Supermemory and LangMem represent two different paths to agent memory. Supermemory is a cloud-native memory engine with strong benchmarks and multi-modal support. LangMem is LangChain's official memory toolkit, designed for teams already committed...
Supermemory and LangMem represent two different paths to agent memory. Supermemory is a cloud-native memory engine with strong benchmarks and multi-modal support. LangMem is LangChain's official memory toolkit, designed for teams already committed to the LangGraph ecosystem.
This comparison covers their architectures, benchmark performance, pricing, and ideal use cases to help you decide.
| Factor | Supermemory | LangMem |
|---|---|---|
| Architecture | Hybrid RAG with fact extraction | Modular memory API with LangGraph integration |
| LongMemEval* | 85.2% | Not published |
| Deployment | Cloud API | Self-hosted with LangGraph |
| Pricing | Not publicly disclosed | Open source |
| GitHub Stars | 21.7K | 1.4K |
| Funding | $2.6M seed (Google/Cloudflare execs) | Part of LangChain ($25M+) |
Supermemory is a cloud-native memory engine for LLMs that combines hybrid RAG with structured fact extraction. Founded by a 19-year-old and backed by $2.6M from Google and Cloudflare executives, it scores 85.2% on LongMemEval.
Beyond basic memory, Supermemory includes connectors for Google Drive, Gmail, Notion, OneDrive, and GitHub—positioning it as both a memory layer and a knowledge integration platform.
Key strengths:
LangMem is LangChain's official long-term memory toolkit. Rather than building a standalone memory system, LangMem integrates directly into LangGraph's storage layer, providing both active memory tools for in-conversation use and automated background handlers for memory distillation.
Backed by LangChain's $25M+ in funding, LangMem benefits from ecosystem integration but depends on LangGraph for deployment and storage.
Key strengths:
Supermemory runs a hybrid retrieval pipeline combining vector search with LLM-based fact extraction. The memory engine extracts structured facts from conversations, handles temporal changes and contradictions, and builds user profiles with ~50ms retrieval latency.
The multi-modal pipeline processes PDFs, images (OCR), videos (transcription), and code alongside conversational data. Connectors pull in external knowledge from Google Drive, Notion, and GitHub.
LangMem provides four core capabilities: a modular memory API compatible with arbitrary storage backends, active memory tools for "hot path" operations during conversations, an automated memory handler for background distillation and refresh, and native LangGraph storage layer integration.
The design assumes you're building within the LangGraph ecosystem. Memory operations run as part of the graph's execution flow, and storage backends are pluggable through LangGraph's abstractions.
Supermemory is a product—a standalone memory engine you call via API. LangMem is a toolkit—a set of building blocks you assemble within LangGraph.
Supermemory handles the full pipeline: extraction, storage, retrieval, and multi-modal processing. LangMem provides the tools for each step but expects you to wire them together in your graph, choose your storage backend, and manage your own infrastructure.
This is the fundamental tradeoff: Supermemory gives you a working system immediately but locks you into their cloud. LangMem gives you flexibility but requires more engineering to reach production quality.
| Benchmark | Supermemory | LangMem |
|---|---|---|
| LongMemEval* | 85.2% | Not published |
Supermemory publishes benchmark results showing 85.2% on LongMemEval. LangMem has not published any public benchmark scores, making direct performance comparison impossible.
The absence of benchmarks from LangMem is notable—teams evaluating memory solutions have no published baseline for what LangMem achieves on standardized retrieval tasks.
Both score below Hypabase (87.4%), which uses AMR-based extraction for higher retrieval accuracy.
Supermemory's pricing is not publicly disclosed. Teams must contact sales for pricing information, which makes cost planning difficult for budget-conscious startups.
LangMem is open source and free to use. However, the total cost includes:
Supermemory bundles everything into an opaque price. LangMem is free as software but requires you to provision and pay for infrastructure separately. For teams already running LangGraph, adding LangMem is marginal cost. For everyone else, the total cost of LangGraph + storage + LangMem can exceed what a managed solution charges.
Choose Supermemory if you:
Supermemory's cloud-only model means no self-hosting option, and opaque pricing makes budget planning uncertain. The retrieval engine hasn't been independently validated.
Choose LangMem if you:
LangMem's lack of published benchmarks and tight coupling to LangGraph limit its appeal outside that ecosystem. The memory engine hasn't demonstrated standalone retrieval quality.
Both Supermemory and LangMem rely on ad-hoc LLM prompts to extract facts—Supermemory through its cloud pipeline, LangMem through whatever you wire into your graph. Neither guarantees consistent extraction across different conversation types. Hypabase takes a fundamentally different path: AMR-based extraction into hyperedges, replacing prompt-dependent extraction with a formal linguistic framework.
| Factor | Supermemory | LangMem | Hypabase |
|---|---|---|---|
| Extraction | LLM-based, hybrid RAG | LLM-based, modular | AMR (formal linguistic framework) |
| Representation | Facts + vector embeddings | Pluggable storage | N-ary hyperedges |
| LongMemEval* | 85.2% | Not published | 87.4% |
| Personalization | — | — | 100% |
Where Supermemory and LangMem depend on LLM prompts that produce inconsistent relation types from run to run, Hypabase uses Abstract Meaning Representation (AMR)—a formal framework from computational linguistics that maps sentences to structured graphs with deterministic role assignments.
AMR extraction produces dense, multi-role facts in PENMAN notation using kāraka semantic roles (from Pāṇini's Sanskrit grammar). Consider a developer onboarding scenario:
"Jack completed the React certification and prefers functional components"
Ad-hoc extraction (Supermemory, LangMem):
(Jack, completed, React certification)
(Jack, prefers, functional components)
AMR extraction (Hypabase):
(complete :agent Jack :object certification :attribute React)
(prefer :agent Jack :object component :attribute functional)
The difference: Hypabase captures structured, role-tagged facts that can be queried precisely—"What certifications does Jack have?" hits :agent Jack + certification directly. Ad-hoc triples require the LLM to have extracted the right relation type, which varies between runs.
Both Supermemory and LangMem are only as good as their extraction prompts. When the prompt misclassifies a preference as a fact, or drops a temporal qualifier, the information is silently lost or corrupted. Hypabase's AMR parser enforces a fixed grammar with 6 kāraka roles, catching malformed extractions at parse time rather than at query time.
| Benefit | How AMR + Hyperedges Deliver It |
|---|---|
| Deterministic roles | 6 kāraka roles replace ad-hoc relation types—no extraction drift between runs |
| Parse-time validation | PENMAN notation has defined grammar; malformed facts rejected immediately |
| Role-based queries | "What does Jack prefer?" hits :agent Jack + prefer regardless of original phrasing |
| No ecosystem lock-in | Works with any LLM provider—no LangGraph dependency, no cloud-only restriction |
This is why Hypabase achieves 100% on personalization tasks—developer preferences, tool choices, and workflow patterns stored as structured hyperedges are precisely retrievable, even when queries use completely different language.
They serve different needs. Supermemory (85.2% LongMemEval) is a managed cloud product with multi-modal support. LangMem is a toolkit for LangGraph users with no published benchmarks. For higher accuracy with structured extraction, consider Hypabase (87.4%).
There's no direct migration path—they use different storage models. Migration requires re-ingesting conversation history through the new system. If you're evaluating both, consider running a small pilot before committing.
Supermemory is a standalone cloud product with built-in connectors and multi-modal support. LangMem is an ecosystem toolkit tightly coupled to LangGraph. Hypabase optimizes for extraction quality using AMR and structured hyperedge representation.
LangMem is self-hostable but requires LangGraph infrastructure plus a storage backend. Supermemory is cloud-only with no self-hosting option. Hypabase runs entirely in a single SQLite file with no external database required—the simplest self-hosting option.
Supermemory scores 85.2% on LongMemEval with strong multi-modal support and productivity tool connectors. Cloud-only with opaque pricing limits flexibility.
LangMem provides LangGraph-native memory tools but has no published benchmark scores. Useful only for teams committed to the LangChain ecosystem.
Hypabase achieves 87.4% through AMR-based extraction into hyperedges—structured knowledge representation that preserves relationships ad-hoc extraction fragments. 100% on personalization tasks.
All three are straightforward to integrate:
LongMemEval scores: Supermemory (85.2%) from published benchmarks. LangMem has no published LongMemEval results. Hypabase (87.4%) from published benchmark harness.