OpenDerisk provides a modular, extensible platform to evaluate and mitigate risks in AI systems. It includes fairness evaluation metrics, privacy leakage detection, adversarial robustness tests, bias monitoring, and output quality checks. Users can configure pre-built probes or develop custom modules to target specific risk domains. Results are aggregated into interactive reports that highlight vulnerabilities and suggest remediation steps. OpenDerisk runs as a CLI and Python SDK, allowing seamless integration into development workflows, continuous integration pipelines, and automated quality gates to ensure safe, reliable AI deployments.

ReasonChain provides a modular pipeline for constructing sequences of LLM-driven operations, allowing each step’s output to feed into the next. Users can define custom chain nodes for prompt generation, API calls to different LLM providers, conditional logic to route workflows, and aggregation functions for final outputs. The framework includes built-in debugging and logging to trace intermediate states, support for vector database lookups, and easy extension through user-defined modules. Whether solving multi-step reasoning tasks, orchestrating data transformations, or building conversational agents with memory, ReasonChain offers a transparent, reusable, and testable environment. Its design encourages experimentation with chain-of-thought strategies, making it ideal for research, prototyping, and production-ready AI solutions.

UnitMesh Framework provides a flexible, modular environment for defining, managing, and executing chains of AI agents. It allows seamless integration with OpenAI, Anthropic, and custom models, supports Python and Node.js SDKs, and offers built-in memory stores, tool connectors, and plugin architecture. Developers can orchestrate parallel or sequential agent workflows, track execution logs, and extend functionality via custom modules. Its event-driven design ensures high performance and scalability across cloud and on-premise deployments.

DND LLM Game leverages large language models to serve as an AI Dungeon Master, dynamically crafting narrative descriptions, quests, and encounters in response to player prompts. It integrates with OpenAI's GPT API and supports customization of adventure settings, difficulty levels, and NPC personalities. As players describe actions or ask questions in the chat interface, the AI generates vivid scene details, dialogues, and branching story paths on the fly. Developers and game masters can configure the engine via Python scripts, adjust model parameters, and extend the framework to include custom modules, making it a flexible tool for solo RPG sessions or AI-assisted tabletop campaigns.

Personal AI Assistant is a modular AI agent built in Python to deliver conversational chat, context-aware memory, and automated task execution. It features a plugin system for web browsing, file management, email sending, and calendar scheduling. Backed by OpenAI or local language models and SQLite-based memory storage, it preserves conversation history and adapts responses over time. Developers can extend capabilities with custom modules, creating a tailored assistant for productivity, research, or home automation.

Swarms is a developer-focused framework enabling the creation, orchestration, and execution of multi-agent AI workflows. You define agents with specific roles, configure their behavior via LLM prompts, and link them to external tools or APIs. Swarms manages inter-agent communication, task planning, and memory persistence. Its plugin architecture allows seamless integration of custom modules—such as retrievers, databases, or monitoring dashboards—while built-in connectors support popular LLM providers. Whether you need coordinated data analysis, automated customer support, or complex decision-making pipelines, Swarms provides the building blocks to deploy scalable, autonomous agent ecosystems.