Product comparison

Memkith vs Supermemory: two different memory models.

Both can give AI tools persistent context through MCP. Supermemory is general memory and content infrastructure. Memkith is built for engineering teams that need memory scoped to the code, branch, and repository where it applies.

Create account Install locally

The useful distinction

The difference is what memory belongs to.

Supermemory remembers for the AI application.

Its broad abstraction accepts conversations, documents, files, and connected sources, then retrieves useful context across AI experiences.

Memkith remembers for the codebase.

Its records describe what a team learned, where that lesson applies, and which git state made it visible to the next coding agent.

Capability model

Compare the job, not a feature checklist.

The products overlap at persistent agent memory. Their organizing units and retrieval boundaries are different.

Dimension	Supermemory	Memkith
Primary job	General memory and context infrastructure for AI applications and assistants.	Shared engineering memory for teams and the coding agents working in their repositories.
Memory unit	Extracted memories and indexed document content from text, files, and conversations.	Deliberate engineering episodes: decisions, failed attempts, review outcomes, constraints, and preferences.
Scope model	Projects, container tags, metadata filters, and connected content sources.	Repository, branch, module, file, and test scopes anchored to the code a memory affects.
Retrieval	Semantic and hybrid search across extracted memories and document chunks.	Keyword relevance plus code-scope proximity, so nearby engineering context ranks higher.
Agent workflow	Persistent memory across MCP clients through memory and recall tools.	Recall, learn, update, and directives tools designed for planning, editing, and reviewing code.
GitHub relationship	A connector synchronizes selected repositories' documentation and text files into a knowledge base.	Memory records carry branch, commit, author, and code scope; local visibility follows git itself.

A Memkith record

Engineering memory carries its address and provenance.

repo acme/api

branch feat/retry-budget

Decision

Retry with a budget, not fixed attempts

Fixed attempt counts overloaded the downstream service during partial recovery. Share one request budget across retries and honor Retry-After before applying backoff.

scope: src/lib/retry.ts:2-3
commit: 8e21f6a
author: sam@acme.dev
source: PR #142

MCP workflow

memkith_recall

Retrieve the relevant episodes for a query and file or module scope.

memkith_learn

Record a durable lesson with the code scope and git provenance attached.

memkith_update

Correct a memory in place when the code or the team's decision changes.

memkith_directives

Provide the always-on operating constraints that apply across the repository.

Choose by use case

Use the layer that matches what you need to preserve.

Choose Supermemory

When you are building a general AI application that needs semantic memory, user profiles, document ingestion, and broad connector coverage.

Choose Memkith

When an engineering team needs decisions and lessons to stay attached to the code, follow git visibility, and remain usable across coding agents.

Sources checked July 2026

Keep engineering memory with the codebase.

Create an account, connect the CLI, and give every supported coding agent the same code-scoped context.