Catalog Systems Dedicated memory layers O-Mem

O-Mem

https://github.com/OPPO-PersonalAI/O-Mem

Omni memory system for personalised, long-horizon, self-evolving agents. From OPPO PersonalAI.

Active · Verified 2026-10-22 · fresh as of 2026-10-22 · 37 cited facts, 0 with a third-party source · Suggest a correction

At a glance

Type
Omni / self-evolving memory repo fix
Tier
T2
Section
Dedicated memory layers
Created
2025-12 repo fix
Latest release
no releases fix
License
Apache-2.0 repo fix
GitHub
OPPO-PersonalAI/O-Mem — ~79 stars, last commit n/a repo fix
Pricing
not applicable — academic research code (no commercial pricing) repo fix
Funding
not applicable — corporate research lab (OPPO PersonalAI Research) repo fix
Freshness
2026-10-22 fix
Layer
agent-infra repo fix
Licensing
open-source repo fix
Deployment locus
local repo fix
Platform vs product
platform repo fix
SDK languages
python repo fix

Memory taxonomy

storage
vector
retrieval
similarity
persistence
lifelong
update
extraction
unit
fact
governance
opaque
conflict
llm-arbitrate

When to use

Optimised for: self-evolving / personalised long-horizon memory

Anti-fit: searched not found

Pros & cons

Pros

Optimized memory store with low-latency reads — strong on agent loops where memory is hit per turn.

Cons

Smaller community; thin track record vs established memory layers.

Claims & capabilities

Apache-2.0; from OPPO PersonalAI Research (corporate lab); arXiv:2511.13593 paper Nov 17 2025; targets self-evolving lifelong personal memory; 4 cites (too recent)

Technical surface

API surface
searched not found
Backend storage
searched not found
Deployment
Self-hosted (research code; no cloud service)
Embedding model
searched not found
Multi-tenancy
searched not found
MCP
not documented publicly
A2A
not supported
OpenTelemetry
not documented publicly

Alternatives to O-Mem → live options ranked by shared design, licence, hosting and MCP support

Similar systems

Other dedicated memory layers in the catalog, ranked by inbound references.

  • Mem0 T1

    Universal memory layer for AI agents. Three concurrent stores (vector + graph + KV); LLM-extracted facts; concurrent retrieval via ThreadPoolExecutor.

  • Zep & Graphiti T1

    Bi-temporal knowledge graph (event time + ingestion time). Strong on chronological reasoning and contradiction tracking. Graphiti is the open-source core.

  • Cognee T1

    "Extract–Cognify–Load" pipeline that turns raw input into a typed, queryable knowledge graph for agent recall.

  • Hindsight (Vectorize) T1

    Standalone memory service from Vectorize. Open source. Biomimetic four-network design (World, Bank, Observation, Opinion). Ships an MCP memory server.

  • Letta / MemGPT T1

    Originated as MemGPT (UC Berkeley), where the model pages its own memory between context, recall and archival tiers. The product now centres on Letta Code, a stateful agent harness whose agents rewrite their own memory, skills and prompts over time.

  • Memori T2

    Structured, persistent memory layer for AI agents. Open source.

Sources (36 cited facts, 0 third-party)

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