Medha FAQs

Question 1

Is Medha designed for production-grade LLM applications?

Accepted Answer

Yes, Medha is built for production environments. It supports PostgreSQL for scalable database management, offers flexible authentication with SAML 2.0 (compatible with DUO/Okta), features optimistic locking for safe concurrent access by multiple AI agents, and includes hourly GitHub auto-synchronization for disaster recovery.

Question 2

How do LLMs interact with Medha to store and retrieve information?

Accepted Answer

Medha exposes a set of 'human-aligned tools' (like `medha_recall`, `medha_remember`, `medha_connect`) that LLMs can easily understand and utilize. It integrates seamlessly as an MCP server with various LLM clients (e.g., Cursor, Claude Desktop), simplifying the process of managing long-term memory within AI workflows.

Question 3

How does Medha ensure the reliability and version control of LLM memories?

Accepted Answer

Medha utilizes a git-backed storage system, treating every memory change as a git commit. This provides a complete, auditable history of all memories, allowing for version control, the ability to 'go back in time,' and ensuring data integrity and reliability for critical AI applications.

Question 4

What is Medha and what problem does it solve for LLMs?

Accepted Answer

Medha is a Model Context Protocol (MCP) server that provides persistent, git-versioned memory storage for large language model (LLM) applications. It enables LLMs to maintain crucial long-term memory, overcoming the limitations of short context windows and enhancing their ability to recall and build upon past interactions and knowledge.

Question 5

What kind of search capabilities does Medha offer for retrieving memories?

Accepted Answer

Medha offers powerful search functionalities, including searching by tags, dates, content, and graph associations. For advanced context retrieval, it provides optional AI-powered semantic search utilizing OpenAI embeddings, allowing LLMs to find relevant information based on meaning, not just keywords.

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