sending user requests to a language model backend (e.g., GPT-4V) and simulating keyboard and mouse inputs to execute the steps. It course-corrects by sending current screenshots to the language models. The tool supports MacOS, Linux, and Windows, and requires setting up the OpenAI API key for access to GPT-4V. It can automate tasks like creating meal plans, setting up custom language model backends, and more. Open Interface is currently not efficient in accurate spatial reasoning, tracking itself in tabular contexts, and navigating complex GUI-rich applications. Future improvements aim to enhance the tool's capabilities with better models trained on video walkthroughs. The tool is cost-effective, with user requests priced between $0.05 - $0.20, and offers features like interrupting the app and primary display visibility in multi-monitor setups.

Xorbits Inference (Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.

EnvScaler is an automated, scalable framework that creates tool-interactive environments for training LLM agents. It consists of SkelBuilder for environment description mining and quality inspection, ScenGenerator for synthesizing multiple environment scenarios, and modules for supervised fine-tuning and reinforcement learning. The tool provides data, models, and evaluation guides for users to build, generate scenarios, collect training data, train models, and evaluate performance. Users can interact with environments, build environments from scratch, and improve LLMs' task-solving abilities in complex environments.

Unoplat-CodeConfluence is a universal code context engine that aims to extract, understand, and provide precise code context across repositories tied through domains. It combines deterministic code grammar with state-of-the-art LLM pipelines to achieve human-like understanding of codebases in minutes. The tool offers smart summarization, graph-based embedding, enhanced onboarding, graph-based intelligence, deep dependency insights, and seamless integration with existing development tools and workflows. It provides a precise context API for knowledge engine and AI coding assistants, enabling reliable code understanding through bottom-up code summarization, graph-based querying, and deep package and dependency analysis.

Deepchecks is a holistic open-source solution for AI & ML validation needs, enabling thorough testing of data and models from research to production. It includes components for testing, CI & testing management, and monitoring. Users can install and use Deepchecks for testing and monitoring their AI models, with customizable checks and suites for tabular, NLP, and computer vision data. The tool provides visual reports, pythonic/json output for processing, and a dynamic UI for collaboration and monitoring. Deepchecks is open source, with premium features available under a commercial license for monitoring components.

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: * **Long-Context LLM** : Activation Beacon * **Fine-tuning of LM** : LM-Cocktail * **Embedding Model** : Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding * **Reranker Model** : llm rerankers, BGE Reranker * **Benchmark** : C-MTEB

EvoAgentX is an open-source framework for building, evaluating, and evolving LLM-based agents or agentic workflows in an automated, modular, and goal-driven manner. It enables developers and researchers to move beyond static prompt chaining or manual workflow orchestration by introducing a self-evolving agent ecosystem. The framework includes features such as agent workflow autoconstruction, built-in evaluation, self-evolution engine, plug-and-play compatibility, comprehensive built-in tools, memory module support, and human-in-the-loop interactions.

Shell-AI (`shai`) is a CLI utility that enables users to input commands in natural language and receive single-line command suggestions. It leverages natural language understanding and interactive CLI tools to enhance command line interactions. Users can describe tasks in plain English and receive corresponding command suggestions, making it easier to execute commands efficiently. Shell-AI supports cross-platform usage and is compatible with Azure OpenAI deployments, offering a user-friendly and efficient way to interact with the command line.

Trace is a new AutoDiff-like tool for training AI systems end-to-end with general feedback. It generalizes the back-propagation algorithm by capturing and propagating an AI system's execution trace. Implemented as a PyTorch-like Python library, users can write Python code directly and use Trace primitives to optimize certain parts, similar to training neural networks.

Magma is a powerful and flexible framework for building scalable and efficient machine learning pipelines. It provides a simple interface for creating complex workflows, enabling users to easily experiment with different models and data processing techniques. With Magma, users can streamline the development and deployment of machine learning projects, saving time and resources.

Policy Synth is a TypeScript class library that empowers better decision-making for governments and companies by integrating collective and artificial intelligence. It streamlines processes through multi-scale AI agent logic flows, robust APIs, and cutting-edge real-time AI-driven web applications. The tool supports organizations in generating, refining, and implementing smarter, data-informed strategies, fostering collaboration with AI to tackle complex challenges effectively.

Blueking Lite is an AI First lightweight operation product with low deployment resource requirements, low usage costs, and progressive experience, providing essential tools for operation administrators.

ComfyUI is a powerful and modular visual AI engine and application that allows users to design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. It provides a user-friendly environment for creating complex Stable Diffusion workflows without the need for coding. ComfyUI supports various models for image, video, audio, and 3D processing, along with features like smart memory management, model loading, embeddings/textual inversion, and offline usage. Users can experiment with different models, create complex workflows, and optimize their processes efficiently.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.