title: Hipocampo MCP emoji: 🧠 colorFrom: blue colorTo: purple sdk: docker pinned: false

Hipocampo

Dual-Memory System with Sparse Selective Caching

Persistent memory for autonomous AI agents · PostgreSQL 17 + pgvector · Hybrid Search · MCP Server

⚠️ Transport Note: SSE transport is deprecated since MCP spec 2025-03-26. Hipocampo now uses Streamable HTTP (single endpoint /mcp) as the recommended remote transport. SSE (/sse) remains available for backward compatibility but will be removed in a future release.

🌐 MCP Server — Live on Hugging Face

Hipocampo runs as a free MCP server on Hugging Face Spaces. Connect from any MCP client:

URL: https://alexbell1-hipocampo-mcp.hf.space/mcp

🧪 Interactive Playground: Try saving and searching memories from your browser at https://alexbell1-hipocampo-mcp.hf.space/ — no registration or MCP client needed.

⚠️ Important: The Hugging Face free tier is ephemeral — data is lost on restart/deploy. This instance is intended for testing only. For persistent storage, run Hipocampo locally (see Quick Start) or connect an external database (Neon, Supabase, etc.).

{
  "mcpServers": {
    "hipocampo": {
      "url": "https://alexbell1-hipocampo-mcp.hf.space/mcp",
      "type": "streamable-http"
    }
  }
}

Embedding model: sentence-transformers/all-MiniLM-L6-v2 (384 dims) via Hugging Face Inference API (free, no credit card required).

⚡ Instalación Zero-Touch — Auto-Installer v6.0

Una sola línea. La terminal hace todo: PostgreSQL + pgvector, embeddings, base de datos, venv, clientes MCP, servicio systemd con timer de mantenimiento automático.

🪟 ¿Usas Windows?

install.sh es un script de Linux/macOS. En Windows necesitas WSL2 (Windows Subsystem for Linux):

powershell

Copy

wsl --install

Abre la terminal de WSL (Ubuntu), actualiza los paquetes y vuelve a ejecutar el instalador:

bash

Copy

sudo apt update && sudo apt install -y python3-venv python3-pip build-essential
curl -fsSL https://raw.githubusercontent.com/carrasquelalex1/hipocampo/main/install.sh | bash

⚠️ En Windows puro (CMD/PowerShell) el instalador NO funciona. Sin WSL2 verás errores como Package 'python3-venv' has no installation candidate o apt: command not found.

🚀 Instalar en 10 segundos

curl -fsSL https://raw.githubusercontent.com/carrasquelalex1/hipocampo/main/install.sh | bash

Máquinas sin interacción (VPS, contenedores):

curl -fsSL https://raw.githubusercontent.com/carrasquelalex1/hipocampo/main/install.sh | bash -s -- --unattended

🔄 Las 8 fases automáticas

🛡️ Características del instalador

Hipocampo is an advanced dual-memory persistence architecture designed for autonomous AI agents. By maintaining both technical knowledge and user profiling data across sessions, Hipocampo provides a reliable, stateful context that enables agents to learn, adapt, and scale efficiently.

Built on top of PostgreSQL 17 with pgvector, it features BIRE v3.7 — a hybrid retrieval engine combining semantic embeddings (1024d), lexical expansion, and GIN trigram search with dynamic score fusion. Also includes Sparse Selective Caching (SSC) as an experimental pipeline.

💡 Why Prompt Compression?

Hipocampo already reduces context through SSC (selective retrieval). But even the top-5 most relevant memories can consume 500-2000+ tokens when concatenated — a significant portion of any LLM's context window.

Hybrid compression adds a second reduction layer:

• Extractive phase: Removes redundant sentences (filtering by keyword relevance to your query). Reduces generic text by 30-50% instantly, with no API calls.
• LLM phase: Summarizes technical/code content using the configured embedding endpoint (Ollama local or NVIDIA NIM). Preserves all code, variable names, and syntax while dropping explanatory verbosity.
• Combined: 20-50% token reduction with near-zero quality loss. A 1500-token memory block becomes 750-1200 tokens — that's real savings on every LLM call.

Real impact: If you call compress_hipocampo before every search_hipocampo → LLM round-trip, you save 200-800 tokens per interaction. At scale (hundreds of queries), this translates to meaningful cost reduction and faster responses.

🚀 Key Features