Catalog Systems Dedicated memory layers Cellon (Memory.inc)

Cellon (Memory.inc)

https://en.wowtale.net/2026/04/30/233992/

Korean startup. Connects scattered internal/external data across multiple chats and services to improve AI answer quality at retrieval. 18-year-old CEO Jang Jun-ha; pitch is that answer quality depends on what the model remembers, not which model. As of Oct 2026 memory.inc redirects to Pogl (about.pogl.com), an AI game and experience maker from the same company (Celon Inc., CEO Junha Jang); the memory product is no longer presented.

Active · Verified 2026-10-29 · fresh as of 2026-10-29 · 27 cited facts, 6 with a third-party source · Suggest a correction

At a glance

Type
Cross-service context stitching / retrieval memory vendor fix
Tier
T2
Section
Dedicated memory layers
Created
searched not found fix
Latest release
not applicable — not OSS fix
License
not applicable — not OSS fix
GitHub
not applicable — no GitHub repo fix
Pricing
searched not found fix
Funding
Seed · 2026-04 third-party fix
Freshness
2026-10-29 fix
Layer
agent-infra repo fix
Licensing
commercial vendor fix

Memory taxonomy

storage
vector
retrieval
similarity
persistence
cross-session
update
extraction
unit
fact
governance
opaque
conflict
none

When to use

Optimised for: cross-service context stitching

Anti-fit: searched not found

Pros & cons

Pros

Cell-based memory abstraction — discrete units with provenance; novel mental model.

Cons

New abstraction means learning curve; small adoption.

Claims & capabilities

Seed (April 2026, Primer + The Ventures, amount undisclosed).

Technical surface

API surface
searched not found
Backend storage
custom
Deployment
searched not found
Embedding model
locked
Multi-tenancy
namespace
MCP
not documented publicly
A2A
not documented publicly
OpenTelemetry
not documented publicly

Alternatives to Cellon (Memory.inc) → 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 (26 cited facts, 6 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).