Letta / MemGPT vs Mem0
Letta / MemGPT vs Mem0: side-by-side comparison of two dedicated memory layers systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Cost & capability
| Letta / MemGPT | Mem0 | |
|---|---|---|
| Capability band | competent | competent |
| Capability composite | 68 | 70 |
| Cost tier | free | free |
| $/Mtok input | 0 | 0 |
| $/Mtok output | 0 | 0 |
| Use cases | Long Running Session, Memory Augmented Chat, Multi Agent Coordination | Long Running Session, Memory Augmented Chat, Multi Agent Coordination |
Where they differ (13)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Letta / MemGPT | Mem0 | |
|---|---|---|
| Capability composite | 68 | 70 |
| Type | Tiered: core / recall / archival | Vector + graph + KV (hybrid) |
| Created | 2023-10 | 2023-06 |
| Latest release | Letta Code v0.34.30 (npm @letta-ai/letta-code, 2026-10-29); PyPI letta v0.34.12 (2026-10-12); last letta-ai/letta server tag v0.16.8 (May 2026) | v2.2.1 (2026-09-25) — mem0ai on PyPI |
| GitHub | letta-ai/letta-code — ~3.7k stars, Apache-2.0 (current source: agent harness, terminal UI, App Server); letta-ai/letta (~25.3k stars, not archived) is now a README pointing to letta-code, with the retired Letta V1 API server on its archive branch | mem0ai/mem0 — ~67.3k stars, last commit 2026-09+ |
| Pricing | Free $0 (3 agents); Pro $20/mo; Developer pay-as-you-go ($0.10/active agent/mo); Teams; Enterprise custom | Free + paid |
| Funding | $10M total $70M val Seed · 2024-09 | $24M total $150M val Series A · 2025-10 |
| Backend storage | Postgres + pgvector | hybrid (vector + graph + KV) |
| API surface | REST, SDK: Python, TS | REST, SDK: Python, Node.js |
| Multi-tenancy | Logical namespace per org/project/agent; self-hosted Docker option for hard tenant isolation | Logical namespace per (user_id, agent_id, run_id); self-hosted/on-prem deployment available for tenant isolation |
| MCP | native (first-party) — Letta serves & consumes MCP | yes — official hosted Mem0 MCP server (docs.mem0.ai/platform/mem0-mcp); the self-hosted mem0-mcp repo was archived 2026-03-24 |
| OpenTelemetry | not documented publicly | via adapter — AgentOps integration |
| Optimised for | LLM-as-OS paradigm; agent autonomy in managing its own memory | developer experience + universal memory layer (model-agnostic, multi-store) |
At a glance
| Letta / MemGPT | Mem0 | |
|---|---|---|
| Section | Dedicated memory layers | Dedicated memory layers |
| Tier | T1 | T1 |
| Type | Tiered: core / recall / archival | Vector + graph + KV (hybrid) |
| Created | 2023-10 | 2023-06 |
| Latest release | Letta Code v0.34.30 (npm @letta-ai/letta-code, 2026-10-29); PyPI letta v0.34.12 (2026-10-12); last letta-ai/letta server tag v0.16.8 (May 2026) | v2.2.1 (2026-09-25) — mem0ai on PyPI |
| License | Apache-2.0 | Apache-2.0 |
| GitHub | letta-ai/letta-code — ~3.7k stars, Apache-2.0 (current source: agent harness, terminal UI, App Server); letta-ai/letta (~25.3k stars, not archived) is now a README pointing to letta-code, with the retired Letta V1 API server on its archive branch | mem0ai/mem0 — ~67.3k stars, last commit 2026-09+ |
| Pricing | Free $0 (3 agents); Pro $20/mo; Developer pay-as-you-go ($0.10/active agent/mo); Teams; Enterprise custom | Free + paid |
| Funding | $10M total $70M val Seed · 2024-09 | $24M total $150M val Series A · 2025-10 |
| Backend storage | Postgres + pgvector | hybrid (vector + graph + KV) |
| Deployment | Both | Both |
| API surface | REST, SDK: Python, TS | REST, SDK: Python, Node.js |
| Embedding | multiple supported | multiple supported |
| Multi-tenancy | Logical namespace per org/project/agent; self-hosted Docker option for hard tenant isolation | Logical namespace per (user_id, agent_id, run_id); self-hosted/on-prem deployment available for tenant isolation |
| MCP | native (first-party) — Letta serves & consumes MCP | yes — official hosted Mem0 MCP server (docs.mem0.ai/platform/mem0-mcp); the self-hosted mem0-mcp repo was archived 2026-03-24 |
| A2A | not documented publicly | not documented publicly |
| OpenTelemetry | not documented publicly | via adapter — AgentOps integration |
| Optimised for | LLM-as-OS paradigm; agent autonomy in managing its own memory | developer experience + universal memory layer (model-agnostic, multi-store) |
| Anti-fit | no anti-fit explicitly stated | no anti-fit explicitly stated |
Taxonomy
| Axis | Letta / MemGPT | Mem0 |
|---|---|---|
| storage | kv | vector |
| retrieval | similarity | similarity |
| persistence | long-term | long-term |
| update | agent-controlled | extraction |
| unit | episode | fact |
| governance | inspectable | opaque |
| conflict | llm-arbitrate | llm-arbitrate |
Pros & cons
Letta / MemGPT
Pros: Most architecturally principled — explicit OS-style tiering makes capacity behavior predictable; UC Berkeley origin + active v1 maturity.
Cons: Tier configuration adds operational complexity vs flat vector memory; latency is sensitive to paging decisions on long histories.
Mem0
Pros: Hybrid (vector + graph + KV) gives the most architectural flexibility of any memory layer.
Cons: LOCOMO benchmark numbers were publicly disputed by Zep in counter-analysis; LLM-extraction approach risks dropping facts that don't fit the prompt.