CaviraOSS/OpenMemory
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc. observed · 2026-08-28
Health v2 · maintenance only
70/100
- Activity 99
- Release rhythm 60
- Longevity 22
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 6.0
- age_days: 318
- days_rel: 264
- days_push: 8
- n_releases_24m: 7
Adoption not part of the score
4466 stars · 501 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
OpenMemory is a local-first, self-hosted cognitive memory engine that gives LLM applications and AI agents persistent long-term memory, with multi-sector embeddings, temporal knowledge graphs, and adaptive memory decay. It ships as Python and Node SDKs (standalone SQLite mode or backend server mode) and integrates with OpenAI, LangChain, CrewAI, AutoGen, and MCP clients like Claude Desktop and Cursor.
Use cases
- give my AI agent persistent memory across sessions
- remember user preferences in a chatbot between conversations
- add long-term memory to Claude Desktop via MCP
- self-hosted alternative to Mem0 or Supermemory
- store and recall episodic and semantic memories for LLM apps
- give each Streamlit app user persistent memory by user_id
- ingest PDFs and web pages into an agent's memory
- reduce token costs by not re-sending context every session
When to choose
- you want local-first, self-hosted memory with full data ownership and no vendor API calls
- you need explainable recall traces and multi-sector (episodic/semantic/emotional) memory rather than plain vector search
- you use LangChain, CrewAI, AutoGen, or MCP clients and want drop-in memory integration
- you want per-user memory scoping in multi-user applications
When to avoid
- you need a battle-tested production system - the project is being rewritten with expected breaking changes and bugs
- you just need a plain vector database or simple RAG over documents
- you require guaranteed stability, long-term support, or enterprise SLAs
- you want a fully managed hosted memory service
Facets
library · maturity experimental
vector-database rag llm-inference agent-framework mcp database sdk artificial-intelligence large-language-models developer-tools self-hosted python cross-platform self-hosted long-term-memory memory-engine cognitive-architecture local-first sqlite postgres temporal-knowledge-graph multi-sector-embeddings memory-decay langchain crewai autogen claude-desktop mcp-server ai-agents retrieval-augmented-generation nodejs docker
6 sources
- readme: https://github.com/CaviraOSS/OpenMemory · fetched 2026-08-28 · 1c81b615cb73
- homepage: https://openmemory.cavira.app · fetched 2026-08-29 · c8da4059a142
- site_page: https://openmemory.cavira.app/docs/introduction · fetched 2026-08-29 · 2920a115e1c2
- site_page: https://openmemory.cavira.app/docs/quick-start · fetched 2026-08-29 · a6a6f9b6d5d1
- site_page: https://openmemory.cavira.app/docs/api/ingestion · fetched 2026-08-29 · f90482645a70
- site_page: https://openmemory.cavira.app/docs/advanced/embedding-modes · fetched 2026-08-29 · 810063cd39ae
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| CaviraOSS/OpenMemory | main | 70 |
For agents
markdown · JSON · MCP: product_card(name="CaviraOSS/OpenMemory")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem