Self-hosted open-source AIaaS with RAG, agents, MCP, and visual logic
Best for: Organizations wanting a self-hosted AI stack with full data control, Developers building multi-project AI apps with RAG, agents, and visual logic
Ontology-first Graph RAG platform turning messy knowledge into reusable packs
Best for: Researchers building private evidence graphs from papers and reports, Domain experts packaging expertise into sellable ontology packs via marketplace
Long-term memory database for AI agents built from scratch in Rust
Best for: AI agent developers needing persistent, structured memory across sessions, Developers of personal AI assistants that learn from user conversations over time
A declarative logic runtime that embeds reasoning into AI systems with a built-in knowledge graph.
Best for: AI researchers building neuro-symbolic systems for reasoning and learning, Developers adding logical constraints to LLM outputs to reduce hallucinations
RAG API platform with hybrid search & pre-built connectors
Best for: Developers building AI assistants that query user data from multiple SaaS tools, SaaS teams embedding RAG into their product with minimal dev time on data pipelines
Open-source context retrieval layer grounding AI agents in real-time data from your apps and databases.
Best for: Developers building AI agents that need real-time business context from SaaS tools and databases, Teams implementing RAG pipelines on top of apps like Stripe, Notion, and Slack
Build AI apps with MongoDB Atlas—native vector search, hybrid search, and reranking in one multi-cloud data platform.
Best for: Developers building generative AI applications with RAG and semantic search, Teams modernizing legacy applications with NoSQL and AI features
Open-source protocol suite for standardizing LLM, vector, graph, and embedding infrastructure.
Best for: AI platform teams standardizing across multiple providers and frameworks, Developers building agentic multi-framework apps needing vendor portability
Mount Supermemory as a real filesystem—ls, cat, and grep become semantic memory operations.
Best for: Developers building autonomous agents (Claude, Codex) who want persistent memory without SDKs, Researchers managing large document corpora and needing semantic search across formats
Local-first vector database for AI agents, RAG, and edge workloads.
Best for: Edge AI engineers building autonomous systems, robotics, and IoT with local vector search, Manufacturing teams running AI in disconnected factory environments
