Grapevine vs OpenCrab

Grapevine is an open-source, self-hostable unified knowledge store designed to ingest information from a company's scattered tools and turn it into one searchable index. It connects to popular services like Slack, Notion, Linear, GitHub, Google Drive, HubSpot, Salesforce, and more, then uses AI embeddings to enable semantic search across all of them. The platform is built around an MCP (Model Context Protocol) server, which lets AI assistants such as Claude query and retrieve company knowledge directly. Beyond search, Grapevine provides an optional Slack bot for AI-powered question answering and a web-based admin UI for configuring data sources and onboarding organizations. Its architecture includes core services like an ingest gatekeeper, tenant steward, and background workers that process webhook data, generate embeddings, and maintain search indices. This makes it a practical choice for teams that want a private, self-hosted knowledge layer rather than relying on a closed SaaS product. Because Grapevine is self-hostable via Docker Compose or Kubernetes, it appeals to developers and enterprises that need control over their data and infrastructure. It supports a wide range of connectors across communication, project management, documentation, code, CRM, design, analytics, meetings, and support. However, it is not a plug-and-play product: it requires PostgreSQL with pgvector, OpenSearch, Redis, WorkOS, and an OpenAI API key, and the repository is provided as-is without active maintenance.

OpenCrab is an Ontology Graph RAG platform that extracts entities, relationships, evidence, and claims from documents to build source-cited ontology graphs. These graphs can be packaged into reusable ontology packs and queried through graph RAG or MCP-compatible AI tools like Claude, ChatGPT, Cursor, and Codex.

• Unified semantic search across 25+ business tools from one self-hosted platform.
• MCP server makes company knowledge accessible to AI assistants like Claude.
• Open source under MIT, with Docker Compose and Kubernetes deployment options.
• Optional Slack bot provides natural language Q&A directly in Slack.

• MCP-based integration with Claude, ChatGPT, Cursor, and Codex
• Graph RAG with grounded source citations
• Supports PDF, URL, OCR, Notion, and GitHub ingestion
• Ontology packs can be shared, versioned, and sold via marketplace

• Repository is provided as-is without active maintenance, so updates and support are limited.
• Requires substantial infrastructure: PostgreSQL, OpenSearch, Redis, WorkOS, and an OpenAI API key.
• Setup complexity is high for non-technical teams due to Docker, environment variables, and multiple services.
• No hosted or managed version is mentioned, so users must handle deployment and scaling themselves.

• Relatively early-stage platform
• Ontology graph concepts may require a learning curve
• Pricing may be limited for team or enterprise features