Deep Memory vs Supermemory

Deep Memory and Supermemory are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Deep Memory

The library pairs a GraphRAG implementation with a Vocabulary system: a shared, schema-enforced dictionary of node types, relationship labels, and property constraints that every agent queries before writing. The result is consistent graph data across sessions without prompting every agent with walls of example documents — the schema replaces the examples, trimming token overhead. Backends include Neo4j, SQL Server, Azure Cosmos DB, and an in-memory option, all wired up via Docker Compose quickstarts the docs describe. Where the ceiling appears: there is no hosted service, no GUI, and no API surface — this is a library you embed and operate, which means your team owns the infra from day one.

Supermemory

Supermemory wraps memory, retrieval, user profiling, data connectors, and document extraction into one API so your agent doesn't reassemble context from scratch on every request. The retrieval layer claims sub-300ms latency using hybrid search with reranking, and the memory layer maintains a knowledge graph that merges contradictions and evolves facts over time rather than appending chunks blindly. Connectors to Slack, Notion, Drive, Gmail, GitHub, and S3 sync automatically — no ETL pipeline to maintain. The core memory engine is proprietary and hosted-only; self-hosting requires an enterprise agreement, so teams with strict data residency requirements hit a wall before they ship.

Attribute	Deep Memory	Supermemory
Pricing	Free	Paid
Price	—	$0 - $399+/mo
Free trial	No	No
Open source	Yes	Yes
Has API	No	Yes
Self-hosted option	Yes	No
Platforms	—	Cloud-hosted (SaaS); MCP server; Browser plugins (Chrome); IDE integrations (Claude Code, Cursor, VS Code)
Released	—	2024
Pros	Shared Vocabulary system enforces node and relationship schemas across every agent that writes to the graph, so two agents running in parallel cannot create conflicting entity types that fracture downstream queries. Schema-as-vocabulary replaces bulky in-prompt document examples, so each agent call carries less context overhead — relevant when token costs compound across high-frequency graph writes. Backend-agnostic design with Neo4j, SQL Server, Cosmos DB, and in-memory options means you can validate the pattern locally against the in-memory store and then swap to a production graph database with a config change, not a rewrite. Docker Compose quickstarts for each backend lower the time from clone to running graph, so evaluation does not require a pre-existing database cluster. Open-source codebase under a stated license, so teams that need to audit what gets written to their graph — or adapt the vocabulary logic to their domain — are not blocked by a closed SDK.	Knowledge graph memory that merges and contradicts facts across sessions, which means your agent doesn't tell a user something they already corrected two conversations ago. Sub-300ms hybrid search with reranking baked into the retrieval layer, so you avoid building and tuning a separate retrieval pipeline to hit production latency targets. Persistent user profiles that carry preference, behavior, and identity context across sessions, which means a support agent or personalized chatbot doesn't reset its understanding of the user on every ticket. Real-time connectors to Slack, Notion, Drive, Gmail, GitHub, and S3 with automatic sync, so your agent's memory reflects live changes in the tools your users actually work in — no manual import jobs to maintain. Multi-format extraction for PDFs, web pages, images, and audio consolidated into one provider, which means you don't wire together separate parsing services before you can ingest mixed document types.
Cons	There is no hosted service, managed API, or GUI: your team provisions, monitors, and scales the graph backend from scratch. Teams without dedicated infra capacity hit this wall at the first production deployment and move to a managed GraphRAG service instead. Vocabulary governance is code-only — there is no visual schema editor or admin UI. When a domain analyst (not an engineer) needs to add a new entity type or review the current schema, they depend on a developer to make and deploy the change, which creates a bottleneck on any team where schema ownership spans roles. The project carries 4 stars and 1 fork at the time of the source scrape, which means community-sourced answers, third-party integrations, and battle-tested patterns are sparse. Teams running into edge cases in the vocabulary merge logic or backend connectors are largely on their own until the maintainer responds.	The core memory engine is not self-hostable without an enterprise agreement — teams with data residency requirements or strict policies against sending user memory to a third-party managed service cannot deploy this in production without negotiating a contract first, and most either wait on procurement or replace the memory layer with a self-managed vector store. The knowledge graph and memory update logic are proprietary and closed; when retrieval behaves unexpectedly — returning stale facts or failing to surface a contradiction — there is no source code to inspect. Teams debugging production retrieval issues work from API responses and vendor support, not from the system itself. The free tier is capped at defined token and query limits, meaning a team validating the tool at scale will exhaust the free tier before they have enough production data to make a confident architecture decision — at which point cost exposure begins before the build is complete. Agent frameworks that manage their own memory or context windows require explicit integration work to hand off to Supermemory rather than their native store; teams already deep in a framework with memory primitives — LangGraph, for example — often find the integration layer adds complexity that exceeds the benefit for their specific architecture and abandon Supermemory in favor of the framework's native memory tooling.

Bottom line

Deep Memory is free while Supermemory is paid; only Supermemory exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Deep Memory and Supermemory?

Deep Memory is Free and open source, while Supermemory is Paid and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Deep Memory better than Supermemory?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.