Memori

https://github.com/MemoriLabs/Memori

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

Active · Verified 2026-10-22 · fresh as of 2026-10-22 · 50 cited facts, 4 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Tier
T2
Created
2025-07 repo fix
Latest release
v3.3.6 (2026-05-28) registry fix
License
Apache-2.0 repo fix
Freshness
2026-10-22 fix
Layer
agent-infra repo fix
Licensing
open-core vendor fix
Deployment locus
hybrid repo fix
Platform vs product
platform repo fix
Maturity
alpha registry fix
SDK languages
python, typescript repo fix
Protocols
mcp-server repo fix

Memory taxonomy

storage
relational
retrieval
similarity
persistence
cross-session
update
extraction
unit
fact
governance
inspectable
conflict
append

When to use

Optimised for: structured persistence + simplicity

Anti-fit: no anti-fit explicitly stated

Pros & cons

Pros

OSS dual-mode model (conscious + autonomous ingestion) lets developers control memory decisions explicitly.

Cons

Smaller community than Mem0/Letta; documentation is uneven.

Claims & capabilities

Apache-2.0; structured persistent memory layer (relational + vector hybrid); +175/mo; v3.3.3 released 2026-05-05; dual-mode (conscious + autonomous ingestion); $3.7M seed (May 2024); founders Harish Mukhami (CEO) and Michael Montero (CTO) NYC

Technical surface

API surface
SDK: Python (memori.enable() one-liner; integrates LiteLLM, OpenAI, Anthropic)
Backend storage
SQL (SQLite/Postgres/MySQL)
Deployment
Both (Memori Cloud zero-config + self-hosted OSS)
Embedding model
multiple supported
Multi-tenancy
not applicable — library; multi-tenancy is the embedding application's concern
MCP
yes — first-party hosted remote MCP endpoint (api.memorilabs.ai/mcp) for Claude Code, Cursor, Codex etc.
A2A
not documented publicly
OpenTelemetry
not documented publicly

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.

  • Memvid T2

    Single-file memory layer (one .mv2 file). No DB, no server. Append-only sequence of immutable Smart Frames with timestamps + checksums. Native Rust core (rewritten from Python).

Sources (49 cited facts, 4 third-party)

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

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-22
    • Latest release “v3.3.3 2026-05-05” → “v3.3.6 (2026-05-28)” source
    • MCP support “community port — third-party MCP wrappers” → “yes — first-party hosted remote MCP endpoint (api.memorilabs.ai/mcp) for Claude Code, Cursor, Codex etc.” source
    #195

Related systems

Referenced by (2)

  • AgentOps integrates with — AgentOps docs have a Memori integration page. (verified 2026-10-29)
  • MongoDB Atlas Vector Search integrates with — MongoDB's Atlas AI Integrations page lists Memori as an integration (some entries are marked community-maintained). (verified 2026-10-29)