The OpenCog AtomSpace is a hypergraph-based knowledge representation system serving as the core component for Artificial General Intelligence (AGI) research. It provides an in-memory database for storing immutable, uniquely-typed atoms (nodes and links) with mutable key-value stores attached for dynamic data. The system supports executable graphs through the Atomese language, features a sophisticated pattern matching and query engine, includes type inheritance and intersection capabilities, and offers multi-language bindings (Scheme, Python). Architecture-wise, it follows a modular design with clear separation between atom management, value systems, execution frameworks, and query processing. The codebase demonstrates strong engineering practices with factory patterns, handle-based memory management, and thread-safe frame stacks for evaluation contexts.
Cognee is an open-source AI memory platform that enables agents and applications to build, store, and query persistent knowledge graphs from documents, code, and conversations. The system provides a Python SDK, a FastAPI REST API, a CLI, a Next.js frontend, and an MCP server for agent integration. Core operations include ingesting data (remember), extracting entities and relationships via LLMs or GLiNER (cognify), retrieving context (recall), and self-improvement loops (improve). The architecture is modular with adapter/registry patterns for graph databases (Ladybug, Neo4j, Turso), vector stores (LanceDB, PGVector), LLM providers (LiteLLM, OpenAI, Anthropic, Ollama), and integrations (Slack, GitHub, Google Drive). The frontend provides a dashboard for dataset management, knowledge graph visualization, and business analytics. The project also includes an evaluation framework for benchmarking retrieval strategies against standard datasets like HotpotQA and BEAM. While production-grade in many areas, some components are marked as demo-quality or have cloud-only stubs.