How SAIHM compares to other AI memory products.

The five properties we emphasise

SAIHM is designed around five emphasised properties; this is the framing we use to compare.

1. User-held encryption keys. The wallet holder, not the operator, is the only party that can read or destroy a memory cell.
2. Cryptographic erasure. “Forget” destroys the encryption key for that specific memory cell — the data cannot be reconstructed by anyone, including the protocol operator. This is what GDPR Article 17 calls right-to-erasure, achieved cryptographically rather than by trust.
3. Public-chain audit anchor. Build commitments and protocol events are anchored to COTI V2 Helium mainnet so independent auditors can reproduce the running state from public artefacts.
4. Cross-vendor sharing with consent. A memory cell can be shared (temporarily or permanently) with another agent — possibly from another vendor — and revoked at any time.
5. Apache 2.0 license. Source is permissively licensed; anyone can self-host, audit, fork, or build on top under the license’s terms.

At-a-glance table

Each entry reflects what each product’s canonical documentation states (research conducted 2026-05-07). Where a property is not addressed in the docs reviewed, we mark “not addressed” rather than asserting the product does not do it.

What each one does well

Honest acknowledgment of each competitor’s actual strengths in their own positioning:

Mem0

Positions itself as a “universal memory layer for AI Agents”, LLM-agnostic by design (works with OpenAI, Anthropic, Ollama, or your own model). Apache-2.0 OSS core plus a managed cloud tier. Backed by a published research paper on the production memory architecture. OSS self-host gives operators full control of stack and data.

Zep

Positions itself as a “Context Engineering & Agent Memory Platform”. Built on a temporal knowledge graph engine (Graphiti, itself Apache-2.0). Strong context-engineering primitives: episode model, business-data integration, behavioral context assembly. Pricing is credit-based (1 credit / 350 bytes / Episode).

Letta (formerly MemGPT)

Positions itself as “the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time”. OS-paradigm tiered memory (Core / Recall / Archival), Agent Development Environment tooling, multi-agent shared memory blocks within a single Letta deployment, Apache 2.0 self-host, plus a managed cloud tier.

LangMem

Positions itself around “helps agents learn and adapt from their interactions over time” — extracts info, refines prompts, maintains long-term memory. MIT-licensed, zero-friction install (“no API keys, no accounts, no monthly bills”), deep LangGraph integration, and storage-agnostic functional primitives.

Pinecone

Positions itself as “The vector database to build knowledgeable AI”. Mature managed vector infrastructure with CMEK + audit logs + HIPAA + PrivateLink + BYOC at the Enterprise tier. Large ecosystem and broad adoption as a vector DB. Note: Pinecone is principally a vector store, not a stateful-memory product per se — included here as the canonical “memory infrastructure” comparator that buyers often evaluate alongside the others.

What is genuinely uncontested

From the canonical documentation reviewed (May 2026), no surveyed competitor publicly addresses:

• Per-memory user-driven cryptographic erasure (Pinecone CMEK is closest, but it is operator-driven and tenant-wide, not per-memory or end-user-driven).
• Public-chain anchored audit trail.
• Consent-based memory sharing across different vendors (Letta has intra-deployment shared blocks; Mem0 is LLM-agnostic but its memory is not portable across vendors).

SAIHM’s positioning is not “do less than Mem0” or “do more than Pinecone” — it is to address a different surface. If those three properties matter for your use case (regulated industries, multi-vendor agentic ecosystems, public-good accountability), SAIHM is currently the only canonical option in this list. If they do not matter, the products above each have legitimate strengths in their own positioning — and several are excellent within those positions.

How to choose

• Need vector search at production scale, no key-sovereignty requirements? Pinecone for the vector layer.
• Want an OSS memory layer that is LLM-agnostic and library-shaped, with optional managed hosting? Mem0.
• Building on LangChain/LangGraph and want zero-config memory primitives under MIT? LangMem.
• Want stateful agents with OS-paradigm tiered memory, comfortable with intra-deployment sharing? Letta.
• Need temporal-knowledge-graph context engineering with business-data integration? Zep.
• Need user-held keys, cryptographic erasure, public-chain audit, cross-vendor consent sharing, all under Apache 2.0? SAIHM.

The categories above are not mutually exclusive. SAIHM is designed to compose with vector stores and other memory layers under it — we are explicit that we are a sovereignty/erasure/audit/sharing layer, not a vector index.

Sources reviewed

All facts above sourced from the canonical pages listed below (research conducted 2026-05-07). Where a fact was not stated in canonical docs, we marked “not addressed” rather than inferring.

• Mem0: mem0.ai · docs.mem0.ai/open-source/overview · github.com/mem0ai/mem0
• Zep: getzep.com · getzep.com/pricing · Zep OSS direction blog
• Letta: letta.com · github.com/letta-ai/letta · docs.letta.com/concepts/memgpt · multi-agent shared memory
• LangMem: langchain-ai.github.io/langmem · LangMem SDK launch · LICENSE (MIT)
• Pinecone: pinecone.io · CMEK guide · security overview · BYOC blog

Get in touch

Questions about this comparison, or want us to update an entry that is now incorrect (positioning changes, license changes, new feature)? Email ops@saihm.coti.global. We revise on receipt of canonical-source disagreement and re-date the page.

Try the protocol →   or build on top — see /competitors