The Context-First AGI Cognitive Context Engine (CCE) Prototype provides a robust toolkit for developers to implement context-aware AI agents. It leverages vector embeddings to store historical user interactions, enabling efficient retrieval of relevant context snippets. The engine automatically summarizes lengthy conversations to fit within LLM token limits, ensuring continuity and coherence in multi-turn dialogues. Developers can configure context prioritization strategies, manage memory lifecycles, and integrate custom retrieval pipelines. CCE supports modular plugin architectures for embedding providers and storage backends, offering flexibility for scaling across projects. With built-in APIs for storing, querying, and summarizing context, CCE streamlines the creation of personalized conversational applications, virtual assistants, and cognitive agents that require long-term memory retention.

CrewAI Code Repo Analyzer is an open-source AI agent that indexes a code repository, creates vector embeddings, and provides semantic search. Developers can ask natural language questions about the code, generate high-level summaries of modules, and explore project structure. It accelerates code understanding, supports legacy code analysis, and automates documentation by leveraging large language models to interpret and explain complex codebases.

eigenDB is a purpose-built vector database tailored for AI and machine learning workloads. It enables users to ingest, index, and query high-dimensional embedding vectors in real time, supporting billions of vectors with sub-second search times. With features such as automated shard management, dynamic scaling, and multi-dimensional indexing, it integrates via RESTful APIs or client SDKs in popular languages. eigenDB also offers advanced metadata filtering, built-in security controls, and a unified dashboard for monitoring performance. Whether powering semantic search, recommendation engines, or anomaly detection, eigenDB delivers a reliable, high-throughput foundation for embedding-based AI applications.

Query-Bot integrates document ingestion, text chunking, and vector embeddings to build a searchable index from PDFs, text files, and Word documents. Using LangChain and OpenAI GPT-3.5 Turbo, it processes user queries by retrieving relevant document passages and generating concise answers. The Streamlit-based UI allows users to upload files, track conversation history, and adjust settings. It can be deployed locally or on cloud environments, offering an extensible framework for custom agents and knowledge bases.

Rawr Agent is a modular, open-source Python framework that empowers developers to build autonomous AI agents by orchestrating complex workflows of LLM interactions. Leveraging LangChain under the hood, Rawr Agent lets you define task sequences either through YAML configurations or Python code, specifying tool integrations such as web APIs, database queries, and custom scripts. It includes memory components for storing conversational history and vector embeddings, caching mechanisms to optimize repeated calls, and robust logging and error handling to monitor agent behavior. Rawr Agent’s extensible architecture allows adding custom tools and adapters, making it suitable for tasks like automated research, data analysis, report generation, and interactive chatbots. With its simple API, teams can rapidly prototype and deploy intelligent agents for diverse applications.

VisQueryPDF processes PDF files by splitting them into chunks, generating vector embeddings via OpenAI or compatible models, and storing those embeddings in a local vector store. Users can submit natural language queries to retrieve the most relevant chunks. Search hits are displayed with highlighted text on the original PDF pages and plotted in a two-dimensional embedding space, allowing interactive exploration of semantic relationships between document segments.

Agentic Shared Memory provides a robust solution for managing contextual data in AI-driven multi-agent environments. Leveraging vector embeddings and efficient data structures, it stores agent observations, decisions, and state transitions, enabling seamless context retrieval and update. Agents can query the shared memory to access past interactions or global knowledge, fostering coherent behavior and collaborative problem-solving. The library supports plug-and-play integration with popular AI frameworks like LangChain or custom agent orchestrators, offering customizable retention strategies, context windowing, and search functions. By abstracting memory management, developers can focus on agent logic while ensuring scalable, consistent memory handling across distributed or centralized deployments. This improves overall system performance, reduces redundant computations, and enhances agent intelligence over time.