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Supermemory vs Cognee: Full Memory Engine vs. Graph Framework

Cognee is a much simpler thing: an open-source framework for building a memory pipeline out of three databases you operate. Supermemory is the full memory engine - ingestion, extraction, profiles, lifecycle, connectors, retrieval - behind one API. Here is the honest comparison.

By Dhravya Shah·7 min read

Cognee and Supermemory both get filed under "AI memory." They are not the same category of thing.

Cognee is a much simpler thing: an open-source framework for building a memory pipeline out of three databases you operate yourself. Supermemory is the full memory engine - ingestion, extraction, profiles, lifecycle, connectors, retrieval - behind one API.

This comparison was reviewed against both products' official documentation on September 16, 2026. It is published by Supermemory. Every claim below is checkable in their docs and ours.

What is Cognee?

Cognee is an open-source framework (also offered as Cognee Cloud) that wires together three stores: a relational store for documents and provenance, a vector store for embeddings, and a graph store for entities and relationships. Its remember() call chunks your data, extracts graph structure, and embeds it; recall() searches across those stores.

Read that architecture list again, because it is the whole product: you are choosing, provisioning, and operating three databases, plus the extraction pipeline on top. Cognee's own docs walk you through Docker Compose services, ports, and backend selection. If your hobby is infrastructure, that is a feature. If your product needs memory, it is a tax you pay forever.

What Cognee does not have, per its own documentation:

  • No user profiles. The only profiles in Cognee's docs are Docker Compose profiles. There is no concept of a standing, always-current profile of each user.
  • No multimodal ingestion. Its remember() operation documents raw text, files, and DataItem objects. No image, audio, video, or PDF extraction anywhere in the pipeline docs.
  • No memory lifecycle. It has a manual forget delete and a temporal query mode, but facts do not get versioned when they change, contradictions are not resolved, and nothing is forgotten automatically. Stale memory is your problem.
  • No managed connectors. Your data sources are your integration work.

What is Supermemory?

Supermemory is context infrastructure: hand it conversations, PDFs, URLs, files, or direct memories, and it extracts facts, links them into a graph, versions them when they change, forgets them when they expire, and maintains a standing profile of every user. Retrieval is one call: hybrid vector + keyword search, the graph around each hit, and the user's profile, with published sub-300ms p50 latency.

Unlike Cognee, Supermemory also has:

  • Profiles. A static + dynamic profile per user, built automatically, attached to every retrieval. Cognee has nothing in this category.
  • A real memory lifecycle. Updates create new versions with full history. An agentic forget operation can mass-expire memories matching a prompt (with dry-run preview). Temporal truth is tracked: what was true, what replaced it, when.
  • Multimodal ingestion. PDFs, images, audio, web pages - extracted, chunked, and indexed. Cognee documents none of this.
  • Managed connectors. Google Drive, Notion, Gmail, OneDrive, S3, GitHub, web crawler - OAuth, sync, and permissions handled.
  • The SDK and integration surface. Official TypeScript and Python SDKs, REST, remote MCP, plugins for Claude Code, Cursor, OpenCode, Windsurf, and OpenClaw, plus first-party integrations for Vercel AI SDK, LangChain, LangGraph, CrewAI, Mastra, Pipecat, Zapier, and n8n. Cognee's integration list is Python, TypeScript, an API, and MCP.
  • Public, reproducible benchmarks. #1 on LongMemEval (85.2%), LoCoMo, and ConvoMem, with the eval platform open-sourced as MemoryBench. Cognee publishes BEAM, its own benchmark measured against mem0.

And the part that surprises people: Supermemory is more directly configurable than Cognee, not less. Org-wide LLM filter prompts decide what gets indexed and what gets skipped. Per-container entity context steers extraction. Profile buckets are definable at org and container level, with LLM-suggested schemas. Container tags partition memory per user, project, or agent with scoped API keys and merge/split controls. Self-hosted, you pick the LLM provider, the embedding model, and the dimensions. Cognee's idea of configuration is choosing which three databases to run.

How do the architectures compare?

Area Cognee Supermemory
What it is Framework for assembling a memory pipeline Complete memory engine behind one API
You operate Relational + vector + graph stores, extraction pipeline, retrieval tuning Nothing (managed) or one binary (self-hosted)
User profiles None Automatic, static + dynamic, attached to retrieval
Multimodal Not documented PDFs, images, audio, URLs, conversations
Memory lifecycle Manual delete; temporal query mode Versioned updates, contradiction handling, automatic forgetting, agentic mass-forget with dry-run
Retrieval Vector + graph search modes Hybrid vector + keyword + graph + profile, one call, sub-300ms p50 published
Configurability Backend/database selection, pipeline internals Filter prompts, entity context, profile buckets, container isolation, scoped keys, model + embedding choice
SDKs & agents Python, TypeScript, API, MCP TS + Python SDKs, REST, remote MCP, coding-agent plugins, Vercel AI SDK, LangChain, LangGraph, CrewAI, Mastra, Pipecat, Zapier, n8n
Connectors Build them Managed: Drive, Notion, Gmail, OneDrive, S3, GitHub, crawler
Benchmarks BEAM (own benchmark, vs mem0) LongMemEval 85.2% #1, LoCoMo #1, ConvoMem #1, open eval platform
Open source Yes Yes (MIT), single-binary offline local mode

When does Cognee make sense?

Honestly? When your product is a side project and memory quality is not important - when you want to spend your time hand-building the memory stack itself: choosing graph backends, writing extraction pipelines, tuning retrieval modes. If the point of the project is to play with memory infrastructure, Cognee gives you the parts.

The moment any of these are true, that trade collapses:

  • the product is real and users depend on it
  • memory quality is the feature, not a toy
  • you need profiles, multimodal ingestion, or managed connectors
  • you do not want to paged by your own memory stack
  • latency and retrieval quality are measured, not vibes

Operating Cognee means owning its headaches (three stores, the pipeline, the retrieval tuning) and still ending up with worse latency, worse retrieval quality, and fewer features than the engine built by a team that does nothing else.

When does Supermemory fit better?

Whenever memory is supposed to work.

Production agents with real users. Products where "the AI remembers me" is the promise. Teams that would rather ship than operate a graph database. Scira AI runs Supermemory in production as the memory layer for a research product with thousands of users - memory went from the weakest part of their product to the strongest, with usage up ~32% after launch.

How should you compare memory quality?

Do not take our word for any of this - run the eval. The checks that actually separate memory systems:

  1. Direct recall. Can it retrieve an explicit fact from an earlier interaction?
  2. Updates. When a preference changes, does the new value replace the old, or do both come back?
  3. Multi-hop context. Can it connect facts spread across documents and sessions?
  4. Noise resistance. Does similar-but-wrong text crowd out the right answer?
  5. Profiles. Ask "what do you know about this user?" and see which system has an answer at all.
  6. Isolation. Can one tenant ever retrieve another tenant's context?
  7. Latency. Measure p50 and p95 with production-sized data, not a demo set.

Supermemory's answers are public: LongMemEval 85.2%, #1 on LoCoMo and ConvoMem, sub-300ms p50, and MemoryBench is open source so you can reproduce every number. Point the same harness at Cognee and compare.

Frequently asked questions

Is Supermemory a Cognee alternative?

Yes, and then some. Cognee is a framework for assembling a memory pipeline from stores you operate. Supermemory is the finished engine: any content in, profiles and lifecycle built in, one retrieval call out, with SDKs and plugins for the tools your team already uses.

Is Cognee open source?

Yes. So is Supermemory (MIT) - and Supermemory's local mode is a single binary that runs fully offline, no Docker Compose fleet required.

Can Supermemory be self-hosted?

Yes. One binary, embedded graph engine, built-in embeddings, offline against Ollama or any OpenAI-compatible model. Same API as the hosted platform - moving between them is a baseURL change.

How should teams choose between Cognee and Supermemory?

Ask what you are buying. Cognee sells you components and the privilege of operating them. Supermemory sells you memory that works. If your goal is a product that remembers, there is one answer.

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