Mem0 vs Supermemory: Two Memory Engines Compared

Mem0 and Supermemory are both memory engines for developers: your application or agent calls an API, a model decides what to remember, and relevant memories come back before the next answer. The difference is where each one starts. Mem0 starts from conversation turns and stores the facts it extracts from them, with an open-source version you can run yourself. Supermemory starts from documents of any kind, including chats, files, URLs and synced drives, and builds a graph of memories that update and extend one another, alongside managed retrieval over the documents themselves. If your memory comes mostly from chat, the two overlap heavily. If it comes from a user’s files and inbox as well, Supermemory does more of the work for you. Everything below is from both vendors’ documentation as of October 8, 2026.

What each one stores

Mem0 stores memories, not transcripts. Its page on how Mem0 works↗ (opens in a new tab) shows a message such as “I prefer aisle seats” becoming the memory “User prefers aisle seats”, written to a SQL store for facts, a vector store for embeddings and an entity store. Extraction is additive; your code calls update or delete to correct a memory.

Supermemory separates two things. Documents are the raw input you send, and memories are the facts it extracts. Supermemory’s guide to how Supermemory works↗ (opens in a new tab) walks a document through extracting, chunking, embedding and indexing, so the document stays searchable for retrieval-augmented answers while the memories describe a person or entity over time. New memories link to old ones in three ways: a fact updates an older one, extends it with detail, or is derived from several others.

Side by side

                    Mem0                         Supermemory
Input               conversation turns, plus     documents: chats, files, URLs,
                    images and PDFs              connector items
Isolation           user_id, agent_id, app_id,   namespace (one per request),
                    run_id filters               own vector index each
Graph               Platform only                built in
Profiles            yes (Platform)               yes
Connectors          framework and tool           Google Drive, Gmail, Notion,
                    integrations                 OneDrive, GitHub, S3 and more
Self-host           open source, Apache 2.0      binary; server not open source
Hosted MCP          yes, OAuth or API key        yes, OAuth, with spaces

Scoping users and tenants

Mem0 tags each memory with identifiers, user_id, agent_id, app_id and run_id on the Platform, and you filter searches by them. Its documentation is candid that the boundary is a filter you must remember to pass, and that facts are attributed to whoever said them, so a search requiring both a user and an agent ID returns nothing on the default path.

Supermemory makes the boundary structural. A namespace↗ (opens in a new tab), formerly called a container tag, goes in the URL of every call, a request can touch only one, and each namespace is backed by a dedicated vector index, which the docs describe as strict rather than best-effort isolation. The trade-off is that reading across several namespaces means one request per namespace.

Keeping memories correct

• Mem0: explicit update and delete, and per-memory history. On the Platform, feedback on retrieved memories, memory decay that dampens stale memories at search time, and Dream, which merges duplicates and supersedes outdated facts in the background.
• Supermemory: when a fact updates an older one, retrieval follows the latest while history can remain. Memories can be forgotten exactly or by meaning. Inferred memories are down-weighted in search until someone approves them, and an API lists them for review.

Both designs assume your application, not the end user, owns correction. If wrong memories matter in your product, build the review screen early with either one.

Self-hosting and licensing

This is the clearest structural difference. Mem0’s open-source version is Apache 2.0 and runs as a library or a Docker server with your choice of model, embedder and vector store, though Mem0’s Platform vs Open Source comparison↗ (opens in a new tab) notes that graph memory, webhooks, export and several search features are Platform-only.

Supermemory offers a self-hosted binary↗ (opens in a new tab) that runs the same API on your machine with local embeddings and any OpenAI-compatible model, including fully offline. Its documentation says the SDKs are open source but the server binary is built from a non-public codebase and is free within a lite license limit, and that connectors and the Supermemory MCP are platform features.

Compliance

Mem0’s site lists SOC 2 (Type 1), HIPAA and GDPR, bring-your-own-key encryption, and Kubernetes, private-cloud or air-gapped deployment. Supermemory’s security and compliance page↗ (opens in a new tab) lists SOC 2 Type II, GDPR, a HIPAA business associate agreement on eligible plans, and says customer content is never used to train models. Ask either vendor for current reports before you rely on them.

Over MCP

Both run hosted MCP servers, so coding tools and AI apps can use memory without code. Mem0’s offers tools such as add_memory and search_memories and signs in through the browser or with an API key. Supermemory’s signs in with OAuth and adds spaces, so a team can keep, say, an engineering launch apart from a legal matter and choose which spaces a client may reach. The general trade-offs of serving memory this way are in agent memory over MCP.

Getting started with each

Both are a key and a few lines away. Mem0 installs with pip install mem0ai or npm install mem0ai, and its CLI can even mint an evaluation key for an AI coding agent without an email or dashboard, which a person claims later. Supermemory issues keys from its developer console, and its SDK is supermemory on npm and PyPI, with a CLI, a skill and a docs MCP server for setting up coding agents.

# Mem0
pip install mem0ai

# Supermemory
pip install supermemory

In both, the first design decision is the same: what your boundary identifier means. Decide whether a user, a workspace or a project is the unit before the first write, because moving memories between boundaries later is a migration, not a setting.

Which one to pick

• Pick Mem0 if your memory comes mainly from conversations, you want an open-source path you control, or your team already works in one of the agent frameworks it integrates with.
• Pick Supermemory if your agent must also search a user’s documents, drives and inbox, and you want strict per-namespace isolation and managed retrieval in one service.
• Run both against the same twenty real conversations before deciding. Supermemory publishes a guide for migrating from Mem0, so switching later is a documented path.

What neither is for

Both are infrastructure for building AI products. They are fast, they scale and they keep thousands of users apart, which no hand-kept record can match. They are not where a team writes down its decisions, tracks its tasks or leaves a note on where today’s session stopped, and nobody on the team reads their memories directly.

fenbs covers that side: a web app where your projects, tasks, Decisions and rules, lessons learned and Where we left off sit in one place that people can see and edit. ChatGPT, Claude, Claude Code, Codex, Cursor and other AI apps connect over MCP and read the same board, rules first. Assistants follow the rules people record and cannot decide a decision themselves, and History shows every change and who made it. Building an app for your users? Use Mem0 or Supermemory. Using AI for your own work, across apps? That is a board’s job.