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Mem0 vs Letta. Rows marked Differs have different values.

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Mem0

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Letta

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TypeStartupStartup
CategoriesDiffers
In one lineThe memory layer for AI apps and agents.Stateful agents with memory, from the team behind MemGPT.
Stage & fundingDiffers—Seed · $10M seed led by Felicis (2024)
GitHub stars——
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Last push——
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People linked22
Events00
Pros
  • MetadataWidely adopted open-source memory layer with about 68k GitHub stars, under Apache-2.0.[1]
  • DocsOffers three deployment paths: a pip/npm library, a self-hosted Docker server with dashboard and auth, or a managed cloud.[1]
  • DocsThe free cloud tier allows 10k adds and 1k retrievals per month, or 50k adds and 10k retrievals with your own LLM key.[1]
  • User reportsIts MemGPT-style memory helps with long-running work: one HN user says it made long-context documentation manageable.[1]
  • MetadataApache-2.0, self-hostable and popular (about 25.3k GitHub stars). The ADE lets you inspect and edit an agent's memory blocks while it runs.[1][2]
  • DocsLetta Code brings its learning memory into a coding-agent harness, with headless and --yolo modes for sandboxes (per Letta staff on HN).[1][2]
  • User reportsOne engineering manager said they would rather build on Letta or Mastra than trust newer personal-agent projects.[1]
Cons
  • DocsMem0's own README says its LoCoMo scores come from the managed platform, which has proprietary optimizations that the open-source SDK lacks.[1]
  • Independent testIts benchmark claims are disputed: Zep published a rebuttal of how Mem0 ran Zep, practitioners report trouble reproducing the LoCoMo numbers, and a full-context baseline scored about 72.9%.[1][2]
  • DocsGraph memory, SSO, audit logs and on-prem deployment are available only on the custom-priced plan.[1]
  • MetadataLarge maintenance backlog: about 437 open issues and 450 open PRs.[1]
  • Independent testAn independent review says to avoid it for stateless apps or when the main problem is coordinating several agents, because it focuses on memory for a single agent.[1]
  • User reportsMemory extraction that runs on an LLM gets expensive at scale; a competitor estimates $1k-3k per month in API calls at 100K memories a month.[1]
  • User reportsA builder who tried it found that memory architectures like Letta's 'simply move the pain to retrieval'.[1]
Best forTeams that want to add user-level memory to chat agents quickly, with an option to self-host.Builders of long-lived, stateful single agents who want memory they can inspect and edit, and can self-host.
Watch out forBenchmark on your own data rather than trusting vendor LoCoMo numbers.Budget for the extra LLM calls that memory management makes, and check retrieval quality on your own data.
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