LangConnect Client
A Modern GUI Interface for Vector Database Management(Supports MCP integration)
What is LangConnect Client?
LangConnect Client is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to modern gui interface for vector database management(supports mcp integration)
A Modern GUI Interface for Vector Database Management(Supports MCP integration)
This server falls under the Databases category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- A Modern GUI Interface for Vector Database Management(Suppor
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx langconnect-clientConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use LangConnect Client
LangConnect Client is a full-stack vector database management platform with a modern GUI and an integrated MCP server that exposes over 10 tools for managing document collections, performing semantic and hybrid search, and ingesting documents from multiple formats. Built on PostgreSQL with pgvector (via Supabase), it combines a Next.js frontend, a FastAPI backend, and an MCP interface, allowing AI assistants to create collections, add PDF or Markdown documents, run multi-query RAG pipelines, and retrieve results with source citations. It is aimed at developers who want a self-hosted, production-ready RAG infrastructure with both a visual management UI and programmatic MCP access for AI agents.
Prerequisites
- Docker and Docker Compose installed
- Node.js 20+ for the frontend
- Python 3.11+ with the uv package manager
- A Supabase account with a project (provides SUPABASE_URL and SUPABASE_KEY)
- An OpenAI API key (OPENAI_API_KEY) for generating text embeddings
Clone the repository
Clone the LangConnect Client repository from GitHub and enter the project directory.
git clone https://github.com/braincrew-lab/langconnect-client.git
cd langconnect-clientConfigure environment variables
Copy the example environment file and fill in your credentials. Required variables include OPENAI_API_KEY, SUPABASE_URL, SUPABASE_KEY, NEXTAUTH_SECRET, NEXTAUTH_URL, and NEXT_PUBLIC_API_URL.
cp .env.example .env
# Edit .env with your credentialsBuild and start all services
Use the provided Makefile to build Docker images and start the frontend, backend, and MCP server containers.
make build
make upGenerate the MCP configuration
Run the mcp make target to produce the MCP client configuration file that points to the running server.
make mcpAccess the management UI
Open http://localhost:3000 in your browser to use the GUI for creating collections and uploading documents. The API docs are available at http://localhost:8080/docs.
Connect your MCP client
Add the generated MCP configuration to your claude_desktop_config.json or Claude Code settings. The MCP server runs on port 8765 by default (configurable via SSE_PORT).
LangConnect Client Examples
Client configuration
Add this block to your claude_desktop_config.json to connect to the running LangConnect MCP server.
{
"mcpServers": {
"langconnect-client": {
"command": "npx",
"args": ["langconnect-client"],
"env": {
"OPENAI_API_KEY": "your_openai_api_key_here",
"SUPABASE_URL": "https://your-project.supabase.co",
"SUPABASE_KEY": "your_supabase_anon_key_here",
"NEXTAUTH_SECRET": "your_nextauth_secret_here",
"NEXTAUTH_URL": "http://localhost:3000",
"NEXT_PUBLIC_API_URL": "http://localhost:8080"
}
}
}
}Prompts to try
These prompts use the MCP tools for collection management, document ingestion, and RAG-based question answering.
- "List all document collections and tell me how many documents are in each one"
- "Create a new collection called 'research-papers' and add the document at /tmp/paper.pdf"
- "Search the 'product-docs' collection for information about authentication and summarize what you find"
- "Run a multi-query search on 'support-tickets' for issues related to payment failures and give me the top results with citations"
- "Delete all documents in the 'test-collection' and then remove the collection itself"Troubleshooting LangConnect Client
Docker containers fail to start with database connection errors
Verify that SUPABASE_URL and SUPABASE_KEY in your .env file are correct. You can find these in your Supabase project settings under API. Ensure the Supabase project is active and not paused.
Document embedding fails with an OpenAI error
Check that OPENAI_API_KEY is set correctly in .env and that your OpenAI account has available quota. The server uses OpenAI's embeddings API to convert documents into vectors — this requires a valid, funded OpenAI account.
MCP client cannot connect to the server on port 8765
Run `make logs` to inspect the container logs for errors. Confirm the services are running with `docker compose ps`. If port 8765 is in use, set SSE_PORT to a different value in .env and rebuild with `make down && make up`.
Frequently Asked Questions about LangConnect Client
What is LangConnect Client?
LangConnect Client is a Model Context Protocol (MCP) server that modern gui interface for vector database management(supports mcp integration) It connects AI assistants to external tools and data sources through a standardized interface.
How do I install LangConnect Client?
Follow the installation instructions on the LangConnect Client GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with LangConnect Client?
LangConnect Client works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is LangConnect Client free to use?
Yes, LangConnect Client is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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