@jaspertvdm
Basic information
Project description
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"rabel": {
"command": "python",
"args": [
"-m",
"mcp_server_rabel"
],
"env": {}
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Mcp Server Rabel (Recidive Active Brain Environment Layer) is a local-first AI memory server that gives AI assistants persistent memory with semantic search, graph relations, and soft pipelines. Inspired by Mem0 and evolved for HumoticaOS, it runs 100% locally with no cloud, no API keys, and no data leaving the machine.
Install via pip: pip install mcp-server-rabel (basic) or pip install mcp-server-rabel[full] (adds vector search and embeddings). Add to Claude CLI with claude mcp add rabel -- python -m mcp_server_rabel. Tools include rabel_add_memory, rabel_search, rabel_add_relation, rabel_get_guidance, and rabel_next_step.
With the basic install, only text storage and LIKE-based search are available. The `[full]` option adds vector search via sqlite-vec and embeddings via Ollama nomic-embed-text. Ollama must be running locally for semantic search.
Yes. It is designed to work entirely offline. No cloud services, tracking, or API keys are needed.
Without the `[full]` extras, text search uses SQL `LIKE` queries. With embeddings, semantic (vector) search becomes available.
Yes. The `rabel_add_relation` tool stores a subject–predicate–object triple, and `rabel_get_relations` retrieves them, enabling a knowledge graph.
Soft pipeline guidance is available in both English and Dutch, more languages are planned.
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