Catalog Systems Dedicated memory layers NeoCognition

NeoCognition

https://www.prnewswire.com/news-releases/neocognition-emerges-from-stealth-with-40-million-seed-round-to-advance-specialized-intelligence-and-expert-agents-302749108.html

Builds agents that learn from experience by constructing a "world model" of the micro-environment they operate in. Specialise over time through Agentic RL loop rather than relying on static prompt context. Targets the ~50% task-completion failure rate in current agents.

Active · Verified 2026-10-28 · fresh as of 2026-10-28 · 30 cited facts, 6 with a third-party source · Suggest a correction

At a glance

Type
World-model / experiential self-learning memory vendor fix
Tier
T1
Section
Dedicated memory layers
Created
2026-04 third-party 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
$40M total Seed · 2026-04 third-party fix
Freshness
2026-10-28 fix
Layer
agent-infra repo fix
Licensing
commercial vendor fix

Memory taxonomy

storage
hybrid
retrieval
agentic
persistence
long-term
update
extraction
unit
episode
governance
opaque
conflict
none

When to use

Optimised for: world-model / experiential learning

Anti-fit: searched not found

Pros & cons

Pros

Cognitive-architecture-inspired model with explicit episodic/semantic separation reflects long-standing memory science.

Cons

More academic than commercial; limited production deployment evidence.

Claims & capabilities

$40M seed (April 2026, Cambium Capital + Walden Catalyst). Angel investors include Intel CEO Lip-Bu Tan, Databricks's Ion Stoica.

Technical surface

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

Alternatives to NeoCognition → 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 (29 cited facts, 6 third-party)

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