BioMCP is an open source toolkit that empowers AI assistants and agents with specialized biomedical knowledge. It follows the Model Context Protocol (MCP) to connect AI systems to authoritative biomedical data sources, enabling precise answers about clinical trials, scientific literature, and genomic variants. It provides structured access to various biomedical data sources, supports natural language queries, and functions as an MCP server for AI assistants and agents. BioMCP integrates with literature sources like PubMed and bioRxiv, clinical and genomic sources like ClinicalTrials.gov and MyVariant.info, and regulatory and safety sources like OpenFDA. It offers 24 specialized tools for biomedical research, including core tools like Think Tool, Search Tool, and Fetch Tool, as well as individual tools for articles, trials, variants, NCI-specific data, genes, diseases, and drugs. BioMCP can be run locally or as an HTTP server, supports Docker deployment, and provides a comprehensive CLI for database interaction.

Strands Evals SDK is a comprehensive evaluation framework for AI agents and LLM applications. It provides tools for output validation, trajectory analysis, interaction evaluation, and automated experiment generation. Users can assess agent behavior, simulate multi-turn conversations, and generate test suites. The SDK offers custom evaluators, experiment management, and built-in scoring tools for comprehensive evaluation of AI systems.

OpenGradient Python SDK is a tool for decentralized model management and inference services on the OpenGradient platform. It provides programmatic access to distributed AI infrastructure with cryptographic verification capabilities. The SDK supports verifiable LLM inference, multi-provider support, TEE execution, model hub integration, consensus-based verification, and command-line interface. Users can leverage this SDK to build AI applications with execution guarantees through Trusted Execution Environments and blockchain-based settlement, ensuring auditability and tamper-proof AI execution.

Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. By integrating cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni helps scientists dramatically enhance research productivity and generate testable hypotheses.

Orra is a tool for building production-ready multi-agent applications that handle complex real-world interactions. It coordinates tasks across existing stack, agents, and tools run as services using intelligent reasoning. With features like smart pre-evaluated execution plans, domain grounding, durable execution, and automatic service health monitoring, Orra enables users to go fast with tools as services and revert state to handle failures. It provides real-time status tracking and webhook result delivery, making it ideal for developers looking to move beyond simple crews and agents.

Memento MCP is a scalable, high-performance knowledge graph memory system designed for LLMs. It offers semantic retrieval, contextual recall, and temporal awareness to any LLM client supporting the model context protocol. The system is built on core concepts like entities and relations, utilizing Neo4j as its storage backend for unified graph and vector search capabilities. With advanced features such as semantic search, temporal awareness, confidence decay, and rich metadata support, Memento MCP provides a robust solution for managing knowledge graphs efficiently and effectively.

CodeEvolve is an open-source framework that combines large language models with evolutionary algorithms to discover and optimize high-performing code solutions. It democratizes algorithmic discovery by making LLM-driven evolutionary search transparent, reproducible, and accessible. CodeEvolve provides a modular foundation for automated code synthesis guided by quantifiable metrics, addressing meta-optimization tasks where complex optimization problems need to be solved. The framework features islands-based genetic algorithm, modular evolutionary operators, quality-diversity optimization, flexible LLM integration, and distributed islands for efficient exploration. Core components include CLI entry point, process runner, evolution engine, program database, exploration schedulers, evaluator, islands coordinator, LLM interface, prompt sampler, and utilities. CodeEvolve demonstrates superior performance on algorithm-discovery benchmarks and is suitable for mathematical discovery, algorithm design, scientific discovery, and software optimization. Reproducibility and determinism are emphasized, with seedable internal algorithmic decisions. Contributions from the community are welcome, focusing on new selection policies, LLM integrations, benchmark problems, documentation, performance optimizations, and bug fixes.

MassGen is a cutting-edge multi-agent system that leverages the power of collaborative AI to solve complex tasks. It assigns a task to multiple AI agents who work in parallel, observe each other's progress, and refine their approaches to converge on the best solution to deliver a comprehensive and high-quality result. The system operates through an architecture designed for seamless multi-agent collaboration, with key features including cross-model/agent synergy, parallel processing, intelligence sharing, consensus building, and live visualization. Users can install the system, configure API settings, and run MassGen for various tasks such as question answering, creative writing, research, development & coding tasks, and web automation & browser tasks. The roadmap includes plans for advanced agent collaboration, expanded model, tool & agent integration, improved performance & scalability, enhanced developer experience, and a web interface.

Mira is an agentic AI library designed for automating company research by gathering information from various sources like company websites, LinkedIn profiles, and Google Search. It utilizes a multi-agent architecture to collect and merge data points into a structured profile with confidence scores and clear source attribution. The core library is framework-agnostic and can be integrated into applications, pipelines, or custom workflows. Mira offers features such as real-time progress events, confidence scoring, company criteria matching, and built-in services for data gathering. The tool is suitable for users looking to streamline company research processes and enhance data collection efficiency.

Code Puppy is an AI-powered code generation agent designed to understand programming tasks, generate high-quality code, and explain its reasoning. It supports multi-language code generation, interactive CLI, and detailed code explanations. The tool requires Python 3.9+ and API keys for various models like GPT, Google's Gemini, Cerebras, and Claude. It also integrates with MCP servers for advanced features like code search and documentation lookups. Users can create custom JSON agents for specialized tasks and access a variety of tools for file management, code execution, and reasoning sharing.

Haiku RAG is a Retrieval-Augmented Generation (RAG) library that utilizes LanceDB as a local vector database. It supports semantic and full-text search, hybrid search with Reciprocal Rank Fusion, multiple embedding and QA providers, default search result reranking, question answering, file monitoring, and various file formats. It can be used via CLI or Python API, and can serve as tools for AI assistants like Claude Desktop. The library offers features for document management and search, with detailed documentation available.

CyberStrikeAI is an AI-native security testing platform built in Go that integrates 100+ security tools, an intelligent orchestration engine, role-based testing with predefined security roles, a skills system with specialized testing skills, and comprehensive lifecycle management capabilities. It enables end-to-end automation from conversational commands to vulnerability discovery, attack-chain analysis, knowledge retrieval, and result visualization, delivering an auditable, traceable, and collaborative testing environment for security teams. The platform features an AI decision engine with OpenAI-compatible models, native MCP implementation with various transports, prebuilt tool recipes, large-result pagination, attack-chain graph, password-protected web UI, knowledge base with vector search, vulnerability management, batch task management, role-based testing, and skills system.

PilottAI is a Python framework for building autonomous multi-agent systems with advanced orchestration capabilities. It provides enterprise-ready features for building scalable AI applications. The framework includes hierarchical agent systems, production-ready features like asynchronous processing and fault tolerance, advanced memory management with semantic storage, and integrations with multiple LLM providers and custom tools. PilottAI offers specialized agents for various tasks such as customer service, document processing, email handling, knowledge acquisition, marketing, research analysis, sales, social media, and web search. The framework also provides documentation, example use cases, and advanced features like memory management, load balancing, and fault tolerance.

LLMxCPG is a framework for vulnerability detection using Code Property Graphs (CPG) and Large Language Models (LLM). It involves a two-phase process: Slice Construction where an LLM generates queries for a CPG to extract a code slice, and Vulnerability Detection where another LLM classifies the code slice as vulnerable or safe. The repository includes implementations of baseline models, information on datasets, scripts for running models, prompt templates, query generation examples, and configurations for fine-tuning models.

Copilot Collections is an opinionated setup for GitHub Copilot tailored for delivery teams. It provides shared workflows, specialized agents, task prompts, reusable skills, and MCP integrations to streamline the software development process. The focus is on building features while letting Copilot handle the glue. The setup requires a GitHub Copilot Pro license and VS Code version 1.109 or later. It supports a standard workflow of Research, Plan, Implement, and Review, with specialized flows for UI-heavy tasks and end-to-end testing. Agents like Architect, Business Analyst, Software Engineer, UI Reviewer, Code Reviewer, and E2E Engineer assist in different stages of development. Skills like Task Analysis, Architecture Design, Codebase Analysis, Code Review, and E2E Testing provide specialized domain knowledge and workflows. The repository also includes prompts and chat commands for various tasks, along with instructions for installation and configuration in VS Code.

Zotero MCP seamlessly connects your Zotero research library with AI assistants like ChatGPT and Claude via the Model Context Protocol. It offers AI-powered semantic search, access to library content, PDF annotation extraction, and easy updates. Users can search their library, analyze citations, and get summaries, making it ideal for research tasks. The tool supports multiple embedding models, intelligent search results, and flexible access methods for both local and remote collaboration. With advanced features like semantic search and PDF annotation extraction, Zotero MCP enhances research efficiency and organization.

BioMCP is an open source toolkit that empowers AI assistants and agents with specialized biomedical knowledge. It follows the Model Context Protocol (MCP) to connect AI systems to authoritative biomedical data sources, enabling precise answers about clinical trials, scientific literature, and genomic variants. It provides structured access to various biomedical data sources, supports natural language queries, and functions as an MCP server for AI assistants and agents. BioMCP integrates with literature sources like PubMed and bioRxiv, clinical and genomic sources like ClinicalTrials.gov and MyVariant.info, and regulatory and safety sources like OpenFDA. It offers 24 specialized tools for biomedical research, including core tools like Think Tool, Search Tool, and Fetch Tool, as well as individual tools for articles, trials, variants, NCI-specific data, genes, diseases, and drugs. BioMCP can be run locally or as an HTTP server, supports Docker deployment, and provides a comprehensive CLI for database interaction.

BioMCP is an open source toolkit that empowers AI assistants and agents with specialized biomedical knowledge. It follows the Model Context Protocol (MCP) to connect AI systems to authoritative biomedical data sources, enabling precise answers about clinical trials, scientific literature, and genomic variants. It provides structured access to various biomedical data sources, supports natural language queries, and functions as an MCP server for AI assistants and agents. BioMCP integrates with literature sources like PubMed and bioRxiv, clinical and genomic sources like ClinicalTrials.gov and MyVariant.info, and regulatory and safety sources like OpenFDA. It offers 24 specialized tools for biomedical research, including core tools like Think Tool, Search Tool, and Fetch Tool, as well as individual tools for articles, trials, variants, NCI-specific data, genes, diseases, and drugs. BioMCP can be run locally or as an HTTP server, supports Docker deployment, and provides a comprehensive CLI for database interaction.

Me LLaMA introduces a suite of open-source medical Large Language Models (LLMs), including Me LLaMA 13B/70B and their chat-enhanced versions. Developed through innovative continual pre-training and instruction tuning, these models leverage a vast medical corpus comprising PubMed papers, medical guidelines, and general domain data. Me LLaMA sets new benchmarks on medical reasoning tasks, making it a significant asset for medical NLP applications and research. The models are intended for computational linguistics and medical research, not for clinical decision-making without validation and regulatory approval.

KG-RAG (Knowledge Graph-based Retrieval Augmented Generation) is a task agnostic framework that combines the explicit knowledge of a Knowledge Graph (KG) with the implicit knowledge of a Large Language Model (LLM). KG-RAG extracts "prompt-aware context" from a KG, which is defined as the minimal context sufficient enough to respond to the user prompt. This framework empowers a general-purpose LLM by incorporating an optimized domain-specific 'prompt-aware context' from a biomedical KG. KG-RAG is specifically designed for running prompts related to Diseases.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

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.

cellseg-models.pytorch is a Python library built upon PyTorch for 2D cell/nuclei instance segmentation models. It provides multi-task encoder-decoder architectures and post-processing methods for segmenting cell/nuclei instances. The library offers high-level API to define segmentation models, open-source datasets for training, flexibility to modify model components, sliding window inference, multi-GPU inference, benchmarking utilities, regularization techniques, and example notebooks for training and finetuning models with different backbones.

AICSImageIO is a Python tool for Image Reading, Metadata Conversion, and Image Writing for Microscopy Images. It supports various file formats like OME-TIFF, TIFF, ND2, DV, CZI, LIF, PNG, GIF, and Bio-Formats. Users can read and write metadata and imaging data, work with different file systems like local paths, HTTP URLs, s3fs, and gcsfs. The tool provides functionalities for full image reading, delayed image reading, mosaic image reading, metadata reading, xarray coordinate plane attachment, cloud IO support, and saving to OME-TIFF. It also offers benchmarking and developer resources.

ceLLama is a streamlined automation pipeline for cell type annotations using large-language models (LLMs). It operates locally to ensure privacy, provides comprehensive analysis by considering negative genes, offers efficient processing speed, and generates customized reports. Ideal for quick and preliminary cell type checks.