Letta (MemGPT) vs RelataDB
TL;DR – Letta (formerly MemGPT) gives an LLM agent an operating-system-style memory hierarchy (core / archival / recall blocks) and a self-hosted agent runtime. RelataDB is the governed knowledge store the agent reads and writes. They answer different questions: "how does the agent hold state?" vs "is the fact provable and governed?"
What each one is
Letta (the company/project behind MemGPT) treats the agent like an OS process: it has memory blocks it pages in and out of a context window, manages its own state, and runs as a self-hosted agent server. The emphasis is the agent runtime and a memory model inspired by operating-system virtual memory.
RelataDB is not an agent runtime — it is the database the agent (any agent, including a Letta one) stores into. Its job is that the stored knowledge is standardized, bi-temporal, provenance-bearing, access-controlled, and recoverable to any past state.
Feature matrix
| Letta (MemGPT) | RelataDB | |
|---|---|---|
| Primary job | Agent runtime + memory hierarchy | Governed temporal knowledge database |
| Memory model | OS-style blocks (core / archival / recall), paged into context | 10 cognitive verbs; rows are bi-temporal, provenance-bearing |
| Is it an agent runtime? | Yes — runs the agent | No — storage/memory layer the agent calls |
| Identity resolution | Not the focus | Deterministic checksum parsers (76 canonical kinds) |
| Bi-temporal history | No | Yes — on every row |
| Provenance / audit | Limited | Hash-chained, tamper-evident per fact |
| Access control | App/runtime-enforced | Cell-level ACL in the scan predicate; per-tenant encryption |
| Query languages | Letta SDK / API | SQL, Cypher, GQL, SPARQL, MCP |
| Talk to existing clients? | Letta SDK | Postgres / S3 / Mongo / Redis / ClickHouse / Neo4j-Bolt / Flight |
When to pick Letta
- You want the agent-as-OS model — the agent manages its own memory blocks and runs as a server.
- Your problem is how the agent holds and pages state across long horizons, not whether each fact is court-grade provable.
When to pick RelataDB
- The knowledge the agent reads/writes must be a governed system of record — auditable, reproducible, access-controlled, multi-tenant.
- You already have an agent runtime (LangGraph, CrewAI, AutoGen, your own) and need the memory/knowledge layer underneath it.
FAQ
Is RelataDB a Letta/MemGPT replacement? No — they're different layers. Letta replaces "how does my agent hold state." RelataDB replaces "where does the governed, provable knowledge live."
MemGPT vs RelataDB for long-term memory? MemGPT's contribution is the memory-hierarchy/paging model for long-running agents. RelataDB's contribution is that every stored memory is bi-temporal, identity-resolved, provenance-bearing, and access-controlled. The two compose.
Does RelataDB run agents? No. Relata is the memory/knowledge layer; it integrates with LangChain, LlamaIndex, CrewAI, AutoGen/AG2, Pydantic-AI, smolagents, and LangGraph via adapters. See Agent Memory.
See also: RelataDB vs the field and Agent Memory.