MegaMemory FAQs

Question 1

What is MegaMemory and how does it help coding agents?

Accepted Answer

MegaMemory provides a persistent knowledge graph that allows AI coding agents to remember project concepts, architecture, and decisions across development sessions. It enhances agents' ability to understand, plan, and execute tasks by querying this stored knowledge.

Question 2

What kind of information can coding agents query or add to MegaMemory?

Accepted Answer

Agents can semantically search for existing concepts using natural language (`understand`). They can also create new concepts, update existing ones, link concepts with typed relationships (e.g., 'depends_on', 'implements'), and list top-level concepts to orient themselves.

Question 3

Does MegaMemory require external API keys for its embedding models?

Accepted Answer

No, MegaMemory utilizes in-process embeddings (all-MiniLM-L6-v2) locally. This means no external API keys are needed, and there are no network calls for embeddings after the initial model download.

Question 4

How does MegaMemory store and represent project knowledge?

Accepted Answer

It stores concepts like features, modules, patterns, and decisions—rather than just code symbols—in a per-project SQLite database. This data is enriched with embeddings, allowing for semantic search and graph-based relationships.

Question 5

Can I visualize the knowledge graph created by MegaMemory?

Accepted Answer

Yes, MegaMemory includes a built-in web explorer. By running `megamemory serve`, you can launch an interactive browser-based visualization of your project's knowledge graph, complete with node details, relationships, and search capabilities.

MegaMemory

MegaMemory

Key Features

Use Cases

Key Features

Use Cases