Memoripy
https://github.com/caspianmoon/memoripy
Memory layer for AI assistants — semantic short-term and long-term memory with concept-graph linking and decay/reinforcement.
At a glance
- Type
- Python OSS memory library (short+long term)
- Tier
- T4
- Section
- Dedicated memory layers
- Created
- 2024-10
- Latest release
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- License
- Apache-2.0
- GitHub
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- Pricing
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- Funding
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Taxonomy
- storage
- vector
- retrieval
- similarity
- persistence
- long-term
- update
- accretion
- unit
- fact
- governance
- plaintext
- conflict
- n/a
When to use
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Pros & cons
Pros
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Cons
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Claims & capabilities
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Technical surface
- API surface
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- Backend storage
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- Deployment
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- Embedding model
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- Multi-tenancy
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- MCP
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- A2A
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- OpenTelemetry
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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.
- 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).
- Supermemory T1
Memory engine with API, app, browser extension, and MCP server. Extracts facts, tracks updates, resolves contradictions, auto-forgets expired info. Plugins for Claude Code, OpenCode, OpenClaw, Hermes.