Grapevine
An open-source, self-hostable knowledge hub that unifies your company's tools and gives AI assistants semantic search via MCP.
What is Grapevine?
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.
SpecificationsAI-estimated
Key Features of Grapevine
Use Cases for Grapevine
Enterprise Knowledge Search
Provide employees a single searchable interface for scattered knowledge from multiple SaaS tools.
AI Assistant Context
Let Claude or other MCP-compatible assistants retrieve company data to answer questions with relevant context.
Slack Q&A
Deploy the optional Slack bot so team members can ask questions and get answers directly in Slack.
Onboarding and Discovery
Help new hires find docs, tickets, and conversations across tools quickly instead of digging through each app.
Customer Support Retrieval
Support agents can search Zendesk, Salesforce, and Intercom together to resolve tickets faster.
Engineering Lookup
Search GitHub, GitLab, Linear, and Jira together for code context, issues, and project history.
Meeting Intelligence
Index Gong and Fireflies transcripts to revisit decisions, action items, and meeting outcomes.
Compliance and Audit
Maintain an aggregated searchable archive of organizational knowledge for internal audit and compliance purposes.
How to use Grapevine?
Clone and prepare the repo
Run git clone https://github.com/gathertown/grapevine.git, cd into the directory, install mise, then run mise install and uv sync to set up the Python toolchain.
Configure environment variables
Copy .env.example to .env and fill in required values for database, OpenSearch, Redis, OpenAI, WorkOS, and AWS.
Start services and initialize the database
Build JavaScript dependencies with cd js-services && yarn install && yarn build && cd .., then run mise dev and mise setup to apply migrations.
Connect data sources through the admin UI
Open http://localhost:5173 to configure connectors, onboard your organization, and start ingesting knowledge.
Enable MCP for AI assistants
Start the MCP server with uv run python -m src.mcp.server and use scripts/generate_mcp_token.py to create service tokens for assistants like Claude.
Pros & Cons of Grapevine
Pros
- 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.
- Extensible connector framework for adding custom data sources.
Cons
- 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.
Frequently Asked Questions
What is Grapevine?
Grapevine is an open-source, self-hostable unified knowledge store that ingests information from corporate sources and exposes semantic search via an MCP server.
Which data sources are supported?
It includes connectors for Slack, Gmail, Intercom, Linear, Jira, Asana, Monday, ClickUp, Trello, Teamwork, Notion, Confluence, Google Drive, GitHub, GitLab, HubSpot, Salesforce, Pipedrive, Attio, Figma, Canva, PostHog, Fireflies, Gong, Zendesk, Pylon, and custom uploads.
What are the prerequisites for self-hosting?
You need Docker and Docker Compose, PostgreSQL 15+ with pgvector, OpenSearch 2.x, Redis 6+, an OpenAI API key for embeddings, and a WorkOS account for authentication.
How does the MCP server work?
The MCP server provides tools like semantic_search, keyword_search, get_document, get_document_metadata, and ask so AI assistants like Claude can search and retrieve company knowledge.
Is Grapevine open source?
Yes, Grapevine is released under the MIT License.
Can I deploy Grapevine in production with Kubernetes?
Yes, the repository includes Kubernetes deployment configurations in kustomize, with approximate resource requirements of 2.5-8 CPU cores and 4-11GB RAM.
Is Grapevine actively maintained?
The repository states that it is provided as-is without active maintenance, so users should expect limited ongoing updates and support.
How do I generate service tokens for the MCP server?
Use uv run python scripts/generate_mcp_token.py --tenant-id <tenant_id>, optionally adding --expires-in and --description flags for custom expiry and descriptions.
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