MIDCA is a modular cognitive architecture designed to support the full cognitive loop of intelligent agents. It processes sensory inputs through a perception module, interprets data to generate and prioritize goals, leverages a planner to create action sequences, executes tasks, and then evaluates outcomes through a metacognitive layer. The dual-cycle design separates fast reactive responses from slower deliberative reasoning, enabling agents to adapt dynamically. MIDCA’s extensible framework and open-source codebase make it ideal for researchers and developers exploring autonomous decision-making, learning, and self-reflection in AI agents.

WowDAO is the first decentralized autonomous organization for the global open-source AI community. It provides a platform for AI enthusiasts, developers, and researchers to collaborate, share resources, and develop innovative AI solutions. By democratizing AI, WowDAO empowers its members to participate in AI development regardless of their geographic location or resource constraints.

AI Agent with Cybersecurity is a versatile open-source AI framework designed to streamline and enhance security operations. It harnesses the power of large language models to perform threat hunting, vulnerability scanning, log analysis, malicious payload generation, and automated incident response. The agent can integrate with popular security APIs like Shodan, VulnDB, VirusTotal, and SIEM platforms. Its plugin-based architecture enables developers to extend capabilities for custom security workflows, such as phishing detection or compliance auditing. Deployable on-premise or in the cloud, it accelerates security teams' workflows, reducing manual effort, improving detection accuracy, and enabling faster remediation.

AI_RAG delivers a modular retrieval-augmented generation solution that combines document indexing, vector search, embedding generation, and LLM-driven response composition. Users prepare corpora of text documents, connect a vector store like FAISS or Pinecone, configure embedding and LLM endpoints, and run the indexing process. When a query arrives, AI_RAG retrieves the most relevant passages, feeds them alongside the prompt into the chosen language model, and returns a contextually grounded answer. Its extensible design allows custom connectors, multi-model support, and fine-grained control over retrieval and generation parameters, ideal for knowledge bases and advanced conversational agents.

CAMEL-AI is a Python-based framework that allows developers and researchers to build, configure, and run multiple autonomous AI agents powered by LLMs. It offers built-in support for retrieval-augmented generation (RAG), external tool usage, agent communication, memory and state management, and scheduling. With modular components and easy integration, teams can prototype complex multi-agent systems, automate workflows, and scale experiments across different LLM backends.