Tensory FAQs

Question 1

What is Tensory and how does it benefit AI agents?

Accepted Answer

Tensory provides zero-setup, claim-native persistent memory for AI agents. It stores atomic facts, detects contradictions, and uses cognitive mechanisms like salience decay to give agents a reliable, long-term knowledge base, enhancing their intelligence and recall without complex infrastructure.

Question 2

What kind of setup or infrastructure is required to use Tensory?

Accepted Answer

Tensory is designed for zero-setup. It stores all its data—raw episodes, claims, embeddings, entities, and relations—within a single SQLite file. This means no Docker, no external databases, and no complex infrastructure setup is required, making it incredibly easy to deploy and manage.

Question 3

How does Tensory handle memory retrieval for AI agents?

Accepted Answer

Tensory employs a powerful hybrid search pipeline, combining FTS5 (full-text search), vector embeddings, and graph traversal. Results are then merged via RRF and diversified via MMR, ensuring comprehensive and highly relevant retrieval of information for agent queries.

Question 4

Can Tensory integrate with existing AI agent frameworks?

Accepted Answer

Yes, Tensory offers a Python library for direct integration, a recommended Claude Code plugin for seamless memory activation, and supports MCP server for clients like Claude Desktop and Cursor. It provides a full set of tools for adding, searching, and managing agent memory across various platforms.

Question 5

What makes Tensory unique compared to other memory solutions?

Accepted Answer

Tensory stands out with its claim-native storage for verifiable statements, built-in collision and contradiction detection, and advanced cognitive mechanisms (salience decay, surprise scoring, priming). It operates entirely within a single SQLite file, eliminating the need for complex external databases or infrastructure.

Tensory

Tensory

Key Features

Use Cases

Key Features

Use Cases