carrasquelalex1/hipocampo

Overview

Hipocampo implements a dual memory system using PostgreSQL with pgvector and Gemini embeddings for expansive relevance search. It stores and retrieves information by combining vector similarity with structured queries. The tool is written in Python and designed for developers building memory-augmented applications.

Best for

Best for
Developers experimenting with dual memory architectures in Python

Use cases

• Building persistent memory for conversational agents
• Retrieving relevant context from large document stores
• Implementing hybrid search combining vector and relational queries

Notes

Hipocampo implements a dual memory system using PostgreSQL with pgvector and Gemini embeddings for expansive relevance search. It stores and retrieves information by combining vector similarity with structured queries. The tool is written in Python and designed for developers building memory-augmented applications.

1 stars on GitHub. Last updated 2026-06-18. Licensed MIT.

Use cases

• Building persistent memory for conversational agents
• Retrieving relevant context from large document stores
• Implementing hybrid search combining vector and relational queries

Pros

• Leverages robust PostgreSQL and pgvector for scalable storage
• Combines vector and structured search for more accurate retrieval
• Open source with clear Python implementation

Cons

• Very limited community adoption (1 star on GitHub)
• Requires Gemini embeddings, creating external dependency
• No documentation or usage examples readily visible

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.