Langchainrb is an open-source Ruby library designed to streamline the development of AI-driven applications by offering a modular framework for agents, chains, and tools. Developers can define prompt templates, assemble chains of LLM calls, integrate memory components to preserve context, and connect custom tools such as document loaders or search APIs. It supports embedding generation for semantic search, built-in error handling, and flexible configuration of models. With agent abstractions, you can implement conversational assistants that decide which tools or chain to invoke based on user input. Langchainrb's extensible architecture allows easy customization, enabling rapid prototyping of chatbots, automated summarization pipelines, QA systems, and complex workflow automation.

rag-services is an extensible platform that breaks down RAG pipelines into discrete microservices. It offers a document store service, a vector index service, an embedder service, multiple LLM inference services, and an orchestrator service to coordinate workflows. Each component exposes REST APIs, allowing you to mix and match databases and model providers. With Docker and Docker Compose support, you can deploy locally or in Kubernetes clusters. The framework enables scalable, fault-tolerant RAG solutions for chatbots, knowledge bases, and automated document Q&A.

RAG for Cybersecurity combines the power of large language models with vector-based retrieval to transform how security teams access and analyze cybersecurity information. Users begin by ingesting documents such as MITRE ATT&CK matrices, CVE entries, and security advisories. The framework then generates embeddings for each document and stores them in a vector database. When a user submits a query, RAG retrieves the most relevant document chunks, passes them to the LLM, and returns precise, context-rich responses. This approach ensures answers are grounded in authoritative sources, reducing hallucinations while improving accuracy. With customizable data pipelines and support for multiple embeddings and LLM providers, teams can tailor the system to their unique threat intelligence needs.

Memonto functions as a middleware library for AI agents, orchestrating the complete memory lifecycle. During each conversation turn, it records user and AI messages, distills salient details, and generates concise summaries. These summaries are converted into embeddings and stored in vector databases or file-based stores. When constructing new prompts, Memonto performs semantic searches to retrieve the most relevant historical memories, enabling agents to maintain context, recall user preferences, and provide personalized responses. It supports multiple storage backends (SQLite, FAISS, Redis) and offers configurable pipelines for embedding, summarization, and retrieval. Developers can seamlessly integrate Memonto into existing agent frameworks, boosting coherence and long-term engagement.

CrewAI Anthropic Similar Company Finder is a command-line AI Agent that processes a user-provided list of company names, sends them to Anthropic Claude for embedding generation, and then calculates cosine similarity scores to rank related companies. By leveraging vector representations, it uncovers hidden relationships and peer groups within datasets. Users can specify parameters such as embedding model, similarity threshold, and number of results to tailor the output to their research and competitive analysis needs.